scholarly journals Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

2020 ◽  
Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Performance ◽  
Detection System ◽  
Random Order ◽  
Colon Polyps ◽  
Clinical Environment ◽  
Polyp Detection ◽  
Link Type ◽  
Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

scholarly journals Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5315
Author(s):  
Chia-Pei Tang ◽  
Kai-Hong Chen ◽  
Tu-Liang Lin
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Early Recognition ◽  
Detection System ◽  
Colon Polyp ◽  
Colon Polyps ◽  
Polyp Detection ◽  
Detection Model ◽  
Real Time Detection ◽  
Polyp Type

Colonoscopies reduce the incidence of colorectal cancer through early recognition and resecting of the colon polyps. However, the colon polyp miss detection rate is as high as 26% in conventional colonoscopy. The search for methods to decrease the polyp miss rate is nowadays a paramount task. A number of algorithms or systems have been developed to enhance polyp detection, but few are suitable for real-time detection or classification due to their limited computational ability. Recent studies indicate that the automated colon polyp detection system is developing at an astonishing speed. Real-time detection with classification is still a yet to be explored field. Newer image pattern recognition algorithms with convolutional neuro-network (CNN) transfer learning has shed light on this topic. We proposed a study using real-time colonoscopies with the CNN transfer learning approach. Several multi-class classifiers were trained and mAP ranged from 38% to 49%. Based on an Inception v2 model, a detector adopting a Faster R-CNN was trained. The mAP of the detector was 77%, which was an improvement of 35% compared to the same type of multi-class classifier. Therefore, our results indicated that the polyp detection model could attain a high accuracy, but the polyp type classification still leaves room for improvement.

scholarly journals Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study

Gut ◽  
2019 ◽  
Vol 68 (10) ◽  
pp. 1813-1819 ◽  
Author(s):  
Pu Wang ◽  
Tyler M Berzin ◽  
Jeremy Romek Glissen Brown ◽  
Shishira Bharadwaj ◽  
Aymeric Becq ◽  
...  
Keyword(s):  
Real Time ◽  
Detection System ◽  
Cost Benefit ◽  
Controlled Study ◽  
Hyperplastic Polyps ◽  
Polyp Detection ◽  
Detection Rates ◽  
Polyp Detection Rate ◽  
Adenoma Detection ◽  
Adenoma Detection Rates

ObjectiveThe effect of colonoscopy on colorectal cancer mortality is limited by several factors, among them a certain miss rate, leading to limited adenoma detection rates (ADRs). We investigated the effect of an automatic polyp detection system based on deep learning on polyp detection rate and ADR.DesignIn an open, non-blinded trial, consecutive patients were prospectively randomised to undergo diagnostic colonoscopy with or without assistance of a real-time automatic polyp detection system providing a simultaneous visual notice and sound alarm on polyp detection. The primary outcome was ADR.ResultsOf 1058 patients included, 536 were randomised to standard colonoscopy, and 522 were randomised to colonoscopy with computer-aided diagnosis. The artificial intelligence (AI) system significantly increased ADR (29.1%vs20.3%, p<0.001) and the mean number of adenomas per patient (0.53vs0.31, p<0.001). This was due to a higher number of diminutive adenomas found (185vs102; p<0.001), while there was no statistical difference in larger adenomas (77vs58, p=0.075). In addition, the number of hyperplastic polyps was also significantly increased (114vs52, p<0.001).ConclusionsIn a low prevalent ADR population, an automatic polyp detection system during colonoscopy resulted in a significant increase in the number of diminutive adenomas detected, as well as an increase in the rate of hyperplastic polyps. The cost–benefit ratio of such effects has to be determined further.Trial registration numberChiCTR-DDD-17012221; Results.

scholarly journals The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study

2020 ◽  
Vol 13 ◽  
pp. 175628482097916
Author(s):  
Peixi Liu ◽  
Pu Wang ◽  
Jeremy R. Glissen Brown ◽  
Tyler M. Berzin ◽  
Guanyu Zhou ◽  
...  
Keyword(s):  
Real Time ◽  
Detection Rate ◽  
Randomized Study ◽  
Control Group ◽  
Colon Polyps ◽  
Polyp Detection ◽  
Polyp Detection Rate ◽  
Adenoma Detection ◽  
Computer Aided ◽  
Cade System

Background: Computer-aided detection (CADe) of colon polyps has been demonstrated to improve colon polyp and adenoma detection during colonoscopy by indicating the location of a given polyp on a parallel monitor. The aim of this study was to investigate whether embedding the CADe system into the primary colonoscopy monitor may serve to increase polyp and adenoma detection, without increasing physician fatigue level. Methods: Consecutive patients presenting for colonoscopies were prospectively randomized to undergo routine colonoscopy with or without the assistance of a real-time polyp detection CADe system. Fatigue level was evaluated from score 0 to 10 by the performing endoscopists after each colonoscopy procedure. The main outcome was adenoma detection rate (ADR). Results: Out of 790 patients analyzed, 397 were randomized to routine colonoscopy (control group), and 393 to a colonoscopy with computer-aided diagnosis (CADe group). The ADRs were 20.91% and 29.01%, respectively (OR = 1.546, 95% CI 1.116–2.141, p = 0.009). The average number of adenomas per colonoscopy (APC) was 0.29 and 0.48, respectively (Change Folds = 1.64, 95% CI 1.299–2.063, p < 0.001). The improvement in polyp detection was mainly due to increased detection of non-advanced diminutive adenomas, serrated adenoma and hyperplastic polyps. The fatigue score for each procedure was 3.28 versus 3.40 for routine and CADe group, p = 0.357. Conclusions: A real-time CADe system employed on the primary endoscopy monitor may lead to improvements in ADR and polyp detection rate without increasing fatigue level during colonoscopy. The integration of a low-latency and high-performance CADe systems may serve as an effective quality assurance tool during colonoscopy. www.chictr.org.cn number, ChiCTR1800018058.

Tu1990 ARTIFICIAL INTELLIGENCE-ASSISTED POLYP DETECTION SYSTEM FOR COLONOSCOPY, BASED ON THE LARGEST AVAILABLE COLLECTION OF CLINICAL VIDEO DATA FOR MACHINE LEARNING

2019 ◽  
Vol 89 (6) ◽  
pp. AB646-AB647 ◽  
Author(s):  
Masashi Misawa ◽  
Shinei Kudo ◽  
Yuichi Mori ◽  
Tomonari Cho ◽  
Shinichi Kataoka ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Detection System ◽  
Video Data ◽  
Polyp Detection

A high performance real-time edge detection system with NEON

2017 ◽  
Author(s):  
Kaixuan Zhang ◽  
Li Ding ◽  
Yujie Cai ◽  
Wenbo Yin ◽  
Fan Yang ◽  
...  
Keyword(s):  
Edge Detection ◽  
Real Time ◽  
High Performance ◽  
Detection System

Fr611 DIAGNOSTIC ACCURACY OF ARTIFICIAL INTELLIGENCE SYSTEM IN DETECTING COLON POLYPS: MULTICENTER, INTERNATIONAL COMPUTER AIDED POLYP DETECTION (C3PO) TRIAL

2021 ◽  
Vol 160 (6) ◽  
pp. S-375-S-376
Author(s):  
Dimpal Bhakta ◽  
Jigar Patel ◽  
Carlos Cifuentes ◽  
Prithvi Patil ◽  
Asmeen Bhatt ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic Accuracy ◽  
Colon Polyps ◽  
Polyp Detection ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Computer Aided

scholarly journals The Assessment of the Colo-rectal Polyps in Order to the New Diagnostic and Therapeutic Strategies

2015 ◽  
Vol 61 (3) ◽  
pp. 161-164
Author(s):  
Diac Andreea Raluca ◽  
Brusnic Olga ◽  
Gabos Gabriella ◽  
Onisor Danusia ◽  
Drasoveanu Silvia Cosmina ◽  
...  
Keyword(s):  
Real Time ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Colorectal Polyps ◽  
Tubular Adenoma ◽  
Polyp Detection ◽  
Time Prediction ◽  
Predominant Type ◽  
Polyp Detection Rate ◽  
Rectal Polyps

Abstract Objective. Assessment of the histological and endoscopic features of the colo-rectal polyps is requered for the application of the new diagnostic and therapeutical strategies in the managment of the diminutive polyps. Methods. This paper is a descriptive retrospective study on 52 pacients reffered for colonoscopy in Gastroenterology Clinic – Clinical County Hospital Targu Mures from January until September 2014. 80 polyps were assessed. Narrow band imaging examination targeted on the protrusive lezions allowed NICE (Narrow Band Imaging International Colorectal Endoscopic) classification and corroboration of the histology prediction and pathological assessment. Results. Polyp detection rate was 48,58%, given the quality of bowel preparation in hospital fair in 84,5%. The predominant histological type was the tubular adenoma (46,25%), and 40% of the polyps were located in the sigmoid. Among the diminutive polyps, 58,33% were hyperplastic(p<0,0001), mainly in the recto-sigmoid (66,67%); the incidence of high grade displasia or cancer was 0. Real –time prediction of the histology of the colorectal polyps using NBI established: NICE 1: 19 polpyps, histology- 16 hyperplastic, (p<0,0001, sensitivity: 100%, specificity: 95%), NICE 2: 59 polyps, histology- 53 adenomatous, (p<0,0001, sensitivity: 96%, specificity: 76%), NICE 3: 2 polyps- histology-cancer. Conclusions. We did not observe any distribution pattern in the topography of the diminutive polyps. Histologicaly the predominant type was the hyperplastic type. NBI was accurate in real-time prediction of the histology of the colo-rectal polyps. The results are relevant for application of the new strategies in the managment of the diminutive polyps.

scholarly journals 256 – Artificial Intelligence for Real-Time Multiple Polyp Detection with Identification, Tracking, and Optical Biopsy During Colonoscopy

2019 ◽  
Vol 156 (6) ◽  
pp. S-48-S-49 ◽  
Author(s):  
Nicolas Guizard ◽  
Sina Hamidi Ghalehjegh ◽  
Milagros Henkel ◽  
Liqiang Ding ◽  
Neal C. Shahidi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Optical Biopsy ◽  
Polyp Detection ◽  
Multiple Polyp
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