Predicting fall parameters from infant skull fractures using machine learning.

When infants are admitted to the hospital with skull fractures, providers must distinguish between cases of accidental and abusive head trauma. Limited information about the incident is available in such cases, and witness statements are not always reliable. In this study, we introduce a novel, data-driven approach to predict fall parameters that lead to skull fractures in infants in order to aid in determinations of abusive head trauma.

Comprehensive craniometry for sagittal synostosis.

Objective: Sagittal synostosis is the most common type of craniosynostosis, resulting in deformity with distinctive morphological characteristics. These include occipital narrowing, parietal narrowing, anteriorly shifted vertex with parietal depression, and exaggerated frontal bossing. The traditional cephalic index affords limited reliability in quantifying initial severity and correction.

Coupled Eulerian-Lagrangian model prediction of neural tissue strain during microelectrode insertion.

Implanted neural microelectrodes are an important tool for recording from and stimulating the cerebral cortex. The performance of chronically implanted devices, however, is often hindered by the development of a reactive tissue response. Previous computational models have investigated brain strain from micromotions of neural electrodes after they have been inserted, to investigate design parameters that might minimize triggers to the reactive tissue response.

Confocal reflectance microscopy for mapping collagen fiber organization in the vitreous gel of the eye.

Vitreous collagen structure plays an important role in ocular mechanics. However, capturing this structure with existing vitreous imaging methods is hindered by the loss of sample position and orientation, low resolution, or a small field of view. The objective of this study was to evaluate confocal reflectance microscopy as a solution to these limitations.

Age-related skull fracture patterns in infants after low-height falls.

Background: Prior research and experience has increased physician understanding of infant skull fracture prediction. However, patterns related to fracture length, nonlinearity, and features of complexity remain poorly understood, and differences across infant age groups have not been previously explored.

Methods: To determine how infant and low-height fall characteristics influence fracture patterns, we collected data from 231 head CT 3D reconstructions and quantified length and nonlinearity using a custom image processing code.