Incidence and Risk Factors for New and Recurrent Infarcts in Adults With Sickle Cell Disease.

BACKGROUND: Most adults with sickle cell disease will experience a silent cerebral infarction (SCI) or overt stroke. Identifying patient subgroups with increased stroke incidence is important for future clinical trials focused on stroke prevention. Our 3-center prospective cohort study tested the primary hypothesis that adults with sickle cell disease and SCIs have a greater incidence of new stroke or SCI compared with those without SCI. A secondary aim focused on identifying additional risk factors for progressive infarcts, particularly traditional risk factors for stroke in adults. METHODS AND RESULTS: This observational study included adults with sickle cell disease and no history of stroke. Magnetic resonance imaging scans of the brain completed at baseline and >1 year later were reviewed by 3 radiologists for baseline SCIs and new or progressive infarcts on follow-up magnetic resonance imaging. Stroke risk factors were abstracted from the medical chart. Time-to-event analysis was utilized for progressive infarcts. Median age was 24.1 years; 45.3% of 95 participants had SCIs on baseline magnetic resonance imaging. Progressive infarcts were present in 17 participants (17.9%), and the median follow-up was 2.1 years. Incidence of new infarcts was 11.95 per 100 patient-years (6.17-20.88) versus 3.74 per 100 patient-years (1.21-8.73) in those with versus without prior SCI. Multivariable Cox regression showed that baseline SCI predicts progressive infarcts (hazard ratio, 3.46 [95% CI, 1.05-11.39]; P=0.041); baseline hypertension was also associated with progressive infarcts (hazard ratio, 3.23 [95% CI, 1.16-9.51]; P=0.025). CONCLUSIONS: Selecting individuals with SCIs and hypertension for stroke prevention trials in sickle cell disease may enrich the study population with those at highest risk for infarct recurrence.

Automated system for classifying uni-bicompartmental knee osteoarthritis by using redefined residual learning with convolutional neural network.

Knee Osteoarthritis (OA) is one of the most common joint diseases that may cause physical disability associated with a significant personal and socioeconomic burden. X-ray imaging is the cheapest and most common method to detect Knee (OA). Accurate classification of knee OA can help physicians manage treatment efficiently and slow knee OA progression. This study aims to classify knee OA X-ray images according to anatomical types, such as uni or bicompartmental. The study proposes a deep learning model for classifying uni or bicompartmental knee OA based on redefined residual learning with CNN. The proposed model was trained, validated, and tested on a dataset containing 733 knee X-ray images (331 normal Knee images, 205 unicompartmental, and 197 bicompartmental knee images). The results show 61.81 % and 68.33 % for accuracy and specificity, respectively. Then, the performance of the proposed model was compared with different pre-trained CNNs. The proposed model achieved better results than all pre-trained CNNs.