Validation of Artificial Intelligence Severity Assessment in Metopic Craniosynostosis

Objective Several severity metrics have been developed for metopic craniosynostosis, including a recent machine learning-derived algorithm. This study assessed the diagnostic concordance between machine learning and previously published severity indices. Design Preoperative computed tomography (CT) scans of patients who underwent surgical correction of metopic craniosynostosis were quantitatively analyzed for severity. Each scan was manually measured to derive manual severity scores and also received a scaled metopic severity score (MSS) assigned by the machine learning algorithm. Regression analysis was used to correlate manually captured measurements to MSS. ROC analysis was performed for each severity metric and were compared to how accurately they distinguished cases of metopic synostosis from controls. Results In total, 194 CT scans were analyzed, 167 with metopic synostosis and 27 controls. The mean scaled MSS for the patients with metopic was 6.18 +/- 2.53 compared to 0.60 +/- 1.25 for controls. Multivariable regression analyses yielded an R-square of 0.66, with significant manual measurements of endocranial bifrontal angle (EBA) (P = 0.023), posterior angle of the anterior cranial fossa (p < 0.001), temporal depression angle (P = 0.042), age (P < 0.001), biparietal distance (P < 0.001), interdacryon distance (P = 0.033), and orbital width (P < 0.001). ROC analysis demonstrated a high diagnostic value of the MSS (AUC = 0.96, P < 0.001), which was comparable to other validated indices including the adjusted EBA (AUC = 0.98), EBA (AUC = 0.97), and biparietal/bitemporal ratio (AUC = 0.95). Conclusions The machine learning algorithm offers an objective assessment of morphologic severity that provides a reliable composite impression of severity. The generated score is comparable to other severity indices in ability to distinguish cases of metopic synostosis from controls.