Correct endoscopic prediction of the histopathology and differentiation between benign, pre-malignant, and malignant colorectal polyps (optical diagnosis) remains difficult. Artificial intelligence has great potential in image analysis in gastrointestinal endoscopy. Aim of this study is to investigate the real-time diagnostic performance of AI4CRP for the classification of diminutive colorectal polyps, and to compare it with the real-time diagnostic performance of commercially available CADx systems.
Correct endoscopic prediction of the histopathology and differentiation between benign, pre-malignant, and malignant colorectal polyps (optical diagnosis) remains difficult. Despite additional training, even experienced endoscopists continue to fail meeting international thresholds set for safe implementation of treatment strategies based on optical diagnosis. Multiple machine learning techniques - computer-aided diagnosis (CADx) systems - have been developed for applications in medical imaging within colonoscopy and can improve endoscopic classification of colorectal polyps. Aim of this study is to explore the feasibility of the workflow using AI4CRP (a CNN based CADx system) real-time in the endoscopy suite, and to investigate the real-time diagnostic performance of AI4CRP for the diagnosis of diminutive (\<5mm) colorectal polyps. Secondary, the real-time performance of commercially available CADx systems will be investigated and compared with AI4CRP performance.
Study Type
OBSERVATIONAL
Enrollment
105
* AI4CRP (artificial intelligence for colorectal polyps), a CNN based computer-aided diagnosis system for diagnosis of colorectal polyps (COMET-OPTICAL research group); * CAD EYE, a computer-aided diagnosis system for diagnosis of colorectal polyps (Fujifilm® Corporation, Tokyo, Japan).
Maastricht University Medical Center
Maastricht, Limburg, Netherlands
RECRUITINGCatharina Ziekenhuis Eindhoven
Eindhoven, North Brabant, Netherlands
COMPLETEDTechnical feasibility of real-time use of AI4CRP.
The technical feasibility of real-time use of AI4CRP in the endoscopy suite regarding a proper reception of the video output from the local endoscopy processor towards AI4CRP (in high definition quality, without any delays in time).
Time frame: 6 months
User interface feasibility of real-time use of AI4CRP.
The user interface feasibility of real-time use of AI4CRP in the endoscopy suite regarding a correct alignment of the user interface of AI4CRP with the video output from the local endoscopy system (resizing image pixels and anonymization).
Time frame: 6 months
The diagnostic accuracy of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The real-time diagnostic accuracy of AI4CRP per image modality (HDWL, BLI, LCI, i-scan). Diagnostic accuracy defined as the percentage of correctly optically diagnosed colorectal polyps.
Time frame: 1 year
The sensitivity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The real-time sensitivity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Time frame: 1 year
The specificity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The real-time specificity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Time frame: 1 year
The negative predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The real-time negative predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Time frame: 1 year
The positive predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
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The real-time positive predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Time frame: 1 year
The Area Under ROC Curve (AUC) of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The real-time Area Under ROC Curve (AUC) of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
Time frame: 1 year
The diagnostic accuracy of AI4CRP per polyp.
The real-time diagnostic accuracy of AI4CRP per polyp (comprising the combination of different imaging modalities).
Time frame: 1 year
The sensitivity of AI4CRP per polyp.
The real-time sensitivity of AI4CRP per polyp (comprising the combination of different imaging modalities).
Time frame: 1 year
The specificity of AI4CRP per polyp.
The real-time specificity of AI4CRP per polyp (comprising the combination of different imaging modalities).
Time frame: 1 year
The negative predictive value of AI4CRP per polyp.
The real-time negative predictive value of AI4CRP per polyp (comprising the combination of different imaging modalities).
Time frame: 1 year
The positive predictive value of AI4CRP per polyp.
The real-time positive predictive value of AI4CRP per polyp (comprising the combination of different imaging modalities).
Time frame: 1 year
The Area Under ROC Curve (AUC) of AI4CRP per polyp.
The real-time Area Under ROC Curve (AUC) of AI4CRP per polyp (comprising the combination of different imaging modalities).
Time frame: 1 year
The diagnostic accuracy of CAD EYE in BLI mode, per polyp.
The real-time diagnostic accuracy of CAD EYE in BLI mode, per polyp.
Time frame: 1 year
The sensitivity of CAD EYE in BLI mode, per polyp.
The real-time sensitivity of CAD EYE in BLI mode, per polyp.
Time frame: 1 year
The specificity of CAD EYE in BLI mode, per polyp.
The real-time specificity of CAD EYE in BLI mode, per polyp.
Time frame: 1 year
The negative predictive value of CAD EYE in BLI mode, per polyp.
The real-time negative predictive value of CAD EYE in BLI mode, per polyp.
Time frame: 1 year
The positive predictive value of CAD EYE in BLI mode, per polyp.
The real-time positive predictive value of CAD EYE in BLI mode, per polyp.
Time frame: 1 year
The Area Under ROC Curve (AUC) of CAD EYE in BLI mode, per polyp.
The real-time Area Under ROC Curve (AUC) of CAD EYE in BLI mode, per polyp.
Time frame: 1 year
The diagnostic accuracy of AI4CRP per patient.
The real-time diagnostic accuracy of AI4CRP per patient (in case of multiple polyps per patient).
Time frame: 1 year
The diagnostic accuracy of CAD EYE per patient.
The real-time diagnostic accuracy of CAD EYE per patient (in case of multiple polyps per patient).
Time frame: 1 year
The localization score of AI4CRP.
The localization score of AI4CRP regarding the number of images in which the heatmap produced by AI4CRP pointed out the area of interest (scale: correct, incorrect, or partly correct area of interest).
Time frame: 1 year
The difference in diagnostic accuracy of endoscopists per polyp before and after AI.
The difference in real-time diagnostic accuracy of endoscopists per polyp before and after AI.
Time frame: 1 year
The difference in sensitivity of endoscopists per polyp before and after AI.
The difference in real-time sensitivity of endoscopists per polyp before and after AI.
Time frame: 1 year
The difference in specificity of endoscopists per polyp before and after AI.
The difference in real-time specificity of endoscopists per polyp before and after AI.
Time frame: 1 year
The difference in negative predictive value of endoscopists per polyp before and after AI.
The difference in real-time negative predictive value of endoscopists per polyp before and after AI.
Time frame: 1 year
The difference in positive predictive value of endoscopists per polyp before and after AI.
The difference in real-time positive predictive value of endoscopists per polyp before and after AI.
Time frame: 1 year
The agreement in surveillance interval based on optical diagnosis and histopathology.
The agreement in surveillance interval based on optical diagnosis of diminutive colorectal polyps and histopathology of small and large colorectal polyps, compared to the surveillance interval based on histopathology of all colorectal polyps (diminutive, small, and large).
Time frame: 1 year