DIA-PINN: A physics-informed machine learning method to estimate global intrinsic diastolic chamber properties of the left ventricle from pressure-volume data

Javier Fernández-Topham, Manuel Guerrero-Hurtado, Juan Carlos del Álamo, Javier Bermejo, Pablo Martinez-Legazpi

Open source

DOI: 10.1152/ajpheart.00109.2026
Published: 2026-05-01
Container: American Journal of Physiology-Heart and Circulatory Physiology
Publisher: American Physiological Society
Open access: unknown

Credibility signals

uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

supportingDOI registered: A matching record was returned by Crossref. Source: Crossref; license: CC0 metadata
supportingDOI resolves: A matching record was returned by Crossref. Source: Crossref; license: CC0 metadata
not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility. Source: DOAJ; license: CC0
not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredROR affiliation: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
supportingMetadata completeness: All 6 scored descriptive metadata groups are present. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

Cite this work

BibTeX

@article{allodium:10.1152/ajpheart.00109.2026,
  title = {DIA-PINN: A physics-informed machine learning method to estimate global intrinsic diastolic chamber properties of the left ventricle from pressure-volume data},
  author = {Javier Fernández-Topham and Manuel Guerrero-Hurtado and Juan Carlos del Álamo and Javier Bermejo and Pablo Martinez-Legazpi},
  year = {2026},
  journal = {American Journal of Physiology-Heart and Circulatory Physiology},
  doi = {10.1152/ajpheart.00109.2026},
  url = {https://doi.org/10.1152/ajpheart.00109.2026}
}

RIS

TY  - JOUR
TI  - DIA-PINN: A physics-informed machine learning method to estimate global intrinsic diastolic chamber properties of the left ventricle from pressure-volume data
AU  - Javier Fernández-Topham
AU  - Manuel Guerrero-Hurtado
AU  - Juan Carlos del Álamo
AU  - Javier Bermejo
AU  - Pablo Martinez-Legazpi
PY  - 2026
JO  - American Journal of Physiology-Heart and Circulatory Physiology
DO  - 10.1152/ajpheart.00109.2026
UR  - https://doi.org/10.1152/ajpheart.00109.2026
ER  -

APA

Fernández-Topham, J., Guerrero-Hurtado, M., Álamo, J. C. D., Bermejo, J., & Martinez-Legazpi, P. (2026). DIA-PINN: A physics-informed machine learning method to estimate global intrinsic diastolic chamber properties of the left ventricle from pressure-volume data. American Journal of Physiology-Heart and Circulatory Physiology. https://doi.org/10.1152/ajpheart.00109.2026

Source records

crossref · retrieved 2026-09-25T18:37:52.031Z