The COVID-19 pandemic generated research interest in automated models to perform classification and segmentation from medical imaging of COVID-19 patients, However, applications in real-world scenarios are still needed. We describe the development and deployment of COVID-19 decision support and segmentation system. A partnership with a Brazilian radiologist consortium, gave us access to 1000s of labeled computed tomography (CT) and X-ray images from São Paulo Hospitals. The system used EfficientNet and EfficientDet networks, state-of-the-art convolutional neural networks for natural images classification and segmentation, in a real-time scalable scenario in communication with a Picture Archiving and Communication System (PACS). Additionally, the system could reject non-related images, using header analysis and classifiers. We achieved CT and X-ray classification accuracies of 0.94 and 0.98, respectively, and Dice coefficient for lung and covid findings segmentations of 0.98 and 0.73, respectively. The median response time was 7 s for X-ray and 4 min for CT.

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Central nervous system (CNS) involvement in childhood-onset systemic lupus erythematosus (cSLE) occurs in more than 50% of patients. Structural magnetic resonance imaging (MRI) has identified global cerebral atrophy, as well as the involvement of the corpus callosum and hippocampus, which is associated with cognitive impairment. In this cross-sectional study we included 71 cSLE (mean age 24.7 years (SD 4.6) patients and a disease duration of 11.8 years (SD 4.8) and two control groups: (1) 49 adult-onset SLE (aSLE) patients (mean age of 33.2 (SD 3.7) with a similar disease duration and (2) 58 healthy control patients (mean age of 29.9 years (DP 4.1)) of a similar age. All of the individuals were evaluated on the day of the MRI scan (Phillips 3T scanner). We reviewed medical charts to obtain the clinical and immunological features and treatment history of the SLE patients. Segmentation of the corpus callosum was performed through an automated segmentation method. Patients with cSLE had a similar mid-sagittal area of the corpus callosum in comparison to the aSLE patients. When compared to the control groups, cSLE and aSLE had a significant reduction in the mid-sagittal area in the posterior region of the corpus callosum. We observed significantly lower FA values and significantly higher MD, RD, and AD values in the total area of the corpus callosum and in the parcels B, C, D, and E in cSLE patients when compared to the aSLE patients. Low complement, the presence of anticardiolipin antibodies, and cognitive impairment were associated with microstructural changes. In conclusion, we observed greater microstructural changes in the corpus callosum in adults with cSLE when compared to those with aSLE. Longitudinal studies are necessary to follow these changes, however they may explain the worse cognitive function and disability observed in adults with cSLE when compared to aSLE.

Objective. The mechanisms driving multiple sclerosis (MS) are still largely unknown, calling for new methods allowing to detect and characterize tissue degeneration since the early stages of the disease. Our aim is to decrypt the microstructural signatures of the Primary Progressive versus the Relapsing-Remitting state of disease based on diffusion and structural magnetic resonance imaging data. Approach. A selection of microstructural descriptors, based on the 3D-Simple Harmonics Oscillator Based Reconstruction and Estimation and the set of new algebraically independent Rotation Invariant spherical harmonics Features, was considered and used to feed convolutional neural networks (CNNs) models. Classical measures derived from diffusion tensor imaging, that are fractional anisotropy and mean diffusivity, were used as benchmark for diffusion MRI (dMRI). Finally, T1-weighted images were also considered for the sake of comparison with the state-of-the-art. A CNN model was fit to each feature map and layerwise relevance propagation (LRP) heatmaps were generated for each model, target class and subject in the test set. Average heatmaps were calculated across correctly classified patients and size-corrected metrics were derived on a set of regions of interest to assess the LRP contrast between the two classes. Main results. Our results demonstrated that dMRI features extracted in grey matter tissues can help in disambiguating primary progressive multiple sclerosis from relapsing-remitting multiple sclerosis patients and, moreover, that LRP heatmaps highlight areas of high relevance which relate well with what is known from literature for MS disease. Significance. Within a patient stratification task, LRP allows detecting the input voxels that mostly contribute to the classification of the patients in either of the two classes for each feature, potentially bringing to light hidden data properties which might reveal peculiar disease-state factors.