19 research outputs found
I-scan optical enhancement for the in vivo prediction of diminutive colorectal polyp histology:results from a prospective three-phased multicentre trial
BACKGROUND AND AIMS:Dye-less chromoendoscopy is an emerging technology for colorectal polyp characterization. Herein, we investigated whether the newly introduced I-scan optical enhancement (OE) can accurately predict polyp histology in vivo in real-time. METHODS:In this prospective three-phased study, 84 patients with 230 diminutive colorectal polyps were included. During the first two study phases, five endoscopists assessed whether analysis of polyp colour, surface and vascular pattern under i-scan OE can differentiate in vivo between adenomatous and hyperplastic polyps. Finally, junior and experienced endoscopists (JE, EE, each n = 4) not involved in the prior study phases made a post hoc diagnosis of polyp histology using a static i-scan OE image database. Histopathology was used as a gold-standard in all study phases. RESULTS:The overall accuracy of i-scan OE for histology prediction was 90% with a sensitivity, specificity, positive (PPV) and negative prediction value (NPV) of 91%, 90%, 86% and 94%, respectively. In high confidence predictions, the diagnostic accuracy increased to 93% with sensitivity, specificity, PPV and NPV of 94%, 91%, 89% and 96%. Colonoscopy surveillance intervals were predicted correctly in ≥ 90% of patients. In the post hoc analysis EE predicted polyp histology under i-scan OE with an overall accuracy of 91%. After a single training session, JE achieved a comparable diagnostic performance for predicting polyp histology with i-scan OE. CONCLUSION:The histology of diminutive colorectal polyps can be accurately predicted with i-scan OE in vivo in real-time. Furthermore, polyp differentiation with i-scan OE appears to require only a short learning curve
Dual Control for Approximate Bayesian Reinforcement Learning
Control of non-episodic, finite-horizon dynamical systems with uncertain
dynamics poses a tough and elementary case of the exploration-exploitation
trade-off. Bayesian reinforcement learning, reasoning about the effect of
actions and future observations, offers a principled solution, but is
intractable. We review, then extend an old approximate approach from control
theory---where the problem is known as dual control---in the context of modern
regression methods, specifically generalized linear regression. Experiments on
simulated systems show that this framework offers a useful approximation to the
intractable aspects of Bayesian RL, producing structured exploration strategies
that differ from standard RL approaches. We provide simple examples for the use
of this framework in (approximate) Gaussian process regression and feedforward
neural networks for the control of exploration.Comment: 30 pages, 7 figure
Strategic exploration in human adaptive control
How do people explore in order to gain rewards in uncertain dynamical systems? Within a reinforcement learning paradigm, control normally involves trading off between exploration (i.e. trying out actions in order to gain more knowledge about the system) and exploitation (i.e. using current knowledge of the system to maximize reward). We study a novel control task in which participants must steer a boat on a grid, assessing whether participants explore strategically in order to produce higher rewards later on. We find that participants explore strategically yet conservatively, exploring more when mistakes are less costly and practicing actions that will be needed later on
Approximate dual control maintaining the value of information with an application to building control
Patient recruitment and study phases.
<p>(A) Patient and polyp characteristics, SSA = sessile serrated adenoma (B) Exemplification of the study phases.</p
Endoscopic criteria under i-scan OE for <i>in vivo</i> assessment of polyp histology (adapted from [20]) and representative images.
<p>Endoscopic criteria under i-scan OE for <i>in vivo</i> assessment of polyp histology (adapted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197520#pone.0197520.ref020" target="_blank">20</a>]) and representative images.</p