- Summary of CADS developed for MS using MRI neuroimaging modalities and details of deep learning architectures.
| Article | Clinical data about cases and controls |
|---|---|
| Afzal el al., 201823 | All patients included fulfilled the McDonald’s criteria. Out of these 21 patients, 10 converted to CDMS after one year, whereas 11 did not convert to CDMS after one year follow up. |
| Afzal el al., 202124 | 21 scans of 5 subjects are available for training purposes and already preprocessed with several steps like skull stripping, denoising, bias correction, and co-registration. These 5 subjects have 4 time points and one subject having 5 time points with a gap of approximately 1 year. These 21 scans are provided for training purposes only. For testing purposes, 61 scans are provided from 14 subjects. |
| Alijamaat et al., 202025 | MRI images of 38 MS patients whose lesions are labeled by several neurologists and approved by radiologists. To increase the number of images, MRI images of 20 healthy individuals have been prepared by the authors and added to the existing data set. |
| Aslani et al., 201926 | 19 subjects divided into two sets, 5 subjects for training and 14 subjects for testing.Each subject has MRI data with a different number of time-points, normally ranging between 4 to 6. |
| Aslani et al., 201927 | 37 MS patients (22 females and 15 males) with mean age 44,6±12,2 years. The patient clinical phenotypes were 24 relapsing remitting MS, 3 primary progressive MS and 10 secondary progressive MS. The mean EDSS was 3,3±2, the mean disease duration was 13.1±8,7 years and the mean lesion load was 6.2±5.7 ml. |
| Coronado et al., 202028 | - |
| Eitel et al., 201929 | 76 patients with relapsing-remitting MS according to the McDonald criteria 2010 and 71 healthy controls. Patients were excluded if they were outside the age range of 18 – 69 or did not have an MRI scan. All patients were examined under supervision of a board-certified neurologist at the NeuroCure Clinical Research Center (Charité – Universitätsmedizin Berlin) between January 2011 and July 2015. |
| Kazancli et al., 201830 | - |
| La Rosa et al., 201831 | -The training dataset was composed of 32 patients, 18 female / 14 male, mean age 34±10 years, with EDSS scores ranged from 1 to 2 (mean 1,6±0,3). Mean lesion volume is 0,11±0,40 ml (range 0.001-7.03 ml). Mean lesion load per case was 6,0±7,2 ml (range 0,3-37,2 ml). -The test dataset was made up of 73 patients, 50 females and 23 males (mean age 38±10 years). EDSS scores ranged from 1 to 7.5 (mean 2,6±1,5). Mean lesion volume was 0,25±3,29 ml (range 0.002-159.827 ml). Mean lesion load per case was 14,3±27,9 ml (range 0.2-162.9 ml). |
| Roy et al., 201832 | 128 patients enrolled in a natural history study of MS, 79 with relapsing-remitting, 30 with secondary progressive, and 19 with primary pro-gressive MS. |
| Shrwan et al., 202133 | - |
| Siar et al., 201934 | 200 patients, including tumors and MS and healthy patients. Totally, the number of trench data for the brain tumor class was 461 images, 791 healthy patients, and 320 MS patients. The total number of data for the most 1286 images and test data was 384 images. Pictures were collected in the range of 6 to 80 years old and the average age was 43. |
| Valverde et al., 201835 | 60 patients with a clinically isolated syndrome (Hospital Vall d’Hebron, Barcelona, Spain) were scanned on a 3 T Siemens with a 12-channel phased-array head coil (Trio Tim, Siemens, Germany) |
| Wang et al., 201836 | - |
| Zhang et al., 201837 | -There are 38 patients in the eHealth dataset. 676 slices associated with plaques were selected. All Brain lesions were identified and delineated by experienced MS neurologists and were confirmed by radiologists. -Age-matched and gender-matched healthy controls (HC) of the eHealth dataset were included. The exclusion criteria for all volunteers were known neurological or psychiatric diseases, brain lesions, taking psychotropic medications, and contraindications to MR imaging. |
ADADELTA: adaptive learning rate method, Adam: A Method for Stochastic Optimization, CADS: Computer-aided detection software, CDMS: clinically defined multiple sclerosis, EDSS: Expanded Disability Status Scale, FMRIB: Functional Magnetic Resonance Imaging of the Brain, MMFF: multi-modal feature fusion block,, MRI: Magnetic resonance imaging, MRIAP: Magnetic Resonance Imaging Automatic Processing, MSFU: multi-scale feature upsampling block, MPR: multi-planes reconstruction, Matlab: matrix laboratory, SGDM: Stochastic Gradient Descent Momentum, UFF: upsampling fused featu