Computational modelling of patient specific spring assisted lambdoid craniosynostosis correction

Optimal untwisting of the orbital bandeau in unicoronal craniosynostosis correction: A finite element analysis

BACKGROUND

Surgical correction of unicoronal craniosynostosis (UCS) is highly complex due to its asymmetric appearance. Although fronto-orbital advancement (FOA) is a versatile technique for craniosynostosis correction, harmonization of the orbital bandeau in UCS is difficult to predict. This study evaluates the biomechanics of the orbital bandeau using different patterns and varying characteristics of inner cortical bone layer osteotomies in a finite element (FE) analysis.

METHOD

An FE model was created using the computed tomography (CT) scan of a 6.5-month-old male infant with a right-sided UCS. The unaffected side of the orbital bandeau was virtually mirrored, and anatomical correction of the orbital bandeau was simulated. Different combinations of osteotomy patterns, numbers, depths, and widths were examined (n = 48) and compared to an uncut model.

RESULTS

Reaction forces and maximum stress values differed significantly (p < 0.01) among osteotomy patterns and between each osteotomy characteristic. Regardless of the osteotomy pattern, higher numbers of osteotomies significantly (p < 0.05) correlated with reductions in reaction force and maximum stress. An X-shaped configuration with three osteotomies deep and wide to the bone was biomechanically the most favorable model.

CONCLUSION

Inner cortical bone layer osteotomy might be an effective modification to the conventional FOA approach in terms of predictable shaping of the orbital bandeau.

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.

Advances in 3D Inner Ear Reconstruction Software for Cochlear Implants: A Comprehensive Review

Auditory impairment stands as a pervasive global issue, exerting significant effects on individuals' daily functioning and interpersonal engagements. Cochlear implants (CIs) have risen as a cutting-edge solution for severe to profound hearing loss, directly stimulating the auditory nerve with electrical signals. The success of CI procedures hinges on precise pre-operative planning and post-operative evaluation, highlighting the significance of advanced three-dimensional (3D) inner ear reconstruction software. Accurate pre-operative imaging is vital for identifying anatomical landmarks and assessing cochlear deformities. Tools like 3D Slicer, Amira and OTOPLAN provide detailed depictions of cochlear anatomy, aiding surgeons in simulating implantation scenarios and refining surgical approaches. Post-operative scans play a crucial role in detecting complications and ensuring CI longevity. Despite technological advancements, challenges such as standardization and optimization persist. This review explores the role of 3D inner ear reconstruction software in patient selection, surgical planning, and post-operative assessment, tracing its evolution and emphasizing features like image segmentation and virtual simulation. It addresses software limitations and proposes solutions, advocating for their integration into clinical practice. Ultimately, this review underscores the impact of 3D inner ear reconstruction software on cochlear implantation, connecting innovation with precision medicine.

Understanding the influence of surgical parameters on craniofacial surgery outcomes: a computational study

Sagittal craniosynostosis (SC) is a congenital condition whereby the newborn skull develops abnormally owing to the premature ossification of the sagittal suture. Spring-assisted cranioplasty (SAC) is a minimally invasive surgical technique to treat SC, where metallic distractors are used to reshape the newborn's head. Although safe and effective, SAC outcomes remain uncertain owing to the limited understanding of skull-distractor interaction and the limited information provided by the analysis of single surgical cases. In this work, an SC population-averaged skull model was created and used to simulate spring insertion by means of the finite-element analysis using a previously developed modelling framework. Surgical parameters were varied to assess the effect of osteotomy and spring positioning, as well as distractor combinations, on the final skull dimensions. Simulation trends were compared with retrospective measurements from clinical imaging (X-ray and three-dimensional photogrammetry scans). It was found that the on-table post-implantation head shape change is more sensitive to spring stiffness than to the other surgical parameters. However, the overall end-of-treatment head shape is more sensitive to spring positioning and osteotomy size parameters. The results of this work suggest that SAC surgical planning should be performed in view of long-term results, rather than immediate on-table reshaping outcomes.

Towards a radiation free numerical modelling framework to predict spring assisted correction of scaphocephaly

Sagittal Craniosynostosis (SC) is a congenital craniofacial malformation, involving premature sagittal suture ossification; spring-assisted cranioplasty (SAC) - insertion of metallic distractors for skull reshaping - is an established method for treating SC. Surgical outcomes are predictable using numerical modelling, however published methods rely on computed tomography (CT) scans availability, which are not routinely performed. We investigated a simplified method, based on radiation-free 3D stereophotogrammetry scans.Eight SAC patients (age 5.1 ± 0.4 months) with preoperative CT and 3D stereophotogrammetry scans were included. Information on osteotomies, spring model and post-operative spring opening were recorded. For each patient, two preoperative models (PREOP) were created: i) CT model and ii) S model, created by processing patient specific 3D surface scans using population averaged skin and skull thickness and suture locations. Each model was imported into ANSYS Mechanical (Analysis System Inc., Canonsburg, PA) to simulate spring expansion. Spring expansion and cranial index (CI - skull width over length) at times equivalent to immediate postop (POSTOP) and follow up (FU) were extracted and compared with in-vivo measurements.Overall expansion patterns were very similar for the 2 models at both POSTOP and FU. Both models had comparable outcomes when predicting spring expansion. Spring induced CI increase was similar, with a difference of 1.2%±0.8% for POSTOP and 1.6%±0.6% for FU.This work shows that a simplified model created from the head surface shape yields acceptable results in terms of spring expansion prediction. Further modelling refinements will allow the use of this predictive tool during preoperative planning.

Spring-assisted posterior vault expansion: a parametric study to improve the intracranial volume increase prediction

Spring-assisted posterior vault expansion has been adopted at the London Great Ormond Street Hospital for Children to treat raised intracranial pressure in patients affected by syndromic craniosynostosis, a congenital calvarial anomaly causing the premature fusion of skull sutures. This procedure involves elastic distractors used to dynamically reshape the skull and increase the intracranial volume (ICV). In this study, we developed and validated a patient-specific model able to predict the ICV increase and carried out a parametric study to investigate the effect of surgical parameters on that final volume. Pre- and post-operative computed tomography data relative to 18 patients were processed to extract simplified patient-specific skull shape, replicate surgical cuts, and simulate spring expansion. A parametric study was performed to quantify each parameter's impact on the surgical outcome: for each patient, the osteotomy location was varied in a pre-defined range; local sensitivity of the predicted ICV to each parameter was analysed and compared. Results showed that the finite element model performed well in terms of post-operative ICV prediction and allowed for parametric optimization of surgical cuts. The study indicates how to optimize the ICV increase according to the type of procedure and provides indication on the most robust surgical strategy.

Automated surgical planning in spring-assisted sagittal craniosynostosis correction using finite element analysis and machine learning

Sagittal synostosis is a condition caused by the fused sagittal suture and results in a narrowed skull in infants. Spring-assisted cranioplasty is a correction technique used to expand skulls with sagittal craniosynostosis by placing compressed springs on the skull before six months of age. Proposed methods for surgical planning in spring-assisted sagittal craniosynostosis correction provide information only about the skull anatomy or require iterative finite element simulations. Therefore, the selection of surgical parameters such as spring dimensions and osteotomy sizes may remain unclear and spring-assisted cranioplasty may yield sub-optimal surgical results. The aim of this study is to develop the architectural structure of an automated tool to predict post-operative surgical outcomes in sagittal craniosynostosis correction with spring-assisted cranioplasty using machine learning and finite element analyses. Six different machine learning algorithms were tested using a finite element model which simulated a combination of various mechanical and geometric properties of the calvarium, osteotomy sizes, spring characteristics, and spring implantation positions. Also, a statistical shape model representing an average sagittal craniosynostosis calvarium in 5-month-old patients was used to assess the machine learning algorithms. XGBoost algorithm predicted post-operative cephalic index in spring-assisted sagittal craniosynostosis correction with high accuracy. Finite element simulations confirmed the prediction of the XGBoost algorithm. The presented architectural structure can be used to develop a tool to predict the post-operative cephalic index in spring-assisted cranioplasty in patients with sagittal craniosynostosis can be used to automate surgical planning and improve post-operative surgical outcomes in spring-assisted cranioplasty.

Artificial Intelligence Applications in Diagnosing and Managing Non-syndromic Craniosynostosis: A Comprehensive Review

Craniosynostosis is characterised by the premature fusion of one or more cranial sutures, resulting in an abnormal head shape. The management of craniosynostosis requires early diagnosis, surgical intervention, and long-term monitoring. With the advancements in artificial intelligence (AI) technologies, there is great potential for AI to assist in various aspects of managing craniosynostosis. The main aim of this article is to review available literature describing the current uses of AI in craniosynostosis. The main applications highlighted include diagnosis, surgical planning, and outcome prediction. Many studies have demonstrated the accuracy of AI in differentiating subtypes of craniosynostosis using machine learning (ML) algorithms to classify craniosynostosis based on simple photographs. This demonstrates its potential to be used as a screening tool and may allow patients to monitor disease progression reducing the need for CT scanning. ML algorithms can also analyse CT scans to aid in the accurate and efficient diagnosis of craniosynostosis, particularly when training junior surgeons. However, the lack of sufficient data currently limits this clinical application. Virtual surgical planning for cranial vault remodelling using prefabricated cutting guides has been shown to allow more precise reconstruction by minimising the subjectivity of the clinicians' assessment. This was particularly beneficial in reducing operating length and preventing the need for blood transfusions. Despite the potential benefits, there are numerous challenges associated with implementing AI in craniosynostosis. The integration of AI in craniosynostosis holds significant promise for improving the management of craniosynostosis. Further collaboration between clinicians, researchers, and AI experts is necessary to harness its full potential.

Utilizing Computer-Assisted Design and Virtual Surgical Planning for Correction of Unilateral Lambdoid Synostosis

Unilateral lambdoid synostosis is the rarest form of single-suture craniosynostosis. Although various surgical approaches have been described, cranial vault remodeling remains the predominant approach. To aid in surgical planning, preoperative virtual surgical modeling using a patient's presenting computed tomography scan can be used to increase reconstructive precision and to reduce operative time. Presented is a 7-month-old male with unilateral lambdoid synostosis who underwent medically modeled cranial vault reconstruction.

Management of Lambdoid Craniosynostosis: A Comprehensive and Systematic Review

BACKGROUND

Craniosynostosis is a condition characterized by the premature fusion of 2 or more skull bones. Craniosynostosis of the lambdoid suture is one of the rarest forms, accounting for 1-4% of all craniosynostoses. Documented cases are separated into simple (single suture), complex (bilateral), and associated with adjacent synostoses ("Mercedes Benz" Pattern) or syndromes (i.e., Crouzon, Sathre-Chotzen, Antley-Bixler). This condition can manifest phenotypic deformities and neurological sequelae that can lead to impaired cognitive function if improperly treated or left undiagnosed. Preferred surgical techniques have varied over time but all maintain the common goals of establishing proper head shape and preventing of complications that could contribute to aforementioned sequelae.

SUMMARY

This comprehensive review highlights demographic distributions, embryological development, pathogenesis, clinical presentation, neurological sequelae, radiologic findings, surgical techniques, surgical outcomes, and postoperative considerations of patients with lambdoid craniosynostosis presentation. In addition, a systematic review was conducted to explore the operative management of lambdoid craniosynostosis using PubMed, Embase, and Scopus databases, with 38 articles included after screening. Key Messages: Due to a low volume of published cases, diagnosis and treatment can vary. Large overlap in presentation can occur in patients that display lambdoid craniosynostosis and posterior plagiocephaly, furthering the need for comprehensive analysis. Possessing the knowledge and tools to properly assess patients with lambdoid craniosynostosis will allow for more precise care and improved outcomes.

Clinical Features and the Use of Three-Dimensional Imaging for Unilateral Lambdoid Synostosis

INTRODUCTION

True isolated lambdoid craniosynostosis is rare. It requires corrective surgery to prevent intracranial pressure and aesthetic stigma by significant dyscrania. We summarize our case series for lambdoid craniosynostosis outlining the pathophysiology, clinical findings and surgical approaches and outcomes.

METHODOLOGY

A retrospective analysis of our data from 2010 to 2020 summarized our cases of true lambdoid synostosis. We have used the medical notes and the radiological findings from computed tomography scans to summarize a case series of isolated lambdoid synostosis.

RESULTS

Our case series demonstrated 7 patients with true isolated lambdoid craniosynostosis. In most cases surgical intervention in the form of posterior cranial vault remodeling utilizing a bandeau, based on occipital advancement techniques, has demonstrated the most consistently favorable aesthetic outcome.

CONCLUSIONS

Surgical referral to a craniofacial center should be sought early in difficult to diagnose cases. Although rare, surgical intervention is indicated to correct potentially increased intracranial pressure and to ameliorate cranial dyscrania.