Artificial intelligence for optimizing otologic surgical video: effects of video inpainting and stabilization on microscopic view.
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 instabili...
| Publicado en: | Acta Oto-Laryngologica Vol. 146; no. 2; pp. 125 - 133 |
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| Autores principales: | , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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. 优化手术室学员的学习体验很重要；然而, 耳部解剖学和耳科手术对学员来说很难掌握。观看耳科手术通常有与诸如视觉干扰和不稳定等视频质量相关的限制。 我们旨在(1)通过使用人工智能(AI)技术提高手术视频(鼓室乳突切除术 [TM])的质量, 以及(2)通过学员的问卷调查评估经处理的视频的有效性。 我们使用人工智能处理的视频修复和稳定技术进行了前瞻性研究。在每个研究组中, 我们都招募了 21 名学员, 要求他们观看处理后的视频并完成问卷调查。 我们发现, 使用隐式神经表征(INR)模型的视频修复技术的手术视频对医学生(0.79 ± 0.58)识别出血点最有帮助。采用点特征匹配稳定技术的视频对初级住院医生 (0.91 ± 0.12) 和医学生 (0.78 ± 0.35) 更有帮助, 可以提高整体可视性并理解手术过程。 使用人工智能视频修复和稳定技术的手术视频有利于教授受训人员, 对于解剖知识和手术经验较少的参与者尤其如此。 |
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