Introduction
Congenital malformations are the leading cause of fetal loss and one of
the top ten causes of mortality in children under five1, 2. It also accounted for 25-38
million disability-adjusted life-years worldwide3,
which causes heavy burden on individuals, families, health-care systems,
and societies4. There are substantial inter-country
differences worldwide in the reported prevalence of congenital
malformations partly due to the unequal capacities of prenatal
screening, leaving many cases undetected, especially in underdeveloped
regions. For example, the reported prevalence of congenital cerebral
anomalies in Europe increased by 2.4% per annum, but a six-fold
difference was found in prevalence across different regions, with an
association between prevalence and prenatal detection rate5. Therefore, early identification of congenital
anomalies with efficiency is crucial in ensuring medical intervention,
minimizing world healthcare disparity, and eventually leading to the
optimization of healthcare resources. This goal calls for not only the
detection equipment but also doctor expertise for prenatal diagnosis.
Yet, training doctors is a timely and costly process, which causes
enormous expense to provide prenatal surveillance for average citizens
all over the world.
The implementation of artificial intelligence (AI) systems has shown its
potential to revolutionize disease diagnosis by performing
classification difficult for human experts 6-11. The
performance of most reported AI shows a promising trend12-18, furthermore, it has significant advantages in
terms of convenient open-source sharing, which have the potential to
provide medical guidance to multiple hospitals simultaneously,
especially for less developed and remote areas 19,20. In the field of fetal congenital malformation
diagnosis, AI development involved the differentiation of images of
normal and abnormal fetuses was rare, only limited progress in
AI-assisted fetal ultrasound identification of normal fetus structure
were reported14-18 , these studies laid a foundation
for the development of AI system to identify abnormal structure in
ultrasound images by training on fetuses with congenital malformation.
We have initially constructed an AI system involving abnormal fetal CNS
ultrasound images to classify fetal CNS ultrasound images as either
normal or abnormal and our system achieved a high
performance21. Nonetheless, this system only
classified images to provide binary outcomes, it is far from making
diagnosis for specific CNS malformation. Here, we sought to further
advance our system from binary classification to multi-classification,
which is capable of detecting multiple types of CNS
malformations. We also assessed
the efficacy of this algorithm in improving clinical doctors’ diagnostic
performance. This is so far the first attempt to construct a deep
learning AI system to aid both the experienced and unexperienced
physicians in the prenatal ultrasound diagnosis on congenital anomalies.