Infants with and without orofacial clefts: how their faces grow in 3D
A scientific essay in Medical Sciences
DOCTORAL THESIS defended in public on 3rd of December 2019
Primary treatment of a complete unilateral or bilateral cleft starts within the first months after birth and may include presurgical infant orthopedics and primary surgical correction of the cleft lip and palate. Different techniques and timings have been described for infant orthopedics and cleft lip closure. The existence of numerous different protocols for primary treatment of orofacial clefts in different treatment centers identifies the lack of clear scientific evidence in favor of one method over the other. Facial morphology is an important outcome variable of cleft treatment and various methods for assessment of facial morphology have been described in literature, like direct physical measurements, rating of standardized clinical photographs as well as two-dimensional x-ray cephalometry. The rise of harmless, accurate and millisecond fast three-dimensional imaging modalities potentially enables early and detailed evaluation of facial growth and treatment outcomes in the youngest patients and healthy control subjects. The rationale and background of this PhD study are explained in chapter 1.
In chapter 2 a systematic review was presented that aimed for developing an overview of soft-tissue based methods for quantitative longitudinal assessment of facial dimensions in children until six years of age and to assess the reliability of these methods in studies with good methodological quality. We included primary publications on longitudinal external facial growth and treatment outcomes in children younger than six years of age. A quality assessment instrument was used to determine the methodological quality. Methods, used in studies with good methodological quality, were assessed for reliability expressed as the magnitude of the measurement error and the correlation coefficient between repeated measurements. In total, 47 studies were included describing 4 methods: 2D x-ray cephalometry; 2D photography; anthropometry; 3D imaging techniques (surface laser scanning, stereophotogrammetry and cone beam computed tomography). In general, the measurement error was below 1 mm and 1° and correlation coefficients ranged from 0.65 to 1.0. We concluded that, at present, stereophotogrammetry seems to be the best 3D method for quantitative longitudinal assessment of facial dimensions in children until six years of age due to its millisecond fast image capture, archival capabilities, high resolution and no exposure to ionizing radiation.
In chapter 3 a systematic review was presented that aimed to provide an overview of soft tissue based methods for quantitative longitudinal assessment of cranial dimensions in children until six years of age and to assess the reliability of these methods in studies with good methodological quality. We included primary publications on longitudinal external cranial growth and treatment outcomes in children younger than six years of age. A quality assessment instrument was used to determine the methodological quality. Methods, used in studies with good methodological quality, were assessed for reliability expressed as the magnitude of the measurement error and the correlation coefficient between repeated measurements. In total, 165 studies were included, forming three groups of methods: head circumference anthropometry, direct anthropometry, and 2D photography and 3D imaging techniques, c.q. surface laser scanning and stereophotogrammetry. In general, the measurement error was below 2 mm, and correlation coefficients between repeated measurements were good. We concluded that stereophotogrammetry is the most versatile method for quantitative longitudinal assessment of cranial dimensions and shapes in children. However, direct anthropometry continues to be the best method for routine clinical assessments of linear cranial dimensions in growing children until 6 years of age.
In chapter 4 we presented a study that aimed to develop a reference frame for 3D facial soft tissue growth analysis in children and to determine its reproducibility. Two observers twice placed the reference frame on 39 3D stereophotogrammetry facial images of children with orofacial clefts and control children. Correlations between observers were analysed with Pearson’s correlation coefficient. The influence of presence of a cleft, absence of one ear in the photograph, and age on the reproducibility of the reference frame was checked using Student’s t test. Results of intraobserver comparisons showed a mean intersurface distance of <0.40 mm, distance variability of <0.51 mm, and P95 of <0.80 mm. For interobserver reliability, the mean intersurface distance was <0.52 mm, distance variability was <0.53 mm, and P95 was <1.10 mm. Presence of a cleft, age, and absence of one ear on the 3D photograph did not have a significant influence on the reproducibility of placing the reference frame. The children’s reference frame is a reproducible method for registration of 3D soft tissue stereophotogrammetry photographs of growing individuals with and without orofacial clefts.
In chapter 5 we presented a study on the influence of involuntary facial expressions on 3D facial stereophotogrammetry reproducibility in children with and without unilateral cleft lip, alveolus and palate aged 3 to 18 months. Three to eight 3D facial images per time-point were acquired within 10 minutes of 31 children with UCLP and 50 controls at 3 months, 12 months and 18 months of age. Best-fit registration of two included 3D facial images per subject per age was performed. Distance kits of the full face and nasolabial area were calculated to assess variation in facial images. Mean variation between two 3D facial images ranged from 0.38–0.88 mm. There were no significant differences within groups for the various ages. Variation between controls and UCLP subjects did not differ significantly. Variation was higher in the nasolabial area than in the full face. We concluded that the influence of involuntary facial expressions on the estimation of facial growth should not be underestimated, especially in the nasolabial region of UCLP subjects aged 3 months. To improve 3D facial imaging reliability, image capturing should be performed by a trained photographer following a meticulous image capturing protocol, including thorough review after capture.
In chapter 6 we presented a study that aimed to develop normative average three-dimensional faces of healthy infants at 3, 6, 9, and 12 months of age and to describe normative average three-dimensional facial growth data from 3 to 12 months of age. Three-dimensional images of 50 healthy children were acquired at 3, 6, 9, and 12 months of age using the 3dMDcranial system. Four average faces with uniform meshes (at 3, 6, 9, and 12 months of age) were developed and superimposed based on the children’s reference frames. Distance maps of growth of the total facial surface and of the nose, upper lip, chin, forehead and cheeks for the intervals 3 to 6 months, 6 to 9 months, and 9 to 12 months of age were calculated. Mean growth of the total facial surface was 3.9 mm, 3.5 mm, and 1.6 mm at 3 to 6 months, 6 to 9 months, and 9 to 12 months, respectively. Regarding the selected regions of the face, the mean growth of the nose and upper lip was the largest between 6 and 9 months of age, that is 3.7 mm and 3.6 mm, respectively. The mean growth of the forehead, cheeks and chin was the largest between 3 and 6 months of age, that is 5.4 mm, 3.2, and 4.7 mm, respectively. For all facial regions, growth clearly diminished from 9 to 12 months of age. This normative data can be used in future studies to identify the effectiveness of treatment of orofacial deformities such as orofacial clefts during the first year of life.
In chapter 7 we aimed to compare 3D facial morphology of infants born with unilateral cleft lip and palate with a normative 3D average face matched for age before and after primary closure of the lip and soft palate. We included 30 infants with a non-syndromic complete unilateral cleft lip, alveolus and palate. Stereophotogrammetric images were acquired at 3, 6, 9 and 12 months of age. The primary surgery protocol consisted of surgical closure of the lip and the soft palate at 6 months of age. 3D images of UCLP patients at age 3, 6, 9, and 12 months were superimposed on normative datasets of average facial morphology using the children’s reference frame. Distance maps of the 3D complete facial surface and of the regions of the nose, upper lip, chin, forehead and cheeks were developed. Assessment of facial morphology of UCLP vs. control subjects by colour distance maps showed a large difference in the upper lip region at the location of the cleft defect and an asymmetry at the nostrils at 3 and 6 months of age. At 9 months of age the labial symmetry is completely restored despite a remaining asymmetry of the tip of the nose towards the unaffected side. At 12 months of age symmetry of the nose increases with only a remaining asymmetry bilateral to the nasal ridge. At all ages the mandibular and chin regions of UCLP subjects are posterior to the average controls for 2.5 to 5 mm. We concluded that deviations from the normal facial anatomy in the regions of the upper lip, nose and even the forehead exist before lip and soft palate closure was performed. After primary lip and soft palate closure symmetry of the upper lip is almost completely restored and the shape of the upper lip shows less variation. At this early age retrusion of the soft tissue mandibular region and chin, however, seems to be developing already.
In chapter 8 methodological issues and results were discussed for the 6 research questions addressed in this thesis. The development and application of 3D facial norms has an enormous potential benefit for early detection of deviations from normal facial development. Surgical procedures that disturb facial growth can be detected early and be abandoned. The same holds true for infant orthopaedic approaches that may not benefit subsequent facial development. Future studies may focus on 3D facial development and treatment outcomes in other cleft subtypes, craniofacial malformations, early childhood cancer and for populations other than Caucasians. Future perspectives include the design of Big Data analyses of facial shapes and variations, automation of 3D facial analysis due to the advancement of artificial intelligence, and the evolution of facial analysis from 3D to 4D imaging . To conclude, stereophotogrammetry should be integrated into the standard documentation dataset for evaluation of outcome of cleft treatment as is proposed in ICHOM+. Engineers in Technical Medicine should be members of the cleft team to enable utilizing the full potential of 3D facial imaging.