A Deep Learning System for the Automated Quantification and Screening of Suspected Ventriculomegaly from 2D Ultrasound Images of the Fetal Brain

European Society of Radiology

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July 17, 2022

Authors

Hari Shankar, Shefali Jain, Pooja Vyas, Nivedita Hegde, Divya Singh, Radhika Kumar, Gopika D, Shashi Kiran, Jagruthi Atada, Roopa PS, Akhila Vasudeva, Prathima Radhakrishnan, Sripad Krishna Devalla

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Purpose
Ventriculomegaly (dilated fetal cerebral ventricles) is a relatively common ﬁnding on prenatal ultrasound and can be considered a soft antenatal marker requiring a specialist referral for a detailed search of associated anomalies. We propose a deep learning system (DLS) for the automated quantification and screening of suspected ventriculomegaly to assist operators to provide timely referrals.

Methods
We obtained retrospective ultrasound (US) examinations of 298 mid-trimester pregnancies (normal [N], unilateral ventriculomegaly [VM]: 259/39) from 2 tertiary referral centers. On 514 2D US images deemed clinically appropriate by fetal medicine specialists (FMS), we trained (ground-truth: FMS caliper points) a DLS to automatically predict the caliper points for measuring the atrial width (AW) of the lateral ventricles. The predicted AW measurements were then classified into normal or suspected VM based on clinical guidelines (ISUOG). The suspected VM cases were further classified into prominent, mild, and severe categories. We assessed the DLS performance in the automated measurement (mean error [ME]) and screening (sensitivity [Sn], specificity [Sp], accuracy [Ac]; with 95% CI) by benchmarking against clinical gold-standard (FMS).

Results
On an independent test set of 226 images (186 cases), the MEs (in mm) in DLS AW measurements were 0.47+-0.56 (normal, 143 cases), 0.41+-0.37 (prominent, 18 cases), 0.71+-0.77 (mild, 20 cases), and 0.77+-0.97 (severe, 5 cases). Further, the normal and suspected VM cases were discriminated with a Sn, Sp, and Ac of 95.18% (92.82 - 97.53%), 95.74% (94.03 - 97.44%), and 95.53% (94.14 - 96.91%), respectively.

Conclusion
We successfully developed and validated a DLS for the automated quantification and screening of suspected VM cases. It’s clinical translation can help expecting mothers in low-resource and remote settings to receive timely referrals for detailed examination.

Limitations

Bilateral VM cases were excluded from the study. The study had a limited dataset size (only mid-trimester cases).

Ethics committee approval‍

This study received the IRB approval from both the tertiary centers, and data were anonymized (tenets of the Declaration of Helsinki).