Evidence map›Paper›PMID 39698122›Full record

ArticleIEEE open journal of engineering in medicine and biology2025

Hybrid Deep Learning-Based Enhanced Occlusion Segmentation in PICU Patient Monitoring.

Mario Francisco Munoz, Hoang Vu Huy, Thanh-Dung Le, Philippe Jouvet, Rita Noumeir

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE open journal of engineering in medicine and biology, 2025. 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

5 authors.

Mario Francisco MunozElectrical Engineering DepartmentÉcole de Technologie Supérieure Montréal QC H3C 1K3 Canada.ORCID https://orcid.org/0009-0003-4857-2315
Hoang Vu HuyBiomedical Information Processing LabÉcole de Technologie Supérieure, University of Québec Montréal H3C 1K3 Canada.ORCID https://orcid.org/0000-0003-2937-9264
Thanh-Dung LeBiomedical Information Processing LabÉcole de Technologie Supérieure, University of Québec Montréal H3C 1K3 Canada.ORCID https://orcid.org/0000-0003-0782-8698
Philippe JouvetCHU Sainte-Justine HospitalUniversity of Montreal Montréal H3C 1K3 Canada.ORCID https://orcid.org/0000-0001-5684-3398
Rita NoumeirBiomedical Information Processing LabÉcole de Technologie Supérieure, University of Québec Montréal H3C 1K3 Canada.ORCID https://orcid.org/0000-0002-4519-1686

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Remote patient monitoring has emerged as a prominent non-invasive method, using digital technologies and computer vision (CV) to replace traditional invasive monitoring. While neonatal and pediatric departments embrace this approach, Pediatric Intensive Care Units (PICUs) face the challenge of occlusions hindering accurate image analysis and interpretation.

Indexed as

Computer visiondata augmentationdeep learningmodel fusionocclusionspediatrics intensive careremote patient monitoring (RPM)segmentation

Identifiers

PMID39698122
PMCPMC11655113

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.