Abstract

LERMA SANCHEZ, Ángel Mario; MEXICANO SANTOYO, Adriana; VILLALOBOS CASTALDI, Fabiola Miroslaba  and  DAMIAN REYES, Pedro. An Automatic Classification of Anastomosis by Convolutional Neural Networks in Fetoscopic Video. RIDE. Rev. Iberoam. Investig. Desarro. Educ [online]. 2021, vol.11, n.22, e018.  Epub May 21, 2021. ISSN 2007-7467.  https://doi.org/10.23913/ride.v11i22.856.

Twin-twin transfusion syndrome (TTTS) is the result of uneven blood flow through placental vascular anastomosis (blood vessel connection) that link the two fetal circulations. Vascular anastomosis in the shared placenta are present in virtually all monocorionic twin pregnancies (MCs), but in only about 10% lead to twin-twin transfusion syndrome. Without intervention, the condition is often fatal for both twins. An alternative to TTTS treatment is the placental laser procedure known as fetal surgery, which consists, in a very general way, of splitting the placenta in two, by laser cauterization of blood vessels between fetuses, thus balancing blood flows.

Currently fetoscopic surgery is a procedure that is performed frequently in Mexico and the appropriate classification of anastomosis is vital for this surgery, since it represents the most recommended treatment. However, die to its degree of complexity, this surgical intervention presents multiple difficulties, such as fetuses moving during the procedure, the orientation of the video used is not suitable for a more accurate analysis, the field of view generated by the fetus is very small. Therefore, it is necessary to have a tool that helps the doctor to be able to differentiate and classify anastomosis in a more appropriate way. The objective of this work is to present the development of a computational tool that contributes to the automatic classification of anastomosis within a fetoscopic video through convolutional neural networks in a way that serves as a support for the unexperienced doctor in his training stage.

A DataSet (image set) was built from fetoscopic videos first unclassified, then cataloged into three categories, selected in conjunction with experts, using an computational tool specifically created for this purpose (VideoLabel). The data augmentation technique served to build artificial images from the actual ones already classified, since the number of tagged images were not sufficient; in the same context, the AlexNet architecture was selected to perform a learning transfer and be trained to obtain results above 90% effectiveness in the classifications made by the computational tool created. This data allows us to conclude that if we did not have a tool that would allow first-time physicians to train in the identification of anastomosis as a practice prior to fetal surgery, their training will take longer since it is done in situ in each fetal surgery. As a result of this research, the design of a software was generated with which it is feasible to automatically classify anastomosis from a fetoscopic video through a convolutional neural network with promising results as a tool to support doctors in their training stage, allowing them to carry out their training in a more agile and less timely way.

Keywords : Machine Learning; Supervised Learning; Artificial Intelligence; Twin to Twin Transfusion Syndrome.

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