Evidence map›Paper›PMID 40766768›Full record

ReviewOpen respiratory archives2024

[Pulmonary Digital Twins].

Ana Fernández-Tena, Carlos Arnedo, Guillaume Houzeaux, Beatriz Eguzkitza

Abstract readEnglish AbstractReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Open respiratory archives, 2024. 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

4 authors.

Ana Fernández-TenaInstituto Nacional de Silicosis, GRUBIPU-ISPA y Facultad de Enfermería, Universidad de Oviedo, Oviedo, España.
Carlos ArnedoBarcelona Supercomputing Center, Department Computer Applications in Science and Engineering, Barcelona, España.
Guillaume HouzeauxBarcelona Supercomputing Center, Department Computer Applications in Science and Engineering, Barcelona, España.
Beatriz EguzkitzaBarcelona Supercomputing Center, Department Computer Applications in Science and Engineering, Barcelona, España.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of lung digital twins (DTs) represents a significant advance in personalized medicine, providing a virtual framework that replicates the structure, function, and pathology of the respiratory system in an individualized manner. DTs integrate clinical data, high-resolution images, and mathematical models to simulate respiratory mechanics, gas diffusion, and fluid dynamics in real time. This technology improves diagnosis, treatment planning, and disease progression monitoring. One of the key applications of lung DTs is the ability to simulate patient-specific response to treatments and predict outcomes, allowing for personalized therapies. Despite advances, the implementation of DTs in clinical practice faces challenges related to data integration, computational efficiency, and ethical considerations regarding data privacy. Nevertheless, lung DTs offer clear promise for improving precision medicine, optimizing patient care, and improving clinical outcomes.

Indexed as

Artificial intelligenceComputational simulationDigital twinsFluid dynamics.LungMachine learningMathematical modelsPersonalized medicine

Identifiers

PMID40766768
PMCPMC12321634

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.