Two-Center Review of Posterior Vault Expansion following a Staged or Expectant Treatment of Crouzon and Apert Craniosynostosis.

BACKGROUND: The timing of posterior cranial expansion for the management of intracranial pressure can be "staged" by age and dysmorphology or "expectant" by pressure monitoring. The authors report shared outcome measures from one center performing posterior vault remodeling (PCVR) or distraction (PVDO) following a staged approach and another performing spring-assisted expansion (SAPVE) following an expectant protocol. METHODS: Apert or Crouzon syndrome patients who underwent posterior expansion younger than 2 years were included. Perioperative outcomes and subsequent cranial operations were recorded up to last follow-up and intracranial volume changes measured and adjusted using growth curves. RESULTS: Thirty-eight patients were included. Following the expectant protocol, Apert patients underwent SAPVE at a younger age (8 months) than Crouzon patients (16 months). The initial surgery time was shorter but total operative time, including device removal, was longer for PVDO (3 hours 52 minutes) and SAPVE (4 hours 34 minutes) than for PCVR (3 hours 24 minutes). Growth-adjusted volume increase was significant and comparable. Fourteen percent of PCVR, 33% of PVDO, and 11% of SAPVE cases had complications, but without long-term deficits. Following the staged approach, 5% underwent only PVDO, 85% had a staged posterior followed by anterior surgery, and 10% required a third expansion. Following the expectant approach, 42% of patients had only posterior expansion at last follow-up, 32% had a secondary cranial surgery, and 26% had a third cranial expansion. CONCLUSION: Two approaches involving posterior vault expansion in young syndromic patients using three techniques resulted in comparable early volume expansion and complication profiles. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.

Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis.

Clinical diagnosis of craniofacial anomalies requires expert knowledge. Recent studies have shown that artificial intelligence (AI) based facial analysis can match the diagnostic capabilities of expert clinicians in syndrome identification. In general, these systems use 2D images and analyse texture and colour. They are powerful tools for photographic analysis but are not suitable for use with medical imaging modalities such as ultrasound, MRI or CT, and are unable to take shape information into consideration when making a diagnostic prediction. 3D morphable models (3DMMs), and their recently proposed successors, mesh autoencoders, analyse surface topography rather than texture enabling analysis from photography and all common medical imaging modalities and present an alternative to image-based analysis. We present a craniofacial analysis framework for syndrome identification using Convolutional Mesh Autoencoders (CMAs). The models were trained using 3D photographs of the general population (LSFM and LYHM), computed tomography data (CT) scans from healthy infants and patients with 3 genetically distinct craniofacial syndromes (Muenke, Crouzon, Apert). Machine diagnosis outperformed expert clinical diagnosis with an accuracy of 99.98%, sensitivity of 99.95% and specificity of 100%. The diagnostic precision of this technique supports its potential inclusion in clinical decision support systems. Its reliance on 3D topography characterisation make it suitable for AI assisted diagnosis in medical imaging as well as photographic analysis in the clinical setting.

Correlation between head shape and volumetric changes following spring-assisted posterior vault expansion.

The aim of the study was to investigate whether different head shapes show different volumetric changes following spring-assisted posterior vault expansion (SA-PVE) and to investigate the influence of surgical and morphological parameters on SA-PVE. Preoperative three-dimensional skull models from patients who underwent SA-PVE were extracted from computed tomography scans. Patient head shape was described using statistical shape modelling (SSM) and principal component analysis (PCA). Preoperative and postoperative intracranial volume (ICV) and cranial index (CI) were calculated. Surgical and morphological parameters included skull bone thickness, number of springs, duration of spring insertion and type of osteotomy. In the analysis, 31 patients were included. SA-PVE resulted in a significant ICV increase (284.1 ± 171.6 cm3, p < 0.001) and a significant CI decrease (-2.9 ± 4.3%, p < 0.001). The first principal component was significantly correlated with change in ICV (Spearman ρ = 0.68, p < 0.001). Change in ICV was significantly correlated with skull bone thickness (ρ = -0.60, p < 0.001) and age at time of surgery (ρ = -0.60, p < 0.001). No correlations were found between the change in ICV and number of springs, duration of spring insertion and type of osteotomy. SA-PVE is effective for increasing the ICV and resolving raised intracranial pressure. Younger, brachycephalic patients benefit more from surgery in terms of ICV increase. Skull bone thickness seems to be a crucial factor and should be assessed to achieve optimal ICV increase. In contrast, insertion of more than two springs, duration of spring insertion or performing a fully cut through osteotomy do not seem to impact the ICV increase. When interpreting ICV increases, normal calvarial growth should be taken into account.

Correlation of Intracranial Volume With Head Surface Volume in Patients With Multisutural Craniosynostosis.

Intracranial volume (ICV) is an important parameter for monitoring patients with multisutural craniosynostosis. Intracranial volume measurements are routinely derived from computed tomography (CT) head scans, which involves ionizing radiation. Estimation of ICV from head surface volumes could prove useful as 3D surface scanners could be used to indirectly acquire ICV information, using a non-invasive, non-ionizing method.Pre- and postoperative 3D CT scans from spring-assisted posterior vault expansion (sPVE) patients operated between 2008 and 2018 in a single center were collected. Patients were treated for multisutural craniosynostosis, both syndromic and non-syndromic. For each patient, ICV was calculated from the CT scans as carried out in clinical practice. Additionally, the 3D soft tissue surface volume (STV) was extracted by 3D reconstruction of the CT image soft tissue of each case, further elaborated by computer-aided design (CAD) software. Correlations were analyzed before surgery, after surgery, combined for all patients and in syndrome subgroups.Soft tissue surface volume was highly correlated to ICV for all analyses: r = 0.946 preoperatively, r = 0.959 postoperatively, and r = 0.960 all cases combined. Subgroup analyses for Apert, Crouzon-Pfeiffer and complex craniosynostosis were highly significant as well (P < 0.001).In conclusion, 3D surface model volumes correlated strongly to ICV, measured from the same scan, and linear equations for this correlation are provided. Estimation of ICV with just a 3D surface model could thus be realized using a simple method, which does not require radiations and therefore would allow closer monitoring in patients through multiple acquisitions over time.

A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery.

Current computational tools for planning and simulation in plastic and reconstructive surgery lack sufficient precision and are time-consuming, thus resulting in limited adoption. Although computer-assisted surgical planning systems help to improve clinical outcomes, shorten operation time and reduce cost, they are often too complex and require extensive manual input, which ultimately limits their use in doctor-patient communication and clinical decision making. Here, we present the first large-scale clinical 3D morphable model, a machine-learning-based framework involving supervised learning for diagnostics, risk stratification, and treatment simulation. The model, trained and validated with 4,261 faces of healthy volunteers and orthognathic (jaw) surgery patients, diagnoses patients with 95.5% sensitivity and 95.2% specificity, and simulates surgical outcomes with a mean accuracy of 1.1 ± 0.3 mm. We demonstrate how this model could fully-automatically aid diagnosis and provide patient-specific treatment plans from a 3D scan alone, to help efficient clinical decision making and improve clinical understanding of face shape as a marker for primary and secondary surgery.

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling.

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face.

Comparison of three-dimensional scanner systems for craniomaxillofacial imaging.

Two-dimensional photographs are the standard for assessing craniofacial surgery clinical outcomes despite lacking three-dimensional (3D) depth and shape. Therefore, 3D scanners have been gaining popularity in various fields of plastic and reconstructive surgery, including craniomaxillofacial surgery. Head shapes of eight adult volunteers were acquired using four 3D scanners: 1.5T Avanto MRI, Siemens; 3dMDface System, 3dMD Inc.; M4D Scan, Rodin4D; and Structure Sensor, Occipital Inc. Accuracy was evaluated as percentage of data within a range of 2 mm from the 3DMDface System reconstruction, by surface-to-surface root mean square (RMS) distances, and with facial distance maps. Precision was determined by RMS. Relative to the 3dMDface System, accuracy was the highest for M4D Scan (90% within 2 mm; RMS of 0.71 mm ± 0.28 mm), followed by Avanto MRI (86%; 1.11 mm ± 0.33 mm) and Structure Sensor (80%; 1.33 mm ± 0.46). M4D Scan and Structure Sensor precision were 0.50 ± 0.04 mm and 0.51 ± 0.03 mm, respectively. Clinical and technical requirements govern scanner choice; however, 3dMDface System and M4D Scan provide high-quality results. It is foreseeable that compact, handheld systems will become more popular in the near future.