Impact of an artificial intelligence decision support system among radiologists with different levels of experience in breast ultrasound: A prospective study in a tertiary center.

PURPOSE

To assess the impact of an artificial intelligence decision support system (Koios DS) on the diagnostic performance of radiologists with different experience in breast ultrasound and to evaluate its potential to reduce unnecessary biopsies.

METHODS

This observational, prospective, single-centre study included consecutive patients scheduled for ultrasound-guided core-needle biopsy of suspicious breast lesions. Three radiologists with different experience in breast ultrasound (senior breast radiologist: 20 years; junior breast radiologist: 3 years; general radiologist: less than 1 year) independently evaluated the lesions, assigning BI-RADS categories before and after Koios DS application. Biopsy reports served as the reference standard. AUCs and the number of unnecessary biopsies before and after implementing Koios DS were compared using DeLong and McNemar's tests.

RESULTS

A total of 222 patients (median age 58 years, interquartile range 46-72 years) with 226 lesions were included, 89/226 (39.4 %) benign and 137/226 (60.6 %) malignant. The application of Koios DS significantly improved (p < 0.001) the AUC of all radiologists, with a 0.078 AUC Δ for the junior breast radiologist (from 0.786 to 0.864), a 0.062 AUC Δ for the general radiologist (from 0.719 to 0.781), and a 0.045 AUC Δ for the senior breast radiologist (from 0.823 to 0.868). Koios DS would have significantly reduced the number of unnecessary biopsies recommended by the senior breast radiologist (from 41/89 [46.1 %] to 30/89 [33.7 %], p < 0.001) and by the junior breast radiologist (from 46/89 [51.7 %] to 29/89 [32.6 %], p = 0.001).

CONCLUSION

The application of Koios DS improved the radiologists' diagnostic performance, particularly for less experienced ones, and could potentially reduce unnecessary biopsies.