Evidence map›Paper›PMID 33110113›Full record

ArticleScientific reports2020

Machine learning for endoleak detection after endovascular aortic repair.

Salmonn Talebi, Mohammad H Madani, Ali Madani, Ashley Chien, Jody Shen, Domenico Mastrodicasa, Dominik Fleischmann, Frandics P Chan, Mohammad R K Mofrad

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

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.4field-weighted citation impact, top 15% 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

13 citing papers in PubMed, 33 citations in OpenAlex.

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  11. Perceptions of Canadian vascular surgeons toward artificial intelligence and machine learning.Journal of vascular surgery cases and innovative techniques · 2022
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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

9 authors at 4 institutions in 1 country.

Salmonn TalebiMolecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, 208A Stanley Hall #1762, Berkeley, CA, 94720-1762, USA.
Mohammad H MadaniDepartment of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
Ali MadaniMolecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, 208A Stanley Hall #1762, Berkeley, CA, 94720-1762, USA.
Ashley ChienMolecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, 208A Stanley Hall #1762, Berkeley, CA, 94720-1762, USA.
Jody ShenDepartment of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
Domenico MastrodicasaDepartment of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
Dominik FleischmannDepartment of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
Frandics P ChanDepartment of Radiology, School of Medicine, Stanford University, Stanford, CA, USA. frandics@stanford.edu.
Mohammad R K MofradMolecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, 208A Stanley Hall #1762, Berkeley, CA, 94720-1762, USA. mofrad@berkeley.edu.
Stanford University · USUniversity of California, Berkeley · USLawrence Berkeley National Laboratory · USSalesforce (United States) · US

Funding

Multi-Disciplinary Training Program in Cardiovascular Imaging at StanfordT32EB009035 · NIBIB · STANFORD UNIVERSITY · PI Koen Nieman, John M. Pauly · 2008 to 2026
$4.3M
NIBIB NIH HHS T32 EB009035
6 · The paper itself

Abstract

Diagnosis of endoleak following endovascular aortic repair (EVAR) relies on manual review of multi-slice CT angiography (CTA) by physicians which is a tedious and time-consuming process that is susceptible to error. We evaluate the use of a deep neural network for the detection of endoleak on CTA for post-EVAR patients using a novel data efficient training approach. 50 CTAs and 20 CTAs with and without endoleak respectively were identified based on gold standard interpretation by a cardiovascular subspecialty radiologist. The Endoleak Augmentor, a custom designed augmentation method, provided robust training for the machine learning (ML) model. Predicted segmentation maps underwent post-processing to determine the presence of endoleak. The model was tested against 3 blinded general radiologists and 1 blinded subspecialist using a held-out subset (10 positive endoleak CTAs, 10 control CTAs). Model accuracy, precision and recall for endoleak diagnosis were 95%, 90% and 100% relative to reference subspecialist interpretation (AUC = 0.99). Accuracy, precision and recall was 70/70/70% for generalist1, 50/50/90% for generalist2, and 90/83/100% for generalist3. The blinded subspecialist had concordant interpretations for all test cases compared with the reference. In conclusion, our ML-based approach has similar performance for endoleak diagnosis relative to subspecialists and superior performance compared with generalists.

Indexed as

Machine LearningAgedAortaComputed Tomography AngiographyEndoleakEndovascular ProceduresFemaleHumansMaleNeural Networks, ComputerReproducibility of Results

Identifiers

PMID33110113
PMCPMC7591558
OpenAlexW3096084844

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.