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E.B. Wolvius

3 records found

Authored

Association between prenatal alcohol exposure and children's facial shape:

a prospective population-based cohort study

Journal article (2023) - X. Liu, Manfred Kayser, Steven A. Kushner, H Tiemeier, F Rivadeneira, Vincent Jaddoe, W.J. Niessen, W.J. Niessen, W.J. Niessen, E.B. Wolvius, G.V. Roshchupkin

STUDY QUESTION: Is there an association between low-to-moderate levels of prenatal alcohol exposure (PAE) and children's facial shape? SUMMARY ANSWER: PAE before and during pregnancy, even at low level (<12 g of alcohol per week), was found associated with the facial shape of children, and these associations were found attenuated as children grow older. WHAT IS KNOWN ALREADY: High levels of PAE during pregnancy can have significant adverse associations with a child's health development resulting in recognizably abnormal facial development. STUDY DESIGN, SIZE, DURATION: This study was based on the Generation R Study, a prospective cohort from fetal life onwards with maternal and offspring data. We analyzed children 3-dimensional (3D) facial images taken at ages 9 (n = 3149) and 13 years (n = 2477) together with the data of maternal alcohol consumption. PARTICIPANTS/MATERIALS, SETTING, METHODS: We defined six levels of PAE based on the frequency and dose of alcohol consumption and defined three tiers based on the timing of alcohol exposure of the unborn child. For the image analysis, we used 3D graph convolutional networks for non-linear dimensionality reduction, which compressed the high-dimensional images into 200 traits representing facial morphology. These 200 traits were used for statistical analysis to search for associations with PAE. Finally, we generated heatmaps to display the facial phenotypes associated with PAE. MAIN RESULTS AND THE ROLE OF CHANCE: The results of the linear regression in the 9-year-old children survived correction for multiple testing with false discovery rate (FDR). In Tier 1 where we examined PAE only before pregnancy (exposed N = 278, unexposed N = 760), we found three traits survived FDR correction. The lowest FDR-P is 1.7e-05 (beta = 0.021, SE = 0.0040) in Trait #29; In Tier 2b where we examine any PAE during first trimester (exposed N = 756; unexposed N = 760), we found eight traits survived FDR correction. The lowest FDR-P is 9.0e-03 (beta = -0.013, SE = 0.0033) in Trait #139. Moreover, more statistically significant facial traits were found in higher levels of PAE. No FDR-significant results were found in the 13-year-old children. We map these significant traits back to the face, and found the most common detected facial phenotypes included turned-up nose tip, shortened nose, turned-out chin, and turned-in lower-eyelid-related regions. LIMITATIONS, REASONS FOR CAUTION: We had no data for alcohol consumption more than three months prior to pregnancy and thus do not know if maternal drinking had chronic effects. The self-reported questionnaire might not reflect accurate alcohol measurements because mothers may have denied their alcohol consumption. WIDER IMPLICATIONS OF THE FINDINGS: Our results imply that facial morphology, such as quantified by the approach we proposed here, can be used as a biomarker in further investigations. Furthermore, our study suggests that for women who are pregnant or want to become pregnant soon, should quit alcohol consumption several months before conception and completely during pregnancy to avoid adverse health outcomes in the offspring. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by Erasmus Medical Centre, Rotterdam, the Erasmus University Rotterdam, and the Netherlands Organization for Health Research. V.W.V.J. reports receipt of funding from the Netherlands Organization for Health Research (ZonMw 90700303). W.J.N. is a founder, a scientific lead, and a shareholder of Quantib BV. TRIAL REGISTRATION NUMBER: N/A.

@en

Contributed

Towards automation of orthognathic surgery planning using deep learning for segmentation and landmark localization on cone beam computed tomography

Master thesis (2024) - B.P.P. Roumen, E.B. Wolvius, Bart Cornelissen, Roy van den Ende

Introduction: 3D imaging has become the standard for orthognathic surgery planning. Manual segmentation and landmark localization are the foundation for assessing deformities and planning of the corrective surgery. However, in 3D imaging, these manual procedures are complex and time consuming. Deep learning has emerged as a promising solution to automate this workflow.

Objective: To develop an automated segmentation and landmark localization workflow to reduce time and efforted required for orthognathic surgery planning using CBCT.

Methods: The dataset consisted of 57 presurgical Cone Beam CT (CBCT) scans. Manual segmentations were created for the mandible, maxilla, skin, mandibular canals (MC) left and right. Additionally, 43 landmarks (13 mandibular, 10 maxillary, 10 dental, and 10 surgical) were manually annotated. Automated landmark localization was approached as a segmentation task, with spherical segmentation of adjacent tissue with radii of 4mm, 5mm, or 6mm around landmarks. The dataset was split into 64% train, 16% validation 20% testset. Seven nnU-Net models were trained: one for segmentation and six (two models for each radius configuration) for landmark localization. Landmark positions were determined by calculating the center of mass of predicted segmentations. Performance was evaluated by comparing results to manual ground truth segmentations and landmark locations.

Results: The nnU-Net was successfully trained to identify five segmentations and 43 landmarks. The overall median [Q1 – Q3] volumetric dice coefficient (vDSC) was 0.91 [0.79 - 0.96] for the segmentation model. The overall median radial error (MRE) for the landmarking models using 4mm, 5mm and 6mm spherical segmentations (each based on two models) were 0.98 [0.58 - 1.60] mm, 1.03 [0.63 - 1.67] mm, and 1.08 [0.66 - 1.82] mm, respectively. The successful detection rate below the clinical acceptability threshold of < 2mm ranged from 78.6% to 81.6%. The inference workflow required 17.7 minutes per patient on average.

Conclusion: The nnU-Net achieved accurate craniomaxillofacial (CMF) anatomy segmentations and precise localization of landmarks, maintaining a clinical acceptability level with an error margin of less than 2mm. This fully automated workflow has the potential to enhance the efficiency of CMF planning.

Predictive modelling of facial features from DNA

Master thesis (2021) - G.C.M. Jans, E.B. Wolvius, G.V. Roshchupkin, X. Zhang

Background: In recent years, attention to the genetic architecture of normal-range variation in facial morphology has risen and through GWAS genetic loci associated with facial morphology have been identified. However these give no insight in how the face is shaped by genetics. To investigate the relationship between the genotype and the phenotype, predictive modelling can be used. Predictive modelling is a term used to describe genetic prediction models: tools that aim to predict a phenotype from the genotype.
Objectives: The objective of this project was to investigate the possibility of predictive modelling of facial features from DNA. This method could be used to visualize the variation in facial features caused by the underlying genetics
Methods: In this project the LDAK genetic prediction model was used, this is a software package for phenotype prediction, which uses multilinear regression. Two different datasets were used for this study: Generation R and the Rotterdam Study. Generation R data existed of subjects at the age of nine years old and from the Rotterdam study subjects ≥ 45 years old were included. The datasets were processed individually. From both datasets, phenotype and genotype data was used. The phenotype data consisted of 3D facial meshes that were reduced to 200 endophenotypes with an auto-encoder prior to this study, and genotype data consisted of SNPs acquired using genotyping arrays. The prediction model was trained on 90% of the data, the other 10% was used for testing, where the facial morphology was predicted based on the SNPs. To evaluate the prediction a similarity measure was computed between the predicted faces and the ground truth faces. The similarity measure was computed between each predicted face and all ground truth faces in the test set, thereafter they were ranked in ascending order based on the computed similarity. Next the rank of the true ground truth was determined. Based on the ranking, an accuracy plot was constructed for both datasets and the accuracy ratios (AR) were computed.
Results: For the Generation R dataset the AR found was 0.06 for the Generation R dataset and 0.02 for the Rotterdam Study dataset. The results indicate there is some predictive power, however the AR’s are only slightly above the lower bound for presence of predictive performance. Furthermore, there was a difference in the AR for the Generation R and Rotterdam Study dataset, which could be the result of increased environmental component in facial morphology which reduced the genetic predictability. However, currently the predictive power is minimal. This could be caused by several factors, such as the number of subjects and prediction model that is restricted to only linear relationships.
Conclusion: The objective of this project was to explore the possibility of predictive modelling of facial features from DNA. It was found that there was some predictive power, however this was very limited. Research on predictive modelling of facial features is still in early stages and further research is required to improve the predictive power.