BACKGROUND: The authors have developed pretrained machine learning (ML) models to evaluate neonatal head shape deformities using top-down and facial orthogonal photographs of the patient's head. In previous preliminary analysis, this approach was tested with images from an open-source data bank. OBJECTIVE: To determine the accuracy of pretrained ML models in identifying craniosynostosis among patients seen in our outpatient neurosurgery clinic. METHODS: We retrospectively reviewed top-down and facial orthogonal images of each patient's head and provider clinical diagnosis from the same encounters. Head shape classifications generated from 3 pretrained ML models (random forest, classification and regression tree, and linear discriminant analysis) were applied to each patient's photograph data set after craniometric extraction using a predefined image processing algorithm. Diagnoses were codified into a binary scheme of craniosynostosis vs noncraniosynostosis. Sensitivity, specificity, and Matthew correlation coefficient were calculated for software vs provider classifications. RESULTS: A total of 174 patients seen for abnormal head shape between May 2020 and February 2021 were included in the analysis. One hundred seven patients (61%) were seen in-person and 67 (39%) through telemedicine. Twenty-three patients (13%) were diagnosed with craniosynostosis. The best-performing model identified craniosynostosis with an accuracy of 94.8% (95% CI 90.4-97.6), sensitivity of 87.0% (95% CI 66.4-97.2), specificity of 96.0% (95% CI 91.6-98.5), and Matthew correlation coefficient of 0.788 (95% CI 0.725-0.839). CONCLUSION: Machine learning-driven image analysis represents a promising strategy for the identification of craniosynostosis in a real-world practice setting. This approach has potential to reduce the need for imaging and facilitate referral by primary care providers.
BACKGROUND: The authors have developed pretrained machine learning (ML) models to evaluate neonatal head shape deformities using top-down and facial orthogonal photographs of the patient's head. In previous preliminary analysis, this approach was tested with images from an open-source data bank. OBJECTIVE: To determine the accuracy of pretrained ML models in identifying craniosynostosis among patients seen in our outpatient neurosurgery clinic. METHODS: We retrospectively reviewed top-down and facial orthogonal images of each patient's head and provider clinical diagnosis from the same encounters. Head shape classifications generated from 3 pretrained ML models (random forest, classification and regression tree, and linear discriminant analysis) were applied to each patient's photograph data set after craniometric extraction using a predefined image processing algorithm. Diagnoses were codified into a binary scheme of craniosynostosis vs noncraniosynostosis. Sensitivity, specificity, and Matthew correlation coefficient were calculated for software vs provider classifications. RESULTS: A total of 174 patients seen for abnormal head shape between May 2020 and February 2021 were included in the analysis. One hundred seven patients (61%) were seen in-person and 67 (39%) through telemedicine. Twenty-three patients (13%) were diagnosed with craniosynostosis. The best-performing model identified craniosynostosis with an accuracy of 94.8% (95% CI 90.4-97.6), sensitivity of 87.0% (95% CI 66.4-97.2), specificity of 96.0% (95% CI 91.6-98.5), and Matthew correlation coefficient of 0.788 (95% CI 0.725-0.839). CONCLUSION: Machine learning-driven image analysis represents a promising strategy for the identification of craniosynostosis in a real-world practice setting. This approach has potential to reduce the need for imaging and facilitate referral by primary care providers.
Authors: Mitch R Paro; William Lambert; Nathan K Leclair; Petronella Stoltz; Jonathan E Martin; David S Hersh; Markus J Bookland Journal: World Neurosurg Date: 2022-04-21 Impact factor: 2.210