Degenerative disease is the result of a continuous process based on degenerative cell changes affecting tissues or organs, which will increasingly deteriorate over time. Alzheimer’s disease (AD) is a degenerative disease that worsens over time, often resulting in progressive memory loss and cognitive decline. Since AD is a complex multifactorial disease, large datasets with multiple data types are essential to identify its risk factors. Artificial Intelligence (AI) and machine learning have resulted in dramatic improvements for biomedical informatics, the future of diagnostic systems, and treatment methods for numerous degenerative diseases, i.e., AD. AI and AD clinical big data can be closely integrated. AD clinical big data requires new structures, algorithms, techniques, and analytics to facilitate the management, visualization, and retrieval of hidden information. Novel AI methods can tackle and extract meaningful insight for AD diagnosis and treatment.

Machine learning and deep learning methods can be trained and tested on large-scale AD data. This Special Issue will address several challenges associated with the development of Computer-Aided Diagnostic Systems that are based on the use of biomedical informatics AD data. Critical challenges include: the development of fast and reliable methods for AD data, the effective use of multiscale techniques in the classification and prediction of degenerative degree, and the scientific rationale for characterizing the behavior of the degenerative tissue.

This Special Issue aims to provide a diverse, but complementary set of contributions to demonstrate new AI developments and applications that covers the existing issues in biomedical informatics of AD data. We would also like to invite successful applications of new methods, including but not limited to data processing, analysis, and knowledge discovery for degenerative diseases. We welcome original research as well as review articles.

Potential topics include but are not limited to the following:

• Degenerative disease
• Alzheimer’s disease in healthcare engineering
• Scientific programming for Alzheimer’s disease data visualization and representation
• Deep learning methods for the classification of lesions, tissue, and disease in ultrasound/CT/MRI
• Machine learning methods for computer-aided detection in ultrasound/CT/MRI
• Machine learning methods for segmentation, 3d rendering from CT/MRI
• Artificial intelligence methods and algorithms in bioinformatics and biomedical images
• Biomechanical analysis
• Biomedical imaging and pattern recognition