Development and Validation of an Artificial Intelligence Based System for the Automated Detection of Choroid Plexus Cyst from Fetal Cranial Sonograms: A Multi-Center Retrospective Study
Background
The ‘choroid plexus (CP) cyst’ refers to the formation of a small round fluid-filled area in the choroid plexus. Although the isolated finding of CP cyst does not alter the management of pregnancies, it is strongly associated with multiple other anomalies and is an important marker for Trisomy 18. Fetuses with trisomy 18 have CP cysts about one-third of the time. The automated detection of CP cysts seen during mid-trimester (1-2% of the cases) ultrasonography (USG) examination is critical in settings that lack well-trained sonographers to provide tertiary/specialist centers referrals for a detailed search of associated anomalies or rule-out as normal variants.
Methods
A total of 2673 2D USG images (non-cystic/cystic CP: 2470/203) of the transventricular (TV) plane were retrospectively obtained from 848 subjects (targeted mid-trimester scans) at 2 tertiary referral centers using 3 commercial ultrasound devices (General Electric [GE] Healthcare; GE Voluson E8/P8/S10). We propose a two-step AI approach for the automated detection of the CP cyst (Step 1: segmentation of the CP from 2D TV USG images; Step 2: classification of the segmented CP as cystic/non-cystic). The segmentation AI network (U-Net based) was trained and tested (performance evaluated using Dice coefficient [scale = 0: no-overlap; 1.0: complete overlap; comparison against manual segmentations]) on 1582 and 588 images respectively. The classification AI network (ResNet 18 based) was trained and tested on 122 and 381 images (equal cystic/non-cystic) to classify the segmented regions as cystic/non-cystic. Sensitivity, specificity, and area under the receiver operating characteristics curve (AUC) were used to evaluate the classifier performance. Clinical impressions (reviewed by fetal medicine specialists) from USG scan reports were used (ground truth) for training and testing the AI networks. We ensured that there was no duplication in data and patient overlap (between datasets) and performed class balancing (USG device; and cystic/non-cystic).
Results
The CP segmentation network achieved a Dice coefficient of 0.85. The cystic/non-cystic classifier achieved a sensitivity, specificity, and AUC of 0.86, 0.89, and 0.94 respectively.
Conclusion
We developed and validated a fully-automated AI system for the automated detection of CP cysts from 2D USG images of the fetal brain. The clinical translation of such frameworks can help expecting mothers in low-resource settings to receive timely referrals for detailed examination.
We observe that, in both the unilateral and bilateral cases of the CP cyst, the system successfully segments the CP (in white) and classifies it as cystic/non-cystic correctly.