Heller Samantha L, Wegener Melanie, Babb James S, Gao Yiming
Department of Radiology, New York University Grossman School of Medicine, New York, NY.
Ultrasound Q. 2020 Dec 28;37(1):10-15. doi: 10.1097/RUQ.0000000000000550.
This study aimed to evaluate the effect of an artificial intelligence (AI) support system on breast ultrasound diagnostic accuracy.In this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved retrospective study, 200 lesions (155 benign, 45 malignant) were randomly selected from consecutive ultrasound-guided biopsies (June 2017-January 2019). Two readers, blinded to clinical history and pathology, evaluated lesions with and without an Food and Drug Administration-approved AI software. Lesion features, Breast Imaging Reporting and Data System (BI-RADS) rating (1-5), reader confidence level (1-5), and AI BI-RADS equivalent (1-5) were recorded. Statistical analysis was performed for diagnostic accuracy, negative predictive value, positive predictive value (PPV), sensitivity, and specificity of reader versus AI BI-RADS. Generalized estimating equation analysis was used for reader versus AI accuracy regarding lesion features and AI impact on low-confidence score lesions. Artificial intelligence effect on false-positive biopsy rate was determined. Statistical tests were conducted at a 2-sided 5% significance level.There was no significant difference in accuracy (73 vs 69.8%), negative predictive value (100% vs 98.5%), PPV (45.5 vs 42.4%), sensitivity (100% vs 96.7%), and specificity (65.2 vs 61.9; P = 0.118-0.409) for AI versus pooled reader assessment. Artificial intelligence was more accurate than readers for irregular shape (74.1% vs 57.4%, P = 0.002) and less accurate for round shape (26.5% vs 50.0%, P = 0.049). Artificial intelligence improved diagnostic accuracy for reader-rated low-confidence lesions with increased PPV (24.7% AI vs 19.3%, P = 0.004) and specificity (57.8% vs 44.6%, P = 0.008).Artificial intelligence decision support aid may help improve sonographic diagnostic accuracy, particularly in cases with low reader confidence, thereby decreasing false-positives.
本研究旨在评估人工智能（AI）支持系统对乳腺超声诊断准确性的影响。在这项符合《健康保险流通与责任法案》且经机构审查委员会批准的回顾性研究中，从连续的超声引导活检（2017年6月至2019年1月）中随机选取了200个病变（155个良性，45个恶性）。两名对临床病史和病理结果不知情的阅片者，分别使用和不使用美国食品药品监督管理局批准的AI软件对病变进行评估。记录病变特征、乳腺影像报告和数据系统（BI-RADS）分级（1-5级）、阅片者信心水平（1-5级）以及AI的BI-RADS等效分级（1-5级）。对阅片者与AI的BI-RADS诊断准确性、阴性预测值、阳性预测值（PPV）、敏感性和特异性进行统计分析。采用广义估计方程分析阅片者与AI在病变特征方面的准确性以及AI对低信心评分病变的影响。确定AI对假阳性活检率的影响。统计检验在双侧显著性水平为5%的情况下进行。与汇总的阅片者评估相比，AI在准确性（73%对69.8%）、阴性预测值（100%对98.5%）、PPV（45.5%对42.4%）、敏感性（100%对96.7%）和特异性（65.2对61.9；P = 0.118 - 0.409）方面无显著差异。AI在不规则形状病变的诊断上比阅片者更准确（74.1%对57.4%，P = 0.002），而在圆形病变上准确性较低（26.5%对50.0%，P = 0.049）。AI提高了阅片者评定为低信心病变的诊断准确性，PPV增加（AI为24.7%对19.3%，P = 0.004），特异性提高（57.8%对44.6%，P = 0.008）。人工智能决策支持辅助工具可能有助于提高超声诊断准确性，特别是在阅片者信心较低的情况下，从而减少假阳性。
