Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AI system for fetal ultrasound in low-resource settings   Despite considerable progress in maternal healthcare, maternal and perinatal
deaths remain high in low-to-middle income countries. Fetal ultrasound is an
important component of antenatal care, but shortage of adequately trained
healthcare workers has limited its adoption. We developed and validated an
artificial intelligence (AI) system that uses novice-acquired "blind sweep"
ultrasound videos to estimate gestational age (GA) and fetal malpresentation.
We further addressed obstacles that may be encountered in low-resourced
settings. Using a simplified sweep protocol with real-time AI feedback on sweep
quality, we have demonstrated the generalization of model performance to
minimally trained novice ultrasound operators using low cost ultrasound devices
with on-device AI integration. The GA model was non-inferior to standard fetal
biometry estimates with as few as two sweeps, and the fetal malpresentation
model had high AUC-ROCs across operators and devices. Our AI models have the
potential to assist in upleveling the capabilities of lightly trained
ultrasound operators in low resource settings.

11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol