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.
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).
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.
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.