CRANIOFACIAL SURGERY
Smarter Image
How can artificial intelligence models use personalized medicine to improve outcomes for children with craniosynostosis ?
An infant ’ s skull is held together by flexible tissues called sutures , which expand to allow bone growth and accommodate a rapidly growing brain . In children with craniosynostosis , sutures fuse prematurely , causing skull malformation and raising risks of elevated intracranial pressure , vision changes and developmental delays . Treatment for craniosynostosis can have varied results because surgeons may not have data that could help them accurately predict patients ’ cranial growth . Now , craniofacial surgery experts at Children ’ s Hospital Colorado are employing artificial intelligence to create personalized , datadriven models that improve outcomes .
Antonio R . Porras , PhD , pediatric plastic and reconstructive surgery research director at Children ’ s Colorado and assistant professor of biostatistics and informatics at the University of Colorado School of Public Health , leads this work . Dr . Porras ’ team uses artificial intelligence ( AI ) software to fuel photogrammetry , which takes images of a child ’ s head from various angles . The resulting anatomical representation not only helps diagnose cranial abnormalities sooner , but also helps predict their progression — all with extraordinary accuracy .
“ When we get a patient in the clinic now , we have methods that can tell us exactly what part of the cranium is abnormal , by how much it is abnormal and how much less volume the brain has in a specific area of their head ,” Dr . Porras says . “ And that is extremely important for surgeons to plan their treatments .”
FORWARD THINKING
AI modeling is a novel approach for craniofacial surgeons , but the data sustaining this software is years in the making .
A decade ago , Children ’ s Colorado ’ s craniofacial clinic co-director Brooke French , MD , launched an image database intended to improve evaluations for patients with craniofacial differences . The database has nearly 1,500 images , including CT images and 3D photograms of craniosynostosis patients before and after surgery . This database was then extended by Dr . Porras ’ team to include CT images of patients without craniosynostosis and those with intracranial hypertension , or elevated head pressure — a potential complication of untreated craniosynostosis . Both sexes are represented across the dataset .
“ The work of my team was to leverage all the data that Dr . French acquired and build a tool that would inform us toward the best course of action for every patient ,” Dr . Porras says .
These images provide a wide range of references for comparison that guide artificial intelligence modeling . This helps quantify even subtle anomalies with greater accuracy . “ We can finally start to know what the right surgery is , for the right patient , at the right time , which will help us personalize their medicine really nicely ,” Dr . French says .
A PATH TO PREVENTION
Today , this technology transcends the limitations of previous cranial models in multiple ways . For one , the noninvasive approach may eliminate the need for multiple CT scans to evaluate
12 | CHILDREN ’ S HOSPITAL COLORADO