Abstract
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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