[Efficient dental panoramic radiographic diagnosis under resource constraints via edge quantization and multi-modal synergy].

Authors

Taotao Zhao, Yuehao Jiao, Ming Ni, Youlu Luo, Shunxing Xia, Yingying Xu, Yating He

Published in

Nan fang yi ke da xue xue bao = Journal of Southern Medical University. Volume 46. Issue 9. Pages 2266-2275.

Abstract

To propose a full-scenario framework for edge-side quantization deployment and multi-modal collaborative diagnosis and treatment based on the prior high-precision algorithm YOLOv11-TDSP.
A data-aware post-training quantization (PTQ) mechanism based on Kullback-Leibler divergence was constructed. The optimal distribution range of the features was determined using the calibration dataset for near-lossless compression of the FP32 model to INT8 precision. A cross-platform heterogeneous compilation pipeline was then established, with in-depth operator fusion and instruction set optimization for Intel Hybrid Architecture, Rockchip RK3588 and Ascend 310P to achieve extreme software-hardware decoupling. An integrated detection-diagnosis collaborative inference architecture for resource-constrained edge devices was constructed. The ultra-quantized detection model was used to free critical computing resources to drive the locally deployed lightweight large model Qwen2.5-3B, which, along with an expert rule base, allowed for an edge-side closed loop from visual detection to text report generation.
Experiments showed that the INT8-quantized model had a weight of only 6.1 MB (57.3% compression ratio), with [email protected] of 95.4% and only a 0.4% precision loss. The model demonstrated excellent generalization on both standard office terminals and resource-limited mobile diagnostic devices. In the mobile diagnostic scenario constrained by computing power and power consumption (Intel i5-12450H), the inference speed of the model reached 22.91 FPS with a 3.64 speedup. The system achieved millisecond-level lesion detection and clinically compliant report generation on devices with only 8 GB RAM in a fully offline environment.
This framework allows deployment of high-precision algorithms on low-end hardware and enables a paradigm shift of panoramic dental radiograph analysis from cloud dependence to edge inclusiveness while ensuring patient privacy, providing a low-cost, high-reliability engineering solution for intelligent upgrading of primary medical facilities.

PMID:
42812070
Bibliographic data and abstract were imported from PubMed on 30 Sep 2026.

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