A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder

Achalia, Rashmin; Sinha, Anannya; Jacob, Arpitha; Achalia, Garimaa; Kaginalkar, Varsha; Venkatasubramanian, Ganesan; Rao, Naren P.

doi:10.1016/j.ajp.2020.101984

Cited by 31 publications

(33 citation statements)

References 52 publications

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“…Among the 33 included articles, 30 were research articles (91%) [ 14 , 18 - 46 ], whereas 3 articles were conference proceedings (9%) [ 24 , 42 , 47 ], as shown in Table 1 and Multimedia Appendix 4 . Articles were published in 14 different countries; China (8, 24%) [ 14 , 18 - 20 , 22 , 25 , 30 , 39 ], India (1, 3%) [ 21 ], Germany (2, 6%) [ 23 , 47 ], United Kingdom (1, 3%) [ 26 ], United States (8, 24%) [ 27 , 28 , 32 , 34 , 37 , 38 , 41 , 45 ], Korea (2, 6%) [ 29 , 36 ], Egypt (1, 3%) [ 31 ], Turkey (2, 6%) [ 31 , 43 ], Italy (1, 3%) [ 33 ], Brazil (1%) [ 47 ], Australia (1%) [ 35 ], the Netherlands (1, 3%) [ 36 ], Norway (1, 3%) [ 37 ], Canada (1, 3%) [ 40 ] and Japan (1, 3%) [ 46 ]; however, the highest numbers of articles were from China and the United States, as observed in Figure 2 . The highest numbers of the articles were published in 2018 and 2019 (7, 21.21%), as shown in Figure 2 and Multimedia Appendices 5 and 6 .…”

Section: Resultsmentioning

confidence: 99%

“…In 9 (28%) of the 33 studies, SVM-based models were used to diagnose BD (specific types are not mentioned) [ 18 - 26 ]. In 1 study [ 18 ], this model was used to diagnose chronic BD and first-episode BD, whereas in 3 studies [ 19 , 21 , 26 ], SVM was used to diagnose type 1 and type 2 BD. However, SVM [ 24 ] was also used to diagnose unspecified types of BD.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review

Jan¹,

Ai-Ansari²,

Mousa³

et al. 2021

J Med Internet Res

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Background Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD. Objective This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes. Methods The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed. Results We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%. Conclusions This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review

Jan¹,

Ai-Ansari²,

Mousa³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…In [ 41 ], using MRI and an SVM classifier the authors found an AUC of 0.71 in differentiating BD from controls. More recently, [ 34 ], also using an SVM classifier, obtained an accuracy of 87.60% considering neuroimaging and neurocognitive measures as input. The results of these lines of study suggest the benefit of undertaking automatic diagnostic studies of BD in which data from OCT, neuroimaging and neurocognitive studies, among others, are considered as inputs.…”

Section: Discussionmentioning

confidence: 99%

“…AI techniques, including Gaussian Naive Bayes, k-nearest neighbors algorithm (KNN), Decision Tree, Artificial Neural Networks and Support Vector Machine (SVM) have already been used to aid diagnosis in BD (see [ 31 ] for a review). More recent AI studies have used MRI [ 32 ] and genomic data [ 33 ], as well as neuroimaging and neuropsychological assessment [ 34 ], as variables. A recent review [ 35 ] focused on diagnosing BD by applying AI techniques to neuroimaging analysis.…”

Section: Introductionmentioning

confidence: 99%

Automatic Diagnosis of Bipolar Disorder Using Optical Coherence Tomography Data and Artificial Intelligence

Sánchez-Morla

Fuentes

Miguel-Jiménez

et al. 2021

JPM

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Background: The aim of this study is to explore an objective approach that aids the diagnosis of bipolar disorder (BD), based on optical coherence tomography (OCT) data which are analyzed using artificial intelligence. Methods: Structural analyses of nine layers of the retina were analyzed in 17 type I BD patients and 42 controls, according to the areas defined by the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. The most discriminating variables made up the feature vector of several automatic classifiers: Gaussian Naive Bayes, K-nearest neighbors and support vector machines. Results: BD patients presented retinal thinning affecting most layers, compared to controls. The retinal thickness of the parafoveolar area showed a high capacity to discriminate BD subjects from healthy individuals, specifically for the ganglion cell (area under the curve (AUC) = 0.82) and internal plexiform (AUC = 0.83) layers. The best classifier showed an accuracy of 0.95 for classifying BD versus controls, using as variables of the feature vector the IPL (inner nasal region) and the INL (outer nasal and inner inferior regions) thickness. Conclusions: Our patients with BD present structural alterations in the retina, and artificial intelligence seem to be a useful tool in BD diagnosis, but larger studies are needed to confirm our findings.

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“…75 Recently, several studies have demonstrated the ability of ML for differentiating unique BD and ADHD subgroups from control participants, based on neurocognitive and neuroimaging data. 76,77…”

Section: Perspectives In Neurocognitive and Neuroimaging Research In mentioning

confidence: 99%

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2020

Psychiatriki

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A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder

Cited by 31 publications

References 52 publications

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review

Automatic Diagnosis of Bipolar Disorder Using Optical Coherence Tomography Data and Artificial Intelligence

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