With the soaring prevalence of psychiatric disorders, the huge demand for effective treatments of psychiatric disorders has increased largely in recent years. However, the complexity of neuronal degeneration and the heterogeneity of patients challenge the early diagnosis and effective treatments of these disorders. Fortunately, the application of machine learning theories and algorithms offers new insights for scientists, clinicians, and patients to meet these challenges.
Machine learning, which includes various methods of feature extraction, selection, and classification, has shown outstanding advantages in the pathological analysis of psychiatric disorders. These methods can learn features from brain neuroimaging data and adapt to the variation of data characteristics, helping to improve the reliability, performance, and accuracy of disease-specific diagnostic systems. In addition, machine learning can accurately assess patients’ conditions and predict the performance of clinical interventions, which is significant for the appropriate interventions and treatments of diseases.
This Research Topic aims to incorporate theoretical and technological innovations and evaluate the performance of machine learning in clinical studies on psychiatric disorders with the application of innovative machine learning algorithms, clinical diagnosis, and clinical interventions, combining the clinical neuroimaging data of the brain. We welcome authors to submit Original Research, including Clinical Case Studies and Methods, as well as Technological Reports, Hypothesis and Theory, and Systematic Reviews. Manuscripts should combine relevant machine learning methods and brain neuroimaging data to provide clinically meaningful research including but not limited to the following topics:
• Assisted diagnosis
• Exploring neural mechanisms and biomarkers
• Selecting interventions and treatments
• Predicting the pathological progression and clinical outcome
• Application of innovative or improved machine learning algorithms
With the soaring prevalence of psychiatric disorders, the huge demand for effective treatments of psychiatric disorders has increased largely in recent years. However, the complexity of neuronal degeneration and the heterogeneity of patients challenge the early diagnosis and effective treatments of these disorders. Fortunately, the application of machine learning theories and algorithms offers new insights for scientists, clinicians, and patients to meet these challenges.
Machine learning, which includes various methods of feature extraction, selection, and classification, has shown outstanding advantages in the pathological analysis of psychiatric disorders. These methods can learn features from brain neuroimaging data and adapt to the variation of data characteristics, helping to improve the reliability, performance, and accuracy of disease-specific diagnostic systems. In addition, machine learning can accurately assess patients’ conditions and predict the performance of clinical interventions, which is significant for the appropriate interventions and treatments of diseases.
This Research Topic aims to incorporate theoretical and technological innovations and evaluate the performance of machine learning in clinical studies on psychiatric disorders with the application of innovative machine learning algorithms, clinical diagnosis, and clinical interventions, combining the clinical neuroimaging data of the brain. We welcome authors to submit Original Research, including Clinical Case Studies and Methods, as well as Technological Reports, Hypothesis and Theory, and Systematic Reviews. Manuscripts should combine relevant machine learning methods and brain neuroimaging data to provide clinically meaningful research including but not limited to the following topics:
• Assisted diagnosis
• Exploring neural mechanisms and biomarkers
• Selecting interventions and treatments
• Predicting the pathological progression and clinical outcome
• Application of innovative or improved machine learning algorithms