A computational modelling tool for prediction of head reshaping following endoscopic strip craniectomy and helmet therapy for the treatment of scaphocephaly.

BACKGROUND: Endoscopic strip craniectomy followed by helmet therapy (ESCH) is a minimally invasive approach for correcting sagittal craniosynostosis. The treatment involves a patient-specific helmet designed to facilitate lateral growth while constraining sagittal expansion. In this study, finite element modelling was used to predict post-treatment head reshaping, improving our comprehension of the necessary helmet therapy duration. METHOD: Six patients (aged 11 weeks to 9 months) who underwent ESCH at Connecticut Children's Hospital were enrolled in this study. Day-1 post-operative 3D scans were used to create skin, skull, and intracranial volume models. Patient-specific helmet models, incorporating areas for growth, were designed based on post-operative imaging. Brain growth was simulated through thermal expansion, and treatments were modelled according to post-operative Imaging available. Mechanical testing and finite element modelling were combined to determine patient-specific mechanical properties from bone samples collected from surgery. Validation compared simulated end-of-treatment skin surfaces with optical scans in terms of shape matching and cranial index estimation. RESULTS: Comparison between the simulated post-treatment head shape and optical scans showed that on average 97.3 ± 2.1 % of surface data points were within a distance range of -3 to 3 mm. The cranial index was also accurately predicted (r = 0.91). CONCLUSIONS: In conclusion, finite element models effectively predicted the ESCH cranial remodeling outcomes up to 8 months postoperatively. This computational tool offers valuable insights to guide and refine helmet treatment duration. This study also incorporated patient-specific material properties, enhancing the accuracy of the modeling approach.

Clinical metrics and tools for provider assessment and tracking of trigonocephaly.

OBJECTIVE: Quantitative measurements of trigonocephaly can be used to characterize and track this phenotype, which is associated with metopic craniosynostosis. Traditionally, trigonocephaly metrics were extracted from CT scans; however, this method exposes patients to ionizing radiation. Three-dimensional optical scans are another option but are not routinely available in most outpatient settings. Recently, the authors developed semiautomated artificial intelligence algorithms that extract craniometric data from orthogonal 2D photographs. Although 2D photographs are safe, inexpensive, and straightforward to obtain, the accuracy of photograph-based craniometrics in comparison to CT and 3D optical scan correlates has not been established. In this study the authors compared the classification power of 2D photograph-based metrics of trigonocephaly with four CT-based metrics and one 3D optical scan-based metric in a heterogeneous series of patients who presented to an outpatient craniofacial clinic. METHODS: In this study the authors performed retrospective craniometric analyses of patient 2D photographs, 3D optical scans, and CT scans. Imaging-derived craniometrics include the 2D photograph-based anterior arc angle (AAA2D-photo), anterior-posterior ratio (APR2D-photo), and anterior-middle ratio (AMR2D-photo); the CT-based anterior arc angle (AAACT), metopic index (MICT), endocranial-bifrontal angle (eBFACT), and interfrontal angle (IFACT); and the 3D optical scan-based anterior arc angle (AAA3D-optical). Receiver operating characteristics (ROCs) were used to identify craniometrics strongly descriptive of trigonocephaly. Interrater comparisons were made between paired trigonocephaly measurements obtained from photographs and either CT scans or 3D optical scans. RESULTS: There were 13 photograph-based and CT-based pairs and 22 paired measurements from 2D photographs and 3D optical scans. AAA displayed the strongest classification capacity across all three imaging modalities. Significant agreement was observed between AAACT and AAA2D-photo (intraclass correlation coefficient [ICC] = 0.68 [95% CI 0.24-0.89], p = 0.0035), and AAA3D-optical and AAA2D-photo (ICC = 0.70 [95% CI 0.41-0.87], p < 0.0001). There was no significant correlation between APR2D-photo or AMR2D-photo and conventional CT-based metrics describing longitudinal width ratios (MICT). CONCLUSIONS: Photograph-based craniometrics are powerful tools that can be used to quantify the severity of trigonocephaly and exhibit high concordance with standard measurements derived from CT scans and 3D optical scans. The authors developed and freely share a research-use application to calculate trigonocephaly metrics from 2D photographs. Given the availability of digital photography, lack of ionizing radiation, and low cost of photograph-based craniometric derivation, this technique may be useful to supplement routine ambulatory care and objectively track outcomes following treatment.