TY - JOUR
T1 - Predictive utility of artificial intelligence on schizophrenia treatment outcomes
T2 - A systematic review and meta-analysis
AU - Saboori Amleshi, Reza
AU - Ilaghi, Mehran
AU - Rezaei, Masoud
AU - Zangiabadian, Moein
AU - Rezazadeh, Hossein
AU - Wegener, Gregers
AU - Arjmand, Shokouh
N1 - Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.
PY - 2024/12/4
Y1 - 2024/12/4
N2 - Identifying optimal treatment approaches for schizophrenia is challenging due to varying symptomatology and treatment responses. Artificial intelligence (AI) shows promise in predicting outcomes, prompting this systematic review and meta-analysis to evaluate various AI models' predictive utilities in schizophrenia treatment. A systematic search was conducted, and the risk of bias was evaluated. The pooled sensitivity, specificity, and diagnostic odds ratio with 95 % confidence intervals between AI models and the reference standard for response to treatment were assessed. Diagnostic accuracy measures were calculated, and subgroup analysis was performed based on the input data of AI models. Out of the 21 included studies, AI models achieved a pooled sensitivity of 70 % and specificity of 76 % in predicting schizophrenia treatment response with substantial predictive capacity and a near-to-high level of test accuracy. Subgroup analysis revealed EEG-based models to have the highest sensitivity (89 %) and specificity (94 %), followed by imaging-based models (76 % and 80 %, respectively). However, significant heterogeneity was observed across studies in treatment response definitions, participant characteristics, and therapeutic interventions. Despite methodological variations and small sample sizes in some modalities, this study underscores AI's predictive utility in schizophrenia treatment, offering insights for tailored approaches, improving adherence, and reducing relapse risk.
AB - Identifying optimal treatment approaches for schizophrenia is challenging due to varying symptomatology and treatment responses. Artificial intelligence (AI) shows promise in predicting outcomes, prompting this systematic review and meta-analysis to evaluate various AI models' predictive utilities in schizophrenia treatment. A systematic search was conducted, and the risk of bias was evaluated. The pooled sensitivity, specificity, and diagnostic odds ratio with 95 % confidence intervals between AI models and the reference standard for response to treatment were assessed. Diagnostic accuracy measures were calculated, and subgroup analysis was performed based on the input data of AI models. Out of the 21 included studies, AI models achieved a pooled sensitivity of 70 % and specificity of 76 % in predicting schizophrenia treatment response with substantial predictive capacity and a near-to-high level of test accuracy. Subgroup analysis revealed EEG-based models to have the highest sensitivity (89 %) and specificity (94 %), followed by imaging-based models (76 % and 80 %, respectively). However, significant heterogeneity was observed across studies in treatment response definitions, participant characteristics, and therapeutic interventions. Despite methodological variations and small sample sizes in some modalities, this study underscores AI's predictive utility in schizophrenia treatment, offering insights for tailored approaches, improving adherence, and reducing relapse risk.
KW - AI
KW - Deep learning
KW - Machine learning
KW - Schizophrenia
KW - Treatment outcomes
UR - http://www.scopus.com/inward/record.url?scp=85211107908&partnerID=8YFLogxK
U2 - 10.1016/j.neubiorev.2024.105968
DO - 10.1016/j.neubiorev.2024.105968
M3 - Review
C2 - 39643220
SN - 0149-7634
VL - 169
SP - 105968
JO - Neuroscience and Biobehavioral Reviews
JF - Neuroscience and Biobehavioral Reviews
M1 - 105968
ER -