Machine Learning Algorithms and Software Tools for Early Detection and Prognosis of Schizophrenia

Schizophrenia is a complex psychiatric disorder with a diversity of symptoms that pose significant challenges to diagnosis and treatment. Despite advances in neuroscience, early detection and prognosis remain difficult due to the complex nature of the disease's presentation and its extensive interindividual variability. With the exponential growth in digitization of medical records and neuroimaging data, it has become increasingly feasible to utilize machine learning algorithms and software tools to advance schizophrenia research.

The primary objective of this Research Topic is to explore the potential of machine learning algorithms and digital tools in the early detection and prognosis of schizophrenia.

-How can computational models be developed and trained to predict the onset of schizophrenia from early symptoms?
-What are the key features that these models should consider?
-Can machine learning algorithms aid in developing a more effective and personalized treatment plan for individuals diagnosed with schizophrenia?

To address these questions, we aim to gather pioneering research employing machine learning, artificial intelligence, and data analytics to transform schizophrenia diagnosis and treatment.

This Research Topic welcomes both empirical and review papers focused on, but not limited to, the following themes:
-Applications of machine learning algorithms in the early detection of schizophrenia.
-Use of digital and computational tools for the prognosis of schizophrenia.
-Machine-learning-based analysis of neuroimaging and/or genomic data in schizophrenia research.
-Challenges, opportunities, and ethical considerations related to the application of AI and machine learning in schizophrenia diagnosis and treatment.
-Machine Learning algorithms' role in individualized interventions and treatment predictions in schizophrenia.

We primarily invite Original Research, Review Papers, Method Articles, and Case Studies that contribute to the understanding and development of innovative machine learning approaches in the field of schizophrenia research.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Schizophrenia, Machine Learning, Digital Tools, Neuroimaging

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.