Quantification of Head Shape from Three-Dimensional Photography for Presurgical and Postsurgical Evaluation of Craniosynostosis

Machine learning applications in craniosynostosis diagnosis and treatment prediction: a systematic review

Craniosynostosis refers to the premature fusion of one or more of the fibrous cranial sutures connecting the bones of the skull. Machine learning (ML) is an emerging technology and its application to craniosynostosis detection and management is underexplored. This systematic review aims to evaluate the application of ML techniques in the diagnosis, severity assessment, and predictive modeling of craniosynostosis. A comprehensive search was conducted on the PubMed and Google Scholar databases using predefined keywords related to craniosynostosis and ML. Inclusion criteria encompassed peer-reviewed studies in English that investigated ML algorithms in craniosynostosis diagnosis, severity assessment, or treatment outcome prediction. Three independent reviewers screened the search results, performed full-text assessments, and extracted data from selected studies using a standardized form. Thirteen studies met the inclusion criteria and were included in the review. Of the thirteen papers examined on the application of ML to the identification and treatment of craniosynostosis, two papers were dedicated to sagittal craniosynostosis, five papers utilized several different types of craniosynostosis in the training and testing of their ML models, and six papers were dedicated to metopic craniosynostosis. ML models demonstrated high accuracy in identifying different types of craniosynostosis and objectively quantifying severity using innovative metrics such as metopic severity score and cranial morphology deviation. The findings highlight the significant strides made in utilizing ML techniques for craniosynostosis diagnosis, severity assessment, and predictive modeling. Predictive modeling of treatment outcomes following surgical interventions showed promising results, aiding in personalized treatment strategies. Despite methodological diversities among studies, the collective evidence underscores ML's transformative potential in revolutionizing craniosynostosis management.

3D Analysis of the Cranial and Facial Shape in Craniosynostosis Patients: A Systematic Review

With increasing interest in 3D photogrammetry, diverse methods have been developed for craniofacial shape analysis in craniosynostosis patients. This review provides an overview of these methods and offers recommendations for future studies. A systematic literature search was used to identify publications on 3D photogrammetry analyses in craniosynostosis patients until August 2023. Inclusion criteria were original research reporting on 3D photogrammetry analyses in patients with craniosynostosis and written in English. Sixty-three publications that had reproducible methods for measuring cranial, forehead, or facial shape were included in the systematic review. Cranial shape changes were commonly assessed using heat maps and curvature analyses. Publications assessing the forehead utilized volumetric measurements, angles, ratios, and mirroring techniques. Mirroring techniques were frequently used to determine facial asymmetry. Although 3D photogrammetry shows promise, methods vary widely between standardized and less conventional measurements. A standardized protocol for the selection and documentation of landmarks, planes, and measurements across the cranium, forehead, and face is essential for consistent clinical and research applications.

Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis

We present the first data-driven pediatric model that explains cranial sutural growth in the pediatric population. We segmented the cranial bones in the neurocranium from the cross-sectional CT images of 2068 normative subjects (age 0-10 years), and we used a 2D manifold-based cranial representation to establish local anatomical correspondences between subjects guided by the location of the cranial sutures. We designed a diffeomorphic spatiotemporal model of cranial bone development as a function of local sutural growth rates, and we inferred its parameters statistically from our cross-sectional dataset. We used the constructed model to predict growth for 51 independent normative patients who had longitudinal images. Moreover, we used our model to simulate the phenotypes of single suture craniosynostosis, which we compared to the observations from 212 patients. We also evaluated the accuracy predicting personalized cranial growth for 10 patients with craniosynostosis who had pre-surgical longitudinal images. Unlike existing statistical and simulation methods, our model was inferred from real image observations, explains cranial bone expansion and displacement as a consequence of sutural growth and it can simulate craniosynostosis. This pediatric cranial suture growth model constitutes a necessary tool to study abnormal development in the presence of cranial suture pathology.

The Use of Artificial Intelligence for the Classification of Craniofacial Deformities

Positional cranial deformities are a common finding in toddlers, yet differentiation from craniosynostosis can be challenging. The aim of this study was to train convolutional neural networks (CNNs) to classify craniofacial deformities based on 2D images generated using photogrammetry as a radiation-free imaging technique. A total of 487 patients with photogrammetry scans were included in this retrospective cohort study: children with craniosynostosis (n = 227), positional deformities (n = 206), and healthy children (n = 54). Three two-dimensional images were extracted from each photogrammetry scan. The datasets were divided into training, validation, and test sets. During the training, fine-tuned ResNet-152s were utilized. The performance was quantified using tenfold cross-validation. For the detection of craniosynostosis, sensitivity was at 0.94 with a specificity of 0.85. Regarding the differentiation of the five existing classes (trigonocephaly, scaphocephaly, positional plagiocephaly left, positional plagiocephaly right, and healthy), sensitivity ranged from 0.45 (positional plagiocephaly left) to 0.95 (scaphocephaly) and specificity ranged from 0.87 (positional plagiocephaly right) to 0.97 (scaphocephaly). We present a CNN-based approach to classify craniofacial deformities on two-dimensional images with promising results. A larger dataset would be required to identify rarer forms of craniosynostosis as well. The chosen 2D approach enables future applications for digital cameras or smartphones.

Can Craniosynostosis be Diagnosed on Physical Examination? A Retrospective Review

Craniosynostosis is a developmental craniofacial defect in which one or more sutures of the skull fuse together prematurely. Uncorrected craniosynostosis may have serious complications including elevated intracranial pressure, developmental delay, and blindness. Proper diagnosis of craniosynostosis requires a physical examination of the head with assessment for symmetry and palpation of sutures for prominence. Often, if craniosynostosis is suspected, computed tomography (CT) imaging will be obtained. Recent literature has posited that this is unnecessary. This study aims to address whether physical examination alone is sufficient for the diagnosis and treatment planning of single suture craniosynostosis. Between 2015 and 2022, the Divisions of Pediatric Neurosurgery and Pediatric Plastic Surgery at UTHealth Houston evaluated 140 children under 36 months of age with suspected craniosynostosis by physical examination and subsequently ordered CT imaging for preoperative planning. Twenty-three patients received a clinical diagnosis of multi-sutural or syndromic craniosynostosis that was confirmed by CT. One hundred seventeen patients were diagnosed with single suture craniosynostosis on clinical examination and follow-up CT confirmed suture fusion in 109 (93.2%) patients and identified intracranial anomalies in 7 (6.0%) patients. These patients underwent surgical correction. Eight (6.8%) patients showed no evidence of craniosynostosis on CT imaging. Treatment for patients without fused sutures included molding helmets and observation alone. This evidence suggests that physical examination alone may be inadequate to accurately diagnose single suture synostosis, and surgery without preoperative CT evaluation could lead to unindicated procedures.

Joint Cranial Bone Labeling and Landmark Detection in Pediatric CT Images Using Context Encoding

Image segmentation, labeling, and landmark detection are essential tasks for pediatric craniofacial evaluation. Although deep neural networks have been recently adopted to segment cranial bones and locate cranial landmarks from computed tomography (CT) or magnetic resonance (MR) images, they may be hard to train and provide suboptimal results in some applications. First, they seldom leverage global contextual information that can improve object detection performance. Second, most methods rely on multi-stage algorithm designs that are inefficient and prone to error accumulation. Third, existing methods often target simple segmentation tasks and have shown low reliability in more challenging scenarios such as multiple cranial bone labeling in highly variable pediatric datasets. In this paper, we present a novel end-to-end neural network architecture based on DenseNet that incorporates context regularization to jointly label cranial bone plates and detect cranial base landmarks from CT images. Specifically, we designed a context-encoding module that encodes global context information as landmark displacement vector maps and uses it to guide feature learning for both bone labeling and landmark identification. We evaluated our model on a highly diverse pediatric CT image dataset of 274 normative subjects and 239 patients with craniosynostosis (age 0.63 ± 0.54 years, range 0-2 years). Our experiments demonstrate improved performance compared to state-of-the-art approaches.

Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry

BACKGROUND AND OBJECTIVE

Accurate and repeatable detection of craniofacial landmarks is crucial for automated quantitative evaluation of head development anomalies. Since traditional imaging modalities are discouraged in pediatric patients, 3D photogrammetry has emerged as a popular and safe imaging alternative to evaluate craniofacial anomalies. However, traditional image analysis methods are not designed to operate on unstructured image data representations such as 3D photogrammetry.

METHODS

We present a fully automated pipeline to identify craniofacial landmarks in real time, and we use it to assess the head shape of patients with craniosynostosis using 3D photogrammetry. To detect craniofacial landmarks, we propose a novel geometric convolutional neural network based on Chebyshev polynomials to exploit the point connectivity information in 3D photogrammetry and quantify multi-resolution spatial features. We propose a landmark-specific trainable scheme that aggregates the multi-resolution geometric and texture features quantified at every vertex of a 3D photogram. Then, we embed a new probabilistic distance regressor module that leverages the integrated features at every point to predict landmark locations without assuming correspondences with specific vertices in the original 3D photogram. Finally, we use the detected landmarks to segment the calvaria from the 3D photograms of children with craniosynostosis, and we derive a new statistical index of head shape anomaly to quantify head shape improvements after surgical treatment.

RESULTS

We achieved an average error of 2.74 ± 2.70 mm identifying Bookstein Type I craniofacial landmarks, which is a significant improvement compared to other state-of-the-art methods. Our experiments also demonstrated a high robustness to spatial resolution variability in the 3D photograms. Finally, our head shape anomaly index quantified a significant reduction of head shape anomalies as a consequence of surgical treatment.

CONCLUSION

Our fully automated framework provides real-time craniofacial landmark detection from 3D photogrammetry with state-of-the-art accuracy. In addition, our new head shape anomaly index can quantify significant head phenotype changes and can be used to quantitatively evaluate surgical treatment in patients with craniosynostosis.

The Impact of Senior Author Profile on Publication Level of Evidence in Plastic and Reconstructive Surgery

Plastic and Reconstructive Surgery (PRS) incorporated the level of evidence (LOE) pyramid in 2011 to highlight evidence-based medicine in plastic surgery. This study aimed to assess the relationship between the profile of senior authors publishing in PRS and the LOE of publications.

Data-driven Normative Reference of Pediatric Cranial Bone Development

Available normative references of cranial bone development and suture fusion are incomplete or based on simplified assumptions due to the lack of large datasets. We present a fully data-driven normative model that represents the age- and sex-specific variability of bone shape, thickness, and density between birth and 10 years of age at every location of the calvaria.

Reconstruction of the fetus face from three-dimensional ultrasound using a newborn face statistical shape model

BACKGROUND AND OBJECTIVE

The fetal face is an essential source of information in the assessment of congenital malformations and neurological anomalies. Disturbance in early stages of development can lead to a wide range of effects, from subtle changes in facial and neurological features to characteristic facial shapes observed in craniofacial syndromes. Three-dimensional ultrasound (3D US) can provide more detailed information about the facial morphology of the fetus than the conventional 2D US, but its use for pre-natal diagnosis is challenging due to imaging noise, fetal movements, limited field-of-view, low soft-tissue contrast, and occlusions.

METHODS

In this paper, we propose the use of a novel statistical morphable model of newborn faces, the BabyFM, for fetal face reconstruction from 3D US images. We test the feasibility of using newborn statistics to accurately reconstruct fetal faces by fitting the regularized morphable model to the noisy 3D US images.

RESULTS

The results indicate that the reconstructions are quite accurate in the central-face and less reliable in the lateral regions (mean point-to-surface error of 2.35 mm vs 4.86 mm). The algorithm is able to reconstruct the whole facial morphology of babies from US scans while handle adverse conditions (e.g. missing parts, noisy data).

CONCLUSIONS

The proposed algorithm has the potential to aid in-utero diagnosis for conditions that involve facial dysmorphology.

Anthropometric Landmarking for Diagnosis of Cranial Deformities: Validation of an Automatic Approach and Comparison with Intra- and Interobserver Variability

Shape analysis of infant's heads is crucial to diagnose cranial deformities and evaluate head growth. Currently available 3D imaging systems can be used to create 3D head models, promoting the clinical practice for head evaluation. However, manual analysis of 3D shapes is difficult and operator-dependent, causing inaccuracies in the analysis. This study aims to validate an automatic landmark detection method for head shape analysis. The detection results were compared with manual analysis in three levels: (1) distance error of landmarks; (2) accuracy of standard cranial measurements, namely cephalic ratio (CR), cranial vault asymmetry index (CVAI), and overall symmetry ratio (OSR); and (3) accuracy of the final diagnosis of cranial deformities. For each level, the intra- and interobserver variability was also studied by comparing manual landmark settings. High landmark detection accuracy was achieved by the method in 166 head models. A very strong agreement with manual analysis for the cranial measurements was also obtained, with intraclass correlation coefficients of 0.997, 0.961, and 0.771 for the CR, CVAI, and OSR. 91% agreement with manual analysis was achieved in the diagnosis of cranial deformities. Considering its high accuracy and reliability in different evaluation levels, the method showed to be feasible for use in clinical practice for head shape analysis.

3D Photography to Quantify the Severity of Metopic Craniosynostosis

This study aims to determine the utility of 3D photography for evaluating the severity of metopic craniosynostosis (MCS) using a validated, supervised machine learning (ML) algorithm.

This single-center retrospective cohort study included patients who were evaluated at our tertiary care center for MCS from 2016 to 2020 and underwent both head CT and 3D photography within a 2-month period.

The analysis method builds on our previously established ML algorithm for evaluating MCS severity using skull shape from CT scans. In this study, we regress the model to analyze 3D photographs and correlate the severity scores from both imaging modalities.

14 patients met inclusion criteria, 64.3% male (n = 9). The mean age in years at 3D photography and CT imaging was 0.97 and 0.94, respectively. Ten patient images were obtained preoperatively, and 4 patients did not require surgery. The severity prediction of the ML algorithm correlates closely when comparing the 3D photographs to CT bone data (Spearman correlation coefficient [SCC] r = 0.75; Pearson correlation coefficient [PCC] r = 0.82).

The results of this study show that 3D photography is a valid alternative to CT for evaluation of head shape in MCS. Its use will provide an objective, quantifiable means of assessing outcomes in a rigorous manner while decreasing radiation exposure in this patient population.

Predictive Statistical Model of Early Cranial Development

OBJECTIVE

We present a data-driven method to build a spatiotemporal statistical shape model predictive of normal cranial growth from birth to the age of 2 years.

METHODS

The model was constructed using a normative cross-sectional computed tomography image dataset of 278 subjects. First, we propose a new standard representation of the calvaria using spherical maps to establish anatomical correspondences between subjects at the cranial sutures - the main areas of cranial bone expansion. Then, we model the cranial bone shape as a bilinear function of two factors: inter-subject anatomical variability and temporal growth. We estimate these factors using principal component analysis on the spatial and temporal dimensions, using a novel coarse-to-fine temporal multi-resolution approach to mitigate the lack of longitudinal images of the same patient.

RESULTS

Our model achieved an accuracy of 1.54 ± 1.05 mm predicting development on an independent longitudinal dataset. We also used the model to calculate the cranial volume, cephalic index and cranial bone surface changes during the first two years of age, which were in agreement with clinical observations.

SIGNIFICANCE

To our knowledge, this is the first data-driven and personalized predictive model of cranial bone shape development during infancy and it can serve as a baseline to study abnormal growth patterns in the population.

Novel Screening and Monitoring Techniques for Deformational Plagiocephaly: A Systematic Review

This article summarizes the current state of diagnostic modalities for infant craniofacial deformities and highlights capable diagnostic tools available currently to pediatricians.

Effectiveness of Automatic Planning of Fronto-orbital Advancement for the Surgical Correction of Metopic Craniosynostosis

BACKGROUND

The surgical correction of metopic craniosynostosis usually relies on the subjective judgment of surgeons to determine the configuration of the cranial bone fragments and the degree of overcorrection. This study evaluates the effectiveness of a new approach for automatic planning of fronto-orbital advancement based on statistical shape models and including overcorrection.

METHODS

This study presents a planning software to automatically estimate osteotomies in the fronto-orbital region and calculate the optimal configuration of the bone fragments required to achieve an optimal postoperative shape. The optimal cranial shape is obtained using a statistical head shape model built from 201 healthy subjects (age 23 ± 20 months; 89 girls). Automatic virtual plans were computed for nine patients (age 10.68 ± 1.73 months; four girls) with different degrees of overcorrection, and compared with manual plans designed by experienced surgeons.

RESULTS

Postoperative cranial shapes generated by automatic interventional plans present accurate matching with normative morphology and enable to reduce the malformations in the fronto-orbital region by 82.01 ± 6.07%. The system took on average 19.22 seconds to provide the automatic plan, and allows for personalized levels of overcorrection. The automatic plans with an overcorrection of 7 mm in minimal frontal breadth provided the closest match (no significant difference) to the manual plans.

CONCLUSIONS

The automatic software technology effectively achieves correct cranial morphometrics and volumetrics with respect to normative cranial shapes. The automatic approach has the potential to reduce the duration of preoperative planning, reduce inter-surgeon variability, and provide consistent surgical outcomes.

Establishment of Objective Clinical Parameters for Assessment of Trigonocephaly: Are Caliper-Derived Clinical Measures Adequate?

OBJECTIVE

Objective clinical parameters characterizing the severity of trigonocephaly are essential given the concern for computerized tomography (CT) scans and radiation in infants. The present study seeks to develop a clinical tool by which to characterize trigonocephaly.

DESIGN

Retrospective cohort study.

SETTING

Tertiary academically affiliated children's medical center.

PARTICIPANTS

A retrospective review identified patients with trigonocephaly for whom surgery was recommended (group 1) and those with metopic ridging without significant trigonocephaly (group 2). Normal age-matched controls were also evaluated (group 3).

INTERVENTIONS

Cranial vault caliper measurements were compared across groups. Two ratios measuring anterior vault constriction were developed: (1) bitemporal width at the mid-forehead to the biparietal width, and (2) bitemporal width at the lateral brow to the biparietal width.

MAIN OUTCOME MEASURES

Bitemporal width to biparietal width (ratio).

RESULTS

Caliper measures were obtained from 19 patients in group 1, 8 patients in group 2, and 19 patients in group 3 (controls). Cranial indices were not significantly different across groups. The bitemporal width at the mid-forehead to the biparietal width ratio was significantly lower in group 1, with no difference between groups 2 and 3. The bitemporal width at the lateral brow to the biparietal width ratio was significantly different between all 3 groups, with group 1 < group 2 < group 3, respectively.

CONCLUSIONS

Bitemporal to biparietal ratios are a quantitative, objective clinical measure that can be used to differentiate patients with significant trigonocephaly from those with metopic ridging but no significant cranial deformity. These findings suggest that caliper-derived indices can assist in characterizing surgically relevant cranial vault deformities secondary to metopic synostosis and may circumvent CT-based analysis.

Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery

INTRODUCTION

Machine learning (ML) is a set of models and methods that can detect patterns in vast amounts of data and use this information to perform various kinds of decision-making under uncertain conditions. This review explores the current role of this technology in plastic surgery by outlining the applications in clinical practice, diagnostic and prognostic accuracies, and proposed future direction for clinical applications and research.

METHODS

EMBASE, MEDLINE, CENTRAL and ClinicalTrials.gov were searched from 1990 to 2020. Any clinical studies (including case reports) which present the diagnostic and prognostic accuracies of machine learning models in the clinical setting of plastic surgery were included. Data collected were clinical indication, model utilised, reported accuracies, and comparison with clinical evaluation.

RESULTS

The database identified 1181 articles, of which 51 articles were included in this review. The clinical utility of these algorithms was to assist clinicians in diagnosis prediction (n=22), outcome prediction (n=21) and pre-operative planning (n=8). The mean accuracy is 88.80%, 86.11% and 80.28% respectively. The most commonly used models were neural networks (n=31), support vector machines (n=13), decision trees/random forests (n=10) and logistic regression (n=9).

CONCLUSIONS

ML has demonstrated high accuracies in diagnosis and prognostication of burn patients, congenital or acquired facial deformities, and in cosmetic surgery. There are no studies comparing ML to clinician's performance. Future research can be enhanced using larger datasets or utilising data augmentation, employing novel deep learning models, and applying these to other subspecialties of plastic surgery.

A Role for Artificial Intelligence in the Classification of Craniofacial Anomalies

ABSTRACT

Development of an objective algorithm to diagnose and assess craniofacial conditions has the potential to facilitate early diagnosis, especially for care providers with limited craniofacial expertise. Deep learning, a branch of artificial intelligence, can automatically analyze and categorize disease without human assistance. Convolutional neural networks (CNN) have excelled in utilizing medical images to automatically classify disease. In this study, the authors developed CNN models to detect and classify non-syndromic craniosynostosis (CS) using 2D images. The authors created an annotated data set of labeled CS (normal, metopic, sagittal, and unicoronal) conditions using standard clinical photography from the image repository at our center. The authors extended this dataset set by adding photographic images of children with craniofacial conditions from the internet. A total of 1076 images were used in this study. The authors developed a CNN model using a pre-trained ResNet-50 model to classify the data as metopic, sagittal, and unicoronal. The testing accuracy for the CS ResNet50 model achieved an overall testing accuracy of 90.6%. The sensitivity and precision were: 100% and 100% for metopic, 93.3% and 100% for sagittal, and 66.7% and 100% for unicoronal, respectively. The CNN model performed with promising accuracy. These results support the idea that deep learning has a role in diagnosis of craniofacial conditions. Using standard 2D clinical photography, such systems can provide automated screening and detection of these conditions. In the future, ML may be applied to prediction and assessment of surgical outcomes, or as an open-source remote diagnostic resource.

Quantitative and qualitative comparison of morphometric outcomes after endoscopic and conventional correction of sagittal and metopic craniosynostosis versus control groups

OBJECTIVE

Surgical correction for sagittal and metopic craniosynostosis (SCS and MCS) aims to alter the abnormal cranial shape to resemble that of the normal population. The achieved correction can be assessed by morphometric parameters. The purpose of the presented study was to compare craniometric parameters of control groups to those same parameters after endoscopic and conventional (open) correction.

METHODS

The authors identified 4 groups of children undergoing surgical treatment for either SCS or MCS, with either endoscopic (SCS, n = 17; MCS, n = 16) or conventional (SCS, n = 29; MCS, n = 18) correction. In addition, normal control groups of nonaffected children who were 6 (n = 30) and 24 (n = 18) months old were evaluated. For all groups, several craniometric indices calculated from 3D photographs were compared for quantitative analysis. For qualitative comparison, averages of all 3D photographs were generated for all groups and superimposed to visualize relative changes.

RESULTS

For children with SCS, the cephalic index and coronal circumference index significantly differed preoperatively from those of the 6-month normal controls. The respective postoperative values were similar to those of the 24-month normal controls after both endoscopic and conventional correction. Similarly, for children with MCS, indices for circumference and diagonal dimension that were significantly different preoperatively became nonsignificantly different from those of 24-month normal controls after both endoscopic and conventional correction. The qualitative evaluation of superimposed average 3D head shapes confirmed changes toward normal controls after both treatment modalities for SCS and MCS. However, in SCS, the volume gain, especially in the biparietal area, was more noticeable after endoscopic correction, while in MCS, relative volume gain of the bilateral forehead was more pronounced after conventional correction. The average 3D head shapes matched more homogeneously with the average of normal controls after endoscopic correction for SCS and after conventional correction for MCS.

CONCLUSIONS

This quantitative analysis confirms that the performed surgical techniques of endoscopic and conventional correction of SCS and MCS alter the head shape toward those of normal controls. However, in a qualitative evaluation, the average head shape after endoscopic technique for SCS and conventional correction for MCS appears to be closer to that of normal controls than after the alternative technique. This study reports on morphometric outcomes after craniosynostosis correction. Only an assessment of the whole multiplicity of outcome parameters based on multicenter data acquisition will allow conclusions of superiority of one surgical technique.

Image processing and machine learning for telehealth craniosynostosis screening in newborns

OBJECTIVE

The authors sought to evaluate the accuracy of a novel telehealth-compatible diagnostic software system for identifying craniosynostosis within a newborn (< 1 year old) population. Agreement with gold standard craniometric diagnostics was also assessed.

METHODS

Cranial shape classification software accuracy was compared to that of blinded craniofacial specialists using a data set of open-source (n = 40) and retrospectively collected newborn orthogonal top-down cranial images, with or without additional facial views (n = 339), culled between April 1, 2008, and February 29, 2020. Based on image quality, midface visibility, and visibility of the cranial equator, 351 image sets were deemed acceptable. Accuracy, sensitivity, and specificity were calculated for the software versus specialist classification. Software agreement with optical craniometrics was assessed with intraclass correlation coefficients.

RESULTS

The cranial shape classification software had an accuracy of 93.3% (95% CI 86.8-98.8; p < 0.001), with a sensitivity of 92.0% and specificity of 94.3%. Intraclass correlation coefficients for measurements of the cephalic index and cranial vault asymmetry index compared to optical measurements were 0.95 (95% CI 0.84-0.98; p < 0.001) and 0.67 (95% CI 0.24-0.88; p = 0.003), respectively.

CONCLUSIONS

These results support the use of image processing-based neonatal cranial deformity classification software for remote screening of nonsyndromic craniosynostosis in a newborn population and as a substitute for optical scanner- or CT-based craniometrics. This work has implications that suggest the potential for the development of software for a mobile platform that would allow for screening by telemedicine or in a primary care setting.

Three-dimensional photography for intraoperative morphometric analysis in metopic craniosynostosis surgery

PURPOSE

Surgical correction of metopic craniosynostosis typically involves open cranial vault remodeling. Accurate translation of the virtual surgical plan into the operating room is challenging due to the lack of tools for intraoperative analysis of the surgical outcome. This study aimed to evaluate the feasibility of using a hand-held 3D photography device for intraoperative evaluation and guidance during cranial vault surgical reconstruction.

METHODS

A hand-held structured light scanner was used for intraoperative 3D photography during five craniosynostosis surgeries, obtaining 3D models of skin and bone surfaces before and after the remodeling. The accuracy of this device for 3D modeling and morphology quantification was evaluated using preoperative computed tomography imaging as gold-standard. In addition, the time required for intraoperative 3D photograph acquisition was measured.

RESULTS

The average error of intraoperative 3D photography was 0.30 mm. Moreover, the interfrontal angle and the transverse forehead width were accurately measured in the 3D photographs with an average error of 0.72 degrees and 0.62 mm. Surgeon's feedback indicates that this technology can be integrated into the surgical workflow without substantially increasing surgical time.

CONCLUSION

Hand-held 3D photography is an accurate technique for objective quantification of intraoperative cranial vault morphology and guidance during metopic craniosynostosis surgical reconstruction. This noninvasive technique does not substantially increase surgical time and does not require exposure to ionizing radiation, presenting a valuable alternative to computed tomography imaging. The proposed methodology can be integrated into the surgical workflow to assist during cranial vault remodeling and ensure optimal surgical outcomes.

Three-Dimensional Stereophotogrammetry in the Evaluation of Craniosynostosis: Current and Potential Use Cases

ABSTRACT

Three-dimensional (3D) stereophotogrammetry is a novel imaging technique that has gained popularity in the medical field as a reliable, non-invasive, and radiation-free imaging modality. It uses optical sensors to acquire multiple 2D images from different angles which are reconstructed into a 3D digital model of the subject's surface. The technique proved to be especially useful in craniofacial applications, where it serves as a tool to overcome the limitations imposed by conventional imaging modalities and subjective evaluation methods. The capability to acquire high-dimensional data in a quick and safe manner and archive them for retrospective longitudinal analyses, provides the field with a methodology to increase the understanding of the morphological development of the cranium, its growth patterns and the effect of different treatments over time.This review describes the role of 3D stereophotogrammetry in the evaluation of craniosynostosis, including reliability studies, current and potential clinical use cases, and practical challenges. Finally, developments within the research field are analyzed by means of bibliometric networks, depicting prominent research topics, authors, and institutions, to stimulate new ideas and collaborations in the field of craniofacial 3D stereophotogrammetry.We anticipate that utilization of this modality's full potential requires a global effort in terms of collaborations, data sharing, standardization, and harmonization. Such developments can facilitate larger studies and novel deep learning methods that can aid in reaching an objective consensus regarding the most effective treatments for patients with craniosynostosis and other craniofacial anomalies, and to increase our understanding of these complex dysmorphologies and associated phenotypes.

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis

Craniosynostosis is a condition in which cranial sutures fuse prematurely, causing problems in normal brain and skull growth in infants. To limit the extent of cosmetic and functional problems, swift diagnosis is needed. The goal of this study is to investigate if a deep learning algorithm is capable of correctly classifying the head shape of infants as either healthy controls, or as one of the following three craniosynostosis subtypes; scaphocephaly, trigonocephaly or anterior plagiocephaly. In order to acquire cranial shape data, 3D stereophotographs were made during routine pre-operative appointments of scaphocephaly (n = 76), trigonocephaly (n = 40) and anterior plagiocephaly (n = 27) patients. 3D Stereophotographs of healthy infants (n = 53) were made between the age of 3-6 months. The cranial shape data was sampled and a deep learning network was used to classify the cranial shape data as either: healthy control, scaphocephaly patient, trigonocephaly patient or anterior plagiocephaly patient. For the training and testing of the deep learning network, a stratified tenfold cross validation was used. During testing 195 out of 196 3D stereophotographs (99.5%) were correctly classified. This study shows that trained deep learning algorithms, based on 3D stereophotographs, can discriminate between craniosynostosis subtypes and healthy controls with high accuracy.

Automated Measurement of Intracranial Volume Using Three-Dimensional Photography

BACKGROUND

Current methods to analyze three-dimensional photography do not quantify intracranial volume, an important metric of development. This study presents the first noninvasive, radiation-free, accurate, and reproducible method to quantify intracranial volume from three-dimensional photography.

METHODS

In this retrospective study, cranial bones and head skin were automatically segmented from computed tomographic images of 575 subjects without cranial abnormality (average age, 5 ± 5 years; range, 0 to 16 years). The intracranial volume and the head volume were measured at the cranial vault region, and their relation was modeled by polynomial regression, also accounting for age and sex. Then, the regression model was used to estimate the intracranial volume of 30 independent pediatric patients from their head volume measured using three-dimensional photography. Evaluation was performed by comparing the estimated intracranial volume with the true intracranial volume of these patients computed from paired computed tomographic images; two growth models were used to compensate for the time gap between computed tomographic and three-dimensional photography.

RESULTS

The regression model estimated the intracranial volume of the normative population from the head volume calculated from computed tomographic images with an average error of 3.81 ± 3.15 percent (p = 0.93) and a correlation (R) of 0.96. The authors obtained an average error of 4.07 ± 3.01 percent (p = 0.57) in estimating the intracranial volume of the patients from three-dimensional photography using the regression model.

CONCLUSION

Three-dimensional photography with image analysis provides measurement of intracranial volume with clinically acceptable accuracy, thus offering a noninvasive, precise, and reproducible method to evaluate normal and abnormal brain development in young children.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Diagnostic, V.

Test-retest validation of a cranial deformity index in unilateral coronal craniosynostosis

Unilateral coronal craniosynostosis (UCS) affects many infants resulting in abnormalities affecting the forehead and orbits. As a result, the deformity caused by UCS is very noticeable and there are several surgical treatment options available to normalize the head shape. However, there is a lack of consistently used outcome measures, resulting in difficulty assessing surgical outcomes and on-going debate over optimal treatments. Current techniques to quantify deformity in UCS are cumbersome, provide limited information, or are based on subjective assessments. In this study, a cranial deformity index was developed to quantify abnormality at the frontal bones for UCS that is accessible, user-friendly, and generates objective surface distance measurements. The cranial deformity index is defined as the Euclidean distance at the point of the largest deviation between the deformed skull compared to a reference skull. In addition, the index was successfully used to quantify post-operative changes in a single case of UCS that underwent corrective surgery. The reproducibility of the index was assessed using test-retest reliability and was demonstrated to be highly reproducible (ICC = 0.93). A user-friendly measurement index that is based on open-source software may be a valuable tool for surgical teams. In addition, this information can augment the consultation experience for patients and their families.