Skinner Garrett, Chen Tina, Jentis Gabriel, Liu Yao, McCulloh Christopher, Harzman Alan, Huang Emily, Kalady Matthew, Kim Peter
Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
Activ Surgical, University at Buffalo, Buffalo, NY, USA.
NPJ Digit Med. 2024 Apr 22;7(1):99. doi: 10.1038/s41746-024-01095-8.
Surgical artificial intelligence (AI) has the potential to improve patient safety and clinical outcomes. To date, training such AI models to identify tissue anatomy requires annotations by expensive and rate-limiting surgical domain experts. Herein, we demonstrate and validate a methodology to obtain high quality surgical tissue annotations through crowdsourcing of non-experts, and real-time deployment of multimodal surgical anatomy AI model in colorectal surgery.
手术人工智能（AI）有潜力提高患者安全性和临床疗效。迄今为止，训练此类人工智能模型来识别组织解剖结构需要由昂贵且数量有限的外科领域专家进行标注。在此，我们展示并验证了一种通过非专家众包来获取高质量手术组织标注，并在结直肠手术中实时部署多模态手术解剖人工智能模型的方法。
