Abstract
Background: The integration of artificial intelligence (AI) into healthcare, especially nursing, offers transformative possibilities for mental health diagnosis. This study explores how MRI-based machine learning can support nurses in accurately identifying schizophrenia, addressing a growing need for innovative diagnostic support in nursing practice.
Objective: To evaluate the effectiveness of AI algorithms utilizing MRI data in differentiating patients with schizophrenia from healthy individuals, focusing on the practical applications for nursing in clinical environments.
Methods: MRI data, including Functional Network Connectivity and Source-Based Morphometry from 86 participants (40 patients with schizophrenia, 46 controls), were used to train machine learning models such as k-NN, SVM, decision trees, random forest, and deep neural networks.
Findings: The deep neural network achieved the highest accuracy (0.76), F1 score (0.73), and AUC (0.86), with SVM as the next best performer (accuracy: 0.65, F1 score: 0.58, AUC: 0.82).
Conclusion: This research underscores AI’s potential to support nursing practice in mental health by enabling early detection and personalized patient care. By integrating AI-driven MRI analysis, nurses can assume a more proactive role in mental health assessment, enhancing patient outcomes and contributing to AI-enabled clinical innovation.
Notes
References:
1. Hosna Tavakoli, Reza Rostami, Reza Shalbaf, Mohammad-Reza Nazem-Zadeh
medRxiv 2024.08.09.24311726; doi: https://doi.org/10.1101/2024.08.09.24311726
2. Zhang J, Rao VM, Tian Y, Yang Y, Acosta N, Wan Z, Lee PY, Zhang C, Kegeles LS, Small SA, Guo J. Detecting schizophrenia with 3D structural brain MRI using deep learning. Sci Rep. 2023 Sep 2;13(1):14433. doi: 10.1038/s41598-023-41359-z. PMID: 37660217; PMCID: PMC10475022.
Sigma Membership
Lambda Beta at-Large
Type
Presentation
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Other
Keywords:
Academic-clinical Partnership, Artificial Intelligence, AI, MRI-based Machine Learning, Schizophrenia Identification, Diagnostics
Recommended Citation
Chiang, Yi Hsuan, "Advancing Nursing Practice with AI: MRI-Enhanced Machine Learning for Schizophrenia Screening" (2025). International Nursing Research Congress (INRC). 250.
https://www.sigmarepository.org/inrc/2025/presentations_2025/250
Conference Name
36th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Seattle, Washington, USA
Conference Year
2025
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Advancing Nursing Practice with AI: MRI-Enhanced Machine Learning for Schizophrenia Screening
Seattle, Washington, USA
Background: The integration of artificial intelligence (AI) into healthcare, especially nursing, offers transformative possibilities for mental health diagnosis. This study explores how MRI-based machine learning can support nurses in accurately identifying schizophrenia, addressing a growing need for innovative diagnostic support in nursing practice.
Objective: To evaluate the effectiveness of AI algorithms utilizing MRI data in differentiating patients with schizophrenia from healthy individuals, focusing on the practical applications for nursing in clinical environments.
Methods: MRI data, including Functional Network Connectivity and Source-Based Morphometry from 86 participants (40 patients with schizophrenia, 46 controls), were used to train machine learning models such as k-NN, SVM, decision trees, random forest, and deep neural networks.
Findings: The deep neural network achieved the highest accuracy (0.76), F1 score (0.73), and AUC (0.86), with SVM as the next best performer (accuracy: 0.65, F1 score: 0.58, AUC: 0.82).
Conclusion: This research underscores AI’s potential to support nursing practice in mental health by enabling early detection and personalized patient care. By integrating AI-driven MRI analysis, nurses can assume a more proactive role in mental health assessment, enhancing patient outcomes and contributing to AI-enabled clinical innovation.
Description
This research explores the use of AI-driven MRI analysis in enhancing nursing capabilities for schizophrenia detection. Participants will learn how integrating machine learning into clinical practice empowers nurses to improve diagnostic accuracy, early intervention, and personalized care for mental health patients.