Can a Convolutional Neural Network Solve the Polyp Size Dilemma? Category Award (Colorectal Cancer Prevention) Presidential Poster Award

Requa, James; Dao, Tyler; Ninh, Andrew; Karnes, William E.

doi:10.14309/00000434-201810001-00282

Cited by 7 publications

(9 citation statements)

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“…Polyp size measurements are important for treatment selection and the establishment of monitoring intervals; however, currently used subjective methods are flawed. Indeed, Requa et al [ 81 ] developed a highly accurate convolutional neural network to estimate the size of polyps in colonoscopy, dividing them into three size-based groups of ≤5, 6–9, and ≥10 mm (model accuracy: 97%, 97%, and 98%, respectively). More recently, Abdelrahim et al [ 82 ] developed a deep learning model based on convolutional neural networks (CNN) with an 80% accuracy in real-time polyp sizing.…”

Section: Artificial Intelligence In Colonoscopymentioning

confidence: 99%

Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening

et al. 2023

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Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.

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Section: Artificial Intelligence In Colonoscopymentioning

confidence: 99%

Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening

et al. 2023

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“…Requa et al developed a CNN to estimate polyp size during live colonoscopy. 28 A total of 8,257 images of polyps were included and labeled into three different size groups-diminutive (≤5 mm), small (6-9 mm), and large (≥10 mm)-by a single expert colonoscopist who has performed over 30,000 colonoscopies with an overall ADR of 50%. The resulting model had an accuracy of 0.97, 0.97, and 0.98 for polyps ≤5 mm, 6-9 mm, and ≥10 mm, respectively, and processed 100 fps, capable of being run during live colonoscopy.…”

Section: Automated Polyp Size Measurementmentioning

confidence: 99%

“…The ability to accurately categorize polyps into three different size groups in real-time colonoscopy may help endoscopists more accurately and consistently determine an appropriate timing of surveillance colonoscopy and additionally document polyp sizes during live colonoscopy. 28…”

Section: Automated Polyp Size Measurementmentioning

confidence: 99%

Artificial Intelligence in Gastrointestinal Endoscopy

et al. 2020

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Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully. clin endosc 2020;53:132-141

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“…The system can help endoscopists discover more polyps in clinical practice(34). Requa et al(35) developed a CNN to estimate the size of polyps on colonoscopy. This system can run during realtime colonoscopy and divide polyps into 3 size-based groups of ≤5, 6-9, and ≥10 mm, with the final model showing an accuracy of 0.97, 0.97 and 0.98, respectively.…”

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confidence: 99%

Use of Artificial Intelligence to Improve the Quality Control of Gastrointestinal Endoscopy

et al. 2021

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With the rapid development of science and technology, artificial intelligence (AI) systems are becoming ubiquitous, and their utility in gastroenteroscopy is beginning to be recognized. Digestive endoscopy is a conventional and reliable method of examining and diagnosing digestive tract diseases. However, with the increase in the number and types of endoscopy, problems such as a lack of skilled endoscopists and difference in the professional skill of doctors with different degrees of experience have become increasingly apparent. Most studies thus far have focused on using computers to detect and diagnose lesions, but improving the quality of endoscopic examination process itself is the basis for improving the detection rate and correctly diagnosing diseases. In the present study, we mainly reviewed the role of AI in monitoring systems, mainly through the endoscopic examination time, reducing the blind spot rate, improving the success rate for detecting high-risk lesions, evaluating intestinal preparation, increasing the detection rate of polyps, automatically collecting maps and writing reports. AI can even perform quality control evaluations for endoscopists, improve the detection rate of endoscopic lesions and reduce the burden on endoscopists.

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Can a Convolutional Neural Network Solve the Polyp Size Dilemma? Category Award (Colorectal Cancer Prevention) Presidential Poster Award

Cited by 7 publications

References 0 publications

Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening

Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening

Artificial Intelligence in Gastrointestinal Endoscopy

Use of Artificial Intelligence to Improve the Quality Control of Gastrointestinal Endoscopy

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