Evidence map›Paper›PMID 27610022›Full record

ArticleWorld journal of gastroenterology2016

Computer-aided texture analysis combined with experts' knowledge: Improving endoscopic celiac disease diagnosis.

Michael Gadermayr, Hubert Kogler, Maximilian Karla, Dorit Merhof, Andreas Uhl, Andreas Vécsei

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Article in World journal of gastroenterology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

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

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Michael GadermayrMichael Gadermayr, Dorit Merhof, Institute of Imaging and Computer Vision, RWTH Aachen University, D-52074 Aachen, Germany.
Hubert KoglerMichael Gadermayr, Dorit Merhof, Institute of Imaging and Computer Vision, RWTH Aachen University, D-52074 Aachen, Germany.
Maximilian KarlaMichael Gadermayr, Dorit Merhof, Institute of Imaging and Computer Vision, RWTH Aachen University, D-52074 Aachen, Germany.
Dorit MerhofMichael Gadermayr, Dorit Merhof, Institute of Imaging and Computer Vision, RWTH Aachen University, D-52074 Aachen, Germany.
Andreas UhlMichael Gadermayr, Dorit Merhof, Institute of Imaging and Computer Vision, RWTH Aachen University, D-52074 Aachen, Germany.
Andreas VécseiMichael Gadermayr, Dorit Merhof, Institute of Imaging and Computer Vision, RWTH Aachen University, D-52074 Aachen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease (CD).

methodsWe assessed a hybrid approach based on the integration of expert knowledge into the computer-based classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique (MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts visually classified each image as normal mucosa (Marsh-0) or villous atrophy (Marsh-3). The experts' decisions were further integrated into state-of-the-art texture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts' diagnoses in 27 different settings.

resultsCompared to the experts' diagnoses, in 24 of 27 classification settings (consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant (P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95% (P < 0.001).

conclusionThe overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.

Indexed as

Celiac DiseaseDiagnosis, Computer-AssistedEndoscopy, GastrointestinalHumansKnowledgeBiopsyCeliac diseaseComputer-aided texture analysisDiagnosisEndoscopyPattern recognition

Identifiers

PMID27610022
PMCPMC4988309

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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.