Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model.
Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model.
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DOI：
10.1136/gutjnl-2017-314547
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发表时间：
2019-01
期刊：
影响因子：
24.5
通讯作者：
Rex DK
中科院分区：
文献类型：
--
作者：
Byrne MF;Chapados N;Soudan F;Oertel C;Linares Pérez M;Kelly R;Iqbal N;Chandelier F;Rex DK
一般来说，学术而不是社区内镜医师已经证明了足够的内镜鉴别准确性，使“切除和丢弃”的范例，为小型结肠直肠息肉可行。视频的计算机分析可能会消除内镜息肉解释中观察者间差异的障碍，并使“切除和切除”得到广泛接受。我们开发了一种人工智能（AI）模型，用于实时评估结直肠息肉的内窥镜视频图像。使用深度卷积神经网络模型。仅使用窄带成像视频帧，在相关多类之间平均分割。来自常规检查的未经修改的视频没有专门设计或适应AI分类，用于训练和验证模型。该模型在连续遇到的被证明是腺瘤或增生性息肉的小息肉的125个视频的单独系列上进行了测试。AI模型采用置信机制，没有产生足够的置信度来预测测试集中19个息肉的组织学，占息肉的15%。对于其余106个小息肉，模型的准确性为94%（95%CI 86%至97%），识别腺瘤的敏感性为98%（95%CI 92%至100%），特异性为83%（95%CI 67%至93%），阴性预测值为97%，阳性预测值为90%。在内窥镜视频上训练的AI模型可以高精度地区分小型腺瘤和增生性息肉。计划在活体患者临床试验环境中对该项目进行额外研究，以解决切除和丢弃问题。
In general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the ‘resect and discard’ paradigm for diminutive colorectal polyps workable. Computer analysis of video could potentially eliminate the obstacle of interobserver variability in endoscopic polyp interpretation and enable widespread acceptance of ‘resect and discard’. We developed an artificial intelligence (AI) model for real-time assessment of endoscopic video images of colorectal polyps. A deep convolutional neural network model was used. Only narrow band imaging video frames were used, split equally between relevant multiclasses. Unaltered videos from routine exams not specifically designed or adapted for AI classification were used to train and validate the model. The model was tested on a separate series of 125 videos of consecutively encountered diminutive polyps that were proven to be adenomas or hyperplastic polyps. The AI model works with a confidence mechanism and did not generate sufficient confidence to predict the histology of 19 polyps in the test set, representing 15% of the polyps. For the remaining 106 diminutive polyps, the accuracy of the model was 94% (95% CI 86% to 97%), the sensitivity for identification of adenomas was 98% (95% CI 92% to 100%), specificity was 83% (95% CI 67% to 93%), negative predictive value 97% and positive predictive value 90%. An AI model trained on endoscopic video can differentiate diminutive adenomas from hyperplastic polyps with high accuracy. Additional study of this programme in a live patient clinical trial setting to address resect and discard is planned.