Tajiri Ayaka, Ishihara Ryu, Kato Yusuke, Inoue Takahiro, Matsueda Katsunori, Miyake Muneaki, Waki Kotaro, Shimamoto Yusaku, Fukuda Hiromu, Matsuura Noriko, Egawa Satoshi, Yamaguchi Shinjiro, Ogiyama Hideharu, Ogiso Kiyoshi, Nishida Tsutomu, Aoi Kenji, Tada Tomohiro
Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan.
AI Medical Service Inc, Tokyo, Japan.
Sci Rep. 2022 Apr 23;12(1):6677. doi: 10.1038/s41598-022-10739-2.
Previous reports have shown favorable performance of artificial intelligence (AI) systems for diagnosing esophageal squamous cell carcinoma (ESCC) compared with endoscopists. However, these findings don't reflect performance in clinical situations, as endoscopists classify lesions based on both magnified and non-magnified videos, while AI systems often use only a few magnified narrow band imaging (NBI) still images. We evaluated the performance of the AI system in simulated clinical situations. We used 25,048 images from 1433 superficial ESCC and 4746 images from 410 noncancerous esophagi to construct our AI system. For the validation dataset, we took NBI videos of suspected superficial ESCCs. The AI system diagnosis used one magnified still image taken from each video, while 19 endoscopists used whole videos. We used 147 videos and still images including 83 superficial ESCC and 64 non-ESCC lesions. The accuracy, sensitivity and specificity for the classification of ESCC were, respectively, 80.9% [95% CI 73.6-87.0], 85.5% [76.1-92.3], and 75.0% [62.6-85.0] for the AI system and 69.2% [66.4-72.1], 67.5% [61.4-73.6], and 71.5% [61.9-81.0] for the endoscopists. The AI system correctly classified all ESCCs invading the muscularis mucosa or submucosa and 96.8% of lesions ≥ 20 mm, whereas even the experts diagnosed some of them as non-ESCCs. Our AI system showed higher accuracy for classifying ESCC and non-ESCC than endoscopists. It may provide valuable diagnostic support to endoscopists.
先前的报告显示，与内镜医师相比，人工智能（AI）系统在诊断食管鳞状细胞癌（ESCC）方面表现良好。然而，这些发现并未反映出临床情况下的表现，因为内镜医师根据放大和未放大的视频对病变进行分类，而人工智能系统通常仅使用少数放大的窄带成像（NBI）静态图像。我们评估了人工智能系统在模拟临床情况下的表现。我们使用了来自1433例浅表ESCC的25048张图像和来自410例非癌性食管的4746张图像来构建我们的人工智能系统。对于验证数据集，我们采集了疑似浅表ESCC的NBI视频。人工智能系统诊断使用从每个视频中获取的一张放大静态图像，而19位内镜医师使用完整视频。我们使用了147个视频和静态图像，包括83例浅表ESCC和64例非ESCC病变。人工智能系统对ESCC分类的准确性、敏感性和特异性分别为80.9%[95%CI 73.6-87.0]、85.5%[76.1-92.3]和75.0%[62.6-85.0]，内镜医师的分别为69.2%[66.4-72.1]、67.5%[61.4-73.6]和71.5%[61.9-81.0]。人工智能系统正确分类了所有侵犯黏膜肌层或黏膜下层的ESCC以及96.8%的≥20mm病变，而即使是专家也将其中一些诊断为非ESCC。我们的人工智能系统在ESCC和非ESCC分类方面比内镜医师具有更高的准确性。它可能为内镜医师提供有价值的诊断支持。
