Publications by authors named "Martina E Bebin"
The desire of parents to obtain a genetic diagnosis for their child with intellectual disability and associated symptoms has long been framed as a diagnostic odyssey, an arduous and sometimes perilous journey focused on the goal of identifying a cause for the child's condition. Semi-structured interviews (N = 60) were conducted with parents of children (N = 59, aged 2-24 years) with intellectual disability and/or developmental delay (IDD) who underwent genome sequencing at a single pediatric multispecialty clinic. Interviews were conducted after parents received their child's sequencing result (positive findings, negative findings, or variants of unknown significance).
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- The study aimed to create and test a deep learning algorithm for detecting cortical tubers in MRI scans of patients with tuberous sclerosis complex (TSC), showcasing the potential of deep learning in rare disorders with limited data.
- Researchers used images from patients and controls, training different convolutional neural network (CNN) models, ultimately finding that the InceptionV3 architecture had the most accurate results.
- The findings revealed high sensitivity, specificity, and accuracy, while a standalone application was developed to allow for local image classification on various operating systems, indicating the clinical feasibility of deep learning for rare neurological disorders.