Artificial Intelligence for Intraoperative Guidance: Using Semantic Segmentation to Identify Surgical Anatomy During Laparoscopic Cholecystectomy.
Artificial Intelligence for Intraoperative Guidance: Using Semantic Segmentation to Identify Surgical Anatomy During Laparoscopic Cholecystectomy.
复制标题
术中指导的人工智能：在腹腔镜胆囊切除术中使用语义分割来识别手术解剖。
DOI：
10.1097/sla.0000000000004594
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发表时间：
2022-08-01
影响因子：
9
通讯作者：
Alseidi A
中科院分区：
文献类型：
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作者：
Madani A;Namazi B;Altieri MS;Hashimoto DA;Rivera AM;Pucher PH;Navarrete-Welton A;Sankaranarayanan G;Brunt LM;Okrainec A;Alseidi A
目的：本研究的目的是开发和评估人工智能（AI）模型的性能，可以识别安全和危险区的解剖，和解剖标志在腹腔镜胆囊切除术（LC）的摘要背景数据：许多不良事件发生在手术过程中，由于错误的视觉感知和判断，导致解剖结构的误解。深度学习是人工智能的一个子领域，可用于提供术中实时指导。方法：开发和训练深度学习模型，以识别LC期间解剖、肝脏、胆囊和肝囊三角的安全（Go）和危险（No-Go）区域。由4名高容量外科医生进行注释。AI预测使用10倍交叉验证对专家外科医生的注释进行评估。主要结果是交叉愈合（IOU）和F1评分（经验证的空间相关性指数），次要结果是像素级准确性，灵敏度，特异性，±standard deviation.Results：AI模型在2627随机帧上进行训练，这些随机帧来自37个国家，136个机构和153名外科医生。AI识别Go区的平均IOU、F1评分、准确度、灵敏度和特异性分别为0.53（±0.24）、0.70（±0.28）、0.94（±0.05）、0.69（±0.20）。和0.94（±0.03）。对于禁区，这些指标分别为0.71（±0.29）、0.83（±0.31）、0.95（±0.06）、0.80（±0.21）和0.98（±0.05）。识别肝脏、胆囊和肝囊三角的平均IOU分别为0.86（±0.12）、0.72（±0.19）和0.65（±0.22）。这项技术最终可能被用于提供实时指导，并将不良事件的风险降至最低。
Objective:The aim of this study was to develop and evaluate the performance of artificial intelligence (AI) models that can identify safe and dangerous zones of dissection, and anatomical landmarks during laparoscopic cholecystectomy (LC).Summary Background Data:Many adverse events during surgery occur due to errors in visual perception and judgment leading to misinterpretation of anatomy. Deep learning, a subfield of AI, can potentially be used to provide real-time guidance intraoperatively.Methods:Deep learning models were developed and trained to identify safe (Go) and dangerous (No-Go) zones of dissection, liver, gallbladder, and hepatocystic triangle during LC. Annotations were performed by 4 high-volume surgeons. AI predictions were evaluated using 10-fold cross-validation against annotations by expert surgeons. Primary outcomes were intersection-over-union (IOU) and F1 score (validated spatial correlation indices), and secondary outcomes were pixel-wise accuracy, sensitivity, specificity,±standard deviation.Results:AI models were trained on 2627 random frames from 290 LC videos, procured from 37 countries, 136 institutions, and 153 surgeons. Mean IOU, F1 score, accuracy, sensitivity, and specificity for the AI to identify Go zones were 0.53 (±0.24), 0.70 (±0.28), 0.94 (±0.05), 0.69 (±0.20). and 0.94 (±0.03), respectively. For No-Go zones, these metrics were 0.71 (±0.29), 0.83 (±0.31), 0.95 (±0.06), 0.80 (±0.21), and 0.98 (±0.05), respectively. Mean IOU for identification of the liver, gallbladder, and hepatocystic triangle were: 0.86 (±0.12), 0.72 (±0.19), and 0.65 (±0.22), respectively.Conclusions:AI can be used to identify anatomy within the surgical field. This technology may eventually be used to provide real-time guidance and minimize the risk of adverse events.