Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?

Hwang, Youngbae; Park, Junseok; Lim, Yun Jeong; Chun, Hoon Jai

doi:10.5946/ce.2018.173

Cited by 38 publications

(18 citation statements)

References 39 publications

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“…52 Prospective trials are necessary for verifying the promising results of CNNs in capsule endoscopy. 53 However, when such CNNs are ready for clinical prime-time, they are expected to revolutionize the practice of capsule endoscopy through drastically improved detection rates and efficiency.…”

Section: Capsule Endoscopymentioning

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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Section: Capsule Endoscopymentioning

confidence: 99%

Artificial Intelligence in Gastrointestinal Endoscopy

et al. 2020

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“…Various artificial intelligence software programs are being developed to aid the diagnostic yield of labor‐intensive endoscope video review sessions in terms of efficiency and diagnostic accuracy . Traditionally, hemorrhages and lesions in images were detected by hand‐crafted features based on color and texture information, and then classified through machine learning algorithms such as support vector machines (SVM), neural networks, or binary classifiers .…”

Section: Wireless Capsule Endoscopesmentioning

confidence: 99%

Robotics in the Gut

Min

Yang

et al. 2019

Advanced Therapeutics

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Since the advent of ingestible temperature sensors and capsule endoscopes, rapid advances in electronics, robotics, nanotechnology, and material sciences have opened the door for the development of novel medical ingestible robots. The untethered robots provide direct, non-invasive access to the entire gastrointestinal (GI) tract. Furthermore, the tissues, gases, and fluids of the gastrointestinal lumen contain a multitude of biomarkers indicative of gut diseases and health. Ingestible medical robots equipped with advanced imaging and sensing techniques can enable the diagnosis and monitoring of diseases, while providing a better pathophysiological understanding of gastrointestinal disorders. In addition, various robotic actuation mechanisms in the macro-and microscale can realize enhanced drug delivery and surgical interventions for the treatment of diseases. In this paper, an overview of recent advances in ingestible robots toward imaging, sensing, drug delivery, and surgical applications in the GI tract is provided. Key challenges and strategies for the development of novel ingestible robots and future directions of ingestible robots toward precision medicine are also discussed.

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“…However, deep-learning methods need large datasets to overcome the fundamental overfitting problem. 39…”

Section: Automated Lesion Detection For Reducing Review Timementioning

confidence: 99%

Recent Development of Computer Vision Technology to Improve Capsule Endoscopy

et al. 2019

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Capsule endoscopy (CE) is a preferred diagnostic method for analyzing small bowel diseases. However, capsule endoscopes capture a sparse number of images because of their mechanical limitations. Post-procedural management using computational methods can enhance image quality. Additional information, including depth, can be obtained by using recently developed computer vision techniques. It is possible to measure the size of lesions and track the trajectory of capsule endoscopes using the computer vision technology, without requiring additional equipment. Moreover, the computational analysis of CE images can help detect lesions more accurately within a shorter time. Newly introduced deep leaning-based methods have shown more remarkable results over traditional computerized approaches. A large-scale standard dataset should be prepared to develop an optimal algorithms for improving the diagnostic yield of CE. The close collaboration between information technology and medical professionals is needed. Clin Endosc 2019;52:328-333

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Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?

Cited by 38 publications

References 39 publications

Artificial Intelligence in Gastrointestinal Endoscopy

Artificial Intelligence in Gastrointestinal Endoscopy

Robotics in the Gut

Recent Development of Computer Vision Technology to Improve Capsule Endoscopy

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