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KMID : 0880420220230050505
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Korean Journal of Radiology
2022 Volume.23 No. 5 p.505 ~ p.516
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Improving the Performance of Radiologists Using Artificial Intelligence-Based Detection Support Software for Mammography: A Multi-Reader Study
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Lee Jeong-Hoon
Kim Ki-Hwan
Lee Eun-Hye
Ahn Jong-Seok
Ryu Jung-Kyu
Park Young-Min
Shin Gi-Won
Kim Young-Joong
Choi Hye-Young
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Abstract
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Objective: To evaluate whether artificial intelligence (AI) for detecting breast cancer on mammography can improve the performance and time efficiency of radiologists reading mammograms.
Materials and Methods: A commercial deep learning-based software for mammography was validated using external data collected from 200 patients, 100 each with and without breast cancer (40 with benign lesions and 60 without lesions) from one hospital. Ten readers, including five breast specialist radiologists (BSRs) and five general radiologists (GRs), assessed all mammography images using a seven-point scale to rate the likelihood of malignancy in two sessions, with and without the aid of the AI-based software, and the reading time was automatically recorded using a web-based reporting system. Two reading sessions were conducted with a two-month washout period in between. Differences in the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and reading time between reading with and without AI were analyzed, accounting for data clustering by readers when indicated.
Results: The AUROC of the AI alone, BSR (average across five readers), and GR (average across five readers) groups was 0.915 (95% confidence interval, 0.876?0.954), 0.813 (0.756?0.870), and 0.684 (0.616?0.752), respectively. With AI assistance, the AUROC significantly increased to 0.884 (0.840?0.928) and 0.833 (0.779?0.887) in the BSR and GR groups, respectively (p = 0.007 and p < 0.001, respectively). Sensitivity was improved by AI assistance in both groups (74.6% vs. 88.6% in BSR, p < 0.001; 52.1% vs. 79.4% in GR, p < 0.001), but the specificity did not differ significantly (66.6% vs. 66.4% in BSR, p = 0.238; 70.8% vs. 70.0% in GR, p = 0.689). The average reading time pooled across readers was significantly decreased by AI assistance for BSRs (82.73 vs. 73.04 seconds, p < 0.001) but increased in GRs (35.44 vs. 42.52 seconds, p < 0.001).
Conclusion: AI-based software improved the performance of radiologists regardless of their experience and affected the reading time.
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KEYWORD
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Breast cancer, Mammography, Screening, Deep-learning, Artificial intelligence, Reading time, Multi-reader study
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