A comparison study of artificial intelligence performance against physicians in benign–malignant classification of pulmonary nodules

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-05-15 DOI:10.1515/oncologie-2023-0319

Weiguo Hu, Jie Zhang, Dingyi Zhou, Shu Xia, Xingxiang Pu, Jianzhong Cao, Mingzhu Zou, Zhangfan Mao, Qibin Song, Xiaodong Zhang

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Abstract

To compare and evaluate the performance of artificial intelligence (AI) against physicians in classifying benign and malignant pulmonary nodules from computerized tomography (CT) images. A total of 506 CT images with pulmonary nodules were retrospectively collected. The AI was trained using in-house software. For comparing the diagnostic performance of artificial intelligence and different groups of physicians in pulmonary nodules, statistical methods of receiver operating characteristic (ROC) curve and area under the curve (AUC) were analyzed. The nodules in CT images were analyzed in a case-by-case manner. The diagnostic accuracy of AI surpassed that of all groups of physicians, exhibiting an AUC of 0.88 alongside a sensitivity of 0.80, specificity of 0.84, and accuracy of 0.83. The area under the curve (AUC) of seven groups of physicians varies between 0.63 and 0.84. The sensitivity of the physicians within these groups varies between 0.4 and 0.76. The specificity of different groups ranges from 0.8 to 0.85. Furthermore, the accuracy of the seven groups ranges from 0.7 to 0.82. The professional insights for enhancing deep learning models were obtained through an examination conducted on a per-case basis. AI demonstrated great potential in the benign–malignant classification of pulmonary nodules with higher accuracy. More accurate information will be provided by AI when making clinical decisions.

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人工智能与医生在肺结节良恶性分类方面的性能比较研究

比较和评估人工智能（AI）与医生在根据计算机断层扫描（CT）图像对肺部结节进行良性和恶性分类方面的性能。回顾性收集了 506 张带有肺结节的 CT 图像。人工智能使用内部软件进行训练。为了比较人工智能和不同组别医生对肺结节的诊断性能，分析了接收者操作特征曲线（ROC）和曲线下面积（AUC）的统计方法。对 CT 图像中的结节进行了逐个分析。人工智能的诊断准确性超过了所有医生组，其 AUC 为 0.88，敏感性为 0.80，特异性为 0.84，准确性为 0.83。七组医生的曲线下面积（AUC）介于 0.63 和 0.84 之间。这些组别中医生的灵敏度介于 0.4 和 0.76 之间。不同组别的特异性介于 0.8 和 0.85 之间。此外，七个组别的准确性介于 0.7 和 0.82 之间。通过对每个病例进行检查，获得了增强深度学习模型的专业见解。人工智能在肺结节的良恶性分类方面表现出巨大潜力，准确率更高。人工智能将为临床决策提供更准确的信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.