1Division of Neurosurgery, Connecticut Children's, Hartford.
2Department of Surgery, University of Connecticut Health Center, Farmington, Connecticut; and.
J Neurosurg Pediatr. 2021 Mar 19;27(5):581-588. doi: 10.3171/2020.9.PEDS20605. Print 2021 May 1.
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.
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.
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.
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.
作者旨在评估一种新型的远程医疗兼容诊断软件系统在识别新生儿（<1 岁）人群颅缝早闭中的准确性。同时评估其与金标准颅测诊断的一致性。
使用开源数据集（n=40）和回顾性收集的新生儿正交顶视图颅像，对颅形分类软件的准确性与盲法颅面专家进行比较，这些颅像有或没有额外的面部视图（n=339），采集时间为 2008 年 4 月 1 日至 2020 年 2 月 29 日。根据图像质量、中面部可视性和颅赤道可视性，351 个图像集被认为是可接受的。计算软件与专家分类的准确性、敏感性和特异性。使用组内相关系数评估软件与光学颅测的一致性。
颅形分类软件的准确率为 93.3%（95%CI 86.8-98.8;p<0.001），灵敏度为 92.0%，特异性为 94.3%。与光学测量相比，头指数和颅穹窿不对称指数的测量的组内相关系数分别为 0.95（95%CI 0.84-0.98;p<0.001）和 0.67（95%CI 0.24-0.88;p=0.003）。
这些结果支持使用基于图像处理的新生儿颅畸形分类软件对新生儿人群中的非综合征性颅缝早闭进行远程筛查，并可替代光学扫描仪或 CT 基于的颅测。这项工作表明，开发适用于移动平台的软件具有潜力，可通过远程医疗或初级保健环境进行筛查。
