Evidence map›Paper›PMID 40109525›Full record

ArticleBiomedical optics express2025

Thermal imaging-based core peripheral temperature difference measurement for neonatal monitoring in the NICU.

Nantao Zhang, Xiaoyan Song, Junli He, Fengchao Liang, Jie Yang, Wenjin Wang

Abstract read
In one paragraph

Article in Biomedical optics express, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

6 authors.

Nantao ZhangSouthern University of Science and Technology, China.
Xiaoyan SongThe Nanfang Hospital of Southern Medical University, China.
Junli HeThe General Hospital of Shenzhen University, China.
Fengchao LiangSouthern University of Science and Technology, China.
Jie YangThe Nanfang Hospital of Southern Medical University, China.
Wenjin WangThe General Hospital of Shenzhen University, China.ORCID https://orcid.org/0000-0001-7832-5444

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The core-peripheral temperature difference (CPTD) refers to the difference between the body's core temperature (e.g., chest or abdomen) and peripheral skin temperature (e.g., hands or feet). It serves as a key biomarker for assessing the hemodynamic status of newborns and is an important early warning indicator of potential shock and severe infection. Measurement of CPTD in clinical practice currently requires the use of an infrared spot thermometer to measure the temperature of multiple body parts of a neonate, which is not possible for continuous and fully automatic long-term monitoring. To address these limitations, we propose a thermal infrared (TIR)-based approach that enables non-contact, fully automatic, and continuous CPTD measurement for neonates. The spatial redundancy property of TIR is utilised and combined with a deep learning-based body parsing model to automatically detect different body parts of a neonate, including the chest and limbs (e.g., hand or foot), and measure the temperatures of these two parts to derive their difference as CPTD. Although accurate measurement of the absolute temperature of the neonatal skin is difficult due to the calibration of the TIR camera and environmental influence, the temperature difference between different body parts that emphasizes the spatial contrast at certain moments can be reliably estimated, and it is independent of the subject and environment. In a prospective clinical trial involving 40 preterm infants, our TIR-based CPTD measurement showed a mean absolute error less than

Identifiers

PMID40109525
PMCPMC11919349

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Registered trials

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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.