CHALLENGING CASES
DISCOVERED BY ENDOSCREENER
“The World’s First Randomized Controlled Trial That Demonstrated Statistically Measurable Improvement In SSL Metrics”2
These Impressive Improvements Were Achieved By A Group Of Participating Physicians With An Already High (44%) Adenoma Detection Rate2
ENDOSCREENER COMPUTER AIDED POLYP DETECTION SYSTEM
ARTIFICIAL INTELLIGENCE CADe
EndoScreener® software utilizes deep learning artificial intelligence (A.I.) and Computer Aided Polyp Detection (CADe) systems to efficiently analyze and identify local abnormalities in the lining of the colon, significantly improving the detection and precision capabilities of a standard colonoscopy. EndoScreener, a software as a medical device (SaMD) product, is compatible with most mainstream endoscope systems.*
Proven Clinical Performance
Supports High Quality Outcomes
- More Than 5,000 Patients In Six Rigorous, Randomized Controlled Trials (RCTs)
- Two Tandem Colonoscopy Randomized Controlled Trials1,2
- Double Blind Randomized Controlled Trial3
“Sessile Serrated Lesion (SSL) Miss Rate Was Significantly Reduced In Our US Trial“
EndoScreener® software utilizes deep learning artificial intelligence (A.I.) and Computer Aided Polyp Detection (CADe) systems to efficiently analyze and identify local abnormalities in the lining of the colon, significantly improving the detection and precision capabilities of a standard
colonoscopy.
Product Resources
Clinical Evidence
Jeremy R. Glissen Brown, Nabil M. Mansour, Pu Wang, Maria Aguilera Chuchuca, Scott B. Minchenberg, Madhuri Chandnani, Lin Liu, Seth A. Gross, Neil Sengupta, and Tyler M. Berzin
Peixi Liu, Pu Wang, Jeremy R. Glissen Brown, Tyler M. Berzin, Guanyu Zhou, Weihui Liu , Xun Xiao, Ziyang Chen, Zhihong Zhang, Chao Zhou, Lei Lei, Fei Xiong Liangping Li and Xiaogang Liu
Pu Wang, Tyler M Berzin, Jeremy Romek Glissen Brown, Shishira Bharadwaj, Aymeric Beca, Xun Xiao, Peixi Liu, Liangping Li, Yan Song, Di Zhang, Yi Li, Guangre Xu, Mengtian Tu, Xiaogang Liu
(Wang, P et al) Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy
Article Published: 10 October 2018 https://www.nature.com/articles/s41551-018-0301-3
First Double Blind Prospective Randomized Controlled Trial of AI In ALL Medicine
(Wang, P et al) Effect of a deep-learning computer aided detection system on adenoma detection during colonoscopy (CADe-DB trial): A double-blind randomised study.
Lancet Gastroenterol Hepatol 2020 Apr; 5(4):343-351.
PMID: 31981517 DOI: 10.1016/S2468-1253(19)30411-X
Erratum in
Correction to Lancet Gastroenterol Hepatol 2020; 5: 343-51.
[No authors listed] Lancet Gastroenterol Hepatol. 2020 Apr;5(4):e3. doi: 10.1016/S2468-1253(20)30051-0. PMID: 32171086 No abstract available.
Abstract
Background: Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system that avoids potential operational bias.
Methods: We did a double-blind randomised trial at the endoscopy centre in Caotang branch hospital of Sichuan Provincial People's Hospital in China. We enrolled consecutive patients (aged 18-75 years) presenting for diagnostic and screening colonoscopy. We excluded patients with a history of inflammatory bowel disease, colorectal cancer, or colorectal surgery or who had a contraindication for biopsy; we also excluded patients who had previously had an unsuccessful colonoscopy and who had a high suspicion for polyposis syndromes, inflammatory bowel disease, and colorectal cancer. We allocated patients (1:1) to colonoscopy with either the CADe system or a sham system. Randomisation was by computer-generated random number allocation. Patients and the endoscopist were unaware of the random assignment. To achieve masking, the output of the system was shown on a second monitor that was only visible to an observer who was responsible for reporting the alerts. The primary outcome was the adenoma detection rate (ADR), which is the proportion of individuals having a complete colonoscopy, from caecum to rectum, who had one or more adenomas detected. The primary analysis was per protocol. We also analysed characteristics of polyps and adenomas missed initially by endoscopists but detected by the CADe system. This trial is complete and is registered with http://www.chictr.org.cn, ChiCTR1800017675.
Findings: Between Sept 3, 2018, and Jan 11, 2019, 1046 patients were enrolled to the study, of whom 36 were excluded before randomisation, 508 were allocated colonoscopy with polyp detection using the CADe system, and 502 were allocated colonoscopy with the sham system. After further excluding patients who met exclusion criteria, 484 patients in the CADe group and 478 in the sham group were included in analyses. The ADR was significantly greater in the CADe group than in the sham group, with 165 (34%) of 484 patients allocated to the CADe system having one or more adenomas detected versus 132 (28%) of 478 allocated to the sham system (odds ratio 1·36, 95% CI 1·03-1·79; p=0·030). No complications were reported among all colonoscopy procedures. Polyps initially missed by the endoscopist but identified by the CADe system were generally small in size, isochromatic, flat in shape, had an unclear boundary, were partly behind colon folds, and were on the edge of the visual field.
Interpretation: Polyps initially missed by the endoscopist had characteristics that are sometimes difficult for skilled endoscopists to recognise. Such polyps could be detected using a high-performance CADe system during colonoscopy. The effect of CADe during colonoscopy on the incidence of interval colorectal cancer should be investigated.
Funding: None. Copyright 2020 Elsevier Ltd. All rights reserved.
First Randomized Tandem Trial of AI in Medicine
(Wang, P et al)Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study
PMID: 32562721 DOI: 10.1053/j.gastro.2020.06.023
Abstract
Background and aims: Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recognized by endoscopists or polyps that are in locations that do not appear on the screen at all. Computer-aided detection (CADe) systems, based on deep learning, might reduce rates of missed adenomas by displaying visual alerts that identify precancerous polyps on the endoscopy monitor in real time. We compared adenoma miss rates of CADe colonoscopy vs routine white-light colonoscopy.
Methods: We performed a prospective study of patients, 18-75 years old, referred for diagnostic, screening, or surveillance colonoscopies at a single endoscopy center of Sichuan Provincial People's Hospital from June 3, 2019 through September 24, 2019. Same day, tandem colonoscopies were performed for each participant by the same endoscopist. Patients were randomly assigned to groups that received either CADe colonoscopy (n=184) or routine colonoscopy (n=185) first, followed immediately by the other procedure. Endoscopists were blinded to the group each patient was assigned to until immediately before the start of each colonoscopy. Polyps that were missed by the CADe system but detected by endoscopists were classified as missed polyps. False polyps were those continuously traced by the CADe system but then determined not to be polyps by the endoscopists. The primary endpoint was adenoma miss rate, which was defined as the number of adenomas detected in the second-pass colonoscopy divided by the total number of adenomas detected in both passes.
Results: The adenoma miss rate was significantly lower with CADe colonoscopy (13.89%; 95% CI, 8.24%-19.54%) than with routine colonoscopy (40.00%; 95% CI, 31.23%-48.77%, P<.0001). The polyp miss rate was significantly lower with CADe colonoscopy (12.98%; 95% CI, 9.08%-16.88%) than with routine colonoscopy (45.90%; 95% CI, 39.65%-52.15%) (P<.0001). Adenoma miss rates in ascending, transverse, and descending colon were significantly lower with CADe colonoscopy than with routine colonoscopy (ascending colon 6.67% vs 39.13%; P=.0095; transverse colon 16.33% vs 45.16%; P=.0065; and descending colon 12.50% vs 40.91%, P=.0364).
Conclusions: CADe colonoscopy reduced the overall miss rate of adenomas by endoscopists using white-light endoscopy. Routine use of CADe might reduce the incidence of interval colon cancers. chictr.org.cn study no: ChiCTR1900023086.
Keywords: AMR; Artificial Intelligence; Early Detection; Neoplasm.
Copyright 2020 AGA Institute. Published by Elsevier Inc. All rights reserved.
Consistent Polyp Detection – All Of The Time
Human Central Vision Has A Tight Focus – Around 13°4
EndoScreener’s Computer Vision Does Not Have That Limitation
EndoScreener Focuses On 100% Of The Screen – 100% Of The Time
Always On – Always Focused – Doesn’t Get Tired Or Distracted
- EndoScreener Utilizes Advanced Mathematical Modeling And Pixel Level, Image Segmentation To Produce An Algorithm Focused On Local Features.
- Pixel Level Awareness of local features can improve an AI Polyp Detection System’s Ability To Be More Sensitive To Difficult Lesions While Producing Fewer False Positives5
Challenging Situations Detected By EndoScreener
Isochromatic
Flat
On the edge of visual field
NO ADDED SECURITY RISKS
NO INTERNET CONNECTIVITY | NO INFORMATION SAVED | NO INFORMATION TRANSFERRED
