Real-time deep learning-based colorectal polyp localization on clinical video footage achievable with a wide array of hardware configurations.

 Several computer-assisted polyp detection systems have been proposed, but they have various limitations, from utilizing outdated neural network architectures to a requirement for multi-graphics processing unit (GPU) processing, to validating on small or non-robust datasets. To address these problems, we developed a system based on a state-of-the-art convolutional neural network architecture able to detect polyps in real time on a single GPU and tested on both public datasets and full clinical examination recordings.  The study comprised 165 colonoscopy procedure recordings and 2678 still photos gathered retrospectively.