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

Abstract

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 photographic 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 (${\mathrm{CI}}_{\mathrm{sagittal}}$ = 71.97 ± 4.33), significant increase in CVAI value and significant decrease in LRHR value is most appropriate for unicoronal suture synostosis diagnosis (${\mathrm{CVAI}}_{\mathrm{unicoronal}}=6.79\pm 3.80$ and ${\mathrm{LRHR}}_{\mathrm{unicoronal}}$ = 0.91 ± 0.05) and significant decrease in APWR and AMWR values could be indicator of metopic synostosis (${\mathrm{AMWR}}_{\mathrm{metopic}}=$ 0.77 ± 0.04 and ${\mathrm{APWR}}_{\mathrm{matopic}}$ = 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.