Artificial intelligence and automation in valvular heart diseases

Artificial intelligence (AI) is gradually changing every aspect of social life, and healthcare is no exception. The clinical procedures that were supposed to, and could previously only be handled by human experts can now be carried out by machines in a more accurate and efficient way. The coming era of big data and the advent of supercomputers provides great opportunities to the development of AI technology for the enhancement of diagnosis and clinical decision-making. This review provides an introduction to AI and highlights its applications in the clinical flow of diagnosing and treating valvular heart diseases (VHDs). More specifically, this review first introduces some key concepts and subareas in AI. Secondly, it discusses the application of AI in heart sound auscultation and medical image analysis for assistance in diagnosing VHDs. Thirdly, it introduces using AI algorithms to identify risk factors and predict mortality of cardiac surgery. This review also describes the state-of-the-art autonomous surgical robots and their roles in cardiac surgery and intervention

Crossmodal Attentive Skill Learner

This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture [Harb et al., 2017] to enable hierarchical reinforcement learning across multiple sensory inputs. We provide concrete examples where the approach not only improves performance in a single task, but accelerates transfer to new tasks. We demonstrate the attention mechanism anticipates and identifies useful latent features, while filtering irrelevant sensor modalities during execution. We modify the Arcade Learning Environment [Bellemare et al., 2013] to support audio queries, and conduct evaluations of crossmodal learning in the Atari 2600 game Amidar. Finally, building on the recent work of Babaeizadeh et al. [2017], we open-source a fast hybrid CPU-GPU implementation of CASL.Comment: International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2018, NIPS 2017 Deep Reinforcement Learning Symposiu

Spectral unmixing of Multispectral Lidar signals

In this paper, we present a Bayesian approach for spectral unmixing of multispectral Lidar (MSL) data associated with surface reflection from targeted surfaces composed of several known materials. The problem addressed is the estimation of the positions and area distribution of each material. In the Bayesian framework, appropriate prior distributions are assigned to the unknown model parameters and a Markov chain Monte Carlo method is used to sample the resulting posterior distribution. The performance of the proposed algorithm is evaluated using synthetic MSL signals, for which single and multi-layered models are derived. To evaluate the expected estimation performance associated with MSL signal analysis, a Cramer-Rao lower bound associated with model considered is also derived, and compared with the experimental data. Both the theoretical lower bound and the experimental analysis will be of primary assistance in future instrument design

Breath-Hold Blood Oxygen Level-Dependent MRI: A Tool for the Assessment of Cerebrovascular Reserve in Children with Moyamoya Disease

BACKGROUND AND PURPOSE: There is a critical need for a reliable and clinically feasible imaging technique that can enable prognostication and selection for revascularization surgery in children with Moyamoya disease. Blood oxygen level-dependent MR imaging assessment of cerebrovascular reactivity, using voluntary breath-hold hypercapnic challenge, is one such simple technique. However, its repeatability and reliability in children with Moyamoya disease are unknown. The current study sought to address this limitation. MATERIALS AND METHODS: Children with Moyamoya disease underwent dual breath-hold hypercapnic challenge blood oxygen level-dependent MR imaging of cerebrovascular reactivity in the same MR imaging session. Within-day, within-subject repeatability of cerebrovascular reactivity estimates, derived from the blood oxygen level-dependent signal, was computed. Estimates were associated with demographics and intellectual function. Interrater reliability of a qualitative and clinically applicable scoring scheme was assessed. RESULTS: Twenty children (11 males; 12.1 ± 3.3 years) with 30 MR imaging sessions (60 MR imaging scans) were included. Repeatability was "good" on the basis of the intraclass correlation coefficient (0.70 ± 0.19). Agreement of qualitative scores was "substantial" (κ = 0.711), and intrarater reliability of scores was "almost perfect" (κ = 0.83 and 1). Younger participants exhibited lower repeatability (P = .027). Repeatability was not associated with cognitive function (P > .05). However, abnormal cerebrovascular reactivity was associated with slower processing speed (P = .015). CONCLUSIONS: Breath-hold hypercapnic challenge blood oxygen level-dependent MR imaging is a repeatable technique for the assessment of cerebrovascular reactivity in children with Moyamoya disease and is reliably interpretable for use in clinical practice. Standardization of such protocols will allow further research into its application for the assessment of ischemic risk in childhood cerebrovascular disease