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Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm.

Jihoon Oh¹, Baek-Lok Oh², Kyong-Uk Lee³

¹Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea.

Frontiers in Psychiatry

|March 3, 2020

Summary

This summary is machine-generated.

Deep learning algorithms accurately detect schizophrenia using structural MRI scans, identifying key brain regions. This approach shows promise for supplementary diagnostic information in clinical settings.

Keywords:

MRI classification deep learning schizophrenia structural abnormalities

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Area of Science:

Neuroimaging
Artificial Intelligence
Psychiatry

Background:

Schizophrenia diagnosis is challenging despite known structural brain abnormalities.
Magnetic resonance imaging (MRI) offers structural insights but lacks definitive diagnostic markers.
Developing automated methods for schizophrenia detection from MRI is crucial.

Purpose of the Study:

To develop and evaluate a deep learning algorithm for detecting schizophrenia using structural MRI data.
To assess the algorithm's performance on independent datasets and identify key brain regions involved.

Main Methods:

A deep convolutional neural network was trained on 873 structural MRI datasets from schizophrenia patients and healthy controls.
The algorithm was tested on both familiar and novel datasets, including one with early-stage patients.
Performance was quantified using the area under the receiver operating characteristic curve (AUC).

Main Results:

The algorithm achieved a high AUC of 0.96 in detecting schizophrenia on randomly selected images.
Performance on an unseen dataset was strong (AUC 0.71-0.90), but decreased to AUC 0.71 for early-stage patients.
The right temporal and parietal areas were most influential in the algorithm's classification.

Conclusions:

Deep learning effectively detects schizophrenia from structural MRI, highlighting significant brain regions.
The algorithm demonstrates acceptable performance for early-stage schizophrenia, suggesting clinical utility.
This AI approach can supplement diagnosis by delineating schizophrenia's structural characteristics.