Endoscopic image dataset links polyposis types to genetics and pathology
ERCPMP-Gx: Endoscopic Image and Video Dataset for Morphological, Histopathological, and Genomic Characterization of Colorectal Polyposis
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Some inherited conditions cause many growths called polyps in the colon, which can lead to cancer. Recognizing and classifying these early helps doctors treat patients better and watch their families closely. The authors created a public dataset of images and videos from endoscopies, along with matching lab and genetic information. This dataset includes both inherited polyposis cases and similar looking growths, helping AI systems learn to tell them apart for better diagnosis.
What this means in practice
- •For medical imaging developers: Develop AI tools that can accurately identify and classify hereditary colorectal polyposis using linked endoscopic images and genetic information.
- •For gastroenterology clinic it teams: Integrate AI-assisted classification systems trained on the dataset to improve patient diagnosis and management of hereditary polyposis syndromes.
Authors
Zahra Ghaffari, Massih Bahar, Mojgan Forootan, Ali Darvishi, Hamidreza Bolhasani
Abstract
Hereditary polyposis syndromes can be precursor lesions to colorectal cancer and are associated with a broad spectrum of extracolonic tumors. Early identification and accurate classification of these syndromes are essential for timely diagnosis, individualized patient management, and targeted surveillance strategies for affected families. However, public endoscopic datasets are largely organized around the individual sporadic polyp, and none links the polyposis phenotype to histopathology and germline findings at the patient level. Here, we present ERCPMP-Gx, an endoscopic, histopathological, and genomic dataset developed to support the application of artificial intelligence (AI) in the recognition, characterization, and classification of colorectal polyposis. Most procedures were performed using the Olympus EVIS X1 system with white-light endoscopy (WLE), narrow-band imaging (NBI), magnifying NBI (M-NBI), and NBI with near focus modes, yielding 160 images and accompanying video clips. Approximately eighty percent of cases represent clinically and/or genetically confirmed hereditary polyposis syndromes (PG), including familial adenomatous polyposis (FAP), Peutz-Jeghers syndrome (PJS), juvenile polyposis syndrome (JPS), and ganglioneuroma syndrome (GNS), while the remaining twenty percent comprise non-hereditary polyps and polyp-mimicking lesions with overlapping morphological features (Non-PG), included to support differential classification. Each released record is linked, where available, to standardized endoscopic annotations, representative histopathology, and clinically reported germline findings, forming an AI-ready, patient-level annotation framework. The dataset is publicly accessible at Mendeley (https://doi.org/10.17632/nzyfc544bx.2). For the latest updates and further information, readers are referred to the DataBioX website: https://databiox.com.