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

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

PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps

Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can improve detection accuracy and efficiency and save the time of the domain experts called endoscopists. Lack of annotated data is a common challenge when building CAD systems. Generating synthetic medical data is an active research area to overcome the problem of having relatively few true positive cases in the medical domain. To be able to efficiently train machine learning (ML) models, which are the core of CAD systems, a considerable amount of data should be used. In this respect, we propose the PolypConnect pipeline, which can convert non-polyp images into polyp images to increase the size of training datasets for training. We present the whole pipeline with quantitative and qualitative evaluations involving endoscopists. The polyp segmentation model trained using synthetic data, and real data shows a 5.1% improvement of mean intersection over union (mIOU), compared to the model trained only using real data. The codes of all the experiments are available on GitHub to reproduce the results.Comment: 6 page

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

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 healthcare 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: Healthcare 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 (<=5mm), when it has sufficient accuracy, is expected to reduce healthcare costs by reducing polypectomies, pathological examinations, or both; Statement 2.2: Healthcare 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 healthcare systems

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

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

High-Definition Colonoscopy Compared With Cuff- and Cap-Assisted Colonoscopy: Results From a Multicenter, Prospective, Randomized Controlled Trial.

BACKGROUND AND AIMS: Mucosal exposure devices including distal attachments such as the cuff and cap have shown variable results in improving adenoma detection rate (ADR) compared with high-definition white light colonoscopy (HDWLE). METHODS: We performed a prospective, multicenter randomized controlled trial in patients undergoing screening or surveillance colonoscopy comparing HDWLE to 2 different types of distal attachments: cuff (CF) (Endocuff Vision) or cap (CP) (Reveal). The primary outcome was ADR. Secondary outcomes included adenomas per colonoscopy, advanced adenoma and sessile serrated lesion detection rate, right-sided ADR, withdrawal time, and adverse events. Continuous variables were compared using Student\u27s t test and categorical variables were compared using chi-square or Fisher\u27s exact test using statistical software Stata version16. A P value RESULTS: A total of 1203 subjects were randomized to either HDWLE (n = 384; mean 62 years of age; 81.3% males), CF (n = 379; mean 62.7 years of age; 79.9% males) or CP (n = 379; mean age 62.1 years of age; 80.5% males). No significant differences were found among 3 groups for ADR (57.3%, 59.1%, and 55.7%; P = .6), adenomas per colonoscopy (1.4 ± 1.9, 1.6 ± 2.4, and 1.4 ± 2; P = .3), advanced adenoma (7.6%, 9.2%, and 8.2%; P = .7), sessile serrated lesion (6.8%, 6.3%, and 5.5%; P = .8), or right ADR (48.2%, 49.3%, and 46.2%; P = .7). The number of polyps per colonoscopy were significantly higher in the CF group compared with HDWLE and CP group (2.7 ± 3.4, 2.3 ± 2.5, and 2.2 ± 2.3; P = .013). In a multivariable model, after adjusting for age, sex, body mass index, withdrawal time, and Boston Bowel Preparation Scale score, there was no impact of device type on the primary outcome of ADR (P = .77). In screening patients, CF resulted in more neoplasms per colonoscopy (CF: 1.7 ± 2.6, HDWLE: 1.3 ± 1.7, and CP: 1.2 ± 1.8; P = .047) with a shorter withdrawal time. CONCLUSIONS: Results from this multicenter randomized controlled trial do not show any significant benefit of using either distal attachment devices (CF or CP) over HDWLE, at least in high-detector endoscopists. The Endocuff may have an advantage in the screening population. (ClinicalTrials.gov, Number: NCT03952611)