轻量AI系统实时解读胆囊超声，准确率超99%且可解释
Interpretable Gallbladder Ultrasound Diagnosis: A Lightweight Web-Mobile Software Platform with Real-Time XAI
- 用轻量混合模型直接分类10类胆囊影像
- 99.85%准确率，仅224万参数，支持实时推理
- 网页与移动端部署，可视化解释助力临床决策
早期精准检测胆囊疾病至关重要，但超声图像解读难度大。为此，我们开发了一款AI诊断软件，集成混合深度学习模型MobResTaNet，可直接从超声图像中对十类（九种疾病类型和正常）胆囊状况进行分类。系统通过可解释人工智能（XAI）可视化提供实时、可解释的预测结果，支持透明的临床决策。该系统在保持仅224万参数的前提下，最高实现99.85%的准确率。软件基于HTML、CSS、JavaScript、Bootstrap和Flutter技术，以网页和移动应用形式部署，为临床诊疗提供高效、便捷且可信的辅助支持。
原文摘要 · Abstract (English)
Early and accurate detection of gallbladder diseases is crucial, yet ultrasound interpretation is challenging. To address this, an AI-driven diagnostic software integrates our hybrid deep learning model MobResTaNet to classify ten categories, nine gallbladder disease types and normal directly from ultrasound images. The system delivers interpretable, real-time predictions via Explainable AI (XAI) visualizations, supporting transparent clinical decision-making. It achieves up to 99.85% accuracy with only 2.24M parameters. Deployed as web and mobile applications using HTML, CSS, JavaScript, Bootstrap, and Flutter, the software provides efficient, accessible, and trustworthy diagnostic support at the point of care
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