Date: December 2020.
Source: Proceedings of the Machine Learning for Health NeurIPS Workshop, PMLR 136:226-237.
Abstract: Craniosynostosis (synostosis) is a serious disease where the sutures of a newborn’s skull fuse prematurely leading to debilitating head shape deformities. Due to the seriousness of this condition many normal infants and those with benign head shape abnormalities are referred to pediatric craniofacial plastic surgeons, leading to a high referral burden and delays in diagnosis for patients. A diagnostic delay beyond 4 months of age excludes patients from being treated with minimally invasive endoscopic procedures, leading to higher risk open surgeries. Machine learning (ML) image classifiers can enhance the triaging process of these referrals through the use of 3D images taken by a multicamera & angle setup during patient visits. In doing so, children with synostosis can be identified earlier, qualifying them for less invasive endoscopic surgical intervention. After training a variety of convolutional neural network (CNN) models on 3D images supplemented with synthetic images using generative adversarial networks (GANs), the best-performing model was found to be a novel approach developed in our study called a multi-view collapsed 3D CNN, which achieved area under the receiver operating curves (AUROC) between 90.00-97.00% for detecting various sub-types of synostosis. These results demonstrate the ability for ML models to potentially streamline the detection of children with synostosis and help overcome challenges associated with high referral burdens for these patients.

Article: 3D Photography Based Neural Network Craniosynostosis Triaging System.
Authors: Pouria Mashouri, Marta Skreta, John Phillips, Dianna McAllister, Melissa Roy, Senthujan Senkaiahliyan, Michael Brudno, Devin Singh. Hospital for Sick Children, Toronto, ON, Canada.