Deep learning model applied to real-time delineation of colorectal polyps.

Journal: BMC medical informatics and decision making
Published Date: Jun 4, 2025

Abstract

BACKGROUND: Deep learning models have shown considerable potential to improve diagnostic accuracy across medical fields. Although YOLACT has demonstrated real-time detection and segmentation in non-medical datasets, its application in medical settings remains underexplored. This study evaluated the performance of a YOLACT-derived Real-time Polyp Delineation Model (RTPoDeMo) for real-time use on prospectively recorded colonoscopy videos.

Authors

  • Moana Gelu-Simeon
    Service d'Hépato-Gastroentérologie, CHU de la Guadeloupe, Pointe- à-Pitre, F-97100, France. moana.simeon@chu-guadeloupe.fr.
  • Adel Mamou
    Biostatistic department, Univ. Montpellier, Montpellier, F-34000, France.
  • Georgette Saint-Georges
    Service d'Hépato-Gastroentérologie, CHU de la Guadeloupe, Pointe- à-Pitre, F-97100, France.
  • Marceline Alexis
    Service d'Hépato-Gastroentérologie, CHU de la Guadeloupe, Pointe- à-Pitre, F-97100, France.
  • Marie Sautereau
    Service d'Hépato-Gastroentérologie, CHU de la Guadeloupe, Pointe- à-Pitre, F-97100, France.
  • Yassine Mamou
    Service de Médecine Nucléaire, CHU de la Guadeloupe, Pointe-à- Pitre, F-97100, France.
  • Jimmy Simeon
    Informatic Department, CHU de la Guadeloupe, Pointe-à-Pitre, F-97100, France.

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