Jin Jing, Zhang Qianqian, Dong Bill, Ma Tao, Mei Xuecan, Wang Xi, Song Shaofang, Peng Jie, Wu Aijiu, Dong Lanfang, Kong Derun
Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
School of Computer Science and Technology, University of Science and Technology of China, Hefei, China.
Front Oncol. 2022 Oct 20;12:927868. doi: 10.3389/fonc.2022.927868. eCollection 2022.
The artificial intelligence (AI)-assisted endoscopic detection of early gastric cancer (EGC) has been preliminarily developed. The currently used algorithms still exhibit limitations of large calculation and low-precision expression. The present study aimed to develop an endoscopic automatic detection system in EGC based on a mask region-based convolutional neural network (Mask R-CNN) and to evaluate the performance in controlled trials. For this purpose, a total of 4,471 white light images (WLIs) and 2,662 narrow band images (NBIs) of EGC were obtained for training and testing. In total, 10 of the WLIs (videos) were obtained prospectively to examine the performance of the RCNN system. Furthermore, 400 WLIs were randomly selected for comparison between the Mask R-CNN system and doctors. The evaluation criteria included accuracy, sensitivity, specificity, positive predictive value and negative predictive value. The results revealed that there were no significant differences between the pathological diagnosis with the Mask R-CNN system in the WLI test (χ = 0.189, P=0.664; accuracy, 90.25%; sensitivity, 91.06%; specificity, 89.01%) and in the NBI test (χ = 0.063, P=0.802; accuracy, 95.12%; sensitivity, 97.59%). Among 10 WLI real-time videos, the speed of the test videos was up to 35 frames/sec, with an accuracy of 90.27%. In a controlled experiment of 400 WLIs, the sensitivity of the Mask R-CNN system was significantly higher than that of experts (χ = 7.059, P=0.000; 93.00% VS 80.20%), and the specificity was higher than that of the juniors (χ = 9.955, P=0.000, 82.67% VS 71.87%), and the overall accuracy rate was higher than that of the seniors (χ = 7.009, P=0.000, 85.25% VS 78.00%). On the whole, the present study demonstrates that the Mask R-CNN system exhibited an excellent performance status for the detection of EGC, particularly for the real-time analysis of WLIs. It may thus be effectively applied to clinical settings.
人工智能（AI）辅助早期胃癌（EGC）的内镜检测已初步得到发展。目前使用的算法仍存在计算量大和精度表达低的局限性。本研究旨在基于基于掩膜区域的卷积神经网络（Mask R-CNN）开发一种EGC的内镜自动检测系统，并在对照试验中评估其性能。为此，共获取了4471张EGC的白光图像（WLI）和2662张窄带图像（NBI）用于训练和测试。总共前瞻性地获取了10段WLI（视频）以检验RCNN系统的性能。此外，随机选择400张WLI用于Mask R-CNN系统与医生之间的比较。评估标准包括准确性、敏感性、特异性、阳性预测值和阴性预测值。结果显示，在WLI测试中，Mask R-CNN系统的病理诊断与（χ = 0.189，P = 0.664；准确性，90.25%；敏感性，91.06%；特异性，89.01%）以及在NBI测试中（χ = 0.063，P = 0.802；准确性，95.12%；敏感性，97.59%）均无显著差异。在10段WLI实时视频中，测试视频的速度高达35帧/秒，准确性为90.27%。在400张WLI的对照实验中，Mask R-CNN系统的敏感性显著高于专家（χ = 7.059，P = 0.000；93.00%对80.20%），特异性高于初级医生（χ = 9.955，P = 0.000，82.67%对71.87%），总体准确率高于高级医生（χ = 7.009，P = 0.000，85.25%对78.00%）。总体而言，本研究表明Mask R-CNN系统在EGC检测方面表现出优异的性能状态，特别是对于WLI的实时分析。因此，它可能有效地应用于临床环境。
