Real-time artificial intelligence for detection of Fetal Intracranial
malformations in Ultrasonic images: A multicenter retrospective
diagnostic study
Abstract
Objective: To develop an artificial intelligence (AI) model to detect
congenital central nervous system (CNS) malformations in fetal
cerebral-cranial ultrasound images, and to assess the efficacy of this
algorithm in improving clinical doctors’ diagnostic performance. Design:
Retrospective, multicenter, diagnostic study Setting: Three Chinese
hospitals Population: a cohort of 2397 fetuses with CNS malformations
and 11316 normal fetuses. Methods: AI model was developed by training on
37450 images from 15264 fetuses and testing on 812 images from 449
fetuses. Three groups of doctors (trainee, competent, expert) were
equipped with the AI system to test its enhancement of diagnosis
performance. Main outcome measures: Diagnostic performance of AI model
and that of doctors. Comparison of performance between AI model and
doctors, and doctors with and without AI assistance. Results: The
performance of AI model was comparable to that of expert in identifying
12 types of CNS malformations in terms of accuracy 79.8% (95% CI
77.0-82.6% ) versus 78.9% (95% CI 75.2-85.2% ), sensitivity 78.4%
(75.3-81.3%) versus 77.5% (73.7-81.4%) , specificity of 94.4%
(86.2-98.4%) versus 93.0% (84.1-100.0%), and AUC 0.864 (0.833-0.895)
versus 0.853 (0.800-0.905). This AI model improved doctors’ diagnostic
performances, the trainee group received maximum improvement, whose
diagnostic performance advanced to the level of expert group in terms of
accuracy (80.2%, 95% CI 75.0-85.3% ) and AUC (0.872, 95% CI
0.861-0.882 ). Conclusions: Our AI system achieved a high diagnostic
performance comparable with that of experienced doctors and can support
unexperienced doctors by improving their diagnostic accuracy to an
expert-level.