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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

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 T2-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 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 (< 5th or > 95th 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.

Key Points

• 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.

• Two new parameters, IOD-ratio and BOD-ratio, are proposed for routine clinical use in ultrasound and MRI. These two ratios are constant across gestational age in normal fetuses, consistent across studies, and differentiate between fetuses with and without hypo/hypertelorism.

• 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 (<5 th or >95 th IOD percentiles).

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Abbreviations

BOD:

Binocular diameter

GA:

Gestational age

IOD:

Interocular diameter

ML:

Machine learning

OD:

Ocular diameter

OV:

Ocular volume

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Acknowledgements

We are grateful to Vicki Myers and Cassandra Kapoor for editorial assistance and MRI technicians for scanning the fetuses.

Funding

This study has received funding from Kamin grants 72126 and 72061 from the Israel Innovation Authority.

Author information

Author notes

  1. Elka Miller and Dafna Ben Bashat contributed equally to this work.

Authors and Affiliations

  1. Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Netanell Avisdris, Daphna Link Sourani & Dafna Ben Bashat

  2. School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel

    Netanell Avisdris & Leo Joskowicz

  3. Division of Pediatric Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo, Israel

    Liat Ben-Sira

  4. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Liat Ben-Sira, Gustavo Malinger & Dafna Ben Bashat

  5. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

    Liat Ben-Sira & Dafna Ben Bashat

  6. Division of Ultrasound in Obstetrics & Gynecology, Lis Maternity Hospital, Tel Aviv, Israel

    Gustavo Malinger

  7. Obstetrics and Gynecology Division, Hadassah Hebrew University Medical Center, Jerusalem, Israel

    Simcha Yagel

  8. Medical Imaging Department, CHEO, University of Ottawa, Ottawa, Canada

    Elka Miller

Authors
  1. Netanell Avisdris
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  2. Liat Ben-Sira
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  3. Leo Joskowicz
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  4. Gustavo Malinger
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  5. Simcha Yagel
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  6. Elka Miller
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  7. Dafna Ben Bashat
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Corresponding authors

Correspondence to Netanell Avisdris or Dafna Ben Bashat.

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The scientific guarantor of this publication is Prof. Dafna Ben Bashat.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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One of the authors has significant statistical expertise.

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Avisdris, N., Link Sourani, D., Ben-Sira, L. et al. Improved differentiation between hypo/hypertelorism and normal fetuses based on MRI using automatic ocular biometric measurements, ocular ratios, and machine learning multi-parametric classification. Eur Radiol 33, 54–63 (2023). https://doi.org/10.1007/s00330-022-08976-0

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  • DOI: https://doi.org/10.1007/s00330-022-08976-0

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