Novel artificial intelligence–enabled deep learning system to enhance adenoma detection: a prospective randomized controlled study
Jesse Lachter, Simon Christopher Schlachter, Robert Scooter Plowman, Roman Goldenberg, Yaffa Raz, Nadav Rabani, Natalie Aizenberg, Alain Suissa, Ehud Rivlin
Abstract
Background and Aims: ), using Fuji 7000 series colonoscopes (Fujifilm, Singapore). Methods: was trained and validated only on white-light imaging, excluding the use of continuous digital chromoendoscopy. Results: < .01). The false alert rate (mean, 4 per examination) was lower than the mean of >20 false alerts reported for other computer-aided detection systems. Withdrawal times were equivalent between arms (mean, 7.2 minutes; not significant). Conclusions: after the completion of the study and after commercial availability. (Clinical trial registration number: MYTRIALS.).
Topics & Concepts
Artificial intelligenceDeep learningComputer scienceRandomized controlled trialPsychologyMachine learningMedicineInternal medicineRadiomics and Machine Learning in Medical ImagingAI in cancer detectionColorectal Cancer Screening and Detection