DB-ATRG: The Density and BI-RADS-Aware Triage and Automatic Report Generation System for Mammography

Background: The growing volume of mammography screenings has created severe radiologist shortages, while standard First-In, First-Out (FIFO) reading queues fail to prioritize urgent or complex cases, delaying critical diagnoses. Objective: This study introduces the Density and BI-RADS--Aware Triage and Report Generation (DB-ATRG) framework to fundamentally restructure mammography workflows by automating diagnostic text generation and enabling risk-based case prioritization. Methods: Utilizing the Digital Mammography Dataset for Breast Cancer Diagnosis Research (DMID), we fine-tuned the 4-billion parameter MedGemma 1.5 vision-language model using Quantization and Low-Rank Adaptation (QLoRA). The extracted biomarkers drive a dual-phase triage algorithm that flags extremely dense breasts (ACR Category D) for supplemental screening and dynamically ranks remaining cases using a calculated Cumulative Urgency Score. The clinical impact of this triage workflow was evaluated against a standard FIFO queue using a simulated cohort of 100 mammography cases. Results: DB-ATRG achieved significant improvements over the AMRG baseline in clinical text generation and classification, securing a ROUGE-L score of 0.8650, a METEOR score of 0.9001, and an ACR Density Accuracy of 0.7039. In clinical simulations, the optimized prioritization queue captured all high-risk malignancies (BI-RADS 4 and 5) within the first 20% of the reading workload, compared to just 40% in the random FIFO queue. This framework effectively accelerated the mean rank position of severe cases from 42.8 down to 3. Conclusion: By accurately automating report generation and aggressively prioritizing severe cases, the DB-ATRG framework can drastically optimize clinical resource allocation and accelerate the time-to-diagnosis for the most vulnerable patients.