The sarcopenia artificial intelligence diagnostic decision support system (SAID DSS) - a multimodal deep learning model

BACKGROUND: Early detection and treatment of sarcopenia are crucial for improving patient outcomes, yet current diagnostic methods often lack the accuracy, accessibility, and efficiency needed for widespread clinical use. The aim of this study was to develop an accurate, secure, and evidence-based multimodal AI model using a point-of-care ultrasound (POCUS) framework combining muscle imaging properties with physical performance for sarcopenia diagnosis. METHODS: The model uses clinical data and POCUS images. Clinical data consisted of age, gender, height, weight, body mass index (BMI) and data on physical performance by Short Physical Performance Battery (SPPB) scores. SPPB scores were chosen since it is recommended by both the European Working Group of Sarcopenia in Older People 2 and the Asian Working Group for Sarcopenia. POCUS data consisted of images from the dominant thigh, focusing on the rectus femoris muscle, using longitudinal and transverse projections. Various Machine Learning (ML) and Deep Learning (DL) algorithms and multimodal architectures were tested. Explainable AI (XAI) methods, including Grad-CAM for ultrasound images and feature-attribution analysis for clinical variables, were integrated to provide transparent interpretation of the multimodal model’s diagnostic decisions. The final model was implemented as part of the Sarcopenia Artificial Intelligence Diagnostic Decision Support System (SAID DSS). RESULTS: Participants (24) were mostly women (63%) with a mean age of 81 years (± 5.2), (age range: 71–91 years) a mean body mass index of 26 kg/m2 (± 4.1), and mean SPPB scores of 5 (± 1.6) and 9 (± 1.6) for sarcopenic and controls. 1060 and 2414 longitudinal and transverse ultrasound events for sarcopenic and control participants, respectively, were used, demonstrating a robust dataset despite the small number of participants. Comprehensive experimental results showed that a feature-level fusion technique using a multilayer perceptron network as classifier and Xception architectures for image feature extraction demonstrated the best performance. The final model yielded a diagnostic accuracy of 85%, an F1-score of 0.85 and an area under the curve (AUC) of 0.84, higher than previous models. CONCLUSION: This study is the first to introduce a clinically oriented, AI-based multimodal model for sarcopenia detection, demonstrating improved performance over existing approaches. In addition, we provided an explanation of the decisions generated by the best-performing detection model. By integrating this model into SAID DSS, we provide a practical and scalable tool with potential for direct application in clinical workflows, supporting early and accurate identification of sarcopenia. CLINICAL TRIAL NUMBER: Not applicable.