To develop and validate an artificial intelligence system (AIS) to automatically obtain 9 key fetal brain measurements
A total of 2435 2D- ultrasound images of transventricular (TV) and transcerebellar (TC) planes were retrospectively obtained from 582 subjects (targeted mid trimester assessment; 3 centres) using 3 ultrasound devices (GE Voluson E8/P8/S10) to train and test a (dataset split = 80:20) a custom AI model (U - Net) to segment 10 fetal brain structures. On an independent test set (144 images; 1 per subject), using the segmentation masks, 9 measurements (biparietal diameter [BPD], occipitofrontal diameter [OFD], cephalic index [CI], head circumference [HC], atrial width [AW], cavum septum pellucidum [CSP] ratio, transcerebellar diameter [TCD], cisterna magna size [CMS], Nuchal Fold Thickness [NFT]) were computed and Benchmarked (intraclass correlation coefficients [ICC], mean error) against the manual measurements of 4 fetal medicine specialists [FMS].
The AIS offered a good segmentation performance (mean Dice coefficient: 0.83). When compared to the 4 FMS, the automated measurements were in excellent (BPD: 0.99, OFD:0.95, HC: 0.98),good (CI: 0.72,TCD: 0.89), and moderate agreements (CSP ratio: 0.51, AW:0.57, CMS: 0.65, NFT: 0.68). The mean intra-rater differences for each FMS were comparable to the absolute error between the AIS and FMS panel.
Conclusions: The proposed AI system can assist novice users in delivering standardized quality prenatal examinations in high volume settings.