Dear Colleagues,

In recent years, we have experienced an exponential growth of artificial intelligence-based applications in many diagnostic fields. In particular, several deep-learning-based algorithms have been refined to extract patterns in high-dimensional clinical and diagnostic imaging datasets, study the relationships between numerous variables, and identify robust biomarkers for a rising number of brain diseases. These techniques have proved particularly effective for studying neurodegenerative diseases, showing promise for early diagnosis, personalized staging, and the development of new therapeutic approaches. However, the successful application of these algorithms in the diagnostic domain requires addressing several issues such as the integration and harmonization of high-dimensional datasets, the robust validation of the performance of the algorithms, the generalization of the techniques developed by using different datasets, and the clinical interpretability of the decisions taken by the algorithms. In this Special Issue, we will collect recent advances based on machine learning and deep learning techniques for the diagnosis of neurodegenerative diseases with imaging, genetic, and clinical data. We encourage work and techniques that aim to strengthen the use of algorithms in diagnostic practice.

Potential topics include but are not limited to the following:

• Machine learning/deep learning algorithms for classification and diagnosis of neurodegenerative diseases;
• Machine learning/deep learning models for subtyping neurodegenerative diseases;
• Harmonization techniques for machine/deep learning;
• Feature selection;
• Multimodal data integration for machine/deep learning models;
• Explainability of machine/deep learning models;
• Transfer learning on large datasets;
• Performance optimization.

Dr. Angela Lombardi
Dr. Domenico Diacono
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Diagnosis
• Machine learning
• Deep learning
• Medical imaging
• Genetics
• Explainability
• Alzheimer’s disease
• Mild cognitive impairment (MCI)
• Neurodegenerative diseases