Hand-crafted AI algorithms have recently given way to convolutional neural networks with the ability to detect polyps in real-time. The first randomized controlled trial comparing an AI system to standard colonoscopy found a 9% increase in adenoma detection rate, but the improvement was restricted to polyps smaller than 10 mm and the results need validation. As this field rapidly evolves, important issues to consider include standardization of outcomes, dataset availability, real-world applications, and regulatory approval. AI has shown great potential for improving colonic polyp detection while requiring minimal training for endoscopists. The question of when AI will enter endoscopic practice depends on whether the technology can be integrated into existing hardware and an assessment of its added value for patient care.
Keyphrases
- artificial intelligence
- deep learning
- machine learning
- convolutional neural network
- big data
- randomized controlled trial
- loop mediated isothermal amplification
- real time pcr
- label free
- primary care
- healthcare
- study protocol
- type diabetes
- ultrasound guided
- transcription factor
- adipose tissue
- quality improvement
- clinical trial
- sensitive detection
- metabolic syndrome
- quantum dots
- double blind
- systematic review