Background Image-enhanced endoscopy (IEE) has been used in the differentiation between neoplastic and non-neoplastic colorectal lesions through microvasculature analysis. This study aimed to evaluate the computer-aided diagnosis (CADx) mode of the CAD EYE system for the optical diagnosis of colorectal lesions and compare it with the performance of an expert, in addition to evaluating the computer-aided detection (CADe) mode in terms of polyp detection rate (PDR) and adenoma detection rate (ADR). Methods A prospective study was conducted to evaluate the performance of CAD EYE using blue light imaging (BLI), dichotomizing lesions into hyperplastic and neoplastic, and of an expert based on the Japan Narrow-Band Imaging Expert Team (JNET) classification for the characterization of lesions. After white light imaging (WLI) diagnosis, magnification was used on all lesions, which were removed and examined histologically. Diagnostic criteria were evaluated, and PDR and ADR were calculated. Results A total of 110 lesions (80 (72.7%) dysplastic lesions and 30 (27.3%) nondysplastic lesions) were evaluated in 52 patients, with a mean lesion size of 4.3 mm. Artificial intelligence (AI) analysis showed 81.8% accuracy, 76.3% sensitivity, 96.7% specificity, 98.5% positive predictive value (PPV), and 60.4% negative predictive value (NPV). The kappa value was 0.61, and the area under the receiver operating characteristic curve (AUC) was 0.87. Expert analysis showed 93.6% accuracy, 92.5% sensitivity, 96.7% specificity, 98.7% PPV, and 82.9% NPV. The kappa value was 0.85, and the AUC was 0.95. Overall, PDR was 67.6% and ADR was 45.9%. Conclusions The CADx mode showed good accuracy in characterizing colorectal lesions, but the expert assessment was superior in almost all diagnostic criteria. PDR and ADR were high.

中文翻译：

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人工智能在结直肠病变表征中的表现。

背景图像增强内窥镜检查（IEE）已被用于通过微血管分析来区分肿瘤性和非肿瘤性结直肠病变。本研究旨在评估 CAD EYE 系统用于结直肠病变光学诊断的计算机辅助诊断 (CADx) 模式，并与专家的表现进行比较，此外还评估了计算机辅助检测 (CADe) 模式在结直肠病变光学诊断中的应用。息肉检出率（PDR）和腺瘤检出率（ADR）。方法进行前瞻性研究，使用蓝光成像 (BLI) 评估 CAD EYE 的性能，将病变分为增生性和肿瘤性，并由专家根据日本窄带成像专家组 (JNET) 分类来表征病变。白光成像（WLI）诊断后，对所有病变进行放大，将其切除并进行组织学检查。评估诊断标准，并计算PDR和ADR。结果 52 例患者共评估 110 个病变（80 个（72.7%）发育异常病变和 30 个（27.3%）非发育异常病变），平均病变大小为 4.3 mm。人工智能 (AI) 分析显示准确度为 81.8%、敏感性为 76.3%、特异性为 96.7%、阳性预测值 (PPV) 为 98.5%、阴性预测值 (NPV) 为 60.4%。kappa值为0.61，受试者工作特征曲线下面积（AUC）为0.87。专家分析显示准确度为93.6%，灵敏度为92.5%，特异性为96.7%，PPV为98.7%，NPV为82.9%。kappa 值为 0.85，AUC 为 0.95。总体而言，PDR 为 67.6%，ADR 为 45.9%。结论 CADx 模式在表征结直肠病变方面显示出良好的准确性，但专家评估在几乎所有诊断标准上均优于专家评估。PDR 和 ADR 较高。