107 research outputs found

    The correlation between static and dynamic facial dysmorphology in unilateral cleft lip and palate (UCLP)

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    Objective – The objective of this study was to assess the correlation between static (3D) facial dysmorphology at both rest and maximum smile and dynamic (4D) facial dysmorphology as represented by the frame of maximum asymmetry during a maximum smile in unilateral cleft lip and palate (UCLP). When maximum asymmetry occurs during a maximum smile was also analysed. Design and setting – This study was designed as a retrospective cross-sectional study utilising quantitative methodology to assess a cohort of patients with a diagnosis of non-syndromic unilateral cleft lip and palate (UCLP). Patients had 4D imaging carried out at Glasgow Dental Hospital and School for audit purposes and as part of their routine care by the local cleft team working under the Managed Clinical Network (MCN) Cleft Care Scotland. Materials and Methods – Thirty-one (31) patients between the ages of 13 to 17 years old with a diagnosis of non-syndromic unilateral cleft lip and palate (UCLP) had 4D images based on passive stereophotogrammetry captured at 60 frames per second (fps). Each patient was captured performing a maximum smile three times over a three second window. Data processing involved the conformation of a generic mesh containing over 7000 vertices or quasi-landmarks to the facial surface before tracking all the vertices during the facial expression. Partial ordinary Procrustes analysis was utilised to calculate an asymmetry score for each frame during the expression. The rest frame (3D), frame of maximum smile (3D), and frame of maximum asymmetry (4D) were used to determine the correlation between static and dynamic facial dysmorphology. Results – Asymmetry scores were higher at maximum smile than at rest and maximum asymmetry was most frequently observed during the relaxation phase following a maximum facial expression (N=27/31; 87.1%). Asymmetry scores for maximum smile were less than but comparable to maximum asymmetry scores. Static (3D) asymmetry at rest and maximum smile was strongly correlated to dynamic (4D) facial asymmetry represented by the frame of maximum asymmetry during a maximum smile. The strongest correlation was seen with analysis using the frame of maximum smile focussing on the nasolabial region. Conclusions – The use of 4D imaging combined with mesh conformation and dense correspondence analysis provides a valid objective measure of facial asymmetry. The fact that asymmetry scores for maximum smile were less than but comparable to maximum asymmetry scores highlights that assessment of facial asymmetry at maximum smile is still a valid assessment of facial dysmorphology despite not depicting the full extent of the asymmetry. Static (3D) asymmetry at rest and maximum smile is strongly correlated to; and likely highly predictive of, dynamic (4D) facial asymmetry represented by the frame of maximum asymmetry during a maximum smile. The strongest correlation is seen with analysis using the frame of maximum smile focussing on the nasolabial region. Future research could use linear regression modelling to predict dynamic (4D) asymmetry scores using static (3D) images

    Craniofacial dysmorphology in Down syndrome is caused by increased dosage of Dyrk1a and at least three other genes

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    Down syndrome (DS), trisomy of human chromosome 21 (Hsa21), occurs in 1 in 800 live births and is the most common human aneuploidy. DS results in multiple phenotypes, including craniofacial dysmorphology, which is characterised by midfacial hypoplasia, brachycephaly and micrognathia. The genetic and developmental causes of this are poorly understood. Using morphometric analysis of the Dp1Tyb mouse model of DS and an associated mouse genetic mapping panel, we demonstrate that four Hsa21-orthologous regions of mouse chromosome 16 contain dosage-sensitive genes that cause the DS craniofacial phenotype, and identify one of these causative genes as Dyrk1a. We show that the earliest and most severe defects in Dp1Tyb skulls are in bones of neural crest (NC) origin, and that mineralisation of the Dp1Tyb skull base synchondroses is aberrant. Furthermore, we show that increased dosage of Dyrk1a results in decreased NC cell proliferation and a decrease in size and cellularity of the NC-derived frontal bone primordia. Thus, DS craniofacial dysmorphology is caused by an increased dosage of Dyrk1a and at least three other genes

    Application of Advanced MRI to Fetal Medicine and Surgery

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    Robust imaging is essential for comprehensive preoperative evaluation, prognostication, and surgical planning in the field of fetal medicine and surgery. This is a challenging task given the small fetal size and increased fetal and maternal motion which affect MRI spatial resolution. This thesis explores the clinical applicability of post-acquisition processing using MRI advances such as super-resolution reconstruction (SRR) to generate optimal 3D isotropic volumes of anatomical structures by mitigating unpredictable fetal and maternal motion artefact. It paves the way for automated robust and accurate rapid segmentation of the fetal brain. This enables a hierarchical analysis of volume, followed by a local surface-based shape analysis (joint spectral matching) using mathematical markers (curvedness, shape index) that infer gyrification. This allows for more precise, quantitative measurements, and calculation of longitudinal correspondences of cortical brain development. I explore the potential of these MRI advances in three clinical settings: fetal brain development in the context of fetal surgery for spina bifida, airway assessment in fetal tracheolaryngeal obstruction, and the placental-myometrial-bladder interface in placenta accreta spectrum (PAS). For the fetal brain, MRI advances demonstrated an understanding of the impact of intervention on cortical development which may improve fetal candidate selection, neurocognitive prognostication, and parental counselling. This is of critical importance given that spina bifida fetal surgery is now a clinical reality and is routinely being performed globally. For the fetal trachea, SRR can provide improved anatomical information to better select those pregnancies where an EXIT procedure is required to enable the fetal airway to be secured in a timely manner. This would improve maternal and fetal morbidity outcomes associated with haemorrhage and hypoxic brain injury. Similarly, in PAS, SRR may assist surgical planning by providing enhanced anatomical assessment and prediction for adverse peri-operative maternal outcome such as bladder injury, catastrophic obstetric haemorrhage and maternal death

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

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    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

    Predicting and optimising the postoperative outcomes of sagittal craniosynostosis correction

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    The neonate skull consists of several flat bones, connected by fibrous joints called sutures. Sutures regulate the bone formation along their adjoining edges, while providing mailability to assist with the early phases of rapid brain growth and passing through the birth canal with minimal restriction. By adolescents, these sutures fuse into solid bone, protecting the brain from impacts. The premature fusion of one or more of these sutures is a medical condition known as craniosynostosis, with its most common form being sagittal craniosynostosis (fusion of the midline suture). The condition results in compensatory overgrowth perpendicular to the fused suture, leading to calvarial deformation and possible neurofunctional defects. Surgeons have developed several surgical techniques to restore the normative shape. This has led to debates as to which surgical option provides the most beneficial long term outcome. The overall aim of this thesis was to develop a computational approach using the finite element (FE) method capable of predicting and optimising the long term outcomes for treating sagittal craniosynostosis. A generic 3D pre-operative FE model was developed using patient specific CT data. The FE model was parameterised to predict the long term calvarial growth, the pattern of suture and bone formation, the pattern of bone healing across the replicated surgical techniques, and the changes in contact pressure levels across the modelled brain. All techniques underwent simulated growth up to the maximum age of 76 months. Morphological results were compared against the patient specific CT data at the same age. Where absent, technique specific follow up CT data were used instead. Results highlighted a good morphological agreement between the predicted models and their comparative CT data. The FE model was highly sensitive to the choice of input parameters. Based on the findings of this thesis, the *** approach proved the most optimal across the predicted outcomes. The novel methodology and platform developed here has huge potential to better inform surgeons of the impact various techniques could have on long term outcomes and continue to improve the quality of care for patients undergoing corrective surgery

    3D statistical shape analysis of the face in Apert syndrome

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    Timely diagnosis of craniofacial syndromes as well as adequate timing and choice of surgical technique are essential for proper care management. Statistical shape models and machine learning approaches are playing an increasing role in Medicine and have proven its usefulness. Frameworks that automate processes have become more popular. The use of 2D photographs for automated syndromic identification has shown its potential with the Face2Gene application. Yet, using 3D shape information without texture has not been studied in such depth. Moreover, the use of these models to understand shape change during growth and its applicability for surgical outcome measurements have not been analysed at length. This thesis presents a framework using state-of-the-art machine learning and computer vision algorithms to explore possibilities for automated syndrome identification based on shape information only. The purpose of this was to enhance understanding of the natural development of the Apert syndromic face and its abnormality as compared to a normative group. An additional method was used to objectify changes as result of facial bipartition distraction, a common surgical correction technique, providing information on the successfulness and on inadequacies in terms of facial normalisation. Growth curves were constructed to further quantify facial abnormalities in Apert syndrome over time along with 3D shape models for intuitive visualisation of the shape variations. Post-operative models were built and compared with age-matched normative data to understand where normalisation is coming short. The findings in this thesis provide markers for future translational research and may accelerate the adoption of the next generation diagnostics and surgical planning tools to further supplement the clinical decision-making process and ultimately to improve patients’ quality of life

    Craniofacial Syndrome Identification Using Convolutional Mesh Autoencoders

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    Background: 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. While these are powerful tools for photographic analysis, they 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. // Methods: 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). // Findings: 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 makes it suitable for AI assisted diagnosis in medical imaging as well as photographic analysis in the clinical setting. // Interpretation: Our study demonstrates the use of 3D convolutional mesh autoencoders for the diagnosis of syndromic craniosynostosis. The topological nature of the tool presents opportunities for this method to be applied as a diagnostic tool across a number of 3D imaging modalities

    The assessment of distorted facial muscles movements in facial palsy

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    Introduction The clinical evaluation of facial palsy remains the routine approach for the assessment of facial muscle movements. However, there is a lack of data to link the mathematical analysis of 3D dynamic facial morphology with the subjective clinical assessments. Quantifying the degree of distortion of facial expressions is a vital step in evaluating the clinical impact of facial palsy. 4D imaging is a reliable modality for recording the dynamics of facial expressions. This study aimed to assess distorted facial muscles movements in unilateral facial palsy and mathematically validate clinical grading indices. Material & Method The study recruited 50 patients who suffered from unilateral facial palsy and a control group of an equal number (50) of age- and sex-matched cases. The dynamics of facial expressions were captured using a stereophotogrammetric 4D imaging system. Six facial expressions were recorded (rest, maximum smile, cheek puff, lip purse, eyebrow-raising, eye closure), each one took 4 seconds and generated about 240 3D images for analysis. An advanced geometric morphometric approach using Dense Surface Models was applied for the mathematical quantification of the 3D facial dysmorphology over time. The asymmetries of 10 facial anatomical regions were calculated. For each participant, six mathematical values which quantify asymmetry were measured per expression (the minimal, mean, median, maximum, range, and standard deviation). The 4D image data of sixteen facial paralysis patients were assessed by 7 expert assessors using two clinical grading indices for the assessment of unilateral facial palsy, the modified Sunnybrook index, and the Glasgow Index. The reproducibility of the clinical gradings between two rating sessions was examined. The measured asymmetries of the 4D images were treated as the gold standard to evaluate the performance of the subjective grading indices. Cross-correlations between the mathematical measurements and the subjective grades were calculated. The Modified Sunnybrook index assessed 8 parameters (3 at rest and 5 at individual facial expression). The Glasgow index assessed 29 parameters for the assessment of dynamic facial abnormalities with considerations for the directionality and severity of asymmetry. The similarities and dissimilarities between the two clinical assessments and to the mathematical measurements were investigated. Results The modified Sunnybrook index was reproducible for grading the dysmorphology and dysfunction of unilateral facial paralysis. The Glasgow Index was reproducible after excluding three parameters of poor reproducibility. The modified Sunnybrook index and the Glasgow index correlated reasonably well with the mathematical measurements of facial asymmetry at rest and with facial expressions. • The minimal value of facial asymmetries of the rest expression had a stronger correlation coefficient than that of other values. • The mean and median values of facial asymmetries of the other five nonverbal expressions had a stronger correlation coefficient than that of other values. The following were the main regions affected by facial dysmorphology which showed a correlation above -0.6 between the subjective and objective assessments: • The full face at rest as well as the forehead, cheek, nose and nasolabial, upper lip, corner of the mouth, and chin regions. • The full face, cheek, nasolabial, upper lip, and lower lip of the smile. • The full face, upper and lower lips of the lip purse. • Most of the facial regions, except the cheek, showed moderate to weak correlations with cheek puff. • A strong correlation was detected between the subjective and objective assessments of the forehead and eye regions with eye closure. Based on the correlation results between the mathematical measurements and clinical evaluation of facial asymmetry in unilateral facial paralysis, the study highlighted the following points: • Smile expression: the assessors encountered difficulties to judge the direction of the asymmetry of the corner of the mouth. It is easier to observe the upper lip and the cheek instead of the corner of the mouth when assessing the smile. • Lip purse: the evaluation of the directionality of lip movement was more accurate and sensitive at the lower lip. • Cheek puff: grading the cheek may not grasp the severity of the asymmetry. We would suggest observing the corner of the mouth and lower lip in cheek puff expressions. • Eyebrow raising expression: grading the 4D movement of the upper margin of the eyebrow may be more sensitive than depending on the assessment of the wrinkles of the forehead. • Eye closure: the clinical assessment of the eyes based on 4D image data was not ideal due to the 4D imaging surface defects secondary to the reflective surface of the cornea. Conclusion The mathematical assessment of the dynamics of facial expressions in unilateral facial palsy using advanced geometric morphometrics provides a state-of-art approach for the quantification and visualization of facial dysmorphology. The Glasgow Index and the Modified Sunnybrook Index were reproducible. The clinical assessors were reasonably consistent in the grading of facial palsy. The significant correlations between the clinical grading of facial palsy and the mathematical calculation of the same facial muscle movements provided satisfactory evidence of objectivity to the clinical assessments. The Glasgow index provided more validated parameters for the assessment of facial palsy in comparison to the modified Sunnybrook index. The mathematical quantification of the 3D facial dysmorphology and the associated dynamic asymmetry provides invaluable information to complement the clinical assessments. This is particularly important for the assessment of regional asymmetries and the directionality of the asymmetry for the evaluation of facial contour (anteroposterior direction), face drooping (vertical direction), especially in cases where surgical rehabilitation is indicated
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