<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0188-9532</journal-id>
<journal-title><![CDATA[Revista mexicana de ingeniería biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. ing. bioméd]]></abbrev-journal-title>
<issn>0188-9532</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Ingeniería Biomédica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-95322025000100102</article-id>
<article-id pub-id-type="doi">10.17488/rmib.46.1.2</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Evaluación de Tubos de Recolección de Muestras de Sangre Utilizando Deep Learning]]></article-title>
<article-title xml:lang="en"><![CDATA[Evaluation of Blood Sample Collection Tubes Using Deep Learning]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Franco-Alucano]]></surname>
<given-names><![CDATA[Ignacio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aguilar-Duque]]></surname>
<given-names><![CDATA[Julian]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Baez-Lopez]]></surname>
<given-names><![CDATA[Yolanda]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Limon-Romero]]></surname>
<given-names><![CDATA[Jorge]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Solís-Quinteros]]></surname>
<given-names><![CDATA[María Marcela]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Tlapa]]></surname>
<given-names><![CDATA[Diego]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Baja California Facultad de Ingeniería, Arquitectura y Diseño ]]></institution>
<addr-line><![CDATA[ Baja California]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Baja California Facultad de Contaduría y Administración ]]></institution>
<addr-line><![CDATA[ Baja California]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2025</year>
</pub-date>
<volume>46</volume>
<numero>1</numero>
<fpage>21</fpage>
<lpage>38</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322025000100102&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322025000100102&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322025000100102&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La flebotomía es un procedimiento para obtener muestras de sangre principalmente para análisis clínicos en laboratorios. La cantidad de sangre, identificación de tubos y el uso del tubo adecuado son características que el profesional de la salud inspecciona visualmente. Al ser una actividad manual, la posibilidad de error está presente pudiendo tener efectos tanto en la calidad, como en el flujo de trabajo y eficiencia. A pesar del avance de las tecnologías de la industria 4.0, incluida la inteligencia artificial (IA), hay poca evidencia de aplicaciones en laboratorios clínicos. Este estudio tiene como objetivo evaluar la idoneidad de utilizar el aprendizaje profundo o deep learning (DL) en la inspección de tubos con muestras de sangre. Particularmente se prueban tres arquitecturas YOLOv5, YOLOv7 y YOLOv8 en la detección de seis clases incluyendo color de tapa y presencia de etiqueta. El mayor desempeño de precisión se presentó con el modelo YOLOv8 obteniendo una precisión de 0.927 en la detección, lo que evidencia una alta capacidad para inspeccionar características importantes en el servicio de flebotomía, siendo DL una alternativa viable para asistir a los profesionales de la salud en actividades de inspección. Trabajo futuro incluye ampliar el número de imágenes de manera balanceada.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Phlebotomy is a procedure to obtain blood samples, mainly for laboratory clinical analysis. The amount of blood, tube identification, and the use of the appropriate tube are characteristics that the health professional visually inspects. Being a manual activity, the possibility of error is latent and can affect quality, workflow, and efficiency. Despite the advancement of industry 4.0 technologies, including artificial intelligence (AI), there is little evidence of applications in clinical laboratories. This study aims to evaluate the suitability of using deep learning (DL) in inspecting tubes with blood samples. Specifically, three architectures, YOLOv5, YOLOv7, and YOLOv8, are tested to detect six classes, including cap color and the presence of labels. The highest precision performance was presented by the YOLOv8 model, obtaining a precision of 0.927 in detection, which shows a high capacity to inspect important characteristics in the phlebotomy service. Therefore, being DL is a suitable alternative to assist health professionals in inspection activities. Future work includes expanding the number of images in a balanced manner.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[deep learning]]></kwd>
<kwd lng="es"><![CDATA[detección de objetos]]></kwd>
<kwd lng="es"><![CDATA[laboratorio clínico]]></kwd>
<kwd lng="es"><![CDATA[muestra de sangre]]></kwd>
<kwd lng="es"><![CDATA[red neuronal convolucional]]></kwd>
<kwd lng="es"><![CDATA[YOLO]]></kwd>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="en"><![CDATA[object detection]]></kwd>
<kwd lng="en"><![CDATA[clinical laboratory]]></kwd>
<kwd lng="en"><![CDATA[blood samples]]></kwd>
<kwd lng="en"><![CDATA[convolutional neural networks]]></kwd>
<kwd lng="en"><![CDATA[YOLO]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>[1]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Singh]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Sharma]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Sharma]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Gupta]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<source><![CDATA[Impact of Adam, Adadelta, SGD on CNN for White Blood Cell Classification]]></source>
<year>2023</year>
<conf-name><![CDATA[ 5International Conference on Smart Systems and Inventive Technology (ICSSIT)]]></conf-name>
<conf-date>2023</conf-date>
<conf-loc>Tirunelveli, India </conf-loc>
<page-range>1702-9</page-range></nlm-citation>
</ref>
<ref id="B2">
<label>[2]</label><nlm-citation citation-type="">
<collab>International Standard Organization</collab>
<source><![CDATA[ISO-15189 Medical laboratories-Requirements for quality and competence]]></source>
<year>2022</year>
</nlm-citation>
</ref>
<ref id="B3">
<label>[3]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lima-Oliveira]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Laboratory Diagnostics and Quality of Blood Collection]]></article-title>
<source><![CDATA[J. Med. Biochem.]]></source>
<year>2015</year>
<volume>34</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>288-94</page-range></nlm-citation>
</ref>
<ref id="B4">
<label>[4]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Plebani]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The detection and prevention of errors in laboratory medicine]]></article-title>
<source><![CDATA[Ann. Clin. Biochem.]]></source>
<year>2010</year>
<volume>47</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>101-10</page-range></nlm-citation>
</ref>
<ref id="B5">
<label>[5]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Aita]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Padoan]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Antonelli]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Sciacovelli]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Plebani]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Patient safety and risk management in medical laboratories: theory and practical application]]></article-title>
<source><![CDATA[J. Lab. Precis. Med.]]></source>
<year>2017</year>
<volume>2</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>75</page-range></nlm-citation>
</ref>
<ref id="B6">
<label>[6]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Nichols]]></surname>
<given-names><![CDATA[J. H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Laboratory quality control based on risk management]]></article-title>
<source><![CDATA[Ann. Saudi Med.]]></source>
<year>2011</year>
<volume>31</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>223-8</page-range></nlm-citation>
</ref>
<ref id="B7">
<label>[7]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Plebani]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Exploring the iceberg of errors in laboratory medicine]]></article-title>
<source><![CDATA[Clin. Chim. Acta]]></source>
<year>2009</year>
<volume>404</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>16-23</page-range></nlm-citation>
</ref>
<ref id="B8">
<label>[8]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hickner]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Testing process errors and their harms and consequences reported from family medicine practices: A study of the American Academy of Family Physicians National Research Network]]></article-title>
<source><![CDATA[Qual Saf. Health Care]]></source>
<year>2008</year>
<volume>17</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>194-200</page-range></nlm-citation>
</ref>
<ref id="B9">
<label>[9]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Javadifard]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<source><![CDATA[Predicting Patient Waiting Time in Phlebotomy Units Using a Deep Learning Method]]></source>
<year>2019</year>
<conf-name><![CDATA[ Innovations in Intelligent Systems and Applications Conference, ASYU]]></conf-name>
<conf-date>2019</conf-date>
<conf-loc>Izmir, Turkey </conf-loc>
<page-range>1-4</page-range></nlm-citation>
</ref>
<ref id="B10">
<label>[10]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fridath]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Gildas]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Dooguy]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[White Blood Cells Recognition and Classification using Convolutional Neural Network]]></source>
<year>2023</year>
<conf-name><![CDATA[ 2International Conference on Applied Artificial Intelligence and Computing (ICAAIC)]]></conf-name>
<conf-date>2023</conf-date>
<conf-loc>Salem, India </conf-loc>
<page-range>145-50</page-range></nlm-citation>
</ref>
<ref id="B11">
<label>[11]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deep Learning in Hematology: From Molecules to Patients]]></article-title>
<source><![CDATA[Clin. Hematol. Int.]]></source>
<year>2024</year>
<volume>6</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>19-42</page-range></nlm-citation>
</ref>
<ref id="B12">
<label>[12]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chandra]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Geico]]></surname>
<given-names><![CDATA[A. K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deep Learning Diagnostics: A Revolutionary Approach to Healthcare Insurance]]></article-title>
<source><![CDATA[NeuroQuantology]]></source>
<year>2021</year>
<volume>19</volume>
<numero>12</numero>
<issue>12</issue>
<page-range>745-54</page-range></nlm-citation>
</ref>
<ref id="B13">
<label>[13]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hou]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial intelligence in the clinical laboratory]]></article-title>
<source><![CDATA[Clin. Chim. Acta]]></source>
<year>2024</year>
<volume>559</volume>
</nlm-citation>
</ref>
<ref id="B14">
<label>[14]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tlapa]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review]]></article-title>
<source><![CDATA[Int. J. Environ. Res. Public Health]]></source>
<year>2022</year>
<volume>19</volume>
<numero>15</numero>
<issue>15</issue>
</nlm-citation>
</ref>
<ref id="B15">
<label>[15]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tortorella]]></surname>
<given-names><![CDATA[G. L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Effects of contingencies on healthcare 4.0 technologies adoption and barriers in emerging economies]]></article-title>
<source><![CDATA[Technol. Forecast Soc. Change]]></source>
<year>2020</year>
<volume>156</volume>
</nlm-citation>
</ref>
<ref id="B16">
<label>[16]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ibrahim]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Augmentation in Healthcare: Augmented Biosignal Using Deep Learning and Tensor Representation]]></article-title>
<source><![CDATA[J. Healthc Eng.]]></source>
<year>2021</year>
<volume>2021</volume>
</nlm-citation>
</ref>
<ref id="B17">
<label>[17]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lee]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Transforming hospital emergency department workflow and patient care]]></article-title>
<source><![CDATA[Interfaces]]></source>
<year>2015</year>
<volume>45</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>58-82</page-range></nlm-citation>
</ref>
<ref id="B18">
<label>[18]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Arcidiacono]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Pieroni]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The revolution Lean Six Sigma 4.0]]></article-title>
<source><![CDATA[Int. J. Adv. Sci. Eng. Inf. Technol.]]></source>
<year>2018</year>
<volume>8</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>141-9</page-range></nlm-citation>
</ref>
<ref id="B19">
<label>[19]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mast]]></surname>
<given-names><![CDATA[J. De]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Process improvement in healthcare: Overall resource efficiency]]></article-title>
<source><![CDATA[Qual. Reliab. Eng. Int.]]></source>
<year>2011</year>
<volume>27</volume>
<numero>8</numero>
<issue>8</issue>
<page-range>1095-106</page-range></nlm-citation>
</ref>
<ref id="B20">
<label>[20]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Marshall]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Selecting a dynamic simulation modeling method for health care delivery research - Part 2: Report of the ISPOR dynamic simulation modeling emerging good practices task force]]></article-title>
<source><![CDATA[Value Health]]></source>
<year>2015</year>
<volume>18</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>147-60</page-range></nlm-citation>
</ref>
<ref id="B21">
<label>[21]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ismael]]></surname>
<given-names><![CDATA[A. M.]]></given-names>
</name>
<name>
<surname><![CDATA[&#350;engür]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deep learning approaches for COVID-19 detection based on chest X-ray images]]></article-title>
<source><![CDATA[Expert. Syst. Appl.]]></source>
<year>2021</year>
<volume>164</volume>
</nlm-citation>
</ref>
<ref id="B22">
<label>[22]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Arreola Minjarez]]></surname>
<given-names><![CDATA[J. I.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Detection of COVID-19 Lung Lesions in Computed Tomography Images Using Deep Learning]]></article-title>
<source><![CDATA[Rev. Mex. Ing. Biomed.]]></source>
<year>2022</year>
<volume>43</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>7-18</page-range></nlm-citation>
</ref>
<ref id="B23">
<label>[23]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Indraswari]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<source><![CDATA[Brain Tumor Detection on Magnetic Resonance Imaging (MRI) Images Using Convolutional Neural Network (CNN)]]></source>
<year>2022</year>
<conf-name><![CDATA[ 9International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)]]></conf-name>
<conf-date>2022</conf-date>
<conf-loc>Jakarta, Indonesia </conf-loc>
<page-range>367-73</page-range></nlm-citation>
</ref>
<ref id="B24">
<label>[24]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Guizani]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Guizani]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Gharsallaoui]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<source><![CDATA[A Hybrid CNN-SVM Prediction Approach for Breast Cancer Ultrasound Imaging]]></source>
<year>2023</year>
<conf-name><![CDATA[ International Wireless Communications and Mobile Computing( IWCMC)]]></conf-name>
<conf-date>2023</conf-date>
<conf-loc>Marrakesh, Morocco </conf-loc>
<page-range>1574-8</page-range></nlm-citation>
</ref>
<ref id="B25">
<label>[25]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Benitez Baltazar]]></surname>
<given-names><![CDATA[V. H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Autonomic Face Mask Detection with Deep Learning: an IoT Application]]></article-title>
<source><![CDATA[Rev. Mex. Ing. Biomed.]]></source>
<year>2021</year>
<volume>42</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>160-70</page-range></nlm-citation>
</ref>
<ref id="B26">
<label>[26]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fan]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Huo]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
</person-group>
<source><![CDATA[A review of one-stage detection algorithms in autonomous driving]]></source>
<year>2020</year>
<conf-name><![CDATA[ 4CAA International Conference on Vehicular Control and Intelligence (CVCI)]]></conf-name>
<conf-date>2020</conf-date>
<conf-loc>Hangzhou, China </conf-loc>
<page-range>210-4</page-range></nlm-citation>
</ref>
<ref id="B27">
<label>[27]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tang]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
</person-group>
<source><![CDATA[A Two-stage Raman Imaging Denoising Algorithm Based on Deep Learning]]></source>
<year>2022</year>
<conf-name><![CDATA[ Asia Communications and Photonics Conference (ACP)]]></conf-name>
<conf-date>2022</conf-date>
<conf-loc>Shenzhen, China </conf-loc>
<page-range>2096-9</page-range></nlm-citation>
</ref>
<ref id="B28">
<label>[28]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Redmon]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<source><![CDATA[You only look once: Unified, real-time object detection]]></source>
<year>2016</year>
<conf-name><![CDATA[ IEEE Conference on Computer Vision and Pattern Recognition (CVPR)]]></conf-name>
<conf-date>2016</conf-date>
<conf-loc>Las Vegas, NV, USA </conf-loc>
<page-range>779-88</page-range></nlm-citation>
</ref>
<ref id="B29">
<label>[29]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[S.-H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A surface defect detection system for golden diamond pineapple based on CycleGAN and YOLOv4]]></article-title>
<source><![CDATA[J. King Saud Univ. - Comput. Inf. Sci.]]></source>
<year>2022</year>
<volume>34</volume>
<numero>10</numero>
<issue>10</issue>
<page-range>8041-53</page-range></nlm-citation>
</ref>
<ref id="B30">
<label>[30]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kimeu]]></surname>
<given-names><![CDATA[J. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deep learning-based mobile application for the enhancement of pneumonia medical imaging analysis: A case-study of West-Meru Hospital]]></article-title>
<source><![CDATA[Inform. Med. Unlocked]]></source>
<year>2024</year>
<volume>50</volume>
</nlm-citation>
</ref>
<ref id="B31">
<label>[31]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shahinfar]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Meek]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Falzon]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[How many images do I need?&#8217; Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring]]></article-title>
<source><![CDATA[Ecol. Inform.]]></source>
<year>2020</year>
<volume>57</volume>
</nlm-citation>
</ref>
<ref id="B32">
<label>[32]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cruz]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence]]></article-title>
<source><![CDATA[Comput. Electron. Agric.]]></source>
<year>2019</year>
<volume>157</volume>
<page-range>63-76</page-range></nlm-citation>
</ref>
<ref id="B33">
<label>[33]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Brown]]></surname>
<given-names><![CDATA[D. E.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data]]></article-title>
<source><![CDATA[BioData Min.]]></source>
<year>2022</year>
<volume>15</volume>
<numero>1</numero>
<issue>1</issue>
</nlm-citation>
</ref>
<ref id="B34">
<label>[34]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
</person-group>
<source><![CDATA[Key Components of Deep Metric Learning]]></source>
<year>2022</year>
<conf-name><![CDATA[ 2International Conference on Consumer Electronics and Computer Engineering (ICCECE)]]></conf-name>
<conf-date>2022</conf-date>
<conf-loc>Guangzhou, China </conf-loc>
<page-range>648-51</page-range></nlm-citation>
</ref>
<ref id="B35">
<label>[35]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gonzalez-Huitron]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4]]></article-title>
<source><![CDATA[Comput. Electron. Agric.]]></source>
<year>2021</year>
<volume>181</volume>
</nlm-citation>
</ref>
<ref id="B36">
<label>[36]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Anh]]></surname>
<given-names><![CDATA[P. T. Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Thuyet]]></surname>
<given-names><![CDATA[D. Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Kobayashi]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Image classification of root-trimmed garlic using multi-label and multi-class classification with deep convolutional neural network]]></article-title>
<source><![CDATA[Postharvest Biol. Technol.]]></source>
<year>2022</year>
<volume>190</volume>
</nlm-citation>
</ref>
<ref id="B37">
<label>[37]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Glu&#269;ina]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Automated Detection and Classification of Returnable Packaging Based on YOLOV4 Algorithm]]></article-title>
<source><![CDATA[Appl. Sci.]]></source>
<year>2022</year>
<volume>12</volume>
<numero>21</numero>
<issue>21</issue>
</nlm-citation>
</ref>
<ref id="B38">
<label>[38]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ke]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<source><![CDATA[Empowering Intelligent Home Safety: Indoor Family Fall Detection with YOLOv5]]></source>
<year>2023</year>
<conf-name><![CDATA[ IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)]]></conf-name>
<conf-date>2023</conf-date>
<conf-loc>Abu Dhabi, United Arab Emirates </conf-loc>
<page-range>0942-9</page-range></nlm-citation>
</ref>
<ref id="B39">
<label>[39]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bhat]]></surname>
<given-names><![CDATA[G. P.]]></given-names>
</name>
<name>
<surname><![CDATA[Cholli]]></surname>
<given-names><![CDATA[N. G.]]></given-names>
</name>
</person-group>
<source><![CDATA[Effective object detection using Tensorflow facilitated YOLOv3 model]]></source>
<year>2021</year>
<conf-name><![CDATA[ IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)]]></conf-name>
<conf-date>2021</conf-date>
<conf-loc>Bangalore, India </conf-loc>
<page-range>1-8</page-range></nlm-citation>
</ref>
<ref id="B40">
<label>[40]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Francies]]></surname>
<given-names><![CDATA[M. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Ata]]></surname>
<given-names><![CDATA[M. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Mohamed]]></surname>
<given-names><![CDATA[M. A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms]]></article-title>
<source><![CDATA[Concurr. Comput. Pract. Exp.]]></source>
<year>2022</year>
<volume>34</volume>
<numero>1</numero>
<issue>1</issue>
</nlm-citation>
</ref>
<ref id="B41">
<label>[41]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ammu]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Sinha]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<source><![CDATA[Small Segment Emphasized Performance Evaluation Metric for Medical Images]]></source>
<year>2020</year>
<conf-name><![CDATA[ International Conference on Signal Processing and Communications (SPCOM)]]></conf-name>
<conf-date>2020</conf-date>
<conf-loc>Bangalore, India </conf-loc>
<page-range>1-5</page-range></nlm-citation>
</ref>
<ref id="B42">
<label>[42]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Raj]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Gupta]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Malhotra]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<source><![CDATA[License Plate Recognition System using Yolov5 and CNN]]></source>
<year>2022</year>
<conf-name><![CDATA[ 8International Conference on Advanced Computing and Communication Systems, ICACCS]]></conf-name>
<conf-date>2022</conf-date>
<conf-loc>Coimbatore, India </conf-loc>
<page-range>372-7</page-range></nlm-citation>
</ref>
<ref id="B43">
<label>[43]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Njoroge]]></surname>
<given-names><![CDATA[S. W.]]></given-names>
</name>
<name>
<surname><![CDATA[Nichols]]></surname>
<given-names><![CDATA[J. H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Risk management in the clinical laboratory]]></article-title>
<source><![CDATA[Ann. Lab. Med.]]></source>
<year></year>
<volume>34</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>274-8</page-range></nlm-citation>
</ref>
<ref id="B44">
<label>[44]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bania]]></surname>
<given-names><![CDATA[R. K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Ensemble of deep transfer learning models for real-time automatic detection of face mask]]></article-title>
<source><![CDATA[Multimed. Tools Appl.]]></source>
<year>2023</year>
<volume>82</volume>
<numero>16</numero>
<issue>16</issue>
<page-range>25131-53</page-range></nlm-citation>
</ref>
<ref id="B45">
<label>[45]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Juanatas]]></surname>
<given-names><![CDATA[I. C.]]></given-names>
</name>
<name>
<surname><![CDATA[Juanatas]]></surname>
<given-names><![CDATA[R. A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Convolution Neural Network Approach for Facial Mask Detection]]></source>
<year>2023</year>
<conf-name><![CDATA[ 12Global Conference on Consumer Electronics (GCCE)]]></conf-name>
<conf-date>2023</conf-date>
<conf-loc>Nara, Japan </conf-loc>
<page-range>1152-5</page-range></nlm-citation>
</ref>
<ref id="B46">
<label>[46]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kamal]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Raj]]></surname>
<given-names><![CDATA[R. J. R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Harnessing deep learning for blood quality assurance through complete blood cell count detection]]></article-title>
<source><![CDATA[e-Prime - Adv. Electr. Eng. Electron.Energy]]></source>
<year>2024</year>
<volume>7</volume>
</nlm-citation>
</ref>
<ref id="B47">
<label>[47]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Henderson]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Ferrari]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
</person-group>
<source><![CDATA[End-to-end training of object class detectors for mean average precision]]></source>
<year>2017</year>
<conf-name><![CDATA[ 13Asian Conference on Computer Vision]]></conf-name>
<conf-loc>Taipei, Taiwan </conf-loc>
<page-range>198-213</page-range></nlm-citation>
</ref>
<ref id="B48">
<label>[48]</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[See]]></surname>
<given-names><![CDATA[J. E.]]></given-names>
</name>
</person-group>
<source><![CDATA[Visual Inspection: A Review of the Literature]]></source>
<year>2012</year>
</nlm-citation>
</ref>
<ref id="B49">
<label>[49]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Graybeal]]></surname>
<given-names><![CDATA[B. A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Visual Inspection of Highway Bridges]]></article-title>
<source><![CDATA[J. Nondestruct. Eval.]]></source>
<year>2002</year>
<volume>21</volume>
<page-range>67-83</page-range></nlm-citation>
</ref>
<ref id="B50">
<label>[50]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Stallard]]></surname>
<given-names><![CDATA[M. M.]]></given-names>
</name>
<name>
<surname><![CDATA[MacKenzie]]></surname>
<given-names><![CDATA[C. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Peters]]></surname>
<given-names><![CDATA[F. E.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A probabilistic model to estimate visual inspection error for metalcastings given different training and judgment types, environmental and human factors, and percent of defects]]></article-title>
<source><![CDATA[J. Manuf. Syst.]]></source>
<year>2018</year>
<volume>48</volume>
<page-range>97-106</page-range></nlm-citation>
</ref>
<ref id="B51">
<label>[51]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Plebani]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Quality indicators in laboratory medicine: A fundamental tool for quality and patient safety]]></article-title>
<source><![CDATA[Clin. Biochem.]]></source>
<year>2013</year>
<volume>46</volume>
<numero>13-14</numero>
<issue>13-14</issue>
<page-range>1170-4</page-range></nlm-citation>
</ref>
<ref id="B52">
<label>[52]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sciacovelli]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Quality Indicators in Laboratory Medicine: The status of the progress of IFCC Working Group &#8216;laboratory Errors and Patient Safety&#8217; project]]></article-title>
<source><![CDATA[Clin. Chem. Lab. Med.]]></source>
<year>2017</year>
<volume>55</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>348-57</page-range></nlm-citation>
</ref>
<ref id="B53">
<label>[53]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shahangian]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Snyder]]></surname>
<given-names><![CDATA[S. R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Laboratory medicine quality indicators: A review of the literature]]></article-title>
<source><![CDATA[Am. J. Clin. Pathol.]]></source>
<year>2009</year>
<volume>131</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>418-31</page-range></nlm-citation>
</ref>
<ref id="B54">
<label>[54]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kuriki]]></surname>
<given-names><![CDATA[P. E. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Kitamura]]></surname>
<given-names><![CDATA[F. C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial Intelligence in Radiology: A Private Practice Perspective From a Large Health System in Latin America]]></article-title>
<source><![CDATA[Semin. Roentgenol.]]></source>
<year>2023</year>
<volume>58</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>203-7</page-range></nlm-citation>
</ref>
<ref id="B55">
<label>[55]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pandian]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Vedanarayanan]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<name>
<surname><![CDATA[Ravi Kumar]]></surname>
<given-names><![CDATA[D. N. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Rajakumar]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Detection and classification of lung cancer using CNN and Google net]]></article-title>
<source><![CDATA[Meas.: Sens.]]></source>
<year>2022</year>
<volume>24</volume>
</nlm-citation>
</ref>
<ref id="B56">
<label>[56]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shahin]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Nassif]]></surname>
<given-names><![CDATA[A. B.]]></given-names>
</name>
<name>
<surname><![CDATA[Alsabek]]></surname>
<given-names><![CDATA[M. B.]]></given-names>
</name>
</person-group>
<source><![CDATA[COVID-19 Electrocardiograms Classification using CNN Models]]></source>
<year>2021</year>
<conf-name><![CDATA[ 14International Conference on Developments in eSystems Engineering, DeSE]]></conf-name>
<conf-date>2021</conf-date>
<conf-loc>Sharjah, Emiratos Árabes Unidos </conf-loc>
<page-range>448-52</page-range></nlm-citation>
</ref>
<ref id="B57">
<label>[57]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sandoval-Cuellar]]></surname>
<given-names><![CDATA[H. J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Image-based Glaucoma Classification Using Fundus Images and Deep Learning]]></article-title>
<source><![CDATA[Rev. Mex. Ing. Biomed.]]></source>
<year>2021</year>
<volume>42</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>28-41</page-range></nlm-citation>
</ref>
<ref id="B58">
<label>[58]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Haq]]></surname>
<given-names><![CDATA[A. U.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system]]></article-title>
<source><![CDATA[J. Ambient. Intell. Humaniz. Comput.]]></source>
<year>2022</year>
<volume>14</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>4695-706</page-range></nlm-citation>
</ref>
<ref id="B59">
<label>[59]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Patgiri]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Ajantha]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<name>
<surname><![CDATA[Bhuvaneswari]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Subramaniyaswamy]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
</person-group>
<source><![CDATA[Intelligent Defect Detection System in Pharmaceutical Blisters Using YOLOv7]]></source>
<year>2024</year>
<conf-name><![CDATA[ SecondInternational Conference on Emerging Trends in Information Technology and Engineering (ICETITE)]]></conf-name>
<conf-date>2024</conf-date>
<conf-loc>Vellore, India </conf-loc>
<page-range>1-7</page-range></nlm-citation>
</ref>
<ref id="B60">
<label>[60]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Pang]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Zeng]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Hu]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[ME-YOLO: Improved YOLOv5 for Detecting Medical Personal Protective Equipment]]></article-title>
<source><![CDATA[Appl. Sci.]]></source>
<year>2022</year>
<volume>12</volume>
<numero>23</numero>
<issue>23</issue>
</nlm-citation>
</ref>
<ref id="B61">
<label>[61]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Do]]></surname>
<given-names><![CDATA[Q. T.]]></given-names>
</name>
<name>
<surname><![CDATA[Chaudri]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<source><![CDATA[Creating Computer Vision Models for Respiratory Status Detection]]></source>
<year>2022</year>
<conf-name><![CDATA[ 44Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)]]></conf-name>
<conf-date>2022</conf-date>
<conf-loc>Glasgow, Reino Unido </conf-loc>
<page-range>1350-3</page-range></nlm-citation>
</ref>
<ref id="B62">
<label>[62]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Aguilar Bucheli]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial Intelligence in Medical Education: Latin American context]]></article-title>
<source><![CDATA[Metro Ciencia]]></source>
<year>2023</year>
<volume>31</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>21-34</page-range></nlm-citation>
</ref>
<ref id="B63">
<label>[63]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Delpino]]></surname>
<given-names><![CDATA[F. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Emergency department use and Artificial Intelligence in Pelotas: design and baseline results]]></article-title>
<source><![CDATA[Rev. Bras. Epidemiol.]]></source>
<year>2023</year>
<volume>26</volume>
</nlm-citation>
</ref>
<ref id="B64">
<label>[64]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Garcia Alonso]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Thoene]]></surname>
<given-names><![CDATA[U.]]></given-names>
</name>
<name>
<surname><![CDATA[Davila Benavides]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Digital Health and Artificial Intelligence: Advancing Healthcare Provision in Latin America]]></article-title>
<source><![CDATA[IT Prof.]]></source>
<year>2022</year>
<volume>24</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>62-8</page-range></nlm-citation>
</ref>
<ref id="B65">
<label>[65]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kitamura]]></surname>
<given-names><![CDATA[F. C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Forging Connections in Latin America to Advance AI in Radiology]]></article-title>
<source><![CDATA[Radiol. Artif. Intell.]]></source>
<year>2022</year>
<volume>4</volume>
<numero>5</numero>
<issue>5</issue>
</nlm-citation>
</ref>
<ref id="B66">
<label>[66]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Álvarez Vega]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Quirós Mora]]></surname>
<given-names><![CDATA[L. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Cortés Badilla]]></surname>
<given-names><![CDATA[M. V.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Inteligencia artificial y aprendizaje automático en medicina]]></article-title>
<source><![CDATA[Rev. Med. Sinerg.]]></source>
<year>2020</year>
<volume>5</volume>
<numero>8</numero>
<issue>8</issue>
</nlm-citation>
</ref>
<ref id="B67">
<label>[67]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fierro]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Pérez]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Mora]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Predicting Unplanned Readmissions with Highly Unstructured Data]]></article-title>
<source><![CDATA[arXiv:2003]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B68">
<label>[68]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jamei]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[redicting all-cause risk of 30-day hospital readmission using artificial neural networks]]></article-title>
<source><![CDATA[PLoS One]]></source>
<year>2017</year>
<volume>12</volume>
<numero>7</numero>
<issue>7</issue>
</nlm-citation>
</ref>
<ref id="B69">
<label>[69]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chang]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A mobile vision inspection system for tiny defect detection on smooth car-body surfaces based on deep ensemble learning]]></article-title>
<source><![CDATA[Meas. Sci. Technol.]]></source>
<year>2019</year>
<volume>30</volume>
<numero>12</numero>
<issue>12</issue>
</nlm-citation>
</ref>
<ref id="B70">
<label>[70]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rachman]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Ratnayake]]></surname>
<given-names><![CDATA[R. M. C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning approach for risk-based inspection screening assessment]]></article-title>
<source><![CDATA[Reliab. Eng. Syst. Saf.]]></source>
<year>2019</year>
<volume>185</volume>
<page-range>518-32</page-range></nlm-citation>
</ref>
<ref id="B71">
<label>[71]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Aust]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Pons]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Comparative Analysis of Human Operators and Advanced Technologies in the Visual Inspection of Aero Engine Blades]]></article-title>
<source><![CDATA[Appl. Sci.]]></source>
<year>2022</year>
<volume>12</volume>
<numero>4</numero>
<issue>4</issue>
</nlm-citation>
</ref>
<ref id="B72">
<label>[72]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wosner]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Farjon]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Bar-Hillel]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Object detection in agricultural contexts: A multiple resolution benchmark and comparison to human]]></article-title>
<source><![CDATA[Comput. Electron. Agric.]]></source>
<year>2021</year>
<volume>189</volume>
</nlm-citation>
</ref>
<ref id="B73">
<label>[73]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kim]]></surname>
<given-names><![CDATA[T. Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A Deep Learning Technique for Optical Inspection of Color Contact Lenses]]></article-title>
<source><![CDATA[Appl. Sci.]]></source>
<year>2023</year>
<volume>13</volume>
<numero>10</numero>
<issue>10</issue>
</nlm-citation>
</ref>
<ref id="B74">
<label>[74]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chan]]></surname>
<given-names><![CDATA[K. Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Ball bonding inspections using a conjoint framework with machine learning and human judgement]]></article-title>
<source><![CDATA[Appl. Soft. Comput.]]></source>
<year>2021</year>
<volume>102</volume>
</nlm-citation>
</ref>
<ref id="B75">
<label>[75]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rio-Torto]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Hybrid Quality Inspection for the Automotive Industry: Replacing the Paper-Based Conformity List through Semi-Supervised Object Detection and Simulated Data]]></article-title>
<source><![CDATA[Appl. Sci.]]></source>
<year>2022</year>
<volume>12</volume>
<numero>11</numero>
<issue>11</issue>
</nlm-citation>
</ref>
<ref id="B76">
<label>[76]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shaikh]]></surname>
<given-names><![CDATA[M. S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Performance evaluation of a coagulation laboratory using Sigma metrics]]></article-title>
<source><![CDATA[Int. J. Health Care Qual. Assur.]]></source>
<year>2018</year>
<volume>31</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>600-8</page-range></nlm-citation>
</ref>
<ref id="B77">
<label>[77]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cheng]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[MicroCrack-Net: A Deep Neural Network with Outline Profile-Guided Feature Augmentation and Attention-Based Multiscale Fusion for MicroCrack Detection of Tantalum Capacitors]]></article-title>
<source><![CDATA[IEEE Trans. Aerosp. Electron. Syst.]]></source>
<year>2022</year>
<volume>58</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>5141-52</page-range></nlm-citation>
</ref>
<ref id="B78">
<label>[78]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Feng]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Dong]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Using deep neural network with small dataset to predict material defects]]></article-title>
<source><![CDATA[Mater. Des.]]></source>
<year>2019</year>
<volume>162</volume>
<page-range>300-10</page-range></nlm-citation>
</ref>
<ref id="B79">
<label>[79]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[He]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Channel pruned YOLO V5s-based deep learning approach for rapid and accurate apple fruitlet detection before fruit thinning]]></article-title>
<source><![CDATA[Biosyst. Eng.]]></source>
<year>2021</year>
<volume>210</volume>
<page-range>271-81</page-range></nlm-citation>
</ref>
<ref id="B80">
<label>[80]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Girshick]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<source><![CDATA[Fast R-CNN]]></source>
<year>2015</year>
<conf-name><![CDATA[ IEEE International Conference on Computer Vision (ICCV)]]></conf-name>
<conf-date>2015</conf-date>
<conf-loc>Santiago, Chile </conf-loc>
<page-range>1440-8</page-range></nlm-citation>
</ref>
<ref id="B81">
<label>[81]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ren]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[aster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks]]></article-title>
<source><![CDATA[IEEE Trans. Pattern. Anal. Mach. Intell.]]></source>
<year>2017</year>
<volume>39</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>1137-49</page-range></nlm-citation>
</ref>
<ref id="B82">
<label>[82]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[He]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<source><![CDATA[Mask R-CNN]]></source>
<year>2017</year>
<conf-name><![CDATA[ IEEE International Conference on Computer Vision (ICCV)]]></conf-name>
<conf-date>2017</conf-date>
<conf-loc>Venecia, Italia </conf-loc>
<page-range>2980-8</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
