Improved differentiation between hypo/hypertelorism and normal fetuses based on MRI using automatic ocular biometric measurements, ocular ratios, and machine learning multi-parametric classification

Objectives To differentiate hypo-/hypertelorism (abnormal) from normal fetuses using automatic biometric measurements and machine learning (ML) classification based on MRI. Methods 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 T-2-weighted sequences and image resolutions, were included. 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). 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. Eight ML classifiers were evaluated to detect abnormalities using measured and computed parameters. Results 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%. In normal fetuses, all four measurements increased with GA. 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. A random forest classifier yielded the best results on an independent test set (n = 58): AUC-ROC = 0.941 and F-1-Score = 0.711 in comparison to AUC-ROC = 0.650 and F-1-Score = 0.385 achieved based on the accepted criteria that define hypo/hypertelorism based on IOD (< 5(th) or > 95(th) percentiles). Using the explainable ML method, the two computed ratios were found as the most contributing parameters. Conclusions The developed fully automatic method demonstrates high performance on varied clinical imaging data. The new BOD and IOD ratios and ML multi-parametric classifier are suggested to improve the differentiation of hypo-/hypertelorism from normal fetuses.

Automated summary

This study successfully differentiated hypo-/hypertelorism in fetuses using automatic biometric measurements and machine learning classification based on MRI. The results showed that the newly defined ratios and the ML multi-parametric classifier improved the accuracy of distinguishing abnormal from normal fetuses with the condition. The developed fully automatic method demonstrated high performance on varied clinical imaging data.