Image processing and machine learning for telehealth craniosynostosis screening in newborns

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  • 1 Division of Neurosurgery, Connecticut Children’s, Hartford;
  • 2 Department of Surgery, University of Connecticut Health Center, Farmington, Connecticut; and
  • 3 Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota
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OBJECTIVE

The authors sought to evaluate the accuracy of a novel telehealth-compatible diagnostic software system for identifying craniosynostosis within a newborn (< 1 year old) population. Agreement with gold standard craniometric diagnostics was also assessed.

METHODS

Cranial shape classification software accuracy was compared to that of blinded craniofacial specialists using a data set of open-source (n = 40) and retrospectively collected newborn orthogonal top-down cranial images, with or without additional facial views (n = 339), culled between April 1, 2008, and February 29, 2020. Based on image quality, midface visibility, and visibility of the cranial equator, 351 image sets were deemed acceptable. Accuracy, sensitivity, and specificity were calculated for the software versus specialist classification. Software agreement with optical craniometrics was assessed with intraclass correlation coefficients.

RESULTS

The cranial shape classification software had an accuracy of 93.3% (95% CI 86.8–98.8; p < 0.001), with a sensitivity of 92.0% and specificity of 94.3%. Intraclass correlation coefficients for measurements of the cephalic index and cranial vault asymmetry index compared to optical measurements were 0.95 (95% CI 0.84–0.98; p < 0.001) and 0.67 (95% CI 0.24–0.88; p = 0.003), respectively.

CONCLUSIONS

These results support the use of image processing–based neonatal cranial deformity classification software for remote screening of nonsyndromic craniosynostosis in a newborn population and as a substitute for optical scanner– or CT-based craniometrics. This work has implications that suggest the potential for the development of software for a mobile platform that would allow for screening by telemedicine or in a primary care setting.

ABBREVIATIONS AAA = anterior arc angle; AMWR = anterior-middle width ratio; CI = cephalic index; CVAI = cranial vault asymmetry index; PAA = posterior arc angle; ROC = receiver operating characteristic; TCLA = transcanthal line angle.

Supplementary Materials

    • eTables and eFigures (PDF 469 kb)

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Contributor Notes

Correspondence Markus J. Bookland: Connecticut Children’s, Hartford, CT. mbookland@connecticutchildrens.org.

INCLUDE WHEN CITING Published online March 19, 2021; DOI: 10.3171/2020.9.PEDS20605.

Disclosures The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

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