Performance of artificial intelligence in the characterization of colorectal lesions.

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Carlos E O Dos Santos, Daniele Malaman, Ivan D Arciniegas Sanmartin, Ari B S Leão, Gabriel S Leão, Júlio C Pereira-Lima
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引用次数: 2

Abstract

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.

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。本研究旨在评估用于结直肠病变光学诊断的CAD EYE系统的计算机辅助诊断(CADx)模式,并将其与专家的性能进行比较,除了从息肉检出率(PDR)和腺瘤检出率(ADR)方面评估计算机辅助检测(CADe)模式外,以及基于日本窄带成像专家组(JNET)分类的用于表征病变的专家。在白光成像(WLI)诊断后,对所有病变进行放大,并对其进行切除和组织学检查。评估诊断标准,计算PDR和ADR。结果:52例患者共评估了110个病变(80个(72.7%)增生异常病变和30个(27.3%)非增生异常病变),平均病变大小为4.3mm。人工智能(AI)分析显示81.8%的准确性、76.3%的敏感性、96.7%的特异性、98.5%的阳性预测值(PPV)和60.4%的阴性预测值(NPV)。κ值为0.61,受试者工作特性曲线下面积(AUC)为0.87。专家分析显示准确率为93.6%,敏感性为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模式在描述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中都优于。PDR和ADR较高。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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