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Abstract #1433

Improved assessment of fetal ocular pathologies in MRI using ocular ratios and machine-learning multi-parametric classification

Netanell Avisdris1,2, Daphna Link Sourani1, Liat Ben Sira3,4,5, Leo Joskowicz2, Gustavo Malinger4,6, Simcha Yagel7, Elka Miller8, and Dafna Ben Bashat1,4,5
1Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, 2School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel, 3Division of Pediatric Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, 4Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel, 5Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, 6Division of Ultrasound in Obstetrics & Gynecology, Lis Maternity Hospital, Tel Aviv, Israel, 7Obstetrics and Gynecology Division, Hadassah Hebrew University Medical Center, Jerusalem, Israel, 8Medical Imaging, CHEO, University of Ottawa, Ottawa, ON, Canada


Brain MRI of 296 fetuses (22-40 weeks’ gestational-age, normal n=244, hypo/hyper-telorism n=52 were included. Binocular, interocular, and ocular diameters, and ocular volume were measured using automatic methods. Two new parameters, binocular-ratio and interocular-ratio, were defined. In normal fetuses, all four measurements increased with gestational-age. However, constant values were detected across all gestational-ages of binocular-ratio=1.56±0.05 and interocular-ratio=0.60±0.05. A random-forest classifier achieved the best results (out of eight classifiers) with AUC-ROC=0.90±0.03 for classification between normal and fetuses with hypo/hyper-telorism. mainly based on the two new ratios. Machine-learning multi-parametric classification and the new ratios are suggested to be used in routine clinical practice.

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