Osteoporosis and sarcopenia · 2026
An integrated deep learning and large language model for automated knee osteoarthritis grading and reporting from the Vietnam Osteoporosis Study.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Nguyen Dinh T, Hoang Duy K, Luu Linh N, Truong An T, Kha Hien Q, Tran Huy T, Ho Phu D, Tran Thach S, Ling Sai Ho, Hunter David J, Ho-Pham Lan T, Nguyen Tuan V
Аннотация
OBJECTIVES: Knee osteoarthritis (KOA) is a prevalent joint condition affecting multiple structures of the knee. Clinicians need reliable tools for early detection and accurate grading to support timely management, but radiographic grading remains subjective and time-consuming. We aimed to develop a deep learning and large language model (LLM) hybrid system for KOA severity classification and automated clinical reporting.
METHODS: We analysed bilateral knee radiographs from 2435 adults (≥40 years) in the Vietnam Osteoporosis Study (VOS), using the Kellgren-Lawrence (KL) scale. Data were split into training (N = 1944) and independent testing (N = 491) sets. A hybrid deep learning pipeline combined knee-region object detection with KL grading, screening and feature localization. Outputs were integrated into a Gemini LLM module to generate structured clinical reports.
RESULTS: The hybrid model achieved 79.1% accuracy and AUC 88.8% for five-class KL grading, and 89.5% accuracy (sensitivity: 87.5%, specificity: 90.2%) for KOA screening. The localization module consistently highlighted KOA-relevant features, including osteophytes, joint space narrowing, and subchondral sclerosis. The Gemini module converted model outputs into coherent, structured reports and provided context-aware answers to clinician queries grounded in the imaging results.
CONCLUSIONS: We developed a deployable deep learning-Gemini system for KOA assessment. This integrated framework offers an interpretable, automated approach to KOA assessment that may improve screening and decision support in resource-limited settings.
DEMO: https://www.youtube.com/watch?v=apXipdeXD5k.
METHODS: We analysed bilateral knee radiographs from 2435 adults (≥40 years) in the Vietnam Osteoporosis Study (VOS), using the Kellgren-Lawrence (KL) scale. Data were split into training (N = 1944) and independent testing (N = 491) sets. A hybrid deep learning pipeline combined knee-region object detection with KL grading, screening and feature localization. Outputs were integrated into a Gemini LLM module to generate structured clinical reports.
RESULTS: The hybrid model achieved 79.1% accuracy and AUC 88.8% for five-class KL grading, and 89.5% accuracy (sensitivity: 87.5%, specificity: 90.2%) for KOA screening. The localization module consistently highlighted KOA-relevant features, including osteophytes, joint space narrowing, and subchondral sclerosis. The Gemini module converted model outputs into coherent, structured reports and provided context-aware answers to clinician queries grounded in the imaging results.
CONCLUSIONS: We developed a deployable deep learning-Gemini system for KOA assessment. This integrated framework offers an interpretable, automated approach to KOA assessment that may improve screening and decision support in resource-limited settings.
DEMO: https://www.youtube.com/watch?v=apXipdeXD5k.