LUCID：一个基于人工智能的实时腹腔镜镜片清洁度分类系统
LUCID: An AI-Powered System for Real-Time Laparoscopic Lens Cleanliness Classification.
摘要(中文译文)
背景 在微创手术（MIS）中，保持持续的光学清晰度对手术安全和效率至关重要。然而，镜头污染事件（LCEs），如起雾和碎屑积聚，仍会干扰可视化，需要频繁移除和清洁摄像头，从而延长手术时间，并与手术损伤相关。 目的 评估人工智能（AI）模型LUCID在检测腹腔镜镜头污染方面的性能。 方法 从德克萨斯州中部地区收集手术视频资料，并进行标注，将镜头状态分类为“清洁”或“污染”。在该数据集上训练AI模型以检测镜头污染。随后由该模型分析另外9例腹腔镜手术，并由人工审核者进行验证，以确定模型的真实性能。 结果 在LUCID分析并由审核者验证的9例病例中，该模型总体准确率为90.42%，精确率为94.45%，灵敏度为91.19%，特异度为88.80%，表明LUCID在评估微创手术光学清晰度方面具有应用潜力。 结论 AI增强的可视化评估为在微创手术中实现视觉清晰度的客观检测提供了一种有前景的方法。鉴于视野受损是已知问题且涉及严重的安全隐患，同时未来AI增强手术的应用需要清晰的可视化以实现高性能，清晰可视化的重要性比以往任何时候都更加突出。未来工作可着眼于标准化不同手术环境中的视觉清晰度，以减少临床结局的变异性。
Abstract
BackgroundIn minimally invasive surgery (MIS), maintaining consistent optical clarity is essential for surgical safety and efficiency. However, lens contamination events (LCEs) such as fogging and debris accumulation continue to disrupt visualization, requiring frequent camera removal and cleaning that prolongs operative time and is linked to surgical injuries.ObjectiveDetermine performance of an artificial intelligence (AI) model, LUCID, for the detection of laparoscopic lens contamination.MethodsSurgical video footage was collected from the central Texas region and annotated to classify the lens state as "clean" or "dirty." An AI model was trained on this dataset to detect lens contamination. 9 additional laparoscopic procedures were analyzed by the model and validated by human reviewers to determine true model performance.ResultsAcross the 9 cases analyzed by LUCID and validated by reviewers, the model achieved an overall accuracy of 90.42%, with a precision of 94.45%, sensitivity of 91.19%, and specificity of 88.80%, demonstrating potential for the use of LUCID to assess optical clarity in MIS.ConclusionAI-enhanced visualization assessment presents a promising approach to enable objective detection of visual clarity in MIS. With compromised vision being a known problem with serious safety concerns, and future applications for AI-enhanced surgery requiring clear visualization for high performance, the importance of clear visualization is paramount more than ever before. Future work may look to standardize visual clarity across operative settings to mitigate variability in clinical outcomes.
如何引用
AMA
Eshan Sayani, Faiza Quadri, Rishit Yokananth, Sriya Dommaraju, Lisa Du, Tony Zhu, et al. LUCID: An AI-Powered System for Real-Time Laparoscopic Lens Cleanliness Classification.. Surgical innovation. 2026; doi:10.1177/15533506261451747.
APA
Eshan Sayani, Faiza Quadri, Rishit Yokananth, Sriya Dommaraju, Lisa Du, Tony Zhu, et al (2026). LUCID: An AI-Powered System for Real-Time Laparoscopic Lens Cleanliness Classification.. Surgical innovation. https://doi.org/10.1177/15533506261451747
GB/T 7714
Eshan Sayani, Faiza Quadri, Rishit Yokananth, Sriya Dommaraju, Lisa Du, Tony Zhu, et al. LUCID: An AI-Powered System for Real-Time Laparoscopic Lens Cleanliness Classification.[J]. Surgical innovation, 2026 doi:10.1177/15533506261451747.
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