• Treffer 1 von 3
Zurück zur Trefferliste

Pilot study of a new freely available computer-aided polyp detection system in clinical practice

Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-324459
  • Purpose Computer-aided polyp detection (CADe) systems for colonoscopy are already presented to increase adenoma detection rate (ADR) in randomized clinical trials. Those commercially available closed systems often do not allow for data collection and algorithm optimization, for example regarding the usage of different endoscopy processors. Here, we present the first clinical experiences of a, for research purposes publicly available, CADe system. Methods We developed an end-to-end data acquisition and polyp detection system named EndoMind.Purpose Computer-aided polyp detection (CADe) systems for colonoscopy are already presented to increase adenoma detection rate (ADR) in randomized clinical trials. Those commercially available closed systems often do not allow for data collection and algorithm optimization, for example regarding the usage of different endoscopy processors. Here, we present the first clinical experiences of a, for research purposes publicly available, CADe system. Methods We developed an end-to-end data acquisition and polyp detection system named EndoMind. Examiners of four centers utilizing four different endoscopy processors used EndoMind during their clinical routine. Detected polyps, ADR, time to first detection of a polyp (TFD), and system usability were evaluated (NCT05006092). Results During 41 colonoscopies, EndoMind detected 29 of 29 adenomas in 66 of 66 polyps resulting in an ADR of 41.5%. Median TFD was 130 ms (95%-CI, 80–200 ms) while maintaining a median false positive rate of 2.2% (95%-CI, 1.7–2.8%). The four participating centers rated the system using the System Usability Scale with a median of 96.3 (95%-CI, 70–100). Conclusion EndoMind’s ability to acquire data, detect polyps in real-time, and high usability score indicate substantial practical value for research and clinical practice. Still, clinical benefit, measured by ADR, has to be determined in a prospective randomized controlled trial.zeige mehrzeige weniger

Volltext Dateien herunterladen

Metadaten exportieren

Weitere Dienste

Teilen auf Twitter Suche bei Google Scholar Statistik - Anzahl der Zugriffe auf das Dokument
Metadaten
Autor(en): Thomas J. Lux, Michael Banck, Zita Saßmannshausen, Joel Troya, Adrian Krenzer, Daniel Fitting, Boban Sudarevic, Wolfram G. Zoller, Frank Puppe, Alexander Meining, Alexander Hann
URN:urn:nbn:de:bvb:20-opus-324459
Dokumentart:Artikel / Aufsatz in einer Zeitschrift
Institute der Universität:Fakultät für Mathematik und Informatik / Institut für Informatik
Medizinische Fakultät / Medizinische Klinik und Poliklinik II
Sprache der Veröffentlichung:Englisch
Titel des übergeordneten Werkes / der Zeitschrift (Englisch):International Journal of Colorectal Disease
Erscheinungsjahr:2022
Band / Jahrgang:37
Heft / Ausgabe:6
Seitenangabe:1349-1354
Originalveröffentlichung / Quelle:International Journal of Colorectal Disease (2022) 37:6, 1349-1354 DOI: 10.1007/s00384-022-04178-8
DOI:https://doi.org/10.1007/s00384-022-04178-8
Allgemeine fachliche Zuordnung (DDC-Klassifikation):6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Freie Schlagwort(e):CADe; artificial intelligence; colonoscopy; deep learning; polyp
Datum der Freischaltung:28.02.2024
Lizenz (Deutsch):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International