A Deep Learning Framework for Automated Triage of Breast Cancer Biopsies in Malaysia: A Simulation Study to Reduce Resource Consumption and Diagnostic Turnaround Time.

BACKGROUND

Breast cancer is a major health burden in Malaysia, where a shortage of pathologists causes long diagnostic delays, patient anxiety, and late treatment. Conventional histopathology workflows use a first-in-first-out (FIFO) system, which is inefficient since most biopsies are benign. This simulation-based study developed and validated a deep learning triage system to prioritize suspicious breast biopsy cases for pathologist review.

MATERIALS AND

METHODS

A convolutional neural network was trained on a large, ethically sourced synthetic dataset of whole-slide images, labelled as benign or suspicious (including atypical, in situ, and invasive carcinoma), and validated on an independent synthetic test set. A discrete-event simulation replicated the pathology workflow of a typical Malaysian hospital, comparing the deep learning triage system with standard FIFO reporting. Outcomes assessed included diagnostic turnaround time (TAT), pathologist workload, and laboratory resource use.

RESULTS

The model achieved an area under the receiver operating characteristic curve of 0.98. Simulation showed a 38.2% reduction in average TAT for suspicious cases (7.2 to 4.5 days), with a small increase for benign cases. Pathologist workload fell by 22.5%, equivalent to saving 422 hours annually, while reagent and slide use declined by 15%.

CONCLUSION

These in-silico findings project potential efficiency gains, though real-world validation is required. By expediting critical case reporting, reducing workload, and conserving resources, this approach offers a promising simulation-informed framework to address diagnostic bottlenecks in resource-constrained healthcare systems.