Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19

Cerebral Microbleeds (CMBs), typically captured as hypointensities from susceptibility-weighted imaging (SWI), are particularly important for the study of dementia, cerebrovascular disease, and normal aging. Recent studies on COVID-19 have shown an increase in CMBs of coronavirus cases. Automatic detection of CMBs is challenging due to the small size and amount of CMBs making the classes highly imbalanced, lack of publicly available annotated data, and similarity with CMB mimics such as calcifications, irons, and veins. Hence, the existing deep learning methods are mostly trained on very limited research data and fail to generalize to unseen data with high variability and cannot be used in clinical setups. To this end, we propose an efficient 3D deep learning framework that is actively trained on multi-domain data. Two public datasets assigned for normal aging, stroke, and Alzheimer's disease analysis as well as an in-house dataset for COVID-19 assessment are used to train and evaluate the models. The obtained results show that the proposed method is robust to low-resolution images and achieves 78% recall and 80% precision on the entire test set with an average false positive of 1.6 per scan.Comment: International Symposium on Biomedical Imaging (ISBI) 202

sj-docx-1-eso-10.1177_23969873231223339 – Supplemental material for Emergency Medical Services dispatcher recognition of stroke: A systematic review

Supplemental material, sj-docx-1-eso-10.1177_23969873231223339 for Emergency Medical Services dispatcher recognition of stroke: A systematic review by Jonathan Wenstrup, Bartal Hofgaard Hestoy, Malini Vendela Sagar, Stig Nikolaj Fasmer Blomberg, Hanne Christensen, Helle Collatz Christensen and Christina Kruuse in European Stroke Journal</p

Validation of Pediatric Idiopathic Generalized Epilepsy Diagnoses from the Danish National Patient Register During 1994‒2019

OBJECTIVE: To identify pediatric idiopathic generalized epilepsy (IGE) during 1994–2019 using ICD-10 codes in the Danish National Patient Register and anti-seizure prescriptions in the Danish Prescription Database. STUDY DESIGN AND SETTING: We reviewed the medical records in children with ICD-10 codes for IGE before 18 years of age, and pediatric neurologists confirmed that the International League Against Epilepsy criteria were met. We estimated positive predictive values (PPV) and sensitivity for ICD-10 alone, including combinations of codes, anti-seizure prescription, and age at first code registration using medical record-validated diagnoses as gold standard. RESULTS: We validated the medical record in 969 children with an ICD-10 code of IGE, and 431 children had IGE (115 childhood absence epilepsy, 97 juvenile absence epilepsy, 192 juvenile myoclonic epilepsy, 27 generalized tonic-clonic seizures alone). By combining ICD-10 codes with antiseizure prescription and age at epilepsy code registration, we found a PPV for childhood absence epilepsy at 44% (95% confidence interval [CI]=34%‒54%) and for juvenile absence epilepsy at 44% (95% CI=36%–52%). However, ethosuximide prescription, age at ethosuximide code registration before age 8 years and a combination of ICD-10 codes yielded a PPV of 59% (95% CI=42%‒75%) for childhood absence epilepsy but the sensitivity was only 17% (20/115 children identified). For juvenile myoclonic epilepsy the highest PPV was 68% (95% CI=62%‒74%) using the code G40.3F plus antiseizure prescription and age at epilepsy code registration after age 8 years, with sensitivity of 85% (164/192 children identified). For generalized tonic-clonic seizures alone the highest PPV was 31% (95% CI=15%‒51%) using G40.3G during 2006–2019 plus antiseizure prescription and age at code registration after age 5 years. CONCLUSION: The Danish National Patient Register and the Danish Prescription Database are not suitable for identifying children with IGE subtypes, except for juvenile myoclonic epilepsy which can be identified with caution