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Colonoscopy is the primary screening method and can identify polyps before they progress, but visual inspection alone cannot ensure consistent, reliable detection in CRC screening videos and images. The study addresses these limits by implementing a YOLOv5 one-stage object detection model for colorectal polyp detection, using multiple training dataset and model configuration strategies to determine key practical factors. Results indicate acceptable performance with transfer learning and show that limited training data is the dominant constraint, with performance improved by 15.6% in average precision after expanding the original training dataset. Clinical analysis further explores causes of false positives, and a quality management framework is proposed for future dataset preparation and model development.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/the-investigation-of-constraints-in-implementing-robust-ai-colorectal-polyp-detection-for-sustainable-healthcare-system/382582/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/the-investigation-of-constraints-in-implementing-robust-ai-colorectal-polyp-detection-for-sustainable-healthcare-system/382582.png","ImageObject",300,407,{"name":92,"@type":93},"WPS_1786070896","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"Why is AI used for colorectal polyp detection in colonoscopy screening?","Question",{"text":112,"@type":113},"Visual inspection by endoscopists is insufficient for consistently reliable detection across colonoscopy videos and images. AI-based object detection helps overcome these limitations and reduce human errors.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which model and approach are used in the study?",{"text":117,"@type":113},"The study uses a YOLOv5 object detection model and evaluates mainstream one-stage approaches. 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PLoS ONE 18(7): e0288376 .  \n[https://doi.org/10.1371/journal.pone.0288376](https://doi.org/10.1371/journal.pone.0288376)  \n[Editor:](Editor: Charfi Said)[ Charfi Said](Editor: Charfi Said), Ibn Zohr University: Universite Ibn Zohr, MOROCCO  \nReceived: February 7, 2023  \nAccepted: June 24, 2023  \nPublished: July 12, 2023  \nCopyright: © 2023 Bian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: Replication Data for: Colonoscopy Polyp Detection and Classification:  \nDataset Creation and Comparative Evaluations: [https://doi.org/10.7910/DVN/FCBUOR](https://doi.org/10.7910/DVN/FCBUOR. Kvasir SEG)[. Kvasir SEG](https://doi.org/10.7910/DVN/FCBUOR. Kvasir SEG)[ ](https://doi.org/10.7910/DVN/FCBUOR. Kvasir SEG)Database: [https://datasets.simula.no/kvasir-seg](https://datasets.simula.no/kvasir-seg). CP-CHILD: [https://doi.org/10.6084/m9.figshare](https://doi.org/10.6084/m9.figshare). 12554042.  \nFunding: This research was funded by the National Natural Science Foundation of China, grant number 52004134.  \nCompeting interests: The authors have declared that no competing interests exist.  \nRESEARCH ARTICLE  \nThe investigation of constraints in implementing robust AI colorectal polyp detection for sustainable healthcare system  \nHaitao Bian1, Min Jiang2, Jingjing Qian3 *  \n1 College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, China, 2 KLA Corporation, Milpitas, California, United States of America, 3 Department of Gastroenterology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China  \n* [fsyy01712@njucm.edu.cn](fsyy01712@njucm.edu.cn)  \nAbstract  \nColorectal cancer (CRC) is one of the significant threats to public health and the sustainable healthcare system during urbanization. As the primary method of screening, colonoscopy can effectively detect polyps before they evolve into cancerous growths. However, the current visual inspection by endoscopists is insufficient in providing consistently reliable polyp detection for colonoscopy videos and images in CRC screening. Artificial Intelligent (AI) based object detection is considered as a potent solution to overcome visual inspection limitations and mitigate human errors in colonoscopy. This study implemented a YOLOv5 object detection model to investigate the performance of mainstream one-stage approaches in colorectal polyp detection. Meanwhile, a variety of training datasets and model structure configurations are employed to identify the determinative factors in practical applications. The designed experiments show that the model yields acceptable results assisted by transfer learning, and highlight that the primary constraint in implementing deep learning polyp detection comes from the scarcity of training data. The model performance was improved by 15 .6% in terms of average precision (AP) when the original training dataset was expanded. Furthermore, the experimental results were analysed from a clinical perspective to identify potential causes of false positives. Besides, the quality management framework is proposed for future dataset preparation and model development in AI-driven polyp detection tasks for smart healthcare solutions.  \n1. Introduction  \nColorectal cancer (CRC) is believed to be strongly associated with urban lifestyle and socioeconomic development [ 1] . Established risk factors for CRC initiation and progression include low physical activity, overweight and obesity, and dietary habits, all of which are linked to lifestyle changes during urbanization [2] . Moreover, studies have also sugg","cbCaiuY1BEgxQHFw","https://ap.wps.com/l/cbCaiuY1BEgxQHFw","pdf",3221619,"English","# Introduction\n# Abstract\n# Methods and Model Design\n# Experiments and Results\n# Clinical Perspective Analysis\n# Proposed Quality Management Framework","[{\"question\":\"Why is AI used for colorectal polyp detection in colonoscopy screening?\",\"answer\":\"Visual inspection by endoscopists is insufficient for consistently reliable detection across colonoscopy videos and images. AI-based object detection helps overcome these limitations and reduce human errors.\"},{\"question\":\"Which model and approach are used in the study?\",\"answer\":\"The study uses a YOLOv5 object detection model and evaluates mainstream one-stage approaches. It also tests different training datasets and model structure configurations.\"},{\"question\":\"What is identified as the primary constraint for deep learning polyp detection?\",\"answer\":\"The scarcity of training data is highlighted as the primary constraint in implementing deep learning polyp detection.\"},{\"question\":\"How much did performance improve after expanding the training dataset?\",\"answer\":\"Performance improved by 15.6% in average precision (AP) when the original training dataset was expanded.\"}]","The investigation of constraints in implementing robust AI colorectal polyp detection for sustainable healthcare system | PDF",1790251145,48]