abc: represent the results of bonferroni
comparison,a significant difference between trainee
and competent, p<0.05;b significant
difference between trainee and expert, p<0.05
Figure 1 Flowchart for the development and test of the
algorithms. M&C, Maternal and Child; W&C, Women and Children’s; CNS,
central nervous system; AI, artificial intelligence.
Figure 2 Flow chart illustrating the entire process of the
network. As shown in the figure, our process contains one input and two
outputs. In the first output, two labels were detected on the same side
of ventricle by the model, which were lateral ventricle (green box, the
label score was 0.597146) and tear-ventricle (lower yellow box, the
label score was 0.871927). After label elimination in the logic output
network according to the scores, only one label with the higher score
remained in output image (tear-ventricle, lower yellow box).
Figure 3 The composite image shows the AI output correctly
labeled with corresponding type of specific malformations in each image,
as well as normal image. ACC, Absence of corpus callosum; ASP, absence
of cavum septi pellucidi; DWNv, Dandy-Walker malformation or variant;
HPE, holoprosencephaly; MCM, Megacisterna magna; CPC, choroid plexus
cyst.
Figure 4 The performance of the AI system and Ultrasonic doctors
in CNS malformations identification a. AI system outperforms the
average of the ultrasonic doctors at CNS malformations identification.
Each point represented the sensitivity and specificity of a single
ultrasonic doctors, the blue points are the average of the doctors, with
error bars denoting one standard deviation. The AI system achieves
superior performance to a doctor if the sensitivity–specificity point
of the lies below the blue curve, which most do. b, The performance of
AI model versus that of experts, competent and trainee doctors.
Figure 5 The improvement of overall performance of three degrees
of doctors in CNS malformations identification with AI assistance (a.
trainee, b. competent, c. expert).