The standard for diagnosing metopic craniosynostosis (CS) utilizes computed tomography (CT) imaging and physical exam, but there is no standardized method for determining disease severity. Previous studies using interfrontal angles have evaluated differences in specific skull landmarks; however, these measurements are difficult to readily ascertain in clinical practice and fail to assess the complete skull contour. This pilot project employs machine learning algorithms to combine statistical shape information with expert ratings to generate a novel objective method of measuring the severity of metopic CS. Expert ratings of normal and metopic skull CT images were collected. Skull-shape analysis was conducted using ShapeWorks software. Machine-learning was used to combine the expert ratings with our shape analysis model to predict the severity of metopic CS using CT images. Our model was then compared to the gold standard using interfrontal angles. Seventeen metopic skull CT images of patients 5 to 15 months old were assigned a severity by 18 craniofacial surgeons, and 65 nonaffected controls were included with a 0 severity. Our model accurately correlated the level of skull deformity with severity (P < 0.10) and predicted the severity of metopic CS more often than models using interfrontal angles (χ 2 = 5.46, P = 0.019). This is the first study that combines shape information with expert ratings to generate an objective measure of severity for metopic CS. This method may help clinicians easily quantify the severity and perform robust longitudinal assessments of the condition.
Background: Orbital blowout fracture reconstruction often requires an implant, which must be shaped at the time of surgical intervention. This process is time-consuming and requires multiple placement trials, possibly risking complications. Three-dimensional printing technology has enabled health care facilities to generate custom anatomical models to which implants can be molded to precisely match orbital anatomy. The authors present their early experience with these models and their use in optimizing orbital fracture fixation. Methods: Maxillofacial computed tomographic scans from patients with orbital floor or wall fractures were prospectively obtained and digitally reconstructed. Both injured-side and mirrored unaffected-side models were produced in-house by stereolithography printing technique. Models were used as templates for molding titanium reconstruction plates, and plates were implanted to reconstruct the patients’ orbital walls. Results: Nine patients (mean age, 15.5 years) were included. Enophthalmos was present in seven patients preoperatively and resolved in six patients with surgery. All patients had excellent conformation of the implant to the fracture site on postoperative computed tomographic scan. Postoperative fracture-side orbital volumes were significantly less than preoperative, and not significantly different from unfractured-side orbital volumes. Total model preparation time was approximately 10 hours. Materials cost was at most $21. Plate bending time was approximately 60 seconds. Conclusions: Patient-specific orbital models can speed the shaping of orbital reconstruction implants and potentially improve surgical correction of orbital fractures. Production of these models with consumer-grade technology confers the same advantages as commercial production at a fraction of the cost and time. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.