Direct comparison of multiple computer-aided polyp detection systems.


Journal

Endoscopy
ISSN: 1438-8812
Titre abrégé: Endoscopy
Pays: Germany
ID NLM: 0215166

Informations de publication

Date de publication:
05 Oct 2023
Historique:
pubmed: 3 8 2023
medline: 3 8 2023
entrez: 2 8 2023
Statut: aheadofprint

Résumé

 Artificial intelligence (AI)-based systems for computer-aided detection (CADe) of polyps receive regular updates and occasionally offer customizable detection thresholds, both of which impact their performance, but little is known about these effects. This study aimed to compare the performance of different CADe systems on the same benchmark dataset.  101 colonoscopy videos were used as benchmark. Each video frame with a visible polyp was manually annotated with bounding boxes, resulting in 129 705 polyp images. The videos were then analyzed by three different CADe systems, representing five conditions: two versions of GI Genius, Endo-AID with detection Types A and B, and EndoMind, a freely available system. Evaluation included an analysis of sensitivity and false-positive rate, among other metrics.  Endo-AID detection Type A, the earlier version of GI Genius, and EndoMind detected all 93 polyps. Both the later version of GI Genius and Endo-AID Type B missed 1 polyp. The mean per-frame sensitivities were 50.63 % and 67.85 %, respectively, for the earlier and later versions of GI Genius, 65.60 % and 52.95 %, respectively, for Endo-AID Types A and B, and 60.22 % for EndoMind.  This study compares the performance of different CADe systems, different updates, and different configuration modes. This might help clinicians to select the most appropriate system for their specific needs.

Sections du résumé

BACKGROUND AND STUDY AIMS OBJECTIVE
 Artificial intelligence (AI)-based systems for computer-aided detection (CADe) of polyps receive regular updates and occasionally offer customizable detection thresholds, both of which impact their performance, but little is known about these effects. This study aimed to compare the performance of different CADe systems on the same benchmark dataset.
METHODS METHODS
 101 colonoscopy videos were used as benchmark. Each video frame with a visible polyp was manually annotated with bounding boxes, resulting in 129 705 polyp images. The videos were then analyzed by three different CADe systems, representing five conditions: two versions of GI Genius, Endo-AID with detection Types A and B, and EndoMind, a freely available system. Evaluation included an analysis of sensitivity and false-positive rate, among other metrics.
RESULTS RESULTS
 Endo-AID detection Type A, the earlier version of GI Genius, and EndoMind detected all 93 polyps. Both the later version of GI Genius and Endo-AID Type B missed 1 polyp. The mean per-frame sensitivities were 50.63 % and 67.85 %, respectively, for the earlier and later versions of GI Genius, 65.60 % and 52.95 %, respectively, for Endo-AID Types A and B, and 60.22 % for EndoMind.
CONCLUSIONS CONCLUSIONS
 This study compares the performance of different CADe systems, different updates, and different configuration modes. This might help clinicians to select the most appropriate system for their specific needs.

Identifiants

pubmed: 37532115
doi: 10.1055/a-2147-0571
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Forum Gesundheitsstandort
ID : 5409.0-001.01/15

Informations de copyright

The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).

Déclaration de conflit d'intérêts

A.M. has received consultancy fees from Ovesco and Olympus. The remaining authors declare that they have no conflict of interest.

Auteurs

Joel Troya (J)

Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany.
Bavarian Cancer Research Center, Würzburg, Germany.

Boban Sudarevic (B)

Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany.
Department of Internal Medicine and Gastroenterology, Katharinenhospital, Stuttgart, Germany.

Adrian Krenzer (A)

Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians-Universität, Würzburg, Germany.

Michael Banck (M)

Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians-Universität, Würzburg, Germany.

Markus Brand (M)

Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany.

Benjamin M Walter (BM)

Department of Internal Medicine I, University Hospital Ulm, Ulm, Germany.

Frank Puppe (F)

Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians-Universität, Würzburg, Germany.

Wolfram G Zoller (WG)

Department of Internal Medicine and Gastroenterology, Katharinenhospital, Stuttgart, Germany.

Alexander Meining (A)

Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany.
Bavarian Cancer Research Center, Würzburg, Germany.

Alexander Hann (A)

Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany.

Classifications MeSH