The diagnosis of amyotrophic lateral sclerosis (ALS) experiences critical delays averaging 9-12 months, limiting therapeutic interventions. We propose a novel architecture that integrates deep learning with blockchain technology for secure and auditable speech ALS detection. Our CNN-BiLSTM architecture with an attention mechanism processes the acoustic characteristics of 217 participants (133 ALS, 84 controls) in the VOC-ALS and Minsk datasets. The model achieves 96.5% accuracy, 95.3% sensitivity, and 97.8% specificity, outperforming traditional approaches. The blockchain implementation on Optimism Layer-2 ensures data integrity through immutable audit trails, IPFS off-chain storage, and smart contract-governed access control. This hybrid approach addresses both diagnostic accuracy and critical data governance challenges in multi-institutional ALS research, demonstrating feasibility for clinical deployment while maintaining patient privacy and regulatory compliance.
Louja et al. (2025) studied this question.