Artificial Intelligence-Based Polyp Detection in Colonoscopy: Where Have We Been, Where Do We Stand, and Where Are We Headed?

被引:7
作者
Wittenberg, Thomas [1 ]
Raithel, Martin [2 ]
机构
[1] Fraunhofer Inst Integrated Circuits IIS, Erlangen, Germany
[2] Malteser Waldkrankenhaus St Marien, Erlangen, Germany
关键词
Artificial intelligence; AI; Colonoscopy; Adenoma and polyp detection; History; PIT-PATTERN-CLASSIFICATION; TEXTURE; ABNORMALITY; VALIDATION; DIAGNOSIS;
D O I
10.1159/000512438
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
Background: In the past, image-based computer-assisted diagnosis and detection systems have been driven mainly from the field of radiology, and more specifically mammography. Nevertheless, with the availability of large image data collections (known as the "Big Data" phenomenon) in correlation with developments from the domain of artificial intelligence (AI) and particularly so-called deep convolutional neural networks, computer-assisted detection of adenomas and polyps in real-time during screening colonoscopy has become feasible. Summary: With respect to these developments, the scope of this contribution is to provide a brief overview about the evolution of AI-based detection of adenomas and polyps during colonoscopy of the past 35 years, starting with the age of "handcrafted geometrical features" together with simple classification schemes, over the development and use of "texture-based features" and machine learning approaches, and ending with current developments in the field of deep learning using convolutional neural networks. In parallel, the need and necessity of large-scale clinical data will be discussed in order to develop such methods, up to commercially available AI products for automated detection of polyps (adenoma and benign neoplastic lesions). Finally, a short view into the future is made regarding further possibilities of AI methods within colonoscopy. Key Messages: Researchofimage-based lesion detection in colonoscopy data has a 35-year-old history. Milestones such as the Paris nomenclature, texture features, big data, and deep learning were essential for the development and availability of commercial AI-based systems for polyp detection.
引用
收藏
页码:428 / 438
页数:11
相关论文
共 66 条
  • [41] Automation of colonoscopy part II: Visual-control aspects
    Phee, SJ
    Ng, WS
    Chen, IM
    Seow-Choen, F
    Davies, BL
    [J]. IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, 1998, 17 (03): : 81 - 88
  • [42] Plagianakos VP, 2001, NNESMED 2001 4 INT C, P59
  • [43] KVASIR: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection
    Pogorelov, Konstantin
    Randel, Kristin Ranheim
    Griwodz, Carsten
    Eskeland, Sigrun Losada
    de Lange, Thomas
    Johansen, Dag
    Spampinato, Concetto
    Dang-Nguyen, Duc-Tien
    Lux, Mathias
    Schmidt, Peter Thelin
    Riegler, Michael
    Halvorsen, Pal
    [J]. PROCEEDINGS OF THE 8TH ACM MULTIMEDIA SYSTEMS CONFERENCE (MMSYS'17), 2017, : 164 - 169
  • [44] Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial
    Repici, Alessandro
    Badalamenti, Matteo
    Maselli, Roberta
    Correale, Loredana
    Radaelli, Franco
    Rondonotti, Emanuele
    Ferrara, Elisa
    Spadaccini, Marco
    Alkandari, Asma
    Fugazza, Alessandro
    Anderloni, Andrea
    Galtieri, Piera Alessia
    Pellegatta, Gaia
    Carrara, Silvia
    Di Leo, Milena
    Craviotto, Vincenzo
    Lamonaca, Laura
    Lorenzetti, Roberto
    Andrealli, Alida
    Antonelli, Giulio
    Wallace, Michael
    Sharma, Prateek
    Rosch, Thomas
    Hassan, Cesare
    [J]. GASTROENTEROLOGY, 2020, 159 (02) : 512 - +
  • [45] High-definition colonoscopy versus Endocuff versus EndoRings versus full-spectrum endoscopy for adenoma detection at colonoscopy: a multicenter randomized trial
    Rex, Douglas K.
    Repici, Alessandro
    Gross, Seth A.
    Hassan, Cesare
    Ponugoti, Prasanna L.
    Garcia, Jonathan R.
    Broadley, Heather M.
    Thygesen, Jack C.
    Sullivan, Andrew W.
    Tippins, William W.
    Main, Samuel A.
    Eckert, George J.
    Vemulapalli, Krishna C.
    [J]. GASTROINTESTINAL ENDOSCOPY, 2018, 88 (02) : 335 - +
  • [46] Circumferential EMR of carcinoma arising in Barrett's esophagus: case report
    Satodate, H
    Inoue, H
    Yoshida, T
    Usui, S
    Iwashita, M
    Fukami, N
    Shiokawa, A
    Kudo, S
    [J]. GASTROINTESTINAL ENDOSCOPY, 2003, 58 (02) : 288 - 292
  • [47] Deep Learning to Improve Breast Cancer Detection on Screening Mammography
    Shen, Li
    Margolies, Laurie R.
    Rothstein, Joseph H.
    Fluder, Eugene
    McBride, Russell
    Sieh, Weiva
    [J]. SCIENTIFIC REPORTS, 2019, 9 (1)
  • [48] Shevchenko N, 2008, TAG 7 JAHR COMP ROB, P205
  • [49] Automatic Colon Polyp Detection Using Region Based Deep CNN and Post Learning Approaches
    Shin, Younghak
    Qadir, Hemin Ali
    Aabakken, Lars
    Bergsland, Jacob
    Balasingham, Ilangko
    [J]. IEEE ACCESS, 2018, 6 : 40950 - 40962
  • [50] Classification of Colon Polyps in NBI Endoscopy using Vascularization Features
    Stehle, Thomas
    Auer, Roland
    Gross, Sebastian
    Behrens, Alexander
    Wulff, Jonas
    Aach, Til
    Winograd, Ron
    Trautwein, Christian
    Tischendorf, Jens
    [J]. MEDICAL IMAGING 2009: COMPUTER-AIDED DIAGNOSIS, 2009, 7260