Deep Learning Based Suture Training System

Background And Objectives: Surgical suturing is a fundamental skill that all medical and dental students learn during their education. Currently, the grading of students' suture skills in the medical faculty during general surgery training is relative, and students do not have the opportunity to learn specific techniques. Recent technological advances, however, have made it possible to classify and measure suture skills using artificial intelligence methods, such as Deep Learning (DL). This work aims to evaluate the success of surgical suture using DL techniques.

Methods: Six Convolutional Neural Network (CNN) models: VGG16, VGG19, Xception, Inception, MobileNet, and DensNet. We used a dataset of suture images containing two classes: successful and unsuccessful, and applied statistical metrics to compare the precision, recall, and F1 scores of the models.

Results: The results showed that Xception had the highest accuracy at 95 %, followed by MobileNet at 91 %, DensNet at 90 %, Inception at 84 %, VGG16 at 73 %, and VGG19 at 61 %. We also developed a graphical user interface that allows users to evaluate suture images by uploading them or using the camera. The images are then interpreted by the DL models, and the results are displayed on the screen.

Conclusions: The initial findings suggest that the use of DL techniques can minimize errors due to inexperience and allow physicians to use their time more efficiently by digitizing the process.