Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy
Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy
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DOI：
10.1038/s41598-019-50567-5
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发表时间：
2019-10-08
影响因子：
4.6
通讯作者：
Hamamoto, Ryuji
中科院分区：
文献类型：
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作者：
Yamada, Masayoshi;Saito, Yutaka;Hamamoto, Ryuji
内窥镜医师在结肠镜检查技能上的差距，主要是由于经验，已经被发现，迫切需要解决方案。因此，开发一种实时可靠的结直肠肿瘤检测系统被认为可以显著降低结肠镜检查过程中漏诊病变的风险。在此，我们开发了一个在结肠镜检查过程中自动检测结直肠癌早期体征的人工智能(AI)系统；AI系统显示，在验证集中，灵敏度和特异度分别为97.3%(95%可信区间=95.9%-98.4%)和99.0%(95%CI=98.6%-99.2%)，曲线下面积为0.975(95%CI=0.964-0.986)。在多体亚组和非多体亚组的灵敏度分别为98.0%(95%CI=96.6%-98.8%)和93.7%(95%CI=87.6%-96.9%)；为了加快检测速度，对训练模型中的张量值进行了分解，系统平均可以预测21.9ms/幅的癌变区域。这些发现表明，该系统足以支持内窥镜医生对光学结肠镜经常遗漏的非息肉样病变的高检测。这种人工智能系统可以实时提醒内窥镜医生，避免在结肠镜检查中遗漏非息肉等异常，提高对这种疾病的早期发现。
Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been identified, and solutions are critically needed. Hence, the development of a real-time robust detection system for colorectal neoplasms is considered to significantly reduce the risk of missed lesions during colonoscopy. Here, we develop an artificial intelligence (AI) system that automatically detects early signs of colorectal cancer during colonoscopy; the AI system shows the sensitivity and specificity are 97.3% (95% confidence interval [CI] = 95.9%-98.4%) and 99.0% (95% CI = 98.6%-99.2%), respectively, and the area under the curve is 0.975 (95% CI = 0.964-0.986) in the validation set. Moreover, the sensitivities are 98.0% (95% CI = 96.6%-98.8%) in the polypoid subgroup and 93.7% (95% CI = 87.6%-96.9%) in the non-polypoid subgroup; To accelerate the detection, tensor metrics in the trained model was decomposed, and the system can predict cancerous regions 21.9 ms/image on average. These findings suggest that the system is sufficient to support endoscopists in the high detection against non-polypoid lesions, which are frequently missed by optical colonoscopy. This AI system can alert endoscopists in real-time to avoid missing abnormalities such as non-polypoid polyps during colonoscopy, improving the early detection of this disease.