Image processing and machine learning for diagnosis and screening of craniosynostosis in children

Objective: craniosynostosis (CSO) is a congenital disorder resulting from early closure of cranial sutures in newborns, while could cause significant cosmetic and neurodevelopmental problems. As a standard method, different craniometric indices are measured directly from child head or from their 3D CT scan of skull for diagnosis or in post-operative follow-up period. We propose a novel telehealth-compatible deep learning neural network-based method for identifying different craniometric indices in non-syndromic CSO patients 2D photo-graphic data.Methods: 624 pre-operative and post-operative top-down cranial digital images of 145 craniosynostotic infants (59 sagittal, 55 metopic and 31 unicoronal synostosis) who had surgery at Mofid Children's Hospital, Tehran, Iran were used in a deep learning neural network algorithm. Head boundary was defined by a faster region-based convolutional neural network (Faster R-CNN) and then different cranial indices (cranial index (CI), cranial vault asymmetry index (CVAI), anterior-posterior width ratio (APWR), anterior-midline width ratio (AMWR) and left-right height ratio (LRHR)) were calculated from segmented images. Accuracy, sensitivity and specificity were calculated for software versus specialist data association between cranial indices were evaluated with inter-class correlation coefficients.Results: The head border was segmented in the proposed images with accuracy of 88.67 +/- 1.94 in comparison with standard hand made procedure with a sensitivity of 86.91 +/- 3.75 and specificity of 88.60 +/- 4.81. Among calculated cranial indices, significant decrease in CI value is most useful for diagnosis of sagittal synostosis (CIsagittal= 71.97 +/- 4.33), significant increase in CVAI value and significant decrease in LRHR value is most appropriate for unicoronal suture synostosis diagnosis (CVAIunicoronal= 6.79 +/- 3.80 and LRHRunicoronal = 0.91 +/- 0.05) and significant decrease in APWR and AMWR values could be indicator of metopic synostosis (AMWRmetopic = 0.77 +/- 0.04 and APWRmatopic = 0.83 +/- 0.05). Conclusion: Deep learning neural network algorithms could have high levels of capability in calculating cranial indices from routine 2D digital images of non-syndromic craniosynostotic children and act as a substitute for optical scanner or 3D CT-based craniometrics. This method could act as a corner stone for developing a software for a mobile platform that that would allow for screening by tele-medicine or in a primary care setting.