Artificial Intelligence System (AIS) to Automatically Obtain Multiple Key Sonographic Measurements of the Fetal Brain in the Axial Views: A Validation Study
Aim
To develop and validate an artificial intelligence system (AIS) to automatically obtain 9 key fetal brain measurements.
Methods
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].
Results
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.