Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enabling faster and more reliable sonographic assessment of gestational
  age through machine learning   Fetal ultrasounds are an essential part of prenatal care and can be used to
estimate gestational age (GA). Accurate GA assessment is important for
providing appropriate prenatal care throughout pregnancy and identifying
complications such as fetal growth disorders. Since derivation of GA from
manual fetal biometry measurements (head, abdomen, femur) are
operator-dependent and time-consuming, there have been a number of research
efforts focused on using artificial intelligence (AI) models to estimate GA
using standard biometry images, but there is still room to improve the accuracy
and reliability of these AI systems for widescale adoption. To improve GA
estimates, without significant change to provider workflows, we leverage AI to
interpret standard plane ultrasound images as well as 'fly-to' ultrasound
videos, which are 5-10s videos automatically recorded as part of the standard
of care before the still image is captured. We developed and validated three AI
models: an image model using standard plane images, a video model using fly-to
videos, and an ensemble model (combining both image and video). All three were
statistically superior to standard fetal biometry-based GA estimates derived by
expert sonographers, the ensemble model has the lowest mean absolute error
(MAE) compared to the clinical standard fetal biometry (mean difference: -1.51
$\pm$ 3.96 days, 95% CI [-1.9, -1.1]) on a test set that consisted of 404
participants. We showed that our models outperform standard biometry by a more
substantial margin on fetuses that were small for GA. Our AI models have the
potential to empower trained operators to estimate GA with higher accuracy
while reducing the amount of time required and user variability in measurement
acquisition.

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The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
2
The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin