| Sumario: | Background: Breast cancer diagnoses are limited in low- and middle-income settings due to lack of medical resources. In these settings, point-of-care ultrasound (POCUS) combined with artificial intelligence (AI)-based interpretation could be a suitable approach. Purpose: To compare the performance of an AI-based breast cancer classification algorithm with radiologists and assess whether POCUS performs comparably to standard breast ultrasound (BUS) as a stand-alone imaging technique. Material and Methods: A total of 70 POCUS and 70 case-matched BUS images (11 malignant, 21 benign, 38 normal) from 40 women (mean age=50.3 ± 16.65) were interpreted by four breast radiologists in a multi-reader, multi-case setup. Readers rated risk of malignancy on single images on a 5-point scale similar to BI-RADS (≥3 considered positive, i.e. malignant). An in-house–developed AI-based algorithm also analyzed the images. The breast cancer detection performance for all modalities was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results: On BUS, AI and radiologists performed comparably (AUC=0.98 [95% confidence interval (CI)=0.93–1.00] vs. 0.97 [95% CI=0.93–1.00]; sensitivity 1.00 vs. 1.00; specificity 0.75 vs. 0.78). The performance was similar on POCUS, for both AI and radiologists (AUC 0.99 [95% CI=0.98–1.00] vs. 0.99 [95% CI=0.96–1.00]; sensitivity 1.00 vs. 1.00; specificity 0.92 vs. 0.77). No statistically significant differences were observed between BUS and POCUS or radiologists and AI. Conclusion: This study demonstrates the potential of reliable AI-based breast cancer detection, both in standard ultrasound imaging and in POCUS imaging.
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