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A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound

机译：使用人工智能的计算机辅助诊断系统用于超声诊断和表征乳腺肿块

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To evaluate the value of the computer-aided diagnosis (CAD) program applied to diagnostic breast ultrasonography (US) based on operator experience.US images of 100 breast masses from 91 women over 2 months (from May to June 2015) were collected and retrospectively analyzed. Three less experienced and 2 experienced breast imaging radiologists analyzed the US features of the breast masses without and with CAD according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and categories. We then compared the diagnostic performance between the experienced and less experienced radiologists and analyzed the interobserver agreement among the radiologists.Of the 100 breast masses, 41 (41%) were malignant and 59 (59%) were benign. Compared with the experienced radiologists, the less experienced radiologists had significantly improved negative predictive value (86.7%–94.7% vs 53.3%–76.2%, respectively) and area under receiver operating characteristics curve (0.823–0.839 vs 0.623–0.759, respectively) with CAD assistance (all P < .05). In contrast, experienced radiologists had significantly improved specificity (52.5% and 54.2% vs 66.1% and 66.1%) and positive predictive value (55.6% and 58.5% vs 64.9% and 64.9%, respectively) with CAD assistance (all P < .05). Interobserver variability of US features and final assessment by categories were significantly improved and moderate agreement was seen in the final assessment after CAD combination regardless of the radiologist's experience.CAD is a useful additional diagnostic tool for breast US in all radiologists, with benefits differing depending on the radiologist's level of experience. In this study, CAD improved the interobserver agreement and showed acceptable agreement in the characterization of breast masses.
机译：为了评估基于操作员经验的计算机辅助诊断（CAD）程序在诊断性乳房超声检查（US）中的价值，收集并回顾性研究了2个月（2015年5月至2015年6月）期间91名女性的100个乳房肿块的美国图像分析。 3名经验不足和2名经验丰富的乳腺影像学放射科医生根据“乳腺影像报告和数据系统”（BI-RADS）词典和类别分析了不使用CAD和使用CAD的乳腺肿块的美国特征。然后我们比较了经验丰富的放射线医生和经验不足的放射线医生的诊断性能，并分析了放射线医生之间的观察者之间的一致性。在100例乳腺肿块中，有41例（41％）是恶性的，而59例（59％）是良性的。与经验丰富的放射科医生相比，经验不足的放射科医生的阴性预测值（分别为86.7％–94.7％和53.3％–76.2％）和接受者工作特征曲线下的面积（分别为0.823–0.839和0.623–0.759）显着提高。 CAD帮助（所有P <.0.05）。相比之下，有经验的放射科医生在CAD辅助下特异性显着提高（52.5％和54.2％，分别为66.1％和66.1％）和阳性预测值（分别为55.6％和58.5％，分别为64.9％和64.9％）（所有P <.05） ）。与放射科医师的经验无关，CAD组合后，美国特征的观察者间差异和按类别进行的最终评估得到了显着改善，并且在最终评估中达到了适度的一致性。CAD是所有放射科医师对乳腺US有用的附加诊断工具，其益处因放射科医生的经验水平。在这项研究中，CAD改善了观察者之间的一致性，并在乳腺肿块的表征中显示出可接受的一致性。

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