Schizophrenia Detection and Classification: A Systematic Review of the Last Decade

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2024 Dec 17

PMID 39682605

Authors

Arghyasree Saha

Seungmin Park

Zong Woo Geem

Pawan Kumar Singh

Affiliations

Soon will be listed here.

Abstract

Background/objectives: Artificial Intelligence (AI) in healthcare employs advanced algorithms to analyze complex and large-scale datasets, mimicking aspects of human cognition. By automating decision-making processes based on predefined thresholds, AI enhances the accuracy and reliability of healthcare data analysis, reducing the need for human intervention. Schizophrenia (SZ), a chronic mental health disorder affecting millions globally, is characterized by symptoms such as auditory hallucinations, paranoia, and disruptions in thought, behavior, and perception. The SZ symptoms can significantly impair daily functioning, underscoring the need for advanced diagnostic tools.

Methods: This systematic review has been conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines and examines peer-reviewed studies from the last decade (2015-2024) on AI applications in SZ detection as well as classification. The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number: CRD42024612364. Research has been sourced from multiple databases and screened using predefined inclusion criteria. The review evaluates the use of both Machine Learning (ML) and Deep Learning (DL) methods across multiple modalities, including Electroencephalography (EEG), Structural Magnetic Resonance Imaging (sMRI), and Functional Magnetic Resonance Imaging (fMRI). The key aspects reviewed include datasets, preprocessing techniques, and AI models.

Results: The review identifies significant advancements in AI methods for SZ diagnosis, particularly in the efficacy of ML and DL models for feature extraction, classification, and multi-modal data integration. It highlights state-of-the-art AI techniques and synthesizes insights into their potential to improve diagnostic outcomes. Additionally, the analysis underscores common challenges, including dataset limitations, variability in preprocessing approaches, and the need for more interpretable models.

Conclusions: This study provides a comprehensive evaluation of AI-based methods in SZ prognosis, emphasizing the strengths and limitations of current approaches. By identifying unresolved gaps, it offers valuable directions for future research in the application of AI for SZ detection and diagnosis.

References

Rahul J, Sharma D, Sharma L, Nanda U, Sarkar A . A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning. Front Hum Neurosci. 2024; 18:1347082. PMC: 10899326. DOI: 10.3389/fnhum.2024.1347082. View

Jain P, Sao A, Minhas A . Analyzing the Effect of Resolution of Network Nodes on the Resting State Functional Connectivity Maps of Schizophrenic Human Brains. Annu Int Conf IEEE Eng Med Biol Soc. 2021; 2021:6695-6698. DOI: 10.1109/EMBC46164.2021.9630822. View

Page M, McKenzie J, Bossuyt P, Boutron I, Hoffmann T, Mulrow C . The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021; 372:n71. PMC: 8005924. DOI: 10.1136/bmj.n71. View

Liu Y, Zhang Y, Lv L, Wu R, Zhao J, Guo W . Abnormal neural activity as a potential biomarker for drug-naive first-episode adolescent-onset schizophrenia with coherence regional homogeneity and support vector machine analyses. Schizophr Res. 2017; 192:408-415. DOI: 10.1016/j.schres.2017.04.028. View

Medalia A, Saperstein A, Hansen M, Lee S . Personalised treatment for cognitive dysfunction in individuals with schizophrenia spectrum disorders. Neuropsychol Rehabil. 2016; 28(4):602-613. PMC: 5951177. DOI: 10.1080/09602011.2016.1189341. View

Rokham H, Pearlson G, Abrol A, Falakshahi H, Plis S, Calhoun V . Addressing Inaccurate Nosology in Mental Health: A Multilabel Data Cleansing Approach for Detecting Label Noise From Structural Magnetic Resonance Imaging Data in Mood and Psychosis Disorders. Biol Psychiatry Cogn Neurosci Neuroimaging. 2020; 5(8):819-832. PMC: 7760893. DOI: 10.1016/j.bpsc.2020.05.008. View

Steardo Jr L, Carbone E, de Filippis R, Pisanu C, Segura-Garcia C, Squassina A . Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review. Front Psychiatry. 2020; 11:588. PMC: 7326270. DOI: 10.3389/fpsyt.2020.00588. View

Shoeibi A, Sadeghi D, Moridian P, Ghassemi N, Heras J, Alizadehsani R . Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models. Front Neuroinform. 2021; 15:777977. PMC: 8657145. DOI: 10.3389/fninf.2021.777977. View

Aslan Z, Akin M . A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals. Phys Eng Sci Med. 2021; 45(1):83-96. DOI: 10.1007/s13246-021-01083-2. View

10.

Rakhmatulin I, Dao M, Nassibi A, Mandic D . Exploring Convolutional Neural Network Architectures for EEG Feature Extraction. Sensors (Basel). 2024; 24(3). PMC: 10856895. DOI: 10.3390/s24030877. View

11.

Lei D, Pinaya W, Young J, van Amelsvoort T, Marcelis M, Donohoe G . Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual. Hum Brain Mapp. 2019; 41(5):1119-1135. PMC: 7268084. DOI: 10.1002/hbm.24863. View

12.

Alam M, Lin H, Deng H, Calhoun V, Wang Y . A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia. J Neurosci Methods. 2018; 309:161-174. PMC: 6415770. DOI: 10.1016/j.jneumeth.2018.08.027. View

13.

Man W, Ding H, Chai C, An X, Liu F, Qin W . Brain age gap as a potential biomarker for schizophrenia: A multi-site structural MRI study. Annu Int Conf IEEE Eng Med Biol Soc. 2021; 2021:4060-4063. DOI: 10.1109/EMBC46164.2021.9631085. View

14.

Ramkiran S, Sharma A, Rao N . Resting-state anticorrelated networks in Schizophrenia. Psychiatry Res Neuroimaging. 2019; 284:1-8. DOI: 10.1016/j.pscychresns.2018.12.013. View

15.

Naveed Iqbal Qureshi M, Oh J, Lee B . 3D-CNN based discrimination of schizophrenia using resting-state fMRI. Artif Intell Med. 2019; 98:10-17. DOI: 10.1016/j.artmed.2019.06.003. View

16.

Naveed Iqbal Qureshi M, Oh J, Cho D, Jo H, Lee B . Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine. Front Neuroinform. 2017; 11:59. PMC: 5596100. DOI: 10.3389/fninf.2017.00059. View

17.

Bae Y, Kumarasamy K, Ali I, Korfiatis P, Akkus Z, Erickson B . Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI. J Digit Imaging. 2017; 31(2):252-261. PMC: 5873464. DOI: 10.1007/s10278-017-0020-4. View

18.

Teixeira F, Costa M, Abreu J, Cabral M, Soares S, Teixeira J . A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges. Bioengineering (Basel). 2023; 10(4). PMC: 10135748. DOI: 10.3390/bioengineering10040493. View

19.

Grasby K, Jahanshad N, Painter J, Colodro-Conde L, Bralten J, Hibar D . The genetic architecture of the human cerebral cortex. Science. 2020; 367(6484). PMC: 7295264. DOI: 10.1126/science.aay6690. View

20.

Wang S, Zhang Y, Lv L, Wu R, Fan X, Zhao J . Abnormal regional homogeneity as a potential imaging biomarker for adolescent-onset schizophrenia: A resting-state fMRI study and support vector machine analysis. Schizophr Res. 2017; 192:179-184. DOI: 10.1016/j.schres.2017.05.038. View