Artificial Intelligence Identifying Polyps in Real-world Colonoscopy
14 grudnia 2018 zaktualizowane przez: 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.
Przegląd badań
Status
Zakończony
Warunki
Interwencja / Leczenie
Szczegółowy opis
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.
Typ studiów
Obserwacyjny
Zapisy (Rzeczywisty)
209
Kontakty i lokalizacje
Ta sekcja zawiera dane kontaktowe osób prowadzących badanie oraz informacje o tym, gdzie badanie jest przeprowadzane.
Lokalizacje studiów
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Shanghai, Chiny, 200433
- Changhai Hospital, Second Military Medical University
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Shanghai, Chiny, 200433
- Changhai Hospital
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Kryteria uczestnictwa
Badacze szukają osób, które pasują do określonego opisu, zwanego kryteriami kwalifikacyjnymi. Niektóre przykłady tych kryteriów to ogólny stan zdrowia danej osoby lub wcześniejsze leczenie.
Kryteria kwalifikacji
Wiek uprawniający do nauki
18 lat do 75 lat (Dorosły, Starszy dorosły)
Akceptuje zdrowych ochotników
Nie
Płeć kwalifikująca się do nauki
Wszystko
Metoda próbkowania
Próbka bez prawdopodobieństwa
Badana populacja
consecutive outpatient who recieved colonoscopy
Opis
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
Plan studiów
Ta sekcja zawiera szczegółowe informacje na temat planu badania, w tym sposób zaprojektowania badania i jego pomiary.
Jak projektuje się badanie?
Szczegóły projektu
- Modele obserwacyjne: Tylko przypadek
- Perspektywy czasowe: Spodziewany
Kohorty i interwencje
Grupa / Kohorta |
Interwencja / Leczenie |
|---|---|
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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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Co mierzy badanie?
Podstawowe miary wyniku
Miara wyniku |
Opis środka |
Ramy czasowe |
|---|---|---|
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sensitivity of the ADS in identifying polyps
Ramy czasowe: 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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Miary wyników drugorzędnych
Miara wyniku |
Opis środka |
Ramy czasowe |
|---|---|---|
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false positves of the ADS per colonoscopy withdrawal
Ramy czasowe: 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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Współpracownicy i badacze
Tutaj znajdziesz osoby i organizacje zaangażowane w to badanie.
Sponsor
Publikacje i pomocne linki
Osoba odpowiedzialna za wprowadzenie informacji o badaniu dobrowolnie udostępnia te publikacje. Mogą one dotyczyć wszystkiego, co jest związane z badaniem.
Publikacje ogólne
- 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.
Daty zapisu na studia
Daty te śledzą postęp w przesyłaniu rekordów badań i podsumowań wyników do ClinicalTrials.gov. Zapisy badań i zgłoszone wyniki są przeglądane przez National Library of Medicine (NLM), aby upewnić się, że spełniają określone standardy kontroli jakości, zanim zostaną opublikowane na publicznej stronie internetowej.
Główne daty studiów
Rozpoczęcie studiów (Rzeczywisty)
1 listopada 2018
Zakończenie podstawowe (Rzeczywisty)
10 grudnia 2018
Ukończenie studiów (Rzeczywisty)
10 grudnia 2018
Daty rejestracji na studia
Pierwszy przesłany
30 listopada 2018
Pierwszy przesłany, który spełnia kryteria kontroli jakości
30 listopada 2018
Pierwszy wysłany (Rzeczywisty)
3 grudnia 2018
Aktualizacje rekordów badań
Ostatnia wysłana aktualizacja (Rzeczywisty)
17 grudnia 2018
Ostatnia przesłana aktualizacja, która spełniała kryteria kontroli jakości
14 grudnia 2018
Ostatnia weryfikacja
1 grudnia 2018
Więcej informacji
Terminy związane z tym badaniem
Dodatkowe istotne warunki MeSH
Inne numery identyfikacyjne badania
- AI-1
Informacje o lekach i urządzeniach, dokumenty badawcze
Bada produkt leczniczy regulowany przez amerykańską FDA
Nie
Bada produkt urządzenia regulowany przez amerykańską FDA
Nie
produkt wyprodukowany i wyeksportowany z USA
Nie
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