DB-ATRG: The Density and BI-RADS–Aware Triage and Automatic Report Generation System for Mammography
Year: 2026
Type: preprint
Abstract: ABSTRACT 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 critiScal 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 0.8012, a METEOR score of 0.8358, and an ACR Density Accuracy of 0.6810. In clinical simulations, the optimized prioritization queue captured 100.0% of high-risk malignancies (BI-RADS 4 and 5) within the first 20% of the reading workload, compared to just 19.2% in the random FIFO queue. This framework effectively accelerated the mean rank position of severe cases from 42.0 down to 7.5. 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. Highlights A vision-language model (MedGemma 1.5 4B) is fine-tuned with QLoRA for automated mammography report generation, achieving ROUGE-L 0.8012 and METEOR 0.8358. A dual-phase Density and BI-RADS–Aware Triage algorithm restructures FIFO reading queues by clinical urgency. The triage system captures 100.0% of high-risk cases (BI-RADS 4/5) within the first 20% of the worklist, versus 19.2% in standard FIFO. ACR breast density classification accuracy reaches 0.6810, enabling reliable density-based complexity filtering.
Source: medRxiv
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7170138787