Diagnostic Accuracy of AI-assisted Mammography in Breast Cancer Detection: A Retrospective Study from a South Asian Population

Abstract

Introduction: Breast cancer remains the most common malignancy among women worldwide, and early detection through mammography is crucial for reducing mortality. However, mammogram interpretation is often subjective and variable. Artificial intelligence (AI) offers a promising solution to enhance diagnostic accuracy and support radiologists.

Research design and Methods: Retrospective, single centre diagnostic accuracy study, was conducted at Breast Care Unit, Rehman Medical Institute (RMI), Peshawar, Pakistan. A total of 200 women with biopsy-confirmed mammographic findings: 100 malignant (BI-RADS 4/5), 50 benign (BI-RADS 2), and 50 normal (BI-RADS 1). Each participant contributed craniocaudal (CC) and mediolateral oblique (MLO) views, yielding 400 annotated mammograms. Deep learning models (InceptionResNetV2 and Vision Transformer [ViT]) were trained using both institutional and public datasets. Image preprocessing included resizing, normalization, and augmentation. Class imbalance was addressed via augmentation and class weighting. A clinician-facing graphical user interface (GUI) was also developed to allow for clinical usability testing. Diagnostic performance of AI models, assessed by accuracy, sensitivity and specificity.

Results: Models trained solely on public datasets achieved limited performance (accuracy: 60%; sensitivity: 37%; specificity: 73%). After fine-tuning on RMI data, the InceptionResNetV2 model achieved an accuracy of 71.67%, with sensitivity of 74% and specificity of 69%. Malignant lesions were significantly associated with older age (mean 54.2 ± 12.8 years, p < 0.001), postmenopausal status (44%), and microcalcifications (53%). Transformer-based models underperformed without transfer learning. This feasibility only, single run analysis was limited by computational resources; ROC/PR curve generation and calibration analyses were not performed and are planned for future validation. The AI system showed potential as a decision support tool in low-resource settings, particularly in aiding early cancer detection.

Conclusions: AI-assisted mammography demonstrates clinically meaningful diagnostic accuracy and sensitivity for breast cancer detection, especially when fine-tuned on local data. Such systems could augment radiologist workflows and improve early diagnosis in under resourced healthcare environments. This retrospective feasibility study evaluates the initial performance of an AI-assisted mammography system in detecting breast cancer. Results are preliminary and not intended to establish diagnostic accuracy or clinical readiness. Further validation in larger, multicentre studies is warranted.