Machine Learning Classification of Neuropsychiatric Systemic Lupus Erythematosus patients using resting-state fMRI functional connectivity

Simos, Nicholas J; Manikis, Georgios C.; Papadaki, Efrosini; Kavroulakis, Eleftherios; Βertsias, George; Marias, Kostas

doi:10.1109/ist48021.2019.9010078

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…Eleven hundred and sixty-six (1166) ineligible articles were excluded after browsing the abstracts and titles, and full texts of the remaining sixty (60) articles were read. Finally, a total of eighteen (18) studies were included, in which ten (10) [15][16][17][18][19][20][21][22][23][24] focused on SLE and the remaining eight (8) [25][26][27][28][29][30][31][32] on NPSLE. Te study selection process is shown in Figure 1, and the characteristics of included studies are shown in Tables 1 (for SLE) and 2 (for NPSLE).…”

Section: Study Selection and Risk Of Bias Assessmentmentioning

confidence: 99%

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Zhou

Wang

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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Background. Application of machine learning (ML) for identification of systemic lupus erythematosus (SLE) has been recently drawing increasing attention, while there is still lack of evidence-based support. Methods. Systematic review and meta-analysis are conducted to evaluate its diagnostic accuracy and application prospect. PubMed, Embase, Cochrane Library, and Web of Science libraries are searched, in combination with manual searching and literature retrospection, for studies regarding machine learning for identifying SLE and neuropsychiatric systemic lupus erythematosus (NPSLE). Quality Assessment of Diagnostic Accuracy Studies (QUADA-2) is applied to assess the quality of included studies. Diagnostic accuracy of the SLE model and NPSLE model is assessed using the bivariate fixed-effect model, and the data are pooled. Summary receiver operator characteristic curve (SROC) is plotted, and area under the curve (AUC) is calculated. Results. Eighteen (18) studies are included, in which ten (10) focused on SLE and eight (8) on NPSLE. The AUC of SLE identification is 0.95, the sensitivity is 0.90, the specificity is 0.89, the PLR is 8.4, the NLR is 0.12, and the DOR is 73. AUC of NPSLE identification is 0.89, the sensitivity is 0.83, the specificity is 0.83, the PLR is 5.0, the NLR is 0.20, and the DOR is 25. Conclusion. Machine learning presented remarkable performance in identification of SLE and NPSLE. Based on the convenience for inclusion factor collection and non-invasiveness of detection, machine learning is expected to be widely applied in clinical practice to assist medical decision making.

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Section: Study Selection and Risk Of Bias Assessmentmentioning

confidence: 99%

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Zhou

Wang

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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“…While building upon an earlier report [12], where a robust machine learning model was developed for the classification of NPSLE patients and HCs using prespecified resting-state functional connectivity networks, two new goals were set. The first improvement was to tackle this diagnostic and exploratory problem using a fully data-driven method.…”

Section: Feature Selection and Classification Algorithmmentioning

confidence: 99%

“…FC can be generally considered as a transformation of the initial high-dimensional fMRI data, quantified by simple metrics such as correlation, covariance or mutual information calculated between the recorded time series of different brain regions. Adaptive algorithms utilizing functional neuroimaging data have not been employed thus far on SLE or NPSLE patients, with the exception of a preliminary study utilizing connectivity indices derived from conventional, a-priori defined functional networks [12].…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

et al. 2020

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Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

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“…In a study of patients with neuropsychiatric SLE (Simos et al, 2019), researchers applied a ML model to enhance current neuropsychiatric SLE diagnosis approaches based on resting-state functional connectivity MRI (fMRI) imaging data of the brain. ML classifiers, including random forest, support vector machine, naïve Bayes and k-nearest neighbours were trained by the fMRI connectivity matrix derived from fMRI images of the brain network of 41 neuropsychiatric SLE patients and 31 healthy controls.…”

Section: Machine Learning For Diagnosismentioning

confidence: 99%

Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges

Peng

Jury

Dönnes³

et al. 2021

Front. Pharmacol.

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In the past decade, the emergence of machine learning (ML) applications has led to significant advances towards implementation of personalised medicine approaches for improved health care, due to the exceptional performance of ML models when utilising complex big data. The immune-mediated chronic inflammatory diseases are a group of complex disorders associated with dysregulated immune responses resulting in inflammation affecting various organs and systems. The heterogeneous nature of these diseases poses great challenges for tailored disease management and addressing unmet patient needs. Applying novel ML techniques to the clinical study of chronic inflammatory diseases shows promising results and great potential for precision medicine applications in clinical research and practice. In this review, we highlight the clinical applications of various ML techniques for prediction, diagnosis and prognosis of autoimmune rheumatic diseases, inflammatory bowel disease, autoimmune chronic kidney disease, and multiple sclerosis, as well as ML applications for patient stratification and treatment selection. We highlight the use of ML in drug development, including target identification, validation and drug repurposing, as well as challenges related to data interpretation and validation, and ethical concerns related to the use of artificial intelligence in clinical research.

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Machine Learning Classification of Neuropsychiatric Systemic Lupus Erythematosus patients using resting-state fMRI functional connectivity

Cited by 9 publications

References 7 publications

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges

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