Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

Abstract

Background Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD. Methods In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed. Results After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5–95.3%), sensitivity of 86.4% (95%CI: 64.0–96.4%), and specificity of 88.9% (95%CI: 63.9–98.0%) in the test data (p = 0.0022). Conclusions A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

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

“…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 .…”

“…The accuracy level ranged from ≤70% to >91%. The accuracy level was ≤70% in 3 studies [ 25 , 32 , 36 ], 71%-78% in 7 studies [ 18 , 23 , 34 , 37 , 43 , 47 , 49 ], 83%-90% in 9 studies [ 14 , 20 , 21 , 26 , 28 , 29 , 31 , 33 , 42 ], and >91% in 5 studies [ 19 , 22 , 24 , 35 , 40 ]. The highest accuracy was 98%, found in only 1 study, whereas the lowest accuracy level was 64%.…”

“…Sensitivity was reported in only 15 studies; it ranged from ≤60% to >90%. Sensitivity was ≤60% in 1 study [ 46 ], 65%-67% in 2 studies [ 37 , 38 ], 75%-78% in 3 studies [ 14 , 18 , 47 ], 80%-88% in 8 studies [ 20 - 22 , 26 , 29 , 31 , 41 , 42 ], and above 90% in 1 study [ 35 ]. The mean value of sensitivity was 78.26%, whereas the median value was 82%.…”

“…Moreover, specificity was only mentioned in 13 studies. The value of specificity ranged from ≤70% to 92% in 1 study [ 46 ], 74%-77% in 3 studies [ 18 , 42 , 49 ], 80%-90% in 6 studies [ 14 , 20 , 29 , 37 , 41 , 47 ], and >90% in 4 studies [ 21 , 22 , 26 , 35 ]. The mean specificity value was 85.36% and the median was 85.4%.…”

“…BD symptoms overlap with other mood disorders, specifically MDD, and this leads to the misdiagnosis of BD [ 20 ]. Future research should explore the main indicator that shows the patient is diagnosed with BD; for example, studies showed that patients diagnosed with BD have abnormal gray matter density in the MRI images of the brain.…”