Human-machine collaboration: bringing artificial intelligence into colonoscopy

Ahmad, Omer F.; Stoyanov, Danail; Lovat, Laurence

doi:10.1136/flgastro-2018-101047

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…One of the potential applications of AI in colonoscopy is to provide an accurate, almost instantaneous prediction of whether a polyp is neoplastic. With a high rate of histological prediction through AI, endoscopists could adopt a resect and discard strategy, obviating the need for complementary histopathological studies in tiny polyps [ 47 ]. Some AI systems already exceed these thresholds in the experimental environment, combining CADx with NBI or endocytoscopy [ 48 , 49 ].…”

Section: Computer-aided Polyp Detection (Cade)mentioning

confidence: 99%

Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?

Gimeno–García

Hernández-Pérez

Díaz

et al. 2023

Cancers

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Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.

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Section: Computer-aided Polyp Detection (Cade)mentioning

confidence: 99%

Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?

Gimeno–García

Hernández-Pérez

Díaz

et al. 2023

Cancers

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“…The most active area of AI development in endoscopy is the detection and classification of lesions, in particular colonic polyps [136,137] but also a growing number of studies with very promising results in upper GI applications like Barret's detection [138] and squamous cell neoplasia [139]. While various endoscopic image understanding methods have been explored for a long time [140], deep-learning based techniques have shown the capability to turn algorithms into clinically valuable computer-aided diagnostic (CAD) tools [141][142][143]. There is a growing number of studies indicating that CAD systems can perform at least as well if not even better than expert endoscopists [138], though additional validation and understanding of the clinical impact is still, without doubt, needed.…”

Section: Artificial Intelligence: An Enabling Factor For Enhancing Romentioning

confidence: 99%

Frontiers of Robotic Colonoscopy: A Comprehensive Review of Robotic Colonoscopes and Technologies

Ciuti

Skonieczna-Żydecka

Marlicz

et al. 2020

JCM

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Flexible colonoscopy remains the prime mean of screening for colorectal cancer (CRC) and the gold standard of all population-based screening pathways around the world. Almost 60% of CRC deaths could be prevented with screening. However, colonoscopy attendance rates are affected by discomfort, fear of pain and embarrassment or loss of control during the procedure. Moreover, the emergence and global thread of new communicable diseases might seriously affect the functioning of contemporary centres performing gastrointestinal endoscopy. Innovative solutions are needed: artificial intelligence (AI) and physical robotics will drastically contribute for the future of the healthcare services. The translation of robotic technologies from traditional surgery to minimally invasive endoscopic interventions is an emerging field, mainly challenged by the tough requirements for miniaturization. Pioneering approaches for robotic colonoscopy have been reported in the nineties, with the appearance of inchworm-like devices. Since then, robotic colonoscopes with assistive functionalities have become commercially available. Research prototypes promise enhanced accessibility and flexibility for future therapeutic interventions, even via autonomous or robotic-assisted agents, such as robotic capsules. Furthermore, the pairing of such endoscopic systems with AI-enabled image analysis and recognition methods promises enhanced diagnostic yield. By assembling a multidisciplinary team of engineers and endoscopists, the paper aims to provide a contemporary and highly-pictorial critical review for robotic colonoscopes, hence providing clinicians and researchers with a glimpse of the major changes and challenges that lie ahead.

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“…CAD systems by the use of advanced AI techniques represent an innovative technology that will likely lead to a paradigm move in the field of diagnostic colonoscopy[ 12 , 13 ]. Since endoscopy is generally related to computer vision technology, this technique allows computers to “see” and decipher visual content[ 14 ].…”

Section: What Is Aimentioning

confidence: 99%

Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped

Sinagra¹,

Badalamenti²,

Maida³

et al. 2020

WJG

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Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or pre-cancerous lesions and the capacity to remove them intra-procedurally. Computer-aided detection and diagnosis (CAD), thanks to the brand new developed innovations of artificial intelligence, and especially deep-learning techniques, leads to a promising solution to human biases in performance by guarantying decision support during colonoscopy. The application of CAD on real-time colonoscopy helps increasing the adenoma detection rate, and therefore contributes to reduce the incidence of interval cancers improving the effectiveness of colonoscopy screening on critical outcome such as colorectal cancer related mortality. Furthermore, a significant reduction in costs is also expected. In addition, the assistance of the machine will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule. The aim of this opinion review is to analyze the clinical applications of CAD and artificial intelligence in colonoscopy, as it is reported in literature, addressing evidence, limitations, and future prospects.

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Human-machine collaboration: bringing artificial intelligence into colonoscopy

Cited by 8 publications

References 11 publications

Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?

Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?

Frontiers of Robotic Colonoscopy: A Comprehensive Review of Robotic Colonoscopes and Technologies

Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped

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