Computer Vision and Deep Learning in Clinical Neuroscience: Methods, Applications and Future Directions
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: 20 March 2026 | Viewed by 65
Special Issue Editors
Interests: computer vision; clinical neurology; movement disorders; neuroimaging; neuromodulation; vestibular system; eye movements; neurodegenerative disease
2. Neurology Department, Royal Hobart Hospital, Hobart, TAS, Australia
Interests: Parkinson's disease; Alzheimer’s disease; preventative health; cognitive disorders; movement disorders; multimodal biomarkers; neurology; precision medicine; preclinical detection; neurodegenerative diseases
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We invite submissions for this Special Issue of Applied Sciences, related to computer vision and deep learning in clinical contexts, with a particular focus on applications in neurology, psychiatry, geriatrics, and movement sciences. As large quantities of visual data from clinical examinations, video recordings, and imaging are often routinely available, artificial intelligence (AI)—particularly computer vision (CV) and deep learning (DL)—offers powerful tools for objective, scalable, and reproducible analysis. As such, these methods hold great potential in overcoming central challenges in modern medical practice. This Special Issue aims to showcase interdisciplinary work that bridges computer science, engineering, medical and neuroscience, and clinical practice. We welcome original research and reviews on CV/DL methods applied to the quantification of clinically meaningful outcomes, as well as diagnosis, prognosis, or treatment monitoring in neurological, psychiatric, and movement-related conditions. Topics of interest include, but are not limited to, video-based motion analysis, facial or eye movement tracking, gait and posture assessment, multimodal data fusion, neuroimaging analysis, explainable AI, and clinical validation frameworks. We particularly encourage contributions addressing real-world clinical integration, ethical considerations, and accessibility in low-resource settings, as well as in underrepresented groups. Both technical and translational studies are welcome.
Dr. Maximilian Friedrich
Prof. Dr. Jane E. Alty
Guest Editors
Manuscript Submission Information
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Keywords
- computer vision
- deep learning
- neurology
- psychiatry
- movement science
- clinical applications
- symptom quantification
- video analysis
- medical AI
- translational neuroscience
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