Gut:人工智能,高精确率识别息肉和腺瘤
创作:小扣啊 审核:szx 2017年11月18日
  • 开发了一种利用深卷积神经网络模型实时评估大肠息肉的内镜视频图像的人工智能(AI)模型;
  • 用该模型检测了测试集中的125个内镜视频,其中19个息肉(15%)无法确认;
  • 在剩余的106个小型息肉中,该模型鉴别的准确性为94%,鉴别腺瘤的敏感性为98%,特异性为83%,阴性预测值为97%,阳性预测值为90%;
  • AI模型可以高准确度地区分增生性息肉中的小型腺瘤,但还需在患者的实际临床试验中进一步研究,以帮助决策是否切除的问题。
主编推荐语
蓝灿辉 | 热心肠先生
这是Gut刚刚online的一篇重要文章,研究者开发了基于深卷积神经网络模型的人工智能算法,对临床录制的肠镜视频进行学习,最终实现了高准确率的区分息肉和腺瘤的目标。人工智能用于肠镜检查,不只再是部分人想象中的技术,而是已经真真切切地发展到了临界爆发点。这是最有潜力的医学人工智能方向之一,值得相关人士特别关注。
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Gut [IF:19.819]

Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model

利用深度学习模型,在分析标准结肠镜原始视频记录过程中,实时区分腺瘤和小型增生性结肠息肉

10.1136/gutjnl-2017-314547

2017-10-24, Article

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BACKGROUND: 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'.
STUDY DESIGN AND METHODS: 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.
RESULTS: 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%.
CONCLUSIONS: 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.

First Authors:
Michael F Byrne

Correspondence Authors:
Michael F Byrne

All Authors:
Michael F Byrne,Nicolas Chapados,Florian Soudan,Clemens Oertel,Milagros Linares Pérez,Raymond Kelly,Nadeem Iqbal,Florent Chandelier,Douglas K Rex

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