Artificial intelligence for optimizing otologic surgical video: effects of video inpainting and stabilization on microscopic view

Optimizing the educational experience of trainees in the operating room is important; however, ear anatomy and otologic surgery are challenging for trainees to grasp. Viewing otologic surgeries often involves limitations related to video quality, such as visual disturbances and instability. We aimed to (1) improve the quality of surgical videos (tympanomastoidectomy [TM]) by using artificial intelligence (AI) techniques and (2) evaluate the effectiveness of processed videos through a questionnaire-based assessment from trainees. We conducted prospective study using video inpainting and stabilization techniques processed by AI. In each study set, we enrolled 21 trainees and asked them to watch processed videos and complete a questionnaire. Surgical videos with the video inpainting technique using the implicit neural representation (INR) model were found to be the most helpful for medical students (0.79 ± 0.58) in identifying bleeding focus. Videos with the stabilization technique via point feature matching were more helpful for low-grade residents (0.91 ± 0.12) and medical students (0.78 ± 0.35) in enhancing overall visibility and understanding surgical procedures. Surgical videos using video inpainting and stabilization techniques with AI were beneficial for educating trainees, especially participants with less anatomical knowledge and surgical experience.