Trained as a computer scientist and engineer, Antonio R. Porras, PhD, became fascinated by craniofacial research – specifically the diagnosis and treatment of craniosynostosis, a skull-growth disorder – while working at Children’s National Hospital in Washington, D.C.
After working with physicians and basic scientists for many years, he realized how much is currently unknown about the normal and abnormal development of the cranial bones in children. His team created computational image analysis and artificial intelligence (AI) methods that, using large datasets, glean important quantitative information about cranial growth in children.
“Our team uses large datasets and AI to accurately quantify deformities, to be able to tell the surgeons how little volume, or how much extra volume, there is in each part of the cranium, so the surgeons can incorporate this personalized patient data into their evaluation and planning,” said Porras, an assistant professor in the Department of Biostatistics & Informatics at the Colorado School of Public Health, and in the departments of Pediatric Plastic and Reconstructive Surgery and Neurosurgery at Children’s Hospital Colorado.
Besides offering surgeons an objective analysis that is used to improve patient evaluation and treatment outcomes, the quantitative analysis offers insights into the temporal development of patient anomalies that can be leveraged to detect pathology early. His team recently had a technical paper published in IEEE Transactions in Biomedical Engineering, a top biomedical engineering journal, presenting a predictive model of cranial bone growth in children.
Here, Porras explains his interest in this area of research and how patients benefit from personalized, data-driven analyses.