Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos)
Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos)
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DOI：
10.1016/j.gie.2019.08.018
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发表时间：
2020-01-01
影响因子：
7.7
通讯作者：
Hu, Bing
中科院分区：
文献类型：
--
作者：
Guo, LinJie;Xiao, Xiao;Hu, Bing
背景和目标：我们开发了一个计算机辅助诊断（CAD）系统，用于实时自动诊断癌前病变和早期食管鳞状细胞癌（ESCCs），以辅助诊断食管cancer.Methods：共6473窄带成像（NBI）图像，包括癌前病变，早期ESCCs，和非癌性病变，用于训练CAD系统。我们使用内窥镜图像和视频数据集验证了CAD系统。基于图像数据集生成CAD系统的受试者工作特性曲线。针对内窥镜图像的每个输入生成人工智能概率热图。在概率热图上，黄色表示癌性病变的可能性高，蓝色表示非癌性病变。当CAD系统检测到任何癌前病变或早期ESCC，感兴趣的病变被掩盖的color.Results：图像数据集包含1480恶性NBI图像从59个连续的癌病例（灵敏度，98.04%）和5191非癌NBI图像从2004例（特异性，95.03%）。曲线下面积为0.989。癌前病变或早期ESCC的视频数据集包括27个非放大视频（每帧灵敏度60.8%，每病变灵敏度100%）和20个放大视频（每帧灵敏度96.1%，每病变灵敏度100%）。未改变的全范围正常食管视频包括33个视频（每帧特异性99.9%，每例特异性90.9%）。结论：深度学习模型对内窥镜图像和视频数据集都表现出高灵敏度和特异性。在不久的将来，实时CAD系统具有很好的潜力，以帮助内镜医师诊断癌前病变和ESCC。
Background and Aims: We developed a system for computer-assisted diagnosis (CAD) for real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinomas (ESCCs) to assist the diagnosis of esophageal cancer.Methods: A total of 6473 narrow-band imaging (NBI) images, including precancerous lesions, early ESCCs, and noncancerous lesions, were used to train the CAD system. We validated the CAD system using both endoscopic images and video datasets. The receiver operating characteristic curve of the CAD system was generated based on image datasets. An artificial intelligence probability heat map was generated for each input of endoscopic images. The yellow color indicated high possibility of cancerous lesion, and the blue color indicated noncancerous lesions on the probability heat map. When the CAD system detected any precancerous lesion or early ESCCs, the lesion of interest was masked with color.Results: The image datasets contained 1480 malignant NBI images from 59 consecutive cancerous cases (sensitivity, 98.04%) and 5191 noncancerous NBI images from 2004 cases (specificity, 95.03%). The area under curve was 0.989. The video datasets of precancerous lesions or early ESCCs included 27 nonmagnifying videos (perframe sensitivity 60.8%, per-lesion sensitivity, 100%) and 20 magnifying videos (per-frame sensitivity 96.1%, per-lesion sensitivity, 100%). Unaltered full-range normal esophagus videos included 33 videos (per-frame specificity 99.9%, per-case specificity, 90.9%).Conclusions: A deep learning model demonstrated high sensitivity and specificity for both endoscopic images and video datasets. The real-time CAD system has a promising potential in the near future to assist endoscopists in diagnosing precancerous lesions and ESCCs.