scholarly journals Artificial Intelligence in Endoscopy

2021 ◽  
pp. 1-5
Author(s):  
Alexander Hann ◽  
Alexander Meining
Keyword(s):  
Artificial Intelligence ◽  
Rapid Development ◽  
Image Interpretation ◽  
Colorectal Polyps ◽  
Screening Colonoscopy ◽  
Current Status ◽  
Great Promise ◽  
Polyp Detection ◽  
Open Questions ◽  
Adenoma Detection

<b><i>Background:</i></b> Owing to their rapid development, artificial intelligence (AI) technologies offer a great promise for gastroenterology practice and research. At present, AI-guided image interpretation has already been used with success for endoscopic detection of early malignant lesions. Nonetheless, there are complex challenges and possible shortcomings that must be considered before full implementation can be realized. <b><i>Summary:</i></b> In this review, the current status of AI in endoscopy is summarized. Future perspectives and open questions for further studies are stressed. <b><i>Key Messages:</i></b> The usage of AI algorithms for polyp detection in screening colonoscopy results in a significant increase in the adenoma detection rate, mainly attributed to the identification of diminutive polyps. Computer-aided characterization of colorectal polyps accompanies the detection, but further studies are needed to evaluate the clinical benefit. In contrast to colonoscopy, usage of AI in gastroscopy is currently rather limited. Regarding other fields of endoscopic imaging, capsule endoscopy is the ideal imaging platform for AI, due to the potential of saving time in the video analysis.

scholarly journals Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis

2021 ◽  
Vol 09 (04) ◽  
pp. E513-E521
Author(s):  
Munish Ashat ◽  
Jagpal Singh Klair ◽  
Dhruv Singh ◽  
Arvind Rangarajan Murali ◽  
Rajesh Krishnamoorthi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Meta Analysis ◽  
Screening Colonoscopy ◽  
Average Risk ◽  
Polyp Detection ◽  
Withdrawal Time ◽  
Detection Rates ◽  
Adenoma Detection ◽  
The Impact

Abstract Background and study aims With the advent of deep neural networks (DNN) learning, the field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials (RCT) have investigated the influence of integrating AI in colonoscopy and its impact on adenoma detection rates (ADRs) and polyp detection rates (PDRs). We performed a systematic review and meta-analysis to reliably assess if the impact is statistically significant enough to warrant the adoption of AI -assisted colonoscopy (AIAC) in clinical practice. Methods We conducted a comprehensive search of multiple electronic databases and conference proceedings to identify RCTs that compared outcomes between AIAC and conventional colonoscopy (CC). The primary outcome was ADR. The secondary outcomes were PDR and total withdrawal time (WT). Results Six RCTs (comparing AIAC vs CC) with 5058 individuals undergoing average-risk screening colonoscopy were included in the meta-analysis. ADR was significantly higher with AIAC compared to CC (33.7 % versus 22.9 %; odds ratio (OR) 1.76, 95 % confidence interval (CI) 1.55–2.00; I2 = 28 %). Similarly, PDR was significantly higher with AIAC (45.6 % versus 30.6 %; OR 1.90, 95 %CI, 1.68–2.15, I2 = 0 %). The overall WT was higher for AIAC compared to CC (mean difference [MD] 0.46 (0.00–0.92) minutes, I2 = 94 %). Conclusions There is an increase in adenoma and polyp detection with the utilization of AIAC.

scholarly journals TOWARDS AN UNIFIED FRAMEWORK FOR VALIDATING DEEP LEARNING METHODS FOR COLORECTAL POLYPS: FIRST STEPS

2021 ◽  
Vol 108 (Supplement_3) ◽  
Author(s):  
L F Sánchez Peralta ◽  
J F Ortega Morán ◽  
Cr L Saratxaga ◽  
J B Pagador ◽  
A Picón ◽  
...  
Keyword(s):  
Deep Learning ◽  
Colorectal Polyps ◽  
Polyp Detection ◽  
Unified Framework ◽  
Endoscopic Image ◽  
Learning Techniques ◽  
Great Utility ◽  
Adenoma Detection ◽  
Definition Of ◽  
Basic Features

Abstract INTRODUCTION Deep learning techniques have significantly contributed to the field of medical imaging analysis. In case of colorectal cancer, they have shown a great utility for increasing the adenoma detection rate at colonoscopy, but a common validation methodology is still missing. In this study, we present preliminary efforts towards the definition of a validation framework. MATERIAL AND METHODS Different models based on different backbones and encoder-decoder architectures have been trained with a publicly available dataset that contains white light and NBI colonoscopy videos, with 76 different lesions from colonoscopy procedures in 48 human patients. A computer aided detection (CADe) demonstrator has been implemented to show the performance of the models. RESULTS This CADe demonstrator shows the areas detected as polyp by overlapping the predicted mask on the endoscopic image. It allows selecting the video to be used, among those from the test set. Although it only present basic features such as play, pause and moving to the next video, it easily loads the model and allows for visualization of results. The demonstrator is accompanied by a set of metrics to be used depending on the aimed task: polyp detection, localization and segmentation. CONCLUSIONS The use of this CADe demonstrator, together with a publicly available dataset and predefined metrics will allow for an easier and more fair comparison of methods. Further work is still required to validate the proposed framework.

scholarly journals Application of Artificial Intelligence in Early Gastric Cancer Diagnosis

Digestion ◽  
2021 ◽  
pp. 1-7
Author(s):  
Zili Xiao ◽  
Danian Ji ◽  
Feng Li ◽  
Zhengliang Li ◽  
Zhijun Bao
Keyword(s):  
Artificial Intelligence ◽  
Gastric Cancer ◽  
New Technologies ◽  
Narrow Band Imaging ◽  
Daily Practice ◽  
Colorectal Polyps ◽  
Current Status ◽  
Randomized Controlled ◽  
Depth Prediction ◽  
Substantial Effort

<b><i>Background:</i></b> With the development of new technologies such as magnifying endoscopy with narrow band imaging, endoscopists achieved better accuracy for diagnosis of gastric cancer (GC) in various aspects. However, to master such skill takes substantial effort and could be difficult for inexperienced doctors. Therefore, a novel diagnostic method based on artificial intelligence (AI) was developed and its effectiveness was confirmed in many studies. AI system using convolutional neural network has showed marvelous results in the ongoing trials of computer-aided detection of colorectal polyps. <b><i>Summary:</i></b> With AI’s efficient computational power and learning capacities, endoscopists could improve their diagnostic accuracy and avoid the overlooking or over-diagnosis of gastric neoplasm. Several systems have been reported to achieved decent accuracy. Thus, AI-assisted endoscopy showed great potential on more accurate and sensitive ways for early detection, differentiation, and invasion depth prediction of gastric lesions. However, the feasibility, effectiveness, and safety in daily practice remain to be tested. <b><i>Key messages:</i></b> This review summarizes the current status of different AI applications in early GC diagnosis. More randomized controlled trails will be needed before AI could be widely put into clinical practice.

scholarly journals Adenoma and Polyp Detection Rates in Colonoscopy according to Indication

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Erika S. Boroff ◽  
Molly Disbrow ◽  
Michael D. Crowell ◽  
Francisco C. Ramirez
Keyword(s):  
Distal Colon ◽  
Proximal Colon ◽  
Screening Colonoscopy ◽  
Bowel Habit ◽  
Polyp Detection ◽  
Age And Gender ◽  
Detection Rates ◽  
Adenoma Detection ◽  
And Gender ◽  
Colon Data

Background. Adenoma detection rate (ADR) is a validated quality measure for screening colonoscopy, but there are little data for other indications. The distribution of adenomas is not well described for these indications. Aim. To describe ADR and the adenoma distribution in the proximal and distal colon based on colonoscopy indication. Methods. Outpatient colonoscopies are subdivided by indication. PDR and ADR for the entire colon and for proximal and distal colon. Data were compared using generalized estimating equations to adjust for clustering amongst endoscopists while controlling for patient age and gender. Results. 3436 colonoscopies were reviewed (51.2%: men (n=1759)). Indications are screening 49.2%, surveillance 29.3%, change in bowel habit 8.4%, bleeding 5.8%, colitides 3.0%, pain 2.8%, and miscellaneous 1.5%. Overall ADR was 37% proximal ADR 28%, and distal ADR 17%. PDR and ADR were significantly higher in surveillance than in screening (PDR: 69% versus 51%; ADR: 50% versus 33%; p=0.0001). Adenomas were more often detected in the proximal than in the distal colon, for all indications. Conclusions. Prevalence of polyps and adenomas differs based on colonoscopy indication. Adenoma detection is highest in surveillance and more commonly detected in the proximal colon. For quality assurance, distinct ADR and PDR targets may need to be established for different colonoscopy indications.

The effectiveness of physical education teaching in college based on Artificial intelligence methods

2020 ◽  
pp. 1-11
Author(s):  
Zihao Li ◽  
Hejin Wang
Keyword(s):  
Artificial Intelligence ◽  
Physical Education ◽  
Rapid Development ◽  
Participation Rate ◽  
Colleges And Universities ◽  
The Body ◽  
Current Status ◽  
Innovative Teaching ◽  
Artificial Intelligence Technology ◽  
Low Activity

Traditional physical education in colleges and universities is difficult to arouse students’ interest in sports, resulting in low activity participation rate and inability to exercise the body. How to effectively improve the effectiveness of physical education in colleges and universities has become one of the hot topics of most concern from all walks of life. In physical education, innovative teaching concepts and methods, teaching methods and processes, and teaching evaluation methods are all conducive to improving the classroom atmosphere of physical education and successfully improve the effectiveness of physical education. This article focuses on analyzing the current status of physical education in colleges and universities. Based on the rapid development of artificial intelligence technology, how to improve the effectiveness of physical education is studied, and an experimental method is used to compare and analyze physical education in a college. The analysis results show that artificial intelligence-based physical education can obviously improve students’ strength quality, speed quality, endurance quality, and agility quality, which provides a more important reference and reference for improving the effectiveness of college physical education.

scholarly journals The Quality of Screening Colonoscopies in an Office-Based Endoscopy Clinic

2009 ◽  
Vol 23 (1) ◽  
pp. 41-47 ◽  
Author(s):  
Douglas Bair ◽  
Joe Pham ◽  
M Bianca Seaton ◽  
Naveen Arya ◽  
Michelle Pryce ◽  
...  
Keyword(s):  
Academic Community ◽  
Screening Colonoscopy ◽  
Polyp Detection ◽  
Cecal Intubation ◽  
Withdrawal Time ◽  
Adenoma Detection ◽  
Adequate Sedation ◽  
Cecal Intubation Rate ◽  
Hospital Practices

BACKGROUND: Wait times for hospital screening colonoscopy have increased dramatically in recent years, resulting in an increase in patient referrals to office-based endoscopy clinics. There is no formal regulation of office endoscopy, and it has been suggested that the quality of service in some office locations may be inferior to hospital procedures.OBJECTIVE: To compare the quality of office-based screening colonos-copies at a clinic in Oakville, Ontario, with published benchmarks for cecal intubation, withdrawal times, polyp detection, adenoma detection, cancer detection and patient complications.METHODS: Demographic information on consecutive patients and colonoscopy reports by all nine gastroenterologists at the Oakville Endoscopy Centre between August 2006 and December 2007 were prospectively obtained.RESULTS: A total of 3741 colonoscopies were analyzed. The mean age of patients was 57.1 years and 51.9% were women. The cecal intubation rate was 98.98% with an average withdrawal time of 9.75 min. A total of 3857 polyps were retrieved from 1725 patients (46.11%), and 1721 adenomas were detected in 953 patients (25.47%). A total of 126 patients (3.37%) had advanced polyps and 18 (0.48%) were diagnosed with colon cancer. One patient (0.027%) had a colonic perforation and two patients had postpolypectomy bleeding (0.053%). These results meet or exceed published benchmarks for quality colonoscopy.CONCLUSIONS: The Oakville Endoscopy Centre data demonstrate that office-based colonoscopies, performed by well-trained physicians using adequate sedation and hospital-grade equipment, result in outcomes at least equal to or better than those of published academic/community hospital practices and are therefore a viable option for the future of screening colonoscopy in Canada.

scholarly journals Artificial intelligence for identification and characterization of colonic polyps

2021 ◽  
Vol 14 ◽  
pp. 263177452110146
Author(s):  
Nasim Parsa ◽  
Michael F. Byrne
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Computer Aided Design ◽  
Colorectal Polyps ◽  
Optical Biopsy ◽  
Machine Learning Techniques ◽  
Withdrawal Time ◽  
Adenoma Detection ◽  
Computer Aided

Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.

scholarly journals S0281 Improvement in Adenoma Detection Using a Novel Artificial Intelligence-Aided Polyp Detection Device

2020 ◽  
Vol 115 (1) ◽  
pp. S136-S136
Author(s):  
Aasma Shaukat ◽  
Daniel Colucci ◽  
Lavi Erisson ◽  
Sloane Phillips ◽  
Jonathan Ng ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Polyp Detection ◽  
Adenoma Detection

scholarly journals PROXIMAL SERRATED POLYP DETECTION RATE: A COMPLEMENTARY QUALITY INDICATOR FOR ADENOMA DETECTION RATE?

2020 ◽  
Vol 57 (4) ◽  
pp. 466-470
Author(s):  
Fernando Antônio Vieira LEITE ◽  
Luiz Cláudio Miranda ROCHA ◽  
Rodrigo Roda Rodrigues SILVA ◽  
Eduardo Garcia VILELA ◽  
Luiz Ronaldo ALBERTI ◽  
...  
Keyword(s):  
Quality Indicator ◽  
Detection Rate ◽  
Adenoma Detection Rate ◽  
Screening Colonoscopy ◽  
Polyp Detection ◽  
Historical Cohort ◽  
Serrated Polyp ◽  
Polyp Detection Rate ◽  
Adenoma Detection ◽  
Significant Difference

ABSTRACT BACKGROUND: The effectiveness of colonoscopy for colorectal cancer (CRC) screening depends on quality indicators, which adenoma detection rate (ADR) being the most important. Proximal serrated polyp detection rate (pSPDR) has been studied as a potential quality indicator for colonoscopy. OBJECTIVE: The aim is to analyze and compare the difference in ADR and pSPDR between patients undergoing screening colonoscopy and an unselected population with other indications for colonoscopy, including surveillance and diagnosis. METHODS: This is a historical cohort of patients who underwent colonoscopy in the digestive endoscopy service of a tertiary hospital. Out of 1554 colonoscopies performed, 573 patients were excluded. The remaining 981 patients were divided into two groups: patients undergoing screening colonoscopy (n=428; 43.6%); patients with other indications including surveillance and diagnosis (n=553; 56.4%). RESULTS: Adenoma detection rate of the group with other indications (50.6%) was higher than that of the screening group (44.6%; P=0.03). In regarding pSPDR, there was no difference between pSPDR in both groups (screening 13.6%; other indications 13.7%; P=0.931). There was no significant difference in the mean age (P=0.259) or in the proportion of men and women (P=0.211) between both groups. CONCLUSION: Proximal serrated polyp detection rate showed an insignificant difference between groups with different indications and could be used as a complementary indicator to adenoma detection rate. This could benefit colonoscopists with low colonoscopy volume or low volume of screening colonoscopies.

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