Computer-aided detection, mucosal exposure device, their combination, and standard colonoscopy for adenoma detection: a randomized controlled trial

Aniwan, Satimai; Mekritthikrai, Krittaya; Kerr, Stephen J.; Tiankanon, Kasenee; Vandaungden, Kunvadee; Sritunyarat, Yingluk; Piyachaturawat, Panida; Luangsukrerk, Thanawat; Kulpatcharapong, Santi; Wisedopas, Naruemon; Kongtub, Natanong; Kullavanijaya, Pinit; Rerknimitr, Rungsun

doi:10.1016/j.gie.2022.09.023

Cited by 24 publications

(9 citation statements)

References 26 publications

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“…Addition of mucosal exposure devices may contribute to further detection of adenomas. 14 While clinical effectiveness is widely considered to be the most important factor for medical innovation, health economic evaluation is another critical area to be considered for implementation in clinical practice. Most health-care systems undertake health technology assessments to inform decisions regarding the adoption and reimbursement of new innovations, with increasing emphasis placed on costeffectiveness given the trend to use scarce resources more efficiently within constrained budgets.…”

Section: Recommendationsmentioning

confidence: 99%

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Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement

Mori

East

Hassan

et al. 2023

Digestive Endoscopy

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The number of artificial intelligence (AI) tools for colonoscopy on the market is increasing with supporting clinical evidence. Nevertheless, their implementation is not going smoothly for a variety of reasons, including lack of data on clinical benefits and cost‐effectiveness, lack of trustworthy guidelines, uncertain indications, and cost for implementation. To address this issue and better guide practitioners, the World Endoscopy Organization (WEO) has provided its perspective about the status of AI in colonoscopy as the position statement. WEO Position Statement: Statement 1.1: Computer‐aided detection (CADe) for colorectal polyps is likely to improve colonoscopy effectiveness by reducing adenoma miss rates and thus increase adenoma detection; Statement 1.2: In the short term, use of CADe is likely to increase health‐care costs by detecting more adenomas; Statement 1.3: In the long term, the increased cost by CADe could be balanced by savings in costs related to cancer treatment (surgery, chemotherapy, palliative care) due to CADe‐related cancer prevention; Statement 1.4: Health‐care delivery systems and authorities should evaluate the cost‐effectiveness of CADe to support its use in clinical practice; Statement 2.1: Computer‐aided diagnosis (CADx) for diminutive polyps (≤5 mm), when it has sufficient accuracy, is expected to reduce health‐care costs by reducing polypectomies, pathological examinations, or both; Statement 2.2: Health‐care delivery systems and authorities should evaluate the cost‐effectiveness of CADx to support its use in clinical practice; Statement 3: We recommend that a broad range of high‐quality cost‐effectiveness research should be undertaken to understand whether AI implementation benefits populations and societies in different health‐care systems.

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Section: Recommendationsmentioning

confidence: 99%

“…However, we should also take into account that CADe cannot pick up any adenomas that are not visualized. Addition of mucosal exposure devices may contribute to further detection of adenomas 14 …”

Section: Recommendationsmentioning

confidence: 99%

Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement

Mori

East

Hassan

et al. 2023

Digestive Endoscopy

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“…CADe was also a dominant strategy for the detection of sessile serrated lesions [ 41 ]. A recent RCT suggested that the combination of CADe plus add-on-devices could be better than each of them analyzed separately [ 42 ].…”

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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“…Recently, deep learning-based algorithms have been developed aiming to assist in adenoma detection. After showing promising results in ex vivo studies [ 26 , 27 , 28 ], the use of artificial intelligence algorithms has been claimed to improve detection of colorectal lesions in several randomized controlled trials [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ] ( Table 1 ).…”

Section: Artificial Intelligence In Colonoscopymentioning

confidence: 99%

“…However, it may be argued that a further increase the performance in adenoma detection of expert endoscopists, such as the ones involved in the first trials [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ], may provide only little additional benefit.…”

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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Computer-aided detection, mucosal exposure device, their combination, and standard colonoscopy for adenoma detection: a randomized controlled trial

Cited by 24 publications

References 26 publications

Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement

Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement

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

Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening

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