&lt;p&gt;Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review&lt;/p&gt;

Filippis, Renato de; Carbone, Elvira Anna; Gaetano, Raffaele; Bruni, Antonella; Pugliese, Valentina; Segura-Garcia, Cristina; Fazio, Pasquale De

doi:10.2147/ndt.s202418

Cited by 96 publications

(80 citation statements)

References 48 publications

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“…It can be used for both classification and regression purposes; it allows data repeatability; it can be used in different fields of study, and it represents a great option for future studies. However, it is expensive, and its interpretation is not simple as it requires an experienced and dedicated team (14,44,45).…”

Section: Discussionmentioning

confidence: 99%

“…Rising attention has been given to machine-learning (ML) techniques (i.e. pattern recognition methods) applied to neuroimaging data (12) to identify phenotypes to be translated into clinical practice for early diagnosis (13,14). ML techniques applied to fMRI analyze highly complex data sets and assess the importance and interactions between variables, exploring brain functionality and making accurate predictions (15,16).…”

Section: Introductionmentioning

confidence: 99%

“…For example, it allows computers to find information, even unknown, without being explicitly told where to look for it (17). Among them, the Support Vector Machine (SVM) represents one of the ML techniques that has shown higher accuracy and precision especially in predicting clinical outcome and severity in schizophrenia patients (14). SVM is a supervised learning model with associated learning algorithms that analyzes data used for classification and regression analysis.…”

Section: Introductionmentioning

confidence: 99%

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Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review

et al. 2020

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Non-invasive measurements of brain function and structure as neuroimaging in patients with mental illnesses are useful and powerful tools for studying discriminatory biomarkers. To date, functional MRI (fMRI), structural MRI (sMRI) represent the most used techniques to provide multiple perspectives on brain function, structure, and their connectivity. Recently, there has been rising attention in using machine-learning (ML) techniques, pattern recognition methods, applied to neuroimaging data to characterize diseaserelated alterations in brain structure and function and to identify phenotypes, for example, for translation into clinical and early diagnosis. Our aim was to provide a systematic review according to the PRISMA statement of Support Vector Machine (SVM) techniques in making diagnostic discrimination between SCZ patients from healthy controls using neuroimaging data from functional MRI as input. We included studies using SVM as ML techniques with patients diagnosed with Schizophrenia. From an initial sample of 660 papers, at the end of the screening process, 22 articles were selected, and included in our review. This technique can be a valid, inexpensive, and non-invasive support to recognize and detect patients at an early stage, compared to any currently available assessment or clinical diagnostic methods in order to save crucial time. The higher accuracy of SVM models and the new integrated methods of ML techniques could play a decisive role to detect patients with SCZ or other major psychiatric disorders in the early stages of the disease or to potentially determine their neuroimaging risk factors in the near future.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review

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“…Diagnosis of schizophrenia is clinically dependent on psychiatric examinations since biomarkers that could accurately classify schizophrenia remain unknown [ 15 , 29 ]. Machine learning algorithms associated with neuroimaging features provide a promising way for schizophrenia diagnosis [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

Guo

Qiu

2020

Brain Sciences

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Structural changes in the hippocampus and amygdala have been demonstrated in schizophrenia patients. However, whether morphological information from these subcortical regions could be used by machine learning algorithms for schizophrenia classification were unknown. The aim of this study was to use volume of the amygdaloid and hippocampal subregions for schizophrenia classification. The dataset consisted of 57 patients with schizophrenia and 69 healthy controls. The volume of 26 hippocampal and 20 amygdaloid subregions were extracted from T1 structural MRI images. Sequential backward elimination (SBE) algorithm was used for feature selection, and a linear support vector machine (SVM) classifier was configured to explore the feasibility of hippocampal and amygdaloid subregions in the classification of schizophrenia. The proposed SBE-SVM model achieved a classification accuracy of 81.75% on 57 patients and 69 healthy controls, with a sensitivity of 84.21% and a specificity of 81.16%. AUC was 0.8241 (p < 0.001 tested with 1000-times permutation). The results demonstrated evidence of hippocampal and amygdaloid structural changes in schizophrenia patients, and also suggested that morphological features from the amygdaloid and hippocampal subregions could be used by machine learning algorithms for the classification of schizophrenia.

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“…ML is widely used in bio-medical sciences, and more recently in clinical and public health research. 3 4 Systematic reviews on health-related applications of ML have explored questions such as the accuracy of ML for diagnosis or outcome prediction, [5][6][7] but most of the research studies included in these reviews have come from high-income countries and the findings may not apply to low/middle-income countries (LMICs) because of the variability in access to healthcare and difference in the disease burden. It is largely unknown what ML applications are available for LMICs that can support and advance clinical medicine and public health.…”

Section: Introductionmentioning

confidence: 99%

Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol

et al. 2020

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Protocol © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.Strengths and limitations of this study ► The search strategy is informed by a solid framework. ► The search will be conducted in several information sources. ► The screening/review process will be performed by two reviewers independently. ► We will not search abstracts or conference proceedings specialised in machine learning. ► We will focus on information sources, where healthrelated outcomes are most likely to be found. AbStrACtIntroduction Machine learning (ML) has been used in biomedical research, and recently in clinical and public health research. However, much of the available evidence comes from high-income countries, where different health profiles challenge the application of this research to low/middleincome countries (LMICs). It is largely unknown what ML applications are available for LMICs that can support and advance clinical medicine and public health. We aim to address this gap by conducting a scoping review of healthrelated ML applications in LMICs.Methods and analysis This scoping review will follow the methodology proposed by Levac et al. The search strategy is informed by recent systematic reviews of ML healthrelated applications. We will search Embase, Medline and Global Health (through Ovid), Cochrane and Google Scholar; we will present the date of our searches in the final review. Titles and abstracts will be screened by two reviewers independently; selected reports will be studied by two reviewers independently. Reports will be included if they are primary research where data have been analysed, ML techniques have been used on data from LMICs and they aimed to improve health-related outcomes. We will synthesise the information following evidence mapping recommendations.Ethics and dissemination The review will provide a comprehensive list of health-related ML applications in LMICs. The results will be disseminated through scientific publications. We also plan to launch a website where ML models can be hosted so that researchers, policymakers and the general public can readily access them.

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<p>Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review</p>

Cited by 96 publications

References 48 publications

Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review

Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review

Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol

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