Pilot study of a new freely available computer-aided polyp detection system in clinical practice.
Artificial intelligence
CADe
Colonoscopy
Deep learning
Polyp
Journal
International journal of colorectal disease
ISSN: 1432-1262
Titre abrégé: Int J Colorectal Dis
Pays: Germany
ID NLM: 8607899
Informations de publication
Date de publication:
Jun 2022
Jun 2022
Historique:
accepted:
01
05
2022
pubmed:
12
5
2022
medline:
9
6
2022
entrez:
11
5
2022
Statut:
ppublish
Résumé
Computer-aided polyp detection (CADe) systems for colonoscopy are already presented to increase adenoma detection rate (ADR) in randomized clinical trials. Those commercially available closed systems often do not allow for data collection and algorithm optimization, for example regarding the usage of different endoscopy processors. Here, we present the first clinical experiences of a, for research purposes publicly available, CADe system. We developed an end-to-end data acquisition and polyp detection system named EndoMind. Examiners of four centers utilizing four different endoscopy processors used EndoMind during their clinical routine. Detected polyps, ADR, time to first detection of a polyp (TFD), and system usability were evaluated (NCT05006092). During 41 colonoscopies, EndoMind detected 29 of 29 adenomas in 66 of 66 polyps resulting in an ADR of 41.5%. Median TFD was 130 ms (95%-CI, 80-200 ms) while maintaining a median false positive rate of 2.2% (95%-CI, 1.7-2.8%). The four participating centers rated the system using the System Usability Scale with a median of 96.3 (95%-CI, 70-100). EndoMind's ability to acquire data, detect polyps in real-time, and high usability score indicate substantial practical value for research and clinical practice. Still, clinical benefit, measured by ADR, has to be determined in a prospective randomized controlled trial.
Identifiants
pubmed: 35543874
doi: 10.1007/s00384-022-04178-8
pii: 10.1007/s00384-022-04178-8
pmc: PMC9167159
doi:
Banques de données
ClinicalTrials.gov
['NCT05006092']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1349-1354Subventions
Organisme : State government of Baden-Württemberg, Funding cluster "Forum Gesundheitsstandort Baden-Württemberg"
ID : 5409.0-001.01/15
Organisme : Interdisziplinäres Zentrum für Klinische Forschung, Universitätsklinikum Würzburg
ID : F-406
Informations de copyright
© 2022. The Author(s).
Références
Corley DA, Levin TR, Doubeni CA (2014) Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med 370:2541. https://doi.org/10.1056/NEJMc1405329
doi: 10.1056/NEJMc1405329
pubmed: 24963577
Liu P, Wang P, Glissen Brown JR et al (2020) The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study. Ther Adv Gastroenterol 13:1756284820979165. https://doi.org/10.1177/1756284820979165
doi: 10.1177/1756284820979165
Hassan C, Spadaccini M, Iannone A et al (2021) Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc 93:77-85.e6. https://doi.org/10.1016/j.gie.2020.06.059
doi: 10.1016/j.gie.2020.06.059
pubmed: 32598963
Repici A, Badalamenti M, Maselli R et al (2020) Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology 159:512-520.e7. https://doi.org/10.1053/j.gastro.2020.04.062
doi: 10.1053/j.gastro.2020.04.062
pubmed: 32371116
Wang P, Liu X, Berzin TM et al (2020) 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 5:343–351. https://doi.org/10.1016/S2468-1253(19)30411-X
doi: 10.1016/S2468-1253(19)30411-X
pubmed: 31981517
Wang P, Berzin TM, Glissen Brown JR et al (2019) Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut 68:1813–1819. https://doi.org/10.1136/gutjnl-2018-317500
doi: 10.1136/gutjnl-2018-317500
pubmed: 30814121
Liu W-N, Zhang Y-Y, Bian X-Q et al (2020) Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol Off J Saudi Gastroenterol Assoc 26:13–19. https://doi.org/10.4103/sjg.SJG_377_19
doi: 10.4103/sjg.SJG_377_19
Su J-R, Li Z, Shao X-J et al (2020) Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest Endosc 91:415-424.e4. https://doi.org/10.1016/j.gie.2019.08.026
doi: 10.1016/j.gie.2019.08.026
pubmed: 31454493
Troya J, Fitting D, Brand M et al (2022) The influence of computer-aided polyp detection systems on reaction time for polyp detection and eye gaze. Endoscopy. https://doi.org/10.1055/a-1770-7353
doi: 10.1055/a-1770-7353
pubmed: 35158384
Kaminski MF, Thomas-Gibson S, Bugajski M et al (2017) Performance measures for lower gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative. Endoscopy 49:378–397. https://doi.org/10.1055/s-0043-103411
doi: 10.1055/s-0043-103411
pubmed: 28268235
Gong D, Wu L, Zhang J et al (2020) Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol 5:352–361. https://doi.org/10.1016/S2468-1253(19)30413-3
doi: 10.1016/S2468-1253(19)30413-3
pubmed: 31981518
Urban G, Tripathi P, Alkayali T et al (2018) Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 155:1069-1078.e8. https://doi.org/10.1053/j.gastro.2018.06.037
doi: 10.1053/j.gastro.2018.06.037
pubmed: 29928897
Hassan C, Wallace MB, Sharma P et al (2020) New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection. Gut 69:799–800. https://doi.org/10.1136/gutjnl-2019-319914
doi: 10.1136/gutjnl-2019-319914
pubmed: 31615835
Pfeifer L, Neufert C, Leppkes M et al (2021) Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience. Eur J Gastroenterol Hepatol 33:e662–e669. https://doi.org/10.1097/MEG.0000000000002209
doi: 10.1097/MEG.0000000000002209
pubmed: 34034272
pmcid: 8734627
Spadaccini M, Hassan C, Alfarone L et al (2022) Comparing number and relevance of false activations between two artificial intelligence CADe SystEms: the NOISE study. Gastrointest Endosc S0016–5107(21):01945–01953. https://doi.org/10.1016/j.gie.2021.12.031
doi: 10.1016/j.gie.2021.12.031
Bangor A, Kortum P, Miller J (2009) Determining what individual SUS scores mean: Adding an adjective rating scale. J Usability Stud 4:114–123