Artificial Intelligence for Surgical Safety

Abstract

Objective: To develop a deep learning model to automatically segment hepatocystic anatomy and assess the criteria defining the critical view of safety (CVS) in laparoscopic cholecystectomy (LC). Background: Poor implementation and subjective interpretation of CVS contributes to the stable rates of bile duct injuries in LC. As CVS is assessed visually, this task can be automated by using computer vision, an area of artificial intelligence aimed at interpreting images. Methods: Still images from LC videos were annotated with CVS criteria and hepatocystic anatomy segmentation. A deep neural network comprising a segmentation model to highlight hepatocystic anatomy and a classification model to predict CVS criteria achievement was trained and tested using 5-fold cross validation. Intersection over union, average precision, and balanced accuracy were computed to evaluate the model performance versus the annotated ground truth. Results: A total of 2854 images from 201 LC videos were annotated and 402 images were further segmented. Mean intersection over union for segmentation was 66.6%. The model assessed the achievement of CVS criteria with a mean average precision and balanced accuracy of 71.9% and 71.4%, respectively. Conclusions: Deep learning algorithms can be trained to reliably segment hepatocystic anatomy and assess CVS criteria in still laparoscopic images. Surgical-technical partnerships should be encouraged to develop and evaluate deep learning models to improve surgical safety.

Dose and administration time of indocyanine green in near-infrared fluorescence cholangiography during laparoscopic cholecystectomy (DOTIG): study protocol for a randomised clinical trial

“… 14 One recent approach that combines artificial intelligence and machine learning has been applied to LC to try to improve the safety of the procedure. 15 On the other hand, intraoperative cholangiography (IOC) has been the most frequently used system for the prevention and early detection of BDI, as well as for the diagnosis of choledocholithiasis. 16 However, some of its disadvantages are the use of ionising radiation, its learning curve, the need for prior surgical dissection and other structural problems and needs.…”

Development and Validation of a Model for Laparoscopic Colorectal Surgical Instrument Recognition Using Convolutional Neural Network–Based Instance Segmentation and Videos of Laparoscopic Procedures

“…Recently, it has also been actively used in the research field of CV in surgery. 24,25 Semantic segmentation attempts to specifically understand the role of each pixel in an image through the process of dividing whole images into pixel groupings that can then be labeled and classified. The boundaries of each object can be delineated; therefore, dense pixel-based predictions can be achieved.…”

“…Semantic segmentation is a CNN-based CV approach that enables pixel-by-pixel image recognition. Recently, it has also been actively used in the research field of CV in surgery . Semantic segmentation attempts to specifically understand the role of each pixel in an image through the process of dividing whole images into pixel groupings that can then be labeled and classified.…”

Computer vision in surgery: from potential to clinical value

“…The endpoint of safe dissection of the hepatocystic triangle is to achieve the critical view of safety (CVS), a universally recommended checkpoint to conclusively identify hepatocystic anatomy and prevent the visual perception illusion causing 97% of major BDIs 43 , 44 . In this regard, Mascagni et al have developed a two-stage CV model to first segment surgical tools and fine-grained hepatocystic anatomy to then predict whether each of the three CVS criteria has been achieved 45 .…”