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AI for Digital Breast Tomosynthesis Enhances Diagnostic Performance

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An artificial intelligence for digital breast tomosynthesis enhances radiologists’ performance and efficiency.

The concurrent use of an artificial intelligence (AI) tool with digital breast tomosynthesis could improve the diagnostic performance of radiologists in the detection of breast cancer. This is according to a study presented at the Radiological Society of North America (RSNA) 2021 Annual Meeting that also found that the addition of AI improved the efficiency of radiologists’ workflow.

“This AI system can be integrated into screening practice to improve both efficiency and accuracy of digital breast tomosynthesis,” the authors wrote.

The results were presented by Pierre Fillard, Ph.D., the founder and chief scientific officer of Therapixel SA in Nice, France.

The retrospective study aimed to determine the benefits that an AI tool could bring to the time and accuracy of digital breast tomosynthesis interpretation. A total of 22 breast specialist radiologists were asked to interpret 240 digital breast tomosynthesis examinations with and without the help of AI. The dataset included 114 biopsy-proven cancers, of which 36 were classified as false negative. Performances were measured in terms of area under the receiver operating characteristic curve (AUC), sensitivity, specificity and reading time.

The average AUC across readers was 0.79 without AI and 0.83 with AI, for an average improvement of 0.04 (95% CI: 0.02 to 0.06, P < .001). Average sensitivity improved from 80% without AI to 82% when using AI (2%; 95% CI: -0.4% to 0.4%), while specificity increased from 56% to 61% (5%; 95% CI: 1.5% to 8.7%). Average reading time was 74.7 seconds without AI and 70.9 seconds with AI (average difference: -3.8; 95% CI: -8.4 to 0.8). The reading time reduction varied in accordance with the likelihood of malignancy assigned by the AI, ranging from -15.6% for cases assigned with a low likelihood of malignancy to -0.5% for the most suspicious cases.

When applying these results to a screening population, the observed reading time was on average 76.2 seconds without the help of AI, which the authors noted “lies in the lower bound of the reading time reported for real-word practice.”

“Nonetheless, an average reduction of 8% was observed in assisted readers, which indicates that a regular batch reading (i.e., a sequential interpretation of screening mammograms without interruption) could include 13% more cases (about seven cases per hour) thus improving the efficiency of the screening program and reducing the time-to-diagnosis for patients,” the authors wrote.

For more coverage of RSNA 2021, click here.

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