This study aims to develop a multi-label classification model based on pelvic X-rays for the diagnosis and assessment of Ankylosing Spondylitis (AS), addressing the challenges of limited expert resources in underdeveloped regions. We propose a deep learning-based multi-label classification model incorporating a prior attention mechanism. This model utilizes predefined bounding boxes to accurately locate key areas, such as the bilateral sacroiliac joints and hip joint, allowing the model to focus on relevant regions during inference. The performance of the model was validated on a multi-center external test set. Experimental results demonstrate that the model achieves significant improvements in diagnostic performance and in the assessment performance of the sacroiliac joints and hip joints. The optimal model’s diagnostic performance achieved an accuracy of 0.875 on a multi-center external test set, surpassing doctors with less experience (0.798) and performing comparably to more experienced human experts (0.880). The accuracy for the assessment of left and right sacroiliac joints and left and right hip joints reached 0.964, 0.970, 0.827, and 0.863, respectively. The proposed multi-label classification model with a prior attention mechanism offers a promising, cost-effective tool for AS diagnosis and assessment, especially in resource-limited settings.

Cadmium (Cd) is a pervasive environmental heavy metal linked to various chronic pathologies; however, its specific role and underlying molecular mechanisms