Evidence map›Paper›PMID 35328143›Full record

ArticleDiagnostics (Basel, Switzerland)2022

Accuracy and Efficiency of Right-Lobe Graft Weight Estimation Using Deep-Learning-Assisted CT Volumetry for Living-Donor Liver Transplantation.

Rohee Park, Seungsoo Lee, Yusub Sung, Jeeseok Yoon, Heung-Il Suk, Hyoungjung Kim, Sanghyun Choi

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Reproducibility and efficiency of liver volumetry using manual method and liver analysis software.HPB : the official journal of the International Hepato Pancreato Biliary Association · 2024
    Article
  8. Article
  9. Article
  10. Review
  11. 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

7 authors.

Rohee ParkDepartment of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.ORCID 0000-0001-7904-4038
Seungsoo LeeDepartment of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.ORCID 0000-0002-5518-2249
Yusub SungDepartment of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.
Jeeseok YoonDepartment of Brain and Cognitive Engineering, Korea University, Seoul 08308, Korea.ORCID 0000-0003-0721-504X
Heung-Il SukDepartment of Brain and Cognitive Engineering, Korea University, Seoul 08308, Korea.
Hyoungjung KimDepartment of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.
Sanghyun ChoiDepartment of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.ORCID 0000-0002-6898-6617

Funding

National Research Foundation of Korea 2020R1F1A1048826
6 · The paper itself

Abstract

CT volumetry (CTV) has been widely used for pre-operative graft weight (GW) estimation in living-donor liver transplantation (LDLT), and the use of a deep-learning algorithm (DLA) may further improve its efficiency. However, its accuracy has not been well determined. To evaluate the efficiency and accuracy of DLA-assisted CTV in GW estimation, we performed a retrospective study including 581 consecutive LDLT donors who donated a right-lobe graft. Right-lobe graft volume (GV) was measured on CT using the software implemented with the DLA for automated liver segmentation. In the development group (

Indexed as

CT volumetrydeep learningliving right liver donorssegmentation

Identifiers

PMID35328143
PMCPMC8946991

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.