CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy

Abstract

Background and study aims Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. With a mean number of 50,000 SB frames per video, SBCE reading is time-consuming and tedious (30 to 60 minutes per video). We describe a large, multicenter database named CAD-CAP (Computer-Assisted Diagnosis for CAPsule Endoscopy, CAD-CAP). This database aims to serve the development of CAD tools for CE reading.

Materials and methods Twelve French endoscopy centers were involved. All available third-generation SB-CE videos (Pillcam, Medtronic) were retrospectively selected from these centers and deidentified. Any pathological frame was extracted and included in the database. Manual segmentation of findings within these frames was performed by two pre-med students trained and supervised by an expert reader. All frames were then classified by type and clinical relevance by a panel of three expert readers. An automated extraction process was also developed to create a dataset of normal, proofread, control images from normal, complete, SB-CE videos.

Results Four-thousand-one-hundred-and-seventy-four SB-CE were included. Of them, 1,480 videos (35 %) containing at least one pathological finding were selected. Findings from 5,184 frames (with their short video sequences) were extracted and delimited: 718 frames with fresh blood, 3,097 frames with vascular lesions, and 1,369 frames with inflammatory and ulcerative lesions. Twenty-thousand normal frames were extracted from 206 SB-CE normal videos. CAD-CAP has already been used for development of automated tools for angiectasia detection and also for two international challenges on medical computerized analysis.

• References

• 1 Iddan G, Meron G, Glukhovsky A. et al. Wireless capsule endoscopy. Nature 2000; 405: 417
• 2 McAlindon ME, Ching H-L, Yung D. et al. Capsule endoscopy of the small bowel. Ann Transl Med 2016; 4: 369
• 3 Rajpurkar P, Irvin J, Zhu K. et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)
• 4 Chilamkurthy S, Ghosh R, Tanamala S. et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. The Lancet 2018; 392: 2388-2396
• 5 Byrne MF, Chapados N, Soudan F. et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 2019; 68: 94-100
• 6 Esteva A, Kuprel B, Novoa RA. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115-118
• 7 Gulshan V, Peng L, Coram M. et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA 2016; 316: 2402-2410
• 8 Commissioner O of the. Press Announcements - FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. https://www.fda.gov/newsevents/newsroom/pressannouncements/ucm604357.htm [Apr 2018]
• 9 Byrne MF, Shahidi N, Rex DK. Will Computer-Aided Detection and Diagnosis Revolutionize Colonoscopy?. Gastroenterology 2017; 153: 1460-1464.e1
• 10 Chen P-J, Lin M-C, Lai M-J. et al. Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis. Gastroenterology 2018; 154: 568-575
• 11 Iakovidis DK, Koulaouzidis A. Automatic lesion detection in capsule endoscopy based on color saliency: closer to an essential adjunct for reviewing software. Gastrointest Endosc 2014; 80: 877-883
• 12 Leenhardt R, Vasseur P, Li C. et al. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy. Gastrointest Endosc 2019; 89: 189-194
• 13 Koulaouzidis A, Iakovidis DK, Yung DE. et al. KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes. Endosc Int Open 2017; 5: E477-E483
• 14 Iakovidis DK, Koulaouzidis A. Software for enhanced video capsule endoscopy: challenges for essential progress. Nat Rev Gastroenterol Hepatol 2015; 12: 172-186
• 15 Lee J-G, Jun S, Cho Y-W. et al. Deep Learning in Medical Imaging: General Overview. Korean J Radiol 2017; 18: 570-584
• 16 Aoki T, Yamada A, Aoyama K. et al. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 2019; 89: 357-363.e2
• 17 Saurin J-C, Delvaux M, Gaudin J-L. et al. Diagnostic value of endoscopic capsule in patients with obscure digestive bleeding: blinded comparison with video push-enteroscopy. Endoscopy 2003; 35: 576-584
• 18 Leenhardt R, Li C, Koulaouzidis A. et al. Nomenclature and Semantic Description of Vascular Lesions in Small Bowel Capsule Endoscopy: an International Delphi Consensus Statement. Endosc Int Open. 2019 07. E372-E379
• 19 Buisson A, Filippi J, Amiot A. et al. Su1229 Definitions of the Endoscopic Lesions in Crohnʼs Disease: Reproductibility Study and GETAID Expert Consensus. Gastroenterology 2015; 148: S445
• 20 Gal E, Geller A, Fraser G. et al. Assessment and validation of the new capsule endoscopy Crohn’s disease activity index (CECDAI). Dig Dis Sci 2008; 53: 1933-1937
• 21 Yung DE, Rondonotti E, Sykes C. et al. Systematic review and meta-analysis: is bowel preparation still necessary in small bowel capsule endoscopy?. Expert Rev Gastroenterol Hepatol 2017; 11: 979-993
• 22 Mori Y, Kudo S-E, Misawa M. et al. Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study. Ann Intern Med 2018; 169: 357-366
• 23 Miller DD, Brown EW. Artificial Intelligence in Medical Practice: The Question to the Answer?. Am J Med 2018; 131: 129-133
• 24 Swager A-F, van der Sommen F, Klomp SR. et al. Computer-aided detection of early Barrett’s neoplasia using volumetric laser endomicroscopy. Gastrointest Endosc 2017; 86: 839-846
• 25 Kashin S. Artificial intelligence: the rise of the machines. United European Gastroenterology Week. 20-24 oct 2018, Vienna, Austria.
• 26 Pietri O, Rezgui G, Histace A. et al. Development and validation of an automated algorithm to evaluate the abundance of bubbles in small bowel capsule endoscopy. Endosc Int Open 2018; 6: E462-E469
• 27 Van Weyenberg SJB, De Leest HTJI, Mulder CJJ. Description of a novel grading system to assess the quality of bowel preparation in video capsule endoscopy. Endoscopy 2011; 43: 406-411
• 28 Abou AliE, Histace A, Camus M. et al. Development and validation of a computed assessment of cleansing score for evaluation of quality of small-bowel visualization in capsule endoscopy. Endosc Int Open 2018; 6: E646-E651
• 29 Becq A, Histace A, Camus M. et al. Development of a computed cleansing score to assess quality of bowel preparation in colon capsule endoscopy. Endosc Int Open 2018; 6: E844-E850
• 30 Fan S, Xu L, Fan Y. et al. Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images. Phys Med Biol 2018; 63: 165001
• 31 Bernal J, Tajkbaksh N, Sanchez FJ. et al. Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge. IEEE Trans Med Imaging 2017; 36: 1231-1249