No abstract

Cerebellofaciodental syndrome is characterized by facial dysmorphisms, intellectual disability, cerebellar hypoplasia, and dental anomalies. It is an autosomal‐recessive condition described in 2015 caused by pathogenic variants in BRF1. Here, we report a Brazilian patient who faced a diagnostic challenge beginning at 11 months of age. Fortunately, whole‐exome sequencing (WES) was performed, detecting the BRF1 variants NM_001519.3:c.1649delG:p.(Gly550Alafs*36) and c.421C>T:p.(Arg141Cys) in compound heterozygosity, thus finally achieving a diagnosis of cerebellofaciodental syndrome. The patient is currently 25 years old and is the oldest patient yet reported. The clinical report and a review of published cases are presented. Atlanto‐occipital fusion, a reduced foramen magnum and basilar invagination leading to compression of the medulla‐spinal cord transition are skeletal findings not reported in previous cases. The description of syndromes with dental findings shows that such anomalies can be an important clue to relevant differential diagnoses. The cooperation of groups from different international centers made possible the resolution of this and other cases and is one of the strategies to bring medical advances to developing countries, where many patients with rare diseases are difficult to diagnose definitively.

To develop a deep learning model for detecting brain abnormalities on MRI. Materials and Methods:In this retrospective study, a deep learning approach using T2-weighted fluid attenuated inversion recovery (FLAIR) images was developed to classify brain MRI as "likely normal" or "likely abnormal." A convolutional neural network model was trained on a large heterogeneous dataset collected from two different continents and covering a broad panel of pathologies including neoplasms, hemorrhage, infarcts, and others. Three datasets were used. Dataset A consisted of 2839 patients, Dataset B consisted of 6442 patients, and Dataset C consisted of 1489 patients and was only used for testing. Dataset A and B were split into training, validation and test sets. A total of three models were trained: Model A (using only Dataset A), Model B (using only Dataset B), and Model A+B (using training datasets from A and B). All three models were tested on subsets from Dataset A, Dataset B and Dataset C separately. The evaluation was performed using annotations based on the images as well as labels based on the radiologic reports.

We describe the curation, annotation methodology, and characteristics of the dataset used in an artificial intelligence challenge for detection and localization of COVID-19 on chest radiographs. The chest radiographs were annotated by an international group of radiologists into four mutually exclusive categories, including “typical,” “indeterminate,” and “atypical appearance” for COVID-19, or “negative for pneumonia,” adapted from previously published guidelines, and bounding boxes were placed on airspace opacities. This dataset and respective annotations are available to researchers for academic and noncommercial use.

We describe the curation, annotation methodology and characteristics of the dataset used in an artificial intelligence challenge for detection and localization of COVID-19 on chest radiographs. The chest radiographs were annotated by an international group of radiologists into four mutually exclusive categories, including “typical”, “indeterminate”, and “atypical appearance” for COVID-19, or “negative for pneumonia”, adapted from previously published guidelines, and bounding boxes were placed on airspace opacities. This dataset and respective annotations are freely available to all researchers for academic and noncommercial use.