Artificial Intelligence System (AIS) to Automatically Obtain Multiple Key Sonographic Measurements of the Fetal Brain in the Axial Views


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.