Craniofacial Scan Research Articles

Feasibility of Magnetic Levator Prosthesis Frame Customization Using Craniofacial Scans and 3-D Printing.

PurposeTo determine the feasibility of a custom frame generation approach for nonsurgical management of severe blepharoptosis with the magnetic levator prosthesis (MLP).MethodsParticipants (n = 8) with severe blepharoptosis (obscuring the visual axis) in one or both eyes who had previously been using a non-custom MLP had a craniofacial scan with a smartphone app to generate a custom MLP frame. A magnetic adhesive was attached to the affected eyelid. The custom MLP frame held a cylindrical magnet near the eyebrow above the affected eyelid, suspending it in the magnetic field while still allowing blinking. The spectacle magnet could be rotated manually, providing adjustable force via angular translation of the magnetic field. Fitting success and comfort were recorded, and interpalpebral fissure (IPF) was measured from video frames after 20 minutes in-office and one-week at-home use. Preference was documented, custom versus non-custom.ResultsOverall, 88% of patients (7/8) were successfully fitted with a median 9/10 comfort (interquartile 7–10) and median ptosis improvement of 2.3 mm (1.3–5.0); P = 0.01). Exact binomial testing suggested, with 80% power, that the true population success rate was significantly greater than 45% (P = 0.05). Five participants took the custom MLP home for one week, with only one case of mild conjunctival redness which resolved without treatment. Highest to lowest force modulation resulted in a marginally significant median IPF adjustment of 1.5 mm (0.8 to 2.7; P = 0.06). All preferred the custom frame.ConclusionsThe three-dimensional custom MLP frame generation approach using a smartphone app–based craniofacial scan is a feasible approach for clinical deployment of the MLP.Translational RelevanceFirst demonstration of customized frame generation for the MLP.

Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans

BackgroundObstructive sleep apnea (OSA) remains massively underdiagnosed, due to limited access to polysomnography (PSG), the highly complex gold standard for diagnosis. Performance scores in predicting OSA are evaluated for machine learning (ML) analysis applied to 3D maxillofacial shapes. MethodsThe 3D maxillofacial shapes were scanned on 280 Caucasian men with suspected OSA. All participants underwent single night in-home or in-laboratory sleep testing with PSG (Nox A1, Resmed, Australia), with concomitant 3D scanning (Sense v2, 3D systems corporation, USA). Anthropometric data, comorbidities, medication, BERLIN, and NoSAS questionnaires were also collected at baseline. The PSG recordings were manually scored at the reference sleep center. The 3D craniofacial scans were processed by geometric morphometrics, and 13 different supervised algorithms, varying from simple to more advanced, were trained and tested. Results for OSAS recognition by ML models were then compared with scores for specificity and sensitivity obtained using BERLIN and NoSAS questionnaires. ResultsAll valid scans (n = 267) were included in the analysis (patient mean age: 59 ± 9 years; BMI: 27 ± 4 kg/m2). For PSG-derived AHI≥15 events/h, the 56% specificity obtained for ML analysis of 3D craniofacial shapes was higher than for the questionnaires (Berlin: 50%; NoSAS: 40%). A sensitivity of 80% was obtained using ML analysis, compared to nearly 90% for NoSAS and 61% for the BERLIN questionnaire. The auROC score was further improved when 3D geometric morphometrics were combined with patient anthropometrics (auROC = 0.75). ConclusionThe combination of 3D geometric morphometrics with ML is proposed as a rapid, efficient, and inexpensive screening tool for OSA. Trial registration number: NCT03632382; Date of registration: 15-08-2018.

Nasal cavity structural anomalies in children and adolescents at high risk of sleep-disordered breathing: An exploratory cone-beam computed tomography study

In this study, we investigated the presence of structural anomalies in the nasal cavity (deviated nasal septum [DNS] and turbinate hypertrophy [TH]) in patients at high risk or not of sleep-disordered breathing (SDB). A retrospective study considering available cone-beam computed tomography scans of 99 patients was conducted. DolphinImaging software (Dolphin Imaging and Management Solutions, Chatsworth, Calif) was used to process the craniofacial scans. A pediatric sleep questionnaire (PSQ) was used to suggest a high risk of SDB. Subjective and objective assessments of DNS and TH were considered. Good to excellent intrareliability and interreliability were attained. The prevalence of a PSQ score suggestive of a high risk of SDB in this sample was 59%. The prevalence of subjective DNS and TH assessment was 64% and 70%, respectively. In contrast, on the basis of objective assessments, 27% of patients presented with DNS and 25% with TH. Cross-tabulation of DNS and TH with PSQ score indicated a statistically significant association between subjective DNS and subjective TH and subjective TH and positive PSQ. A positive correlation between age and subjective and objective DNS assessments was also observed. Older patients are more likely to present with DNS. Only the presence of subjectively determined TH in patients is associated with a high risk for SDB. The study reveals that assessment of DNS and TH using cone-beam computed tomography imaging is not likely suitable to strongly suggest patients at high risk for SDB. DNS subjective assessments were capable of identifying less than 5% of deviation.

Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans.

Obstructive sleep apnea (OSA) is characterized by decreased breathing events that occur through the night, with severity reported as the apnea-hypopnea index (AHI), which is associated with certain craniofacial features. In this study, we used data from 1366 patients collected as part of Stanford Technology Analytics and Genomics in Sleep (STAGES) across 11 US and Canadian sleep clinics and analyzed 3D craniofacial scans with the goal of predicting AHI, as measured using gold standard nocturnal polysomnography (PSG). First, the algorithm detects pre-specified landmarks on mesh objects and aligns scans in 3D space. Subsequently, 2D images and depth maps are generated by rendering and rotating scans by 45-degree increments. Resulting images were stacked as channels and used as input to multi-view convolutional neural networks, which were trained and validated in a supervised manner to predict AHI values derived from PSGs. The proposed model achieved a mean absolute error of 11.38 events/hour, a Pearson correlation coefficient of 0.4, and accuracy for predicting OSA of 67% using 10-fold cross-validation. The model improved further by adding patient demographics and variables from questionnaires. We also show that the model performed at the level of three sleep medicine specialists, who used clinical experience to predict AHI based on 3D scan displays. Finally, we created topographic displays of the most important facial features used by the model to predict AHI, showing importance of the neck and chin area. The proposed algorithm has potential to serve as an inexpensive and efficient screening tool for individuals with suspected OSA.

Prediction of Patient Demographics using 3D Craniofacial Scans and Multi-view CNNs.

3D data is becoming increasingly popular and accessible for computer vision tasks. A popular format for 3D data is the mesh format, which can depict a 3D surface accurately and cost-effectively by connecting points in the (x, y, z) plane, known as vertices, into triangles that can be combined to approximate geometrical surfaces. However, mesh objects are not suitable for standard deep learning techniques due to their non-euclidean structure. We present an algorithm which predicts the sex, age, and body mass index of a subject based on a 3D scan of their face and neck. This algorithm relies on an automatic pre-processing technique, which renders and captures the 3D scan from eight different angles around the x-axis in the form of 2D images and depth maps. Subsequently, the generated data is used to train three convolutional neural networks, each with a ResNet18 architecture, to learn a mapping between the set of 16 images per subject (eight 2D images and eight depth maps from different angles) and their demographics. For age and body mass index, we achieved a mean absolute error of 7.77 years and 4.04 kg/m2 on the respective test sets, while Pearson correlation coefficients of 0.76 and 0.80 were obtained, respectively. The prediction of sex yielded an accuracy of 93%. The developed framework serves as a proof of concept for prediction of more clinically relevant variables based on 3D craniofacial scans stored in mesh objects.

Length of the ramus of the mandible as an indicator of chronological age and sex: A study in a group of Egyptians

BackgroundThe determination of age and sex is crucial for establishing a human identity. The mandible is an accessible and a durable bone that would be very useful in this respect. AimTo test if the length of the ramus of the mandible can be used to predict the age and the sex in a group of Egyptians Subjects and methodsThe length of the ramus of the mandible was measured in lateral craniofacial scans of 213 individuals (99 males and 114 females) with an age range of 7–58 years using cone-beam computerized tomography (CBCT). ResultsSexual dimorphism was not observed until the age of 17 years in this sample of population. There was not found any significant differences in the length of the ramus of the mandible between males and females in the age range of 7 to less than 17 years (P-value = 0.2495). There was found a significant difference between males and females for the mean length of the mandible ramus in the age range of 17–58 years (P-value < 0.0000). It predicts sex with an accuracy of 67 %. There was a positive correlation between ln (age) and ln (ramus) (r = 0.73, P-value < 0.0001 for females and r = 0.85, P-value <0.0001 for males). ConclusionThe mandibular ramus length is valuable in age estimation and less valuable in sex determination. Computerized tomography examination is a safe and an accurate procedure with minimal radiation exposure, and provides valuable precise information regarding mandible measurements.

Mucocèle ethmoïdale découverte devant un épiphora

To report a case of anterior ethmoid mucocele with atypical location and ophthalmic symptomatology. A 18-year-old patient presented with epiphora and swelling of the left medial canthus; craniofacial scan revealed an ethmoid mucocele. We describe a surgical technique for the treatment of mucoceles, illustrated with CT and endoscopy images. Mucoceles present with various clinical and radiological manifestations, and in particular may have an ophthalmologic impact. Treatment is surgical, with various techniques available. Epiphora may be an unusual presentation of an ethmoid mucocele, due to lacrimal duct compression.

A C1-inhibitor rare mutation: Early diagnosis of hereditary angioedema in a paediatric patient

Hereditary angioedema secondary to C1-inhibitor deficiency is a rare autosomal dominant disorder characterized by a deficiency of C1 esterase inhibitor.An eight-year-old girl showed periorbital painless swelling, diagnosed as ethmoiditis. A craniofacial scan did not evidence a paranasal sinus involvement, C1INH levels were undetectable, with low C4 levels: 7.6 mg/dl and C1INH: <8.46 mg/dl. The genetic study identified a rare mutation of the C1INH gene. This clinical report is of relieve because paediatric cases described in literature are rare, did not presented a positive family history, and received a diagnosis after many attacks. Furthermore our girl received a prompt diagnosis of HAE at the first attack of angioedema.

Residents’ journal review

Residents’ journal review