Evidence map›Paper›PMID 41102035›Full record

ArticleUltrasound in medicine & biology2026

Enhancing Newborn Health Assessment: Ultrasound-based Body Composition Prediction Using Deep Learning Techniques.

Keshi He, Julia Hohenberg, Yi Li, Audrey Xiao, Hayoung Cho, Emily Nagel, Sara Ramel, Katherine A Bell, Donglai Wei, Jinhee Park and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ultrasound in medicine & biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Keshi HeDepartment of Engineering, Boston College, Chestnut Hill, MA, USA.
Julia HohenbergDepartment of Computer Science, Boston College, Chestnut Hill, MA, USA.
Yi LiDepartment of Computer Science, Boston College, Chestnut Hill, MA, USA.
Audrey XiaoDepartment of Engineering, Boston College, Chestnut Hill, MA, USA.
Hayoung ChoDepartment of Engineering, Boston College, Chestnut Hill, MA, USA.
Emily NagelDepartment of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
Sara RamelDepartment of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
Katherine A BellDepartment of Pediatrics, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Donglai WeiDepartment of Computer Science, Boston College, Chestnut Hill, MA, USA.
Jinhee ParkConnell School of Nursing, Boston College, Chestnut Hill, MA, USA.
Bryan J RangerDepartment of Engineering, Boston College, Chestnut Hill, MA, USA; Connell School of Nursing, Boston College, Chestnut Hill, MA, USA. Electronic address: bryan.ranger@bc.edu.

Funding

The Impact of Zinc Intake on Nutritional Status and Brain Development Among Preterm InfantsK23HD104000 · NICHD · BRIGHAM AND WOMEN'S HOSPITAL · PI Katherine Bell · 2022 to 2026
$833k
NICHD NIH HHS K23 HD104000
6 · The paper itself

Abstract

objectiveThis study investigates the feasibility of deep learning to predict body composition with ultrasound, specifically fat mass (FM) and fat-free mass (FFM), to improve newborn health assessments.

methodsWe analyzed 721 ultrasound images of the biceps, quadriceps and abdomen from 65 pre-term infants. A deep learning model incorporating a modified U-Net architecture was developed to predict FM and FFM using air displacement plethysmography as ground truth labels for training. Model performance was assessed using mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE), along with Bland-Altman plots to evaluate mean bias and limits of agreement. We tested different image combinations to determine the contribution of anatomical regions. Grad-CAM was applied to identify image regions with the strongest influence on predictions.

resultsCombining biceps, quadriceps and abdominal ultrasound images to predict whole-body composition showed strong agreement with ground truth values, with low MAE (FM: 0.0145 kg, FFM: 0.0794 kg), MSE (FM: 0.0003 kg

conclusionDeep learning provides a promising approach to predicting body composition with ultrasound and could be a valuable tool for assessing nutritional status in neonatal care.

Indexed as

Body CompositionDeep LearningAbdomenFeasibility StudiesFemaleHumansInfant, NewbornMaleUltrasonographyBody composition predictionDeep learningMalnutritionNewborn and child healthUltrasound imaging

Identifiers

PMID41102035
PMCPMC12614439

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.