Artificial intelligence technologies for the detection of colorectal lesions: The future is now

Several studies have shown a significant adenoma miss rate up to 35% during screening colonoscopy, especially in patients with diminutive adenomas. The use of artificial intelligence (AI) in colonoscopy has been gaining popularity by helping endoscopists in polyp detection, with the aim to increase their adenoma detection rate (ADR) and polyp detection rate (PDR) in order to reduce the incidence of interval cancers. The efficacy of deep convolutional neural network (DCNN)-based AI system for polyp detection has been trained and tested in ex vivo settings such as colonoscopy still images or videos. Recent trials have evaluated the real-time efficacy of DCNN-based systems showing promising results in term of improved ADR and PDR. In this review we reported data from the preliminary ex vivo experiences and summarized the results of the initial randomized controlled trials.

[1]  C. Hassan,et al.  Performance of artificial intelligence for colonoscopy regarding adenoma and polyp detection: a meta-analysis. , 2020, Gastrointestinal endoscopy.

[2]  M. Wallace,et al.  Computer-aided detection-assisted colonoscopy: classification and relevance of false positives. , 2020, Gastrointestinal endoscopy.

[3]  C. Hassan,et al.  Efficacy and Tolerability of High- vs Low-Volume Split-Dose Bowel Cleansing Regimens for Colonoscopy: a Systematic Review and Meta-analysis. , 2020, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.

[4]  M. Wallace,et al.  Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial. , 2020, Gastroenterology.

[5]  Tsuyoshi Ozawa,et al.  Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks , 2020, Therapeutic advances in gastroenterology.

[6]  Xiu-Li Zuo,et al.  Impact of real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with video). , 2020, Gastrointestinal endoscopy.

[7]  Peixi Liu,et al.  Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. , 2020, The lancet. Gastroenterology & hepatology.

[8]  W. Zhou,et al.  Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. , 2020, The lancet. Gastroenterology & hepatology.

[9]  S. Gross,et al.  Artificial Intelligence and Polyp Detection , 2020, Current Treatment Options in Gastroenterology.

[10]  Y. Hayashi,et al.  COLON POLYP DETECTION USING LINKED COLOR IMAGING COMPARED TO WHITE LIGHT IMAGING: A SYSTEMATIC REVIEW AND META-ANALYSIS , 2019, Endoscopy.

[11]  Luis A. de Souza,et al.  A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology , 2019, Endoscopy International Open.

[12]  Alessandro Repici,et al.  New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection , 2019, Gut.

[13]  J. Sese,et al.  Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy , 2019, Scientific Reports.

[14]  T. Lui,et al.  1062 USE OF ARTIFICIAL INTELLIGENCE IMAGE CLASSIFER FOR REAL-TIME DETECTION OF COLONIC POLYPS , 2019, Gastrointestinal Endoscopy.

[15]  K. Mori,et al.  Tu1990 ARTIFICIAL INTELLIGENCE-ASSISTED POLYP DETECTION SYSTEM FOR COLONOSCOPY, BASED ON THE LARGEST AVAILABLE COLLECTION OF CLINICAL VIDEO DATA FOR MACHINE LEARNING , 2019, Gastrointestinal Endoscopy.

[16]  F. Maes,et al.  Tu1959 BLI AND LCI IMPROVE POLYP DETECTION AND DELINEATION ACCURACY FOR DEEP LEARNING NETWORKS , 2019, Gastrointestinal Endoscopy.

[17]  R. Ishihara,et al.  Tu2003 APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS COULD DETECT ALL LATERALLY SPREADING TUMOR IN COLONOSCOPIC IMAGES , 2019, Gastrointestinal Endoscopy.

[18]  R. Kiesslich,et al.  G-EYE colonoscopy is superior to standard colonoscopy for increasing adenoma detection rate: an international randomized controlled trial (with videos). , 2019, Gastrointestinal endoscopy.

[19]  T. Wittenberg,et al.  Automated polyp detection in the colorectum: a prospective study (with videos). , 2019, Gastrointestinal endoscopy.

[20]  T. Berzin,et al.  Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study , 2019, Gut.

[21]  Xuqiang Bian,et al.  Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy , 2019, Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association.

[22]  Jun Ki Min,et al.  Overview of Deep Learning in Gastrointestinal Endoscopy , 2019, Gut and liver.

[23]  Danail Stoyanov,et al.  Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. , 2019, The lancet. Gastroenterology & hepatology.

[24]  P. Baldi,et al.  Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. , 2018, Gastroenterology.

[25]  Seth D. Crockett,et al.  Incidence of interval colorectal cancer attributable to an endoscopist in clinical practice. , 2018, Gastrointestinal endoscopy.

[26]  A. Jemal,et al.  Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.

[27]  K. Mori,et al.  Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy , 2018, Annals of Internal Medicine.

[28]  Hayato Itoh,et al.  Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience. , 2018, Gastroenterology.

[29]  D. Hewett,et al.  Sa1923 DETECTION AND DIAGNOSIS OF SESSILE SERRATED ADENOMA/POLYPS USING CONVOLUTIONAL NEURAL NETWORK (ARTIFICIAL INTELLIGENCE) , 2018, Gastrointestinal Endoscopy.

[30]  Michael Riegler,et al.  Deep Learning and Hand-Crafted Feature Based Approaches for Polyp Detection in Medical Videos , 2018, 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS).

[31]  M. Pioche,et al.  Effect of Endocuff-assisted colonoscopy on adenoma detection rate: meta-analysis of randomized controlled trials , 2018, Endoscopy.

[32]  Aymeric Histace,et al.  Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis , 2017, CARE/CLIP@MICCAI.

[33]  Sajjad Waheed,et al.  An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features , 2017, Int. J. Biomed. Imaging.

[34]  Y. Jung,et al.  Miss rate of colorectal neoplastic polyps and risk factors for missed polyps in consecutive colonoscopies , 2017, Intestinal research.

[35]  E. Kuipers,et al.  Performance measures for lower gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) quality improvement initiative , 2017, United European gastroenterology journal.

[36]  K. Geetha,et al.  Automatic Colorectal Polyp Detection in Colonoscopy Video Frames , 2016 .

[37]  Michael Goodman,et al.  Effectiveness of screening colonoscopy in reducing the risk of death from right and left colon cancer: a large community-based study , 2016, Gut.

[38]  G. Fernández-Esparrach,et al.  Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps , 2016, Endoscopy.

[39]  Sun Young Park,et al.  Colonoscopic polyp detection using convolutional neural networks , 2016, SPIE Medical Imaging.

[40]  Nima Tajbakhsh,et al.  Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information , 2016, IEEE Transactions on Medical Imaging.

[41]  Fernando Vilariño,et al.  WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians , 2015, Comput. Medical Imaging Graph..

[42]  Jung-Hwan Oh,et al.  Polyp-Alert: Near real-time feedback during colonoscopy , 2015, Comput. Methods Programs Biomed..

[43]  Vani Konda,et al.  ASGE Technology Committee systematic review and meta-analysis assessing the ASGE PIVI thresholds for adopting real-time endoscopic assessment of the histology of diminutive colorectal polyps. , 2015, Gastrointestinal endoscopy.

[44]  Jung-Hwan Oh,et al.  Part-Based Multiderivative Edge Cross-Sectional Profiles for Polyp Detection in Colonoscopy , 2014, IEEE Journal of Biomedical and Health Informatics.

[45]  M. Murad,et al.  Prevalence, Risk Factors, and Outcomes of Interval Colorectal Cancers: A Systematic Review and Meta-Analysis , 2014, The American Journal of Gastroenterology.

[46]  H. Brenner,et al.  Effect of screening sigmoidoscopy and screening colonoscopy on colorectal cancer incidence and mortality: systematic review and meta-analysis of randomised controlled trials and observational studies , 2014, BMJ : British Medical Journal.

[47]  Christopher D. Jensen,et al.  Adenoma detection rate and risk of colorectal cancer and death. , 2014, The New England journal of medicine.

[48]  Sun Young Park,et al.  A Colon Video Analysis Framework for Polyp Detection , 2012, IEEE Transactions on Biomedical Engineering.

[49]  T. Ponchon3,et al.  Miss rate for colorectal neoplastic polyps: a prospective multicenter study of back-to-back video colonoscopies , 2008 .

[50]  D. Heresbach,et al.  Miss rate for colorectal neoplastic polyps: a prospective multicenter study of back-to-back video colonoscopies , 2008, Endoscopy.

[51]  Jung-Hwan Oh,et al.  Polyp Detection in Colonoscopy Video using Elliptical Shape Feature , 2007, 2007 IEEE International Conference on Image Processing.

[52]  P Glasziou,et al.  Screening for colorectal cancer using the faecal occult blood test, Hemoccult. , 2007, The Cochrane database of systematic reviews.

[53]  Anna K. Jerebko,et al.  Symmetric Curvature Patterns for Colonic Polyp Detection , 2006, MICCAI.

[54]  Dimitris A. Karras,et al.  Computer-aided tumor detection in endoscopic video using color wavelet features , 2003, IEEE Transactions on Information Technology in Biomedicine.

[55]  Dimitris A. Karras,et al.  Computer Methods and Programs in Biomedicine , 2022 .

[56]  Martínez Barellas Mr,et al.  [Continuing education. 41. Subject: pediatric nursing. Topic: How to care for the nursing infant?]. , 1989 .

[57]  Hao Chen,et al.  Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos. , 2017, IEEE journal of biomedical and health informatics.

[58]  Carmen C. Y. Poon,et al.  Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain , 2017, IEEE Journal of Biomedical and Health Informatics.

[59]  Hao Chen,et al.  Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos , 2017, IEEE Journal of Biomedical and Health Informatics.

[60]  Stefan Wesarg,et al.  Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures , 2017, Lecture Notes in Computer Science.

[61]  H. Khalil Treating TB in people with HIV , 2011 .

[62]  C. Villanueva López,et al.  [Continuing education. 41. Subject: pediatric nursing. Topic: How to care for the nursing infant?]. , 1989, Revista de enfermeria.