Published Paper Details:

This paper presents a multimodal AI-based arthritis detection system integrating five independent diagnostic modules: a Vision Module (knee X-ray classification, spine MRI degeneration analysis, and synovial fluid microscopy), a Lab Report Module, a Wearable Sensor Module, a Genomics Module, and a Voice Emotion Detection Module. The Vision Module employs DenseNet121 trained on 5,778 knee X-ray images, achieving 82.40% classification accuracy across five Kellgren-Lawrence (KL) grades. Spine MRI degeneration is assessed via signal intensity analysis, while synovial fluid inflammation is detected using ResNet50 with K-Means clustering. The Lab Report Module applies Random Forest classification on clinical biomarkers. The Wearable Module employs a neural network on MotionSense sensor data for risk stratification. The Genomics Module classifies arthritis-associated SNP markers, and the Voice Module detects pain and fatigue using MFCC-based feature extraction. All modules produce structured outputs integrated into an automated PDF medical report generator, providing a scalable, interpretable, and accessible clinical decision support system.