Object and anatomical feature recognition in surgical video images based on a convolutional neural network.

PURPOSE: Artificial intelligence-enabled techniques can process large amounts of surgical data and may be utilized for clinical decision support to recognize or forecast adverse events in an actual intraoperative scenario. To develop an image-guided navigation technology that will help in surgical education, we explored the performance of a convolutional neural network (CNN)-based computer vision system in detecting intraoperative objects. METHODS: The surgical videos used for annotation were recorded during surgeries conducted in the Department of Surgery of Tokyo Women's Medical University from 2019 to 2020. Abdominal endoscopic images were cut out from manually captured surgical videos. An open-source programming framework for CNN was used to design a model that could recognize and segment objects in real time through IBM Visual Insights. The model was used to detect the GI tract, blood, vessels, uterus, forceps, ports, gauze and clips in the surgical images. RESULTS: The accuracy, precision and recall of the model were 83%, 80% and 92%, respectively. The mean average precision (mAP), the calculated mean of the precision for each object, was 91%. Among surgical tools, the highest recall and precision of 96.3% and 97.9%, respectively, were achieved for forceps. Among the anatomical structures, the highest recall and precision of 92.9% and 91.3%, respectively, were achieved for the GI tract. CONCLUSION: The proposed model could detect objects in operative images with high accuracy, highlighting the possibility of using AI-based object recognition techniques for intraoperative navigation. Real-time object recognition will play a major role in navigation surgery and surgical education.

Ulcerative colitis followed by the development of Behçet's disease.

A 34-year-old man who had a history of ulcerative colitis (UC) was admitted to our hospital with complaints of arthralgia, erythema nodosum, recurrent oral aphthous ulcers and bloody stools. A colonoscopy revealed multiple aphthous ulcers on his cecum and colon and also revealed a transmural ulcer on his rectum consistent with a diagnosis of UC. The patient was HLA-B51 positive. Based on clinical evidence [recurrent oral ulcers, skin lesions (erythema nodosum), positivity for pathergy test] this patient was diagnosed as having Behçet's disease with gastrointestinal involvement. We describe this rare case of Behçet's disease with colitis and discuss the difficulties in making a differential diagnosis between Behçet's disease and the inflammatory bowel diseases.