Optical Diagnosis of Neoplasia Using Artificial Intelligence
NCT ID: NCT07158203
Last Updated: 2025-09-05
Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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Recruitment Status
NOT_YET_RECRUITING
Clinical Phase
NA
Total Enrollment
70 participants
Study Classification
INTERVENTIONAL
Study Start Date
2025-09-30
Study Completion Date
2025-12-31
Brief Summary
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Computer-aided diagnosis (CADx) for colonoscopy aims to enhance optical diagnosis but often underperforms when used alongside humans due to under-reliance on AI. Psychological interventions like cognitive forcing, such as delaying CADx suggestions, may improve human-AI interaction by fostering critical assessment. However, their impact on patient-important outcomes remains unexplored.
The investigators will conduct an ex-vivo randomized study with 70 endoscopists assessing 100 polyp videos (≤5 mm) using a CADx tool (GI Genius, Medtronic). Participants will be randomized to either:
* Intervention group: CADx suggestions will be shown in the last 3 seconds of the 15 second polyp video.
* Control group: CADx suggestions will be shown in real-time throughout the playback of the 15 second polyp video.
The primary endpoint is sensitivity for high-confidence neoplasia detection, with secondary endpoints assessing endoscopists' reliance on AI.
CADx systems on the market function in various ways, such as real-time, delayed, or on-demand diagnosis. Our study aims to inform users and manufacturers whether cognitive forcing through delayed CADx suggestions enhances human-AI interaction, leading to improved clinical outcomes.
The investigators will conduct an ex-vivo randomized study with 70 endoscopists assessing 100 polyp videos (≤5 mm) using a CADx tool (GI Genius, Medtronic). Participants will be randomized to either:
* Intervention group: CADx suggestions will be shown in the last 3 seconds of the 15 second polyp video.
* Control group: CADx suggestions will be shown in real-time throughout the playback of the 15 second polyp video.
The primary endpoint is sensitivity for high-confidence neoplasia detection, with secondary endpoints assessing endoscopists' reliance on AI.
CADx systems on the market function in various ways, such as real-time, delayed, or on-demand diagnosis. Our study aims to inform users and manufacturers whether cognitive forcing through delayed CADx suggestions enhances human-AI interaction, leading to improved clinical outcomes.
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Detailed Description
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Computer-Aided Diagnosis and cognitive forcing Computer-aided diagnosis (CADx) for colonoscopy is expected to improve physicians' ability to predict colorectal polyp pathologies (optical diagnosis). However, recent randomized trials indicate that collaboration between humans and CADx yields lower performance than CADx alone.
This suggests suboptimal human-AI interaction due to users' under-reliance on CADx advice, favoring their inherent biases over critically assessing AI suggestions. Psychological interventions, such as cognitive forcing, aim to address this by encouraging crucial assessment of AI suggestions. One example, delayed display of CADx suggestions may promote proactive thinking by giving endoscopists enough time to consider polyp pathologies before receiving CADx suggestions, potentially leading to critical and optimal human-AI interaction. The effectiveness of such cognitive forcing was observed in experimental studies of AI for mammography reading.
However, despite its potential, no studies have evaluated the impact of such interventions on optical diagnosis accuracy in colonoscopy. To investigate the value of psychological intervention in optical diagnosis, the investigators will conduct an ex-vivo randomised controlled study.
Study aim:
This is an ex-vivo randomised controlled study. The hypothesis of our study is that cognitive forcing by delaying display of CADx suggestions facilitates physicians' critical thinking, leading to better clinical outcomes in optical diagnosis in colonoscopy.
Comercially CADx device and video details:
In this study, the investigators are going to use the comercially available CADx system (GI Genius CADx, manufactured by Cosmo Intelligent Medical Devices and distributed by Medtronic Corp). The GI Genius CADx highlights the suspicious area for polyps on-screen with bounding boxes and provides optical diagnosis prediction (i.e. adenoma, non-adenoma). All the endoscopists will be given the information that the CADx tool used in the study is GI Genius with a link to their product overview.
The investigators will collect 100 colonoscopy videos of 100 different diminutive polyps from the Polyp Image BAnk database (PIBAdb). In accordance with the real-world prevalence, 65 polyps will be neoplastic while the remaining 35 will be non-neoplastic. The duration of each video will be adjusted to contain 15 second appearance of the lesion including wite light (WL) and narrow band imaging (NBI). All the polyps should be \< = 5 mm. The original videos were recorded without having any CADx interaction.
PIBAdb contains 507 videos of diminutive colorectal polyps with WL and NBI, of which 231 have a duration of 15 seconds or more. All the videos contain polyps with their histopathology available (i.e. adenoma, sessile serrated lesions, traditional serrated adenomas, invasive, hyperplastic and non-neoplastic). The polyps that have no histology category are going to be excluded from the study.
The investigators will use this database as a pool to select the 100 study videos. First, the investigators are going to split the pool of data into two groups according to polyp histology: one pool containing neoplasia (i.e. adenoma, sessile serrated lesions and traditional serrated adenoma) and the other containing non-neoplasia (i.e. hyperplastic and non-epithelial neoplastic). In each pool, 20 videos will be randomly selected as a first step. These 20 videos will be assessed if they meet our inclusion and exclusion criteria (see below), leaving only eligible videos. This selection process will be repeatedly done until the investigators collect 65 videos of polyps with neoplasia and 35 videos of polyps with non-neoplasia. The figure below shows how the video selection process takes place.
GI Genius´s specification The GI Genius system processes the videos at 50-60 frames per second and in high-definition format to have a good quality of the videos. To process the videos, the GI Genius splits them into two different streams by dedicated video card. One stream is transmitted to the first path to the output without any processing of the AI algorithm. The second stream is sent to the AI algorithm (i.e. second path) and after appropriate computation, if there is a polyp present on-screen appears an overlay in the output highlighting the polyp. The system was designed to work on unaltered WL video streams, but it works under both WL and NBI similarly.The transmission of the video stream to GI Genius is through a Serial Digital Interface (SDI) cable, acquiring the output stream from the video displaying in a computer. Finally, the SDI output stream is transmitted to the monitor containing the original video stream with additional markers superimposed on it.
How to make CADx overlaid videos In the present study, the selected videos of colorectal lesions will be stored in our high-spec computer system first. Then, these videos will be transmitted to GI Genius with an SDI cable. A capture card (DeckLink 8K Pro Mini, Blackmagic) is integrated into the computer system that converts the recorded video output to SDI signal, which allows GI Genius to process the transmitted videos.
All 100 selected videos will be processed by GI Genius in two different ways. In the first set, only the frames from the last 3 seconds of each 15-second video will be processed, and CADx suggestions will appear only in that final 3-second segment. This first set will be shown to endoscopists who are allocated to the intervention arm. In the second set, all frames from the entire 15-second duration will be processed, and CADx suggestions will be overlaid on every frame. This first set will be shown to endoscopists who are allocated to the control arm.
This suggests suboptimal human-AI interaction due to users' under-reliance on CADx advice, favoring their inherent biases over critically assessing AI suggestions. Psychological interventions, such as cognitive forcing, aim to address this by encouraging crucial assessment of AI suggestions. One example, delayed display of CADx suggestions may promote proactive thinking by giving endoscopists enough time to consider polyp pathologies before receiving CADx suggestions, potentially leading to critical and optimal human-AI interaction. The effectiveness of such cognitive forcing was observed in experimental studies of AI for mammography reading.
However, despite its potential, no studies have evaluated the impact of such interventions on optical diagnosis accuracy in colonoscopy. To investigate the value of psychological intervention in optical diagnosis, the investigators will conduct an ex-vivo randomised controlled study.
Study aim:
This is an ex-vivo randomised controlled study. The hypothesis of our study is that cognitive forcing by delaying display of CADx suggestions facilitates physicians' critical thinking, leading to better clinical outcomes in optical diagnosis in colonoscopy.
Comercially CADx device and video details:
In this study, the investigators are going to use the comercially available CADx system (GI Genius CADx, manufactured by Cosmo Intelligent Medical Devices and distributed by Medtronic Corp). The GI Genius CADx highlights the suspicious area for polyps on-screen with bounding boxes and provides optical diagnosis prediction (i.e. adenoma, non-adenoma). All the endoscopists will be given the information that the CADx tool used in the study is GI Genius with a link to their product overview.
The investigators will collect 100 colonoscopy videos of 100 different diminutive polyps from the Polyp Image BAnk database (PIBAdb). In accordance with the real-world prevalence, 65 polyps will be neoplastic while the remaining 35 will be non-neoplastic. The duration of each video will be adjusted to contain 15 second appearance of the lesion including wite light (WL) and narrow band imaging (NBI). All the polyps should be \< = 5 mm. The original videos were recorded without having any CADx interaction.
PIBAdb contains 507 videos of diminutive colorectal polyps with WL and NBI, of which 231 have a duration of 15 seconds or more. All the videos contain polyps with their histopathology available (i.e. adenoma, sessile serrated lesions, traditional serrated adenomas, invasive, hyperplastic and non-neoplastic). The polyps that have no histology category are going to be excluded from the study.
The investigators will use this database as a pool to select the 100 study videos. First, the investigators are going to split the pool of data into two groups according to polyp histology: one pool containing neoplasia (i.e. adenoma, sessile serrated lesions and traditional serrated adenoma) and the other containing non-neoplasia (i.e. hyperplastic and non-epithelial neoplastic). In each pool, 20 videos will be randomly selected as a first step. These 20 videos will be assessed if they meet our inclusion and exclusion criteria (see below), leaving only eligible videos. This selection process will be repeatedly done until the investigators collect 65 videos of polyps with neoplasia and 35 videos of polyps with non-neoplasia. The figure below shows how the video selection process takes place.
GI Genius´s specification The GI Genius system processes the videos at 50-60 frames per second and in high-definition format to have a good quality of the videos. To process the videos, the GI Genius splits them into two different streams by dedicated video card. One stream is transmitted to the first path to the output without any processing of the AI algorithm. The second stream is sent to the AI algorithm (i.e. second path) and after appropriate computation, if there is a polyp present on-screen appears an overlay in the output highlighting the polyp. The system was designed to work on unaltered WL video streams, but it works under both WL and NBI similarly.The transmission of the video stream to GI Genius is through a Serial Digital Interface (SDI) cable, acquiring the output stream from the video displaying in a computer. Finally, the SDI output stream is transmitted to the monitor containing the original video stream with additional markers superimposed on it.
How to make CADx overlaid videos In the present study, the selected videos of colorectal lesions will be stored in our high-spec computer system first. Then, these videos will be transmitted to GI Genius with an SDI cable. A capture card (DeckLink 8K Pro Mini, Blackmagic) is integrated into the computer system that converts the recorded video output to SDI signal, which allows GI Genius to process the transmitted videos.
All 100 selected videos will be processed by GI Genius in two different ways. In the first set, only the frames from the last 3 seconds of each 15-second video will be processed, and CADx suggestions will appear only in that final 3-second segment. This first set will be shown to endoscopists who are allocated to the intervention arm. In the second set, all frames from the entire 15-second duration will be processed, and CADx suggestions will be overlaid on every frame. This first set will be shown to endoscopists who are allocated to the control arm.
Conditions
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Polyps Colorectal
Colonoscopy
Optical Biopsy
Colorectal Cancer Control and Prevention
Colorectal Cancer Screening
Behavior Change
Psychological Factors
Psychological Intervention
Study Design
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Allocation Method
RANDOMIZED
Intervention Model
PARALLEL
The investigators will conduct an ex vivo image-interpretation study where the endoscopists are going to watch 100 short colonoscopy videos of 100 diminutive (\<=5mm) colonic lesions and make optical diagnoses of them. The endoscopists will be given support of computer-aided diagnosis (CADx) before making their decision. The endoscopists will be randomized to the following two arms with a 1:1 ratio, and they are given CADx support accordingly:
* Intervention group: CADx suggestions will be shown in the last 3 seconds of the 15 second polyp video.
* Control group: CADx suggestions will be shown in real-time throughout the playback of the 15 second polyp video.
The endoscopists are going to classify the lesions in the videos as neoplastic (i.e. cancer, adenomas, serrated lesions) or non-neoplastic (i.e. hyperplastic lesions and non-epithelial neoplastic). In each diagnosis, the endoscopists give their confidence level (i.e. high or low) in optical diagnosis.
* Intervention group: CADx suggestions will be shown in the last 3 seconds of the 15 second polyp video.
* Control group: CADx suggestions will be shown in real-time throughout the playback of the 15 second polyp video.
The endoscopists are going to classify the lesions in the videos as neoplastic (i.e. cancer, adenomas, serrated lesions) or non-neoplastic (i.e. hyperplastic lesions and non-epithelial neoplastic). In each diagnosis, the endoscopists give their confidence level (i.e. high or low) in optical diagnosis.
Primary Study Purpose
DIAGNOSTIC
Blinding Strategy
SINGLE
Outcome Assessors
The endoscopists are not blinded to study intervention. Those who analyze the data will be blinded to randomization allocation.
Study Groups
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CADx suggestions will be shown in 15 second polyp video.
CADx suggestions will be shown in real-time throughout the playback of the 15 second polyp video.
Group Type
ACTIVE_COMPARATOR
CADx simultaneously
Intervention Type
DEVICE
The investigators showed the CADx suggestion during the 15-second playback of the video
CADx suggestions will be shown in the last 3 seconds of the 15 second polyp video.
CADx suggestions will be shown in the last 3 seconds of the 15 second polyp video.
Group Type
EXPERIMENTAL
CADx delayed
Intervention Type
BEHAVIORAL
During the 15-seconds polyp video the CADx suggestion appear only in the last 3 seconds of the video
Interventions
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CADx simultaneously
The investigators showed the CADx suggestion during the 15-second playback of the video
Intervention Type
DEVICE
CADx delayed
During the 15-seconds polyp video the CADx suggestion appear only in the last 3 seconds of the video
Intervention Type
BEHAVIORAL
Eligibility Criteria
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Inclusion Criteria
* Videos with a duration of 15 seconds including both WL and NBI.
Exclusion Criteria
* Endoscopists who are involved in the development of the protocol of the present study.
* Videos with no clear image of the polyps.
* Videos with more than one polyp on-screen.
* Inflammatory bowel disease
* Polyposis
* Hereditary colorectal disease
* Videos which CADx cannot provide sufficient number of outputs.
* Videos with no clear image of the polyps.
* Videos with more than one polyp on-screen.
* Inflammatory bowel disease
* Polyposis
* Hereditary colorectal disease
* Videos which CADx cannot provide sufficient number of outputs.
Minimum Eligible Age
18 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
Yes
Sponsors
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Fundacin Biomedica Galicia Sur
OTHER
Sponsor Role
lead
Responsible Party
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Responsibility Role
SPONSOR
Principal Investigators
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Yuichi Mori, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Clinical Effectiveness Research group
Pedro Davila Piñón, Masters degree biotechnology
Role: PRINCIPAL_INVESTIGATOR
Research Group in Gastrointestinal Oncology Ourense / Galicia-Sur Public Galician Foundation
Joaquin Cubiella, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
University Hospital of Ourense
Locations
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Clinical Effectiveness Research Group
Oslo, , Norway
Site Status
Research Group in Gastrointestinal Oncology Ourense
Ourense, , Spain
Site Status
Countries
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Norway
Spain
Central Contacts
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Pedro Davila Piñón, Master degree in biotechnology
Role: CONTACT
+34 988385047 ext. 285047
Facility Contacts
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Pedro Davila, Master Degree in biotechnology
Role: primary
+34 988385047 ext. 285047
References
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Zana Buçinca, Maja Barbara Malaya, and Krzysztof Z. Gajos. 2021. To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making. Proc. ACM Hum.-Comput. Interact. 5, CSCW1, Article 188 (April 2021), 21 pages. https://doi.org/10.1145/3449287
Reference Type
BACKGROUND
Kunar MA, Watson DG. Framing the fallibility of Computer-Aided Detection aids cancer detection. Cogn Res Princ Implic. 2023 May 24;8(1):30. doi: 10.1186/s41235-023-00485-y.
Reference Type
BACKGROUND
Kaminski MF, Anderson J, Valori R, Kraszewska E, Rupinski M, Pachlewski J, Wronska E, Bretthauer M, Thomas-Gibson S, Kuipers EJ, Regula J. Leadership training to improve adenoma detection rate in screening colonoscopy: a randomised trial. Gut. 2016 Apr;65(4):616-24. doi: 10.1136/gutjnl-2014-307503. Epub 2015 Feb 10.
Reference Type
BACKGROUND
Mori Y, Kudo SE, Chiu PW, Singh R, Misawa M, Wakamura K, Kudo T, Hayashi T, Katagiri A, Miyachi H, Ishida F, Maeda Y, Inoue H, Nimura Y, Oda M, Mori K. Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study. Endoscopy. 2016 Dec;48(12):1110-1118. doi: 10.1055/s-0042-113609. Epub 2016 Aug 5.
Reference Type
BACKGROUND
Mori Y, Jin EH, Lee D. Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy. Dig Liver Dis. 2024 Jul;56(7):1140-1143. doi: 10.1016/j.dld.2023.11.033. Epub 2023 Dec 16.
Reference Type
BACKGROUND
Meinikheim M, Mendel R, Palm C, Probst A, Muzalyova A, Scheppach MW, Nagl S, Schnoy E, Rommele C, Schulz DAH, Schlottmann J, Prinz F, Rauber D, Ruckert T, Matsumura T, Fernandez-Esparrach G, Parsa N, Byrne MF, Messmann H, Ebigbo A. Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett's esophagus: a tandem randomized and video trial. Endoscopy. 2024 Sep;56(9):641-649. doi: 10.1055/a-2296-5696. Epub 2024 Mar 28.
Reference Type
BACKGROUND
Stanislaw H, Todorov N. Calculation of signal detection theory measures. Behav Res Methods Instrum Comput. 1999 Feb;31(1):137-49. doi: 10.3758/bf03207704.
Reference Type
BACKGROUND
Kim J, Lim SH, Kang HY, Song JH, Yang SY, Chung GE, Jin EH, Choi JM, Bae JH. Impact of 3-second rule for high confidence assignment on the performance of endoscopists for the real-time optical diagnosis of colorectal polyps. Endoscopy. 2023 Oct;55(10):945-951. doi: 10.1055/a-2073-3411. Epub 2023 May 12.
Reference Type
BACKGROUND
Jin EH, Lee D, Bae JH, Kang HY, Kwak MS, Seo JY, Yang JI, Yang SY, Lim SH, Yim JY, Lim JH, Chung GE, Chung SJ, Choi JM, Han YM, Kang SJ, Lee J, Chan Kim H, Kim JS. Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations. Gastroenterology. 2020 Jun;158(8):2169-2179.e8. doi: 10.1053/j.gastro.2020.02.036. Epub 2020 Feb 29.
Reference Type
BACKGROUND
Cherubini A, Dinh NN. A Review of the Technology, Training, and Assessment Methods for the First Real-Time AI-Enhanced Medical Device for Endoscopy. Bioengineering (Basel). 2023 Mar 24;10(4):404. doi: 10.3390/bioengineering10040404.
Reference Type
BACKGROUND
Suna N, Koksal AS, Yildiz H, Parlak E, Kuzu UB, Yuksel M, Aydinli O, Turhan N, Sakaogullari SZ, Yalinkilic ZM, Ozin Y, Sasmaz N. Prevalence of advanced histologic features in diminutive colon polyps. Acta Gastroenterol Belg. 2015 Jul-Sep;78(3):287-91.
Reference Type
BACKGROUND
Vleugels JLA, Hassan C, Senore C, Cassoni P, Baron JA, Rex DK, Ponugoti PL, Pellise M, Parejo S, Bessa X, Arnau-Collell C, Kaminski MF, Bugajski M, Wieszczy P, Kuipers EJ, Melson J, Ma KH, Holman R, Dekker E, Pohl H. Diminutive Polyps With Advanced Histologic Features Do Not Increase Risk for Metachronous Advanced Colon Neoplasia. Gastroenterology. 2019 Feb;156(3):623-634.e3. doi: 10.1053/j.gastro.2018.10.050. Epub 2018 Nov 2.
Reference Type
BACKGROUND
Djinbachian R, Haumesser C, Taghiakbari M, Pohl H, Barkun A, Sidani S, Liu Chen Kiow J, Panzini B, Bouchard S, Deslandres E, Alj A, von Renteln D. Autonomous Artificial Intelligence vs Artificial Intelligence-Assisted Human Optical Diagnosis of Colorectal Polyps: A Randomized Controlled Trial. Gastroenterology. 2024 Jul;167(2):392-399.e2. doi: 10.1053/j.gastro.2024.01.044. Epub 2024 Feb 7.
Reference Type
BACKGROUND
Other Identifiers
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2025/055
Identifier Type: -
Identifier Source: org_study_id
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