Ratings of 597 unique individuals from the Chicago Face Database were used. The aims of this study were to confirm and extend the association of proportionality with perceived levels of attractiveness and character traits and determine differences in attractiveness and character ratings between 'anomalous' and 'typical' faces using a large dataset. Multi-parametric machine learning classification based on automatic measurements and the two new ratios improves the identification of fetal ocular anomalies beyond the accepted criteria (95th IOD percentiles).įacial proportionality and symmetry are positively associated with perceived levels of facial attractiveness.These two ratios are constant across gestational age in normal fetuses, consistent across studies, and differentiate between fetuses with and without hypo/hypertelorism. Two new parameters, IOD-ratio and BOD-ratio, are proposed for routine clinical use in ultrasound and MRI.A fully automatic method for computing fetal ocular biometry from MRI is proposed, achieving high performance, comparable to that of an expert fetal neuro-radiologist.The new BOD and IOD ratios and ML multi-parametric classifier are suggested to improve the differentiation of hypo-/hypertelorism from normal fetuses. The developed fully automatic method demonstrates high performance on varied clinical imaging data. Using the explainable ML method, the two computed ratios were found as the most contributing parameters. A random forest classifier yielded the best results on an independent test set (n = 58): AUC-ROC = 0.941 and F1-Score = 0.711 in comparison to AUC-ROC = 0.650 and F1-Score = 0.385 achieved based on the accepted criteria that define hypo/hypertelorism based on IOD ( 95th percentiles). Constant values were detected for BOD-ratio = 1.56 ± 0.05 and IOD-ratio = 0.60 ± 0.05 across all GA and when calculated from previously published reference data of both MRI and ultrasound. In normal fetuses, all four measurements increased with GA. The automatic method yielded a mean difference of BOD = 0.70 mm, IOD = 0.81 mm, OD = 1.00 mm, and a 3D-Dice score of OV = 93.7%. Eight ML classifiers were evaluated to detect abnormalities using measured and computed parameters. Two new parameters, BOD-ratio and IOD-ratio, were defined as the ratio between BOD/IOD relative to the sum of both globes' OD, respectively. A fully automatic method including deep learning and geometric algorithms was developed to measure the binocular (BOD), inter-ocular (IOD), ocular (OD) diameters, and ocular volume (OV). MRI data of normal (n = 244) and abnormal (n = 52) fetuses of 22-40 weeks' gestational age (GA), scanned between March 2008 and June 2020 on 1.5/3T systems with various T2-weighted sequences and image resolutions, were included. To differentiate hypo-/hypertelorism (abnormal) from normal fetuses using automatic biometric measurements and machine learning (ML) classification based on MRI.

Golden ratio perfect face