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.
None
Endoscopists experienced with more than 100 colonoscopies. Exclusion criteria
Endoscopists who are involved in the development of the protocol of the present study.
Videos with a duration of 15 seconds including both WL and NBI.
Videos with diminutive polyps with confirmed pathology. Exclusion criteria.
Parallel
CADx simultaneously
DeviceThe investigators showed the CADx suggestion during the 15-second playback of the video
CADx delayed
BehavioralDuring the 15-seconds polyp video the CADx suggestion appear only in the last 3 seconds of the video
Sensitivity of the optical diagnosis of neoplastic lesions.
\- Sensitivity of each endoscopist and in each arm in the optical diagnosis of neoplastic or non-neoplastic lesions with high confidence level
The reliance level on artificial intelligence, measured using the C value of the signal detection theory
Measured by the C value of the signal detection theory
Discrimination (d´) level of neoplastic lesions based on the signal detection theory
Measured by the d' value of the signal detection theory
Receiver Operating characteristic (ROC) curve to determine overall discrimination in the signal detection theory
Overall discrimination assessment by the d' measured by the Receiver Operating characteristic Curve
Proportion of high confidence diagnosis
Proportion of lesions classified as neoplastic or non-neoplastic with high confidence diagnosis
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