Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy

Urban, Gregor; Tripathi, Priyam; Alkayali, Talal; Mittal, Mohit; Jalali, Farid; Karnes, William E.; Baldi, Pierre

doi:10.1053/j.gastro.2018.06.037

Cited by 578 publications

(449 citation statements)

References 34 publications

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“…35 A CNN (trained on >8000 images from 2000 patients) performing in real time demonstrated excellent diagnostic accuracy of 96% and was superior to conventional colonoscopy for polyp detection (45 vs. 36). 36 The first RCT of CAD assisted colonoscopy versus conventional colonoscopy demonstrated a higher adenoma detection rate (ADR) using CAD (0.29 vs. 0.2) and a sensitivity of >90% using a combination of static images and videos. 37…”

Section: Polyp Detectionmentioning

confidence: 99%

The future of endoscopy: Advances in endoscopic image innovations

Gulati

Patel

Emmanuel

et al. 2019

Digestive Endoscopy

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The latest state of the art technological innovations have led to a palpable progression in endoscopic imaging and may facilitate standardisation of practice. One of the most rapidly evolving modalities is artificial intelligence with recent studies providing real-time diagnoses and encouraging results in the first randomised trials to conventional endoscopic imaging. Advances in functional hypoxia imaging offer novel opportunities to be used to detect neoplasia and the assessment of colitis. Three-dimensional volumetric imaging provides spatial information and has shown promise in the increased detection of small polyps. Studies to date of self-propelling colonoscopes demonstrate an increased caecal intubation rate and possibly offer patients a more comfortable procedure. Further development in robotic technology has introduced ex vivo automated locomotor upper gastrointestinal and small bowel capsule devices. Eye-tracking has the potential to revolutionise endoscopic training through the identification of differences in experts and non-expert endoscopist as trainable parameters. In this review, we discuss the latest innovations of all these technologies and provide perspective into the exciting future of diagnostic luminal endoscopy.

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Section: Polyp Detectionmentioning

confidence: 99%

The future of endoscopy: Advances in endoscopic image innovations

Gulati

Patel

Emmanuel

et al. 2019

Digestive Endoscopy

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“…The serrated polyp was localized both with the (A) narrow‐band imaging mode and (B) with the white light mode. (Image courtesy of Urban et al …”

Section: Automated Polyp Detectionmentioning

confidence: 99%

Artificial intelligence and colonoscopy: Current status and future perspectives

Kudo

Mori

Misawa

et al. 2019

Digestive Endoscopy

119

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Background and Aim Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer‐aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps. Methods and Results Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy. Conclusion We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence‐assisted medical devices.

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“…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 . Recently, deep learning‐based approaches such as convolutional neural networks (CNN) have shown improved performance for image‐based recognition and classification via learned features . However, deep learning‐based computer‐aided diagnosis requires a large database, and overfitting can be an issue.…”

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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Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy

Cited by 578 publications

References 34 publications

The future of endoscopy: Advances in endoscopic image innovations

The future of endoscopy: Advances in endoscopic image innovations

Artificial intelligence and colonoscopy: Current status and future perspectives

Robotics in the Gut

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