Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Clin Transl Gastroenterol. 2020 Dec 22;12(1):e00282. doi: 10.14309/ctg.0000000000000282.
Conventional gastrointestinal (GI) endoscopy reports written by physicians are time consuming and might have obvious heterogeneity or omissions, impairing the efficiency and multicenter consultation potential. We aimed to develop and validate an image recognition-based structured report generation system (ISRGS) through a multicenter database and to assess its diagnostic performance.
First, we developed and evaluated an ISRGS combining real-time video capture, site identification, lesion detection, subcharacteristics analysis, and structured report generation. White light and chromoendoscopy images from patients with GI lesions were eligible for study inclusion. A total of 46,987 images from 9 tertiary hospitals were used to train, validate, and multicenter test (6:2:2). Moreover, 5,699 images were prospectively enrolled from Qilu Hospital of Shandong University to further assess the system in a prospective test set. The primary outcome was the diagnosis performance of GI lesions in multicenter and prospective tests.
The overall accuracy in identifying early esophageal cancer, early gastric cancer, early colorectal cancer, esophageal varices, reflux esophagitis, Barrett's esophagus, chronic atrophic gastritis, gastric ulcer, colorectal polyp, and ulcerative colitis was 0.8841 (95% confidence interval, 0.8775-0.8904) and 0.8965 (0.8883-0.9041) in multicenter and prospective tests, respectively. The accuracy of cecum and upper GI site identification were 0.9978 (0.9969-0.9984) and 0.8513 (0.8399-0.8620), respectively. The accuracy of staining discrimination was 0.9489 (0.9396-0.9568). The relative error of size measurement was 4.04% (range 0.75%-7.39%).
ISRGS is a reliable computer-aided endoscopic report generation system that might assist endoscopists working at various hospital levels to generate standardized and accurate endoscopy reports (http://links.lww.com/CTG/A485).
传统的胃肠（GI）内镜报告由医生书写，耗时且可能存在明显的异质性或遗漏，从而降低效率和限制多中心会诊的潜力。我们旨在通过多中心数据库开发和验证一种基于图像识别的结构化报告生成系统（ISRGS），并评估其诊断性能。
首先，我们开发并评估了一种 ISRGS，该系统结合了实时视频捕获、部位识别、病变检测、亚特征分析和结构化报告生成。GI 病变患者的白光和染色内镜图像符合研究纳入标准。来自 9 家三级医院的 46987 张图像用于训练、验证和多中心测试（6:2:2）。此外，前瞻性纳入了山东大学齐鲁医院的 5699 张图像，以进一步在前瞻性测试集中评估该系统。主要结局是多中心和前瞻性测试中 GI 病变的诊断性能。
在多中心和前瞻性测试中，识别早期食管癌、早期胃癌、早期结直肠癌、食管静脉曲张、反流性食管炎、Barrett 食管、慢性萎缩性胃炎、胃溃疡、结直肠息肉和溃疡性结肠炎的总体准确率分别为 0.8841（95%置信区间，0.8775-0.8904）和 0.8965（0.8883-0.9041）。盲肠和上 GI 部位识别的准确率分别为 0.9978（0.9969-0.9984）和 0.8513（0.8399-0.8620）。染色鉴别准确率为 0.9489（0.9396-0.9568）。大小测量的相对误差为 4.04%（范围 0.75%-7.39%）。
ISRGS 是一种可靠的计算机辅助内镜报告生成系统，可帮助不同级别医院的内镜医生生成标准化和准确的内镜报告（http://links.lww.com/CTG/A485）。
