Background and Aims

In the treatment of ulcerative colitis (UC), an incremental benefit of achieving histologic healing beyond that of endoscopic mucosal healing has been suggested; persistent histologic inflammation increases the risk of exacerbation and dysplasia. However, identification of persistent histologic inflammation is extremely difficult using conventional endoscopy. Furthermore, the reproducibility of endoscopic disease activity is poor. We developed and evaluated a computer-aided diagnosis (CAD) system to predict persistent histologic inflammation using endocytoscopy (EC; 520-fold ultra-magnifying endoscope).

Methods

We evaluated the accuracy of the CAD system using test image sets. First, we retrospectively reviewed the data of 187 patients with UC from whom biopsy samples were obtained after endocytoscopic observation. EC images and biopsy samples of each patient were collected from 6 colorectal segments: cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum. All EC images were tagged with reference to the biopsy sample’s histologic activity. For validation samples, 525 validation sets of 525 independent segments were collected from 100 patients, and 12,900 EC images from the remaining 87 patients were used for machine learning to construct CAD. The primary outcome measure was the diagnostic ability of CAD to predict persistent histologic inflammation. Its reproducibility for all test images was also assessed.

Results

CAD provided diagnostic sensitivity, specificity, and accuracy as follows: 74% (95% confidence interval, 65%-81%), 97% (95% confidence interval, 95%-99%), and 91% (95% confidence interval, 83%-95%), respectively. Its reproducibility was perfect (κ = 1).

Conclusions

Our CAD system potentially allows fully automated identification of persistent histologic inflammation associated with UC.

中文翻译：

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全自动诊断系统，具有使用内窥镜检查的人工智能功能，可识别与溃疡性结肠炎相关的组织学炎症的存在（视频）

背景和目标

在溃疡性结肠炎（UC）的治疗中，已经提出了实现组织学愈合的内在好处，远胜于内窥镜黏膜愈合。持续的组织学炎症增加了病情加重和不典型增生的风险。然而，使用常规内窥镜检查很难鉴别出持续的组织学炎症。此外，内窥镜疾病活动的再现性很差。我们开发并评估了计算机辅助诊断（CAD）系统，以使用内窥镜检查（EC； 520倍超放大内窥镜）预测持续的组织学炎症。

方法

我们使用测试图像集评估了CAD系统的准确性。首先，我们回顾性回顾了187例UC患者的数据，这些患者在进行内镜检查后获得了活检样本。从盲肠，升结肠，横结肠，降结肠，乙状结肠和直肠的6个结直肠部位收集每位患者的EC图像和活检样本。参照活检样本的组织学活性标记所有EC图像。对于验证样本，从100位患者中收集了525个验证集（共525个独立段），并将其余87位患者的12,900张EC图像用于机器学习以构建CAD。主要结果指标是CAD预测持续性组织学炎症的诊断能力。还评估了其对所有测试图像的可重复性。

结果

CAD提供的诊断敏感性，特异性和准确性如下：74％（95％置信区间，65％-81％），97％（95％置信区间，95％-99％）和91％（95％置信区间） ，分别为83％-95％）。其重现性极佳（κ= 1）。

结论

我们的CAD系统可能会自动识别与UC相关的持续性组织学炎症。