Osteoporosis is a common condition that increases fracture risk, especially in older adults. Early diagnosis is vital for preventing fractures, reducing treatment costs, and preserving mobility. However, healthcare providers face challenges like limited labeled data and difficulties in processing medical images. This study presents a novel multi-modal learning framework that integrates clinical and imaging data to improve diagnostic accuracy and model interpretability. The model utilizes three pre-trained networks-VGG19, InceptionV3, and ResNet50-to extract deep features from X-ray images. These features are transformed using PCA to reduce dimensionality and focus on the most relevant components. A clustering-based selection process identifies the most representative components, which are then combined with preprocessed clinical data and processed through a fully connected network (FCN) for final classification. A feature importance plot highlights key variables, showing that Medical History, BMI, and Height were the main contributors, emphasizing the significance of patient-specific data. While imaging features were valuable, they had lower importance, indicating that clinical data are crucial for accurate predictions. This framework promotes precise and interpretable predictions, enhancing transparency and building trust in AI-driven diagnoses for clinical integration.

翻译：骨质疏松症是一种常见疾病，会增加骨折风险，尤其在老年人群中。早期诊断对于预防骨折、降低治疗成本和维持活动能力至关重要。然而，医疗保健提供者面临着诸如标记数据有限和医学图像处理困难等挑战。本研究提出了一种新颖的多模态学习框架，该框架整合了临床数据和影像数据，以提高诊断准确性和模型可解释性。该模型利用三个预训练网络——VGG19、InceptionV3和ResNet50——从X射线图像中提取深度特征。这些特征通过主成分分析（PCA）进行转换以降低维度，并聚焦于最相关的成分。基于聚类的选择过程识别出最具代表性的成分，随后将其与预处理后的临床数据相结合，并通过全连接网络（FCN）进行处理以完成最终分类。特征重要性图突出了关键变量，显示病史、身体质量指数（BMI）和身高是主要贡献因素，强调了患者特异性数据的重要性。虽然影像特征具有价值，但其重要性较低，表明临床数据对于准确预测至关重要。该框架促进了精确且可解释的预测，增强了透明度，并建立了对人工智能驱动诊断临床整合的信任。