Artificial Intelligence Identifying Polyps in Real-world Colonoscopy
14 december 2018 uppdaterad av: Zhaoshen Li
Validating the Performance of Artificial Intelligence in Identifying Polyps in Real-world Colonoscopy
Recently, artificial intelligence (AI) assisted image recognition has made remarkable breakthroughs in various medical fields with the developing of deep learning and conventional neural networks (CNNs).
However, all current AI assisted-diagnosis systems (ADSs) were established and validated on endoscopic images or selected videos, while its actual assisted-diagnosis performance in real-world colonoscopy is up to now unknown.
Therefore, we validated the performance of an ADS in real-world colonoscopy, which is based on deep learning algorithm and CNNs, trained and tested in multicenter datasets of 20 endoscopy centers.
Studieöversikt
Status
Avslutad
Betingelser
Intervention / Behandling
Detaljerad beskrivning
The ADS were established in changhai digestive endoscopy center to assess its efficacy in clinical practice.
The ADS automatically initiated once the ileocecal valve was pictured by the colonoscopist or the colonoscopist recorded any image of colon during the insertion.
When colonoscopists withdrew the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the ADS, which made it feasible to identify and classify lesions in real time.
Colonoscopists were invited to respond if they doubted potential polyps in the screen, and the ADS also made a voice when identifying potential polyps, followed by repeatedly inspecting to confirm the existence of lesions.
The voice of ADS could be real-time heard by colonoscopists, while the screen of ADS was placed right behind colonoscopists, where polyps identified by ADS could be seen after the colonoscopists' turning but not simultaneously.
The lesion detection by ADS or colonoscopists were determined as follow: A. polyps only identified by ADS, which was considered to be missed by colonoscopists: polyps were reported by the ADS and the colonoscopists did not know the location of polyps without reminder of the ADS until the polyps disappeared from the view; B. polyps first identified by ADS: polyps were first reported by the ADS and the colonoscopists also later knew the location of polyps by themselves; C. polyps simultaneously identified by the ADS and colonoscopists: the time of reporting polyps was closely synchronal (within 1 second); D. polyps first reported by colonoscopists: polyps were first reported by the colonoscopists and the ADS also later identified the location of polyps before the colonoscopists unfolded and pictured the polyps; E. polyps only reported by colonoscopists, which was considered to be missed by the ADS: polyps were reported by the colonoscopists and the ADS did not identify the location of polyps until colonoscopists unfolded and pictured the polyps.
Besides, the false-positives of real-world ADS were also reported with potential causes analyzed by colonoscopists.
Studietyp
Observationell
Inskrivning (Faktisk)
209
Kontakter och platser
Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.
Studieorter
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Shanghai, Kina, 200433
- Changhai Hospital, Second Military Medical University
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Shanghai, Kina, 200433
- Changhai Hospital
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Deltagandekriterier
Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.
Urvalskriterier
Åldrar som är berättigade till studier
18 år till 75 år (Vuxen, Äldre vuxen)
Tar emot friska volontärer
Nej
Kön som är behöriga för studier
Allt
Testmetod
Icke-sannolikhetsprov
Studera befolkning
consecutive outpatient who recieved colonoscopy
Beskrivning
Inclusion Criteria:
- patients receiving screening colonoscopy
- patients receiving surveillance colonoscopy
- patients receiving diagnostic colonoscopy
Exclusion Criteria:
- patients with declined consent
- patients with poor bowel preparation
- patients with failed cecal intubation
- patients with colonic resection
- patients with inflammatory bowel diseases
- patients with polyposis
Studieplan
Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.
Hur är studien utformad?
Designdetaljer
- Observationsmodeller: Endast fall
- Tidsperspektiv: Blivande
Kohorter och interventioner
Grupp / Kohort |
Intervention / Behandling |
|---|---|
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colonoscopy withdrawal with the ADS monitoring
The ADS automatically initiated once the ileocecal valve was pictured by the colonoscopist or the colonoscopist recorded any image of colon during the insertion.
When colonoscopists withdrew the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the ADS, which made it feasible to identify and classify lesions in real time.
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During the testing of trained ADS, when the system doubts colonic lesions from the input data of the test images, a rectangular frame was displayed in the endoscopic image to surround the lesion.
If the system confirmed it as the colonic lesions, a sound of reminder will be played and the types of lesions (non-adenomatous polyps, adenomatous polyps and colorectal cancers) will be classified by the system.
We adopted several standards to define the identification and classification of colonic lesions: 1) when the system identified and confirmed any lesion in the images of no polyps or cancers, the results were judged to be false-positive.
2) when the system both confirmed and correctly localized the lesions in images (IoU > 0.3), the results were judged to be true-positive.
3) when the system did not confirm or correctly localize the lesions, the results were judged as false-negative.
4) when system confirmed no lesions in the normal images, the results were judged to be true-negative.
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Vad mäter studien?
Primära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
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sensitivity of the ADS in identifying polyps
Tidsram: 1 hour
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Polyps that were only reported by colonoscopists were considered to be missed by the ADS (polyps were reported by the colonoscopists and the ADS did not identify the location of polyps until colonoscopists unfolded and pictured the polyps.)
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1 hour
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Sekundära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
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false positves of the ADS per colonoscopy withdrawal
Tidsram: 1 hour
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when the system identified and confirmed any lesion in the images with no polyps or cancers appearing, the results were judged to be false-positive.
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1 hour
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Samarbetspartners och utredare
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Sponsor
Publikationer och användbara länkar
Den som ansvarar för att lägga in information om studien tillhandahåller frivilligt dessa publikationer. Dessa kan handla om allt som har med studien att göra.
Allmänna publikationer
- Byrne MF, Chapados N, Soudan F, Oertel C, Linares Perez M, Kelly R, Iqbal N, Chandelier F, Rex DK. 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 Jan;68(1):94-100. doi: 10.1136/gutjnl-2017-314547. Epub 2017 Oct 24.
- Wang Z, Meng Q, Wang S, Li Z, Bai Y, Wang D. Deep learning-based endoscopic image recognition for detection of early gastric cancer: a Chinese perspective. Gastrointest Endosc. 2018 Jul;88(1):198-199. doi: 10.1016/j.gie.2018.01.029. No abstract available.
- Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18.
- Wang Z, Zhao S, Bai Y. Artificial Intelligence as a Third Eye in Lesion Detection by Endoscopy. Clin Gastroenterol Hepatol. 2018 Sep;16(9):1537. doi: 10.1016/j.cgh.2018.04.032. No abstract available.
Studieavstämningsdatum
Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.
Studera stora datum
Studiestart (Faktisk)
1 november 2018
Primärt slutförande (Faktisk)
10 december 2018
Avslutad studie (Faktisk)
10 december 2018
Studieregistreringsdatum
Först inskickad
30 november 2018
Först inskickad som uppfyllde QC-kriterierna
30 november 2018
Första postat (Faktisk)
3 december 2018
Uppdateringar av studier
Senaste uppdatering publicerad (Faktisk)
17 december 2018
Senaste inskickade uppdateringen som uppfyllde QC-kriterierna
14 december 2018
Senast verifierad
1 december 2018
Mer information
Termer relaterade till denna studie
Ytterligare relevanta MeSH-villkor
Andra studie-ID-nummer
- AI-1
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produkt tillverkad i och exporterad från U.S.A.
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