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

Abstract

Background: 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.

Objectives: 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.

Materials and methods: 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.

Results: 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.

Conclusions and significance: Surgical videos using video inpainting and stabilization techniques with AI were beneficial for educating trainees, especially participants with less anatomical knowledge and surgical experience.