Evidence map›Paper›PMID 37808255›Full record

ArticleFrontiers in surgery2023

Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons.

Beatriz Pontes Balanza, Juan M Castillo Tuñón, Daniel Mateos García, Javier Padillo Ruiz, José C Riquelme Santos, José M Álamo Martinez, Carmen Bernal Bellido, Gonzalo Suarez Artacho, Carmen Cepeda Franco, Miguel A Gómez Bravo and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in surgery, 2023. 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
1.3field-weighted citation impact, top 20% of its field
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, 4 citations in OpenAlex.

  1. 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

11 authors at 3 institutions in 1 country.

Beatriz Pontes BalanzaDepartment of Computer Languages and Systems, Sevilla University, Seville, Spain.
Juan M Castillo TuñónHPB Surgery Unit, Virgen Macarena University Hospital, Seville, Spain.
Daniel Mateos GarcíaDepartment of Computer Languages and Systems, Sevilla University, Seville, Spain.
Javier Padillo RuizHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville, Spain.
José C Riquelme SantosDepartment of Computer Languages and Systems, Sevilla University, Seville, Spain.
José M Álamo MartinezHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville, Spain.
Carmen Bernal BellidoHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville, Spain.
Gonzalo Suarez ArtachoHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville, Spain.
Carmen Cepeda FrancoHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville, Spain.
Miguel A Gómez BravoHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville, Spain.
Luis M Marín GómezHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville, Spain.
Hospital Universitario Virgen del Rocío · ESUniversidad de Sevilla · ESHospital Universitario Virgen Macarena · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decision-making process whether to accept a graft or not using the initial variables available to it. Material and method: Liver graft samples candidate for liver transplantation after donor brain death were studied. All of them were evaluated " Results: A total of 350 liver grafts that were evaluated for liver transplantation were studied. Steatosis was the most frequent reason for classifying grafts as no transplantable, and the main risk factors identified in the univariant study were age, dyslipidemia, personal medical history, personal surgical history, bilirubinemia, and the result of previous liver ultrasound ( Conclusion: The tool presented in this study obtains a high accuracy in predicting whether a liver graft will be transplanted or deemed non-transplantable based on the initial variables assigned to it. The inherent capacity for improvement in the system causes the rate of correct predictions to increase as new data are entered. Therefore, we believe it is a tool that can help optimize the graft pool for liver transplantation.

Indexed as

artificial intelligencedecision-making processliver graft assessmentliver transplantsmachine learning

Identifiers

PMID37808255
PMCPMC10559881
OpenAlexW4386962448

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.