Automated kinematic analysis of pre-pluse inhibition in larval zebrafish

honors thesisCollege of EngineeringBiomedical EngineeringAdam DouglassAnimals differentially ignore or attend to sensory information depending on their immediate environment. A significant example of this phenomenon is audiomotorpre-pulse inhibition (PPI), in which the startle response to a loud noise is suppressed by a preceding stimulus of lower intensity. This ability to optimize behavior in response to environmental context is anessential brain function. Defects in PPI are associated with neurological disorders such as obsessive-compulsive disorder, Tourette Syndrome, and schizophrenia. As part of an effort to develop a restrained, larval zebrafish model of PPI, we created new software to analyze swim kinematics in behaving fish. Our programs automatically extract several kinematic parameters from image sequences of behaving animals and use them to classify behavior into one of three, stereotyped categories. Correct classification is reported in 96.32 percent of trials (n = 162). This automated analysis will now permit a more robust study of PPI in these animals, where the brain's experimental accessibility will allow us to discover the cellular bases of sensorimot filtering

Pediatric sex estimation using AI-enabled ECG analysis: influence of pubertal development

Abstract AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987–2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0–7 years), peripubertal (8–14 years), and postpubertal (15–18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model’s discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy