Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.
By A. Rodríguez-Ruiz, K. Lång, A. Gubern-Mérida, M. Broeders, G. Gennaro, P. Clauser, T. Helbich, M. Chevalier, T. Tan, Thomas Mertelmeier and 5 more. Journal of the National Cancer Institute, 2019
591 citations recorded by the source corpus.
BACKGROUND Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM. METHODS Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05. RESULTS The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists. CONCLUSIONS The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation.
Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/77b01422d4fa697454e1bec218433532d7c1e906
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Citations in the corpus
- Artificial Intelligence for Clinical Trial Design. (2019, background): “ML technologies have been proposed [27] – and recently approved [28,29] – for screening applications for the rapid detection of diseases from medical images.”
- Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging (2023, background): “AI models have been engineered to distinguish between benign and malignant tumours in mammography, achieving performance metrics comparable to human radiologists, thus facilitating early detection of breast cancer [57].”
- Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening (2019, unknown): “91), [36] (0.”
- Artificial intelligence in oncology (2020, background): “22,43 A second strategy, known as transfer learning, involves reuse for the new purpose of medical imaging of features extracted by deep learning from natu-ral image datasets such as ImageNet.”