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Artificial Intelligence-Aid Colonoscopy Vs. Conventional Colonoscopy for Polyp and Adenoma Detection: A Systematic Review of 7 Discordant Meta-Analyses.

ABSTRACT: Objectives: Multiple meta-analyses which investigated the comparative efficacy and safety of artificial intelligence (AI)-aid colonoscopy (AIC) vs. conventional colonoscopy (CC) in the detection of polyp and adenoma have been published. However, a definitive conclusion has not yet been generated. This systematic review selected from discordant meta-analyses to draw a definitive conclusion about whether AIC is better than CC for the detection of polyp and adenoma. Methods: We comprehensively searched potentially eligible literature in PubMed, Embase, Cochrane library, and China National Knowledgement Infrastructure (CNKI) databases from their inceptions until to April 2021. Assessment of Multiple Systematic Reviews (AMSTAR) instrument was used to assess the methodological quality. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was used to assess the reporting quality. Two investigators independently used the Jadad decision algorithm to select high-quality meta-analyses which summarized the best available evidence. Results: Seven meta-analyses met our selection criteria finally. AMSTAR score ranged from 8 to 10, and PRISMA score ranged from 23 to 26. According to the Jadad decision algorithm, two high-quality meta-analyses were selected. These two meta-analyses suggested that AIC was superior to CC for colonoscopy outcomes, especially for polyp detection rate (PDR) and adenoma detection rate (ADR). Conclusion: Based on the best available evidence, we conclude that AIC should be preferentially selected for the route screening of colorectal lesions because it has potential value of increasing the polyp and adenoma detection. However, the continued improvement of AIC in differentiating the shape and pathology of colorectal lesions is needed.

SUBMITTER: Pan H

PROVIDER: S-EPMC8792899 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRemoval of colorectal polyps during screening could reduce the incidence of colorectal cancer (CRC). However, there is a lack of data on risk factors associated with recurrence of polyps, including conventional adenomas and serrated polyps (SPs). This study aimed to determine risk factors for recurrence of colorectal polyps and their subtypes based on the characteristics of the patients and polyps.MethodsA total of 1,165 patients diagnosed with conventional adenoma or SP in the Sixth Affiliated Hospital of Sun Yat-sen University between January 2013 and December 2019 were enrolled in this study, including 668 cases with conventional adenomas, 385 with SPs, and 112 with coexistence of adenomas and SPs. Univariate analysis and multivariate logistic regression were used to identify potential risk factors for polyp recurrence. A nomogram was established according to risk factors and the performance was evaluated using calibration plots.ResultsDuring a median follow-up of 24 months, recurrent polyps were observed in 531 (45.6%) cases. Male, age ≥50 years, body mass index (BMI) ≥24 kg/m2, at least three polyps, smoking, alcohol consumption, family history of polyps, and family history of CRC were independent risk factors for polyp recurrence. The Harrell's C-index of the nomogram developed with these parameters was 0.69 and the calibration plots showed good agreement between actual polyp recurrence and nomogram-predicted recurrence probability. In the subtype analyses, conventional adenomas had the same risk factors for recurrence as all polyps, while smoking, alcohol consumption, family history of polyps, and family history of CRC were not risk factors for SP recurrence.ConclusionsWe identified several risk factors for recurrence of colorectal polyps and found that some of them could increase the risk of adenoma recurrence but not SP recurrence, including smoking, alcohol consumption, and family history of polyps/CRC, which might help us to understand different etiology and biology between conventional adenomas and SPs.

Project description:AimTo compare magnetic imaging-assisted colonoscopy (MIC) with conventional colonoscopy (CC).MethodsMagnetic imaging technology provides a computer-generated image of the shape and position of the colonoscope onto a monitor to give visual guidance to the endoscopist. It is designed to improve colonoscopy performance and tolerability for patients by enabling visualization of loop formation and endoscope position. Recently, a new version of MIC technology was developed for which there are limited data.To evaluate this latest generation of MIC among experienced rather than inexperienced or trainee endoscopists, a prospective randomized trial was performed using only gastroenterologists with therapeutic endoscopy training. Consecutive patients undergoing elective outpatient colonoscopy were randomized to MIC or CC, with patients blinded to their group assignment. Endoscopic procedural metrics and quantities of conscious sedation medications were recorded during the procedures. The procedure was classified as "usual" or "difficult" by the endoscopist at the conclusion of each case based on the need for adjunctive maneuvers to facilitate endoscope advancement. After more than one hour post-procedure, patients completed a 10 cm visual analogue pain scale to reflect the degree of discomfort experienced during their colonoscopy. The primary outcome was patient comfort expressed by the visual analogue pain score. Secondary outcomes consisted of endoscopic procedural metrics as well as a sedation score derived from standardized dose increments of the conscious sedation medications.ResultsTwo hundred fifty-three patients were randomized and underwent MIC or CC between September 2011 and October 2012. The groups were similar in terms of the indications for colonoscopy and patient characteristics. There were no differences in cecal intubation rates (100% vs 99%), insertion distance-to-cecum (82 cm vs 83 cm), time-to-cecum (6.5 min vs 7.2 min), or polyp detection rate (47% vs 52%) between the MIC and CC groups. The primary outcome of mean pain score (1.0 vs 0.9 out of 10, P = 0.41) did not differ between MIC and CC groups, nor did the mean sedation score (8.2 vs 8.5, P = 0.34). Within the subgroup of cases considered more challenging or difficult, time-to-cecum was significantly faster with MIC compared to CC, 10.1 min vs 13.4 min respectively (P = 0.01). Sensitivity analyses confirmed a similar pattern of overall findings when each endoscopist was considered separately, demonstrating that the mean results for the entire group were not unduly influenced by outlier results from any one endoscopist.ConclusionAlthough the latest version of MIC resulted in faster times-to-cecum within a subgroup of more challenging cases, overall it was no better than CC in terms of patient comfort, sedation requirements and endoscopic procedural metrics, when performed in experienced hands.

Project description:Debilitating hearing loss (HL) affects ~6% of the human population. Only 20% of the people in need of a hearing assistive device will eventually seek and acquire one. The number of people that are satisfied with their Hearing Aids (HAids) and continue using them in the long term is even lower. Understanding the personal, behavioral, environmental, or other factors that correlate with the optimal HAid fitting and with users' experience of HAids is a significant step in improving patient satisfaction and quality of life, while reducing societal and financial burden. In SMART BEAR we are addressing this need by making use of the capacity of modern HAids to provide dynamic logging of their operation and by combining this information with a big amount of information about the medical, environmental, and social context of each HAid user. We are studying hearing rehabilitation through a 12-month continuous monitoring of HL patients, collecting data, such as participants' demographics, audiometric and medical data, their cognitive and mental status, their habits, and preferences, through a set of medical devices and wearables, as well as through face-to-face and remote clinical assessments and fitting/fine-tuning sessions. Descriptive, AI-based analysis and assessment of the relationships between heterogeneous data and HL-related parameters will help clinical researchers to better understand the overall health profiles of HL patients, and to identify patterns or relations that may be proven essential for future clinical trials. In addition, the future state and behavioral (e.g., HAids Satisfiability and HAids usage) of the patients will be predicted with time-dependent machine learning models to assist the clinical researchers to decide on the nature of the interventions. Explainable Artificial Intelligence (XAI) techniques will be leveraged to better understand the factors that play a significant role in the success of a hearing rehabilitation program, constructing patient profiles. This paper is a conceptual one aiming to describe the upcoming data collection process and proposed framework for providing a comprehensive profile for patients with HL in the context of EU-funded SMART BEAR project. Such patient profiles can be invaluable in HL treatment as they can help to identify the characteristics making patients more prone to drop out and stop using their HAids, using their HAids sufficiently long during the day, and being more satisfied by their HAids experience. They can also help decrease the number of needed remote sessions with their Audiologist for counseling, and/or HAids fine tuning, or the number of manual changes of HAids program (as indication of poor sound quality and bad adaptation of HAids configuration to patients' real needs and daily challenges), leading to reduced healthcare cost.

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Artificial Intelligence-Aid Colonoscopy Vs. Conventional Colonoscopy for Polyp and Adenoma Detection: A Systematic Review of 7 Discordant Meta-Analyses.

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