Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study.
Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study.
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DOI：
10.1007/s00330-021-07992-w
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发表时间：
2021-11
影响因子：
5.9
通讯作者：
Mann RM
中科院分区：
文献类型：
--
作者：
van Winkel SL;Rodríguez-Ruiz A;Appelman L;Gubern-Mérida A;Karssemeijer N;Teuwen J;Wanders AJT;Sechopoulos I;Mann RM
数字化乳腺断层成像(DBT)提高了乳房X光检查的灵敏度，并越来越多地应用于乳腺癌筛查。然而，大量的图像增加了读取错误和读取时间的风险。这项研究旨在调查乳房放射科医生在人工智能(AI)支持系统的帮助下，读取广角DBT的准确性是否会提高。此外，还评估了对阅读时间的影响，并将AI系统在检测恶性肿瘤方面的独立性能与普通放射科医生进行了比较。对240例双侧乳腺DBT检查(癌病变71例，良性病变70例，正常339例)进行了多读数、多病例研究。检查由18名放射科医生解释，在有和没有人工智能支持的情况下，提供每个乳房的癌症怀疑分数。在人工智能的支持下，放射科医生可以看到基于考试和基于区域的癌症可能性分数。使用混合模型方差分析比较不同阅读条件下的受试者工作特征曲线下面积(AUC)和每次检查的阅读时间。平均而言，使用人工智能支持的AUC更高(0.863比0.833；p=0.0025)。使用人工智能支持，每次DBT考试的阅读时间从41次(95%CI=39-42 S)减少到36次S(95%CI=35-37 S)(p<；0.001)。独立AI系统的AUC值不低于普通放射科医生的AUC值(+0.007，p=0.8115)。放射科医生在使用人工智能阅读支持系统评估DBT检查时，改进了他们的癌症检测并减少了阅读时间。·放射科医生在使用人工智能系统支持时，提高了数字乳房断层合成(DBT)中的癌症检测精度，同时减少了读取时间。·人工智能系统的独立乳腺癌检测性能不逊于放射科医生阅读数字乳房断层合成检查的平均性能。·使用人工智能支持系统可以使先进和更可靠的成像技术更容易获得，并可以使用DBT进行更具成本效益的乳房筛查项目。
Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist. A multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance. On average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39–42 s) to 36 s (95% CI = 35– 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115). Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system. • Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time. • The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams. • The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.
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影响因子：
5.9
作者：
Rodriguez-Ruiz A;Gubern-Merida A;Imhof-Tas M;Lardenoije S;Wanders AJT;Andersson I;Zackrisson S;Lång K;Dustler M;Karssemeijer N;Mann RM;Sechopoulos I
通讯作者：
Sechopoulos I
影响因子：
10.6
作者：
Wu, Nan;Phang, Jason;Geras, Krzysztof J.
通讯作者：
Geras, Krzysztof J.
影响因子：
64.8
作者：
McKinney, Scott Mayer;Sieniek, Marcin;Shetty, Shravya
通讯作者：
Shetty, Shravya
影响因子：
10.3
作者：
Rodriguez-Ruiz, Alejandro;Lang, Kristina;Sechopoulos, Ioannis
通讯作者：
Sechopoulos, Ioannis
DOI：
10.2214/ajr.18.20294
发表时间：
2019-01-01
期刊：
ARCHAEOLOGY OF IDENTITY AND DISSONANCE
影响因子：
--
作者：
George, Diane F.;Kurchin, Bernice;Britt, Kelly M.
通讯作者：
Britt, Kelly M.