Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep-learning models improve on community-level diagnosis for common
  congenital heart disease lesions   Prenatal diagnosis of tetralogy of Fallot (TOF) and hypoplastic left heart
syndrome (HLHS), two serious congenital heart defects, improves outcomes and
can in some cases facilitate in utero interventions. In practice, however, the
fetal diagnosis rate for these lesions is only 30-50 percent in community
settings. Improving fetal diagnosis of congenital heart disease is therefore
critical. Deep learning is a cutting-edge machine learning technique for
finding patterns in images but has not yet been applied to prenatal diagnosis
of congenital heart disease. Using 685 retrospectively collected
echocardiograms from fetuses 18-24 weeks of gestational age from 2000-2018, we
trained convolutional and fully-convolutional deep learning models in a
supervised manner to (i) identify the five canonical screening views of the
fetal heart and (ii) segment cardiac structures to calculate fetal cardiac
biometrics. We then trained models to distinguish by view between normal
hearts, TOF, and HLHS. In a holdout test set of images, F-score for
identification of the five most important fetal cardiac views was 0.95. Binary
classification of unannotated cardiac views of normal heart vs. TOF reached an
overall sensitivity of 75% and a specificity of 76%, while normal vs. HLHS
reached a sensitivity of 100% and specificity of 90%, both well above average
diagnostic rates for these lesions. Furthermore, segmentation-based
measurements for cardiothoracic ratio (CTR), cardiac axis (CA), and ventricular
fractional area change (FAC) were compatible with clinically measured metrics
for normal, TOF, and HLHS hearts. Thus, using guideline-recommended imaging,
deep learning models can significantly improve detection of fetal congenital
heart disease compared to the common standard of care.

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The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
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The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom