By Trend News Line 2024-03-23 11:47:54.
**New AI Study Uses MRI Data to Predict Risk of Psychosis**
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A recent study conducted by researchers from the ENIGMA Clinical High Risk for Psychosis working group has utilized structural MRI data to predict the risk of subsequent psychosis in high-risk individuals. The findings of the study, which have been published in the journal “Molecular Psychiatry,” could potentially aid in the early detection of psychosis.
**Identifying High-Risk Individuals Using Machine Learning**
The central objective of the study was to develop a method that could use structural magnetic resonance imaging (sMRI) data to identify individuals at high risk of developing psychosis. The international research team collected and analyzed MRI data from a total of 1165 individuals, including those at clinically elevated risk for psychosis (CHR), participants who later developed psychosis (CHR-PS+), individuals who did not develop psychosis (CHR-PS-), and those with unclear follow-up status (CHR-UNK). Data from 1029 healthy controls (HCs) were also included for comparison.
**Evaluating the Performance of the Classifier**
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To assess the effectiveness of the classifier, the data set was divided into various subsets, including a training dataset for building the classifier, a test dataset for accuracy checks, and an independent confirmation dataset for validation on new data. The study also utilized the ComBat statistical method to harmonize MRI data across different sites, improving the reliability of classification results.
**Structural Differences in the Brains of At-Risk Individuals**
Previous studies have indicated structural differences in the brains of individuals at increased risk for psychosis, including a reduction in gray matter in specific brain regions. In the current analysis, the study highlighted the superior temporal lobe, insular cortex, and superior frontal areas as critical for differentiation. The accuracy of the classifier on training and independent confirmatory datasets was reported to be 85 percent and 73 percent, respectively.
**Potential Clinical Applications**
The classifier developed in this study was not only able to identify individuals at high risk of developing psychosis but also those who did not go on to develop psychosis and those with an unclear course, mostly classified as healthy controls. The researchers believe that their AI-based approach could potentially be used as a clinical tool for risk stratification, complementing existing risk assessments. Further research is required to validate and optimize the classifier for clinical use.
**Understanding Psychosis and the Importance of Early Detection**
Psychosis is characterized by symptoms such as delusions and hallucinations that interfere with a person’s contact with reality. It can be triggered by various factors, including genetic predisposition, brain developmental disorders, stress, trauma, and substance use. Early detection of psychosis is crucial, as it can lead to more favorable recovery outcomes. Each year, 15 to 100 out of every 100,000 people develop psychosis, highlighting the importance of early intervention and prevention.
**Conclusion**
The recent study using structural MRI data to predict the risk of subsequent psychosis in high-risk individuals represents a significant advancement in the field of mental health research. By harnessing the power of machine learning and sMRI, researchers have made strides towards early detection and intervention for psychosis. As further research is conducted to refine and validate the classifier, there is hope for improved outcomes for individuals at risk of developing psychosis..
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