Paper-Based Optode Devices (PODs) for Selective Quantification of Potassium in Biological Fluids.

This paper describes the design, fabrication, and feasibility of paper-based optode devices (PODs) for sensing potassium selectively in biological fluids. PODs operate in exhaustive mode and integrate with a handheld, smartphone-connected optical reader. This integrated measuring system provides significant advantages over traditional optode membranes and other paper-based designs, by obtaining a linear optical response to potassium concentration via a simple, stackable design and by harnessing a smartphone to provide an easy-to-use interface, thus enabling remote monitoring of diseases.

Habits Heart App for Patient Engagement in Heart Failure Management: Pilot Feasibility Randomized Trial.

BACKGROUND: Due to the complexity and chronicity of heart failure, engaging yet simple patient self-management tools are needed. OBJECTIVE: This study aimed to assess the feasibility and patient engagement with a smartphone app designed for heart failure. METHODS: Patients with heart failure were randomized to intervention (smartphone with the Habits Heart App installed and Bluetooth-linked scale) or control (paper education material) groups. All intervention group patients were interviewed and monitored closely for app feasibility while receiving standard of care heart failure management by cardiologists. The Atlanta Heart Failure Knowledge Test, a quality of life survey (Kansas City Cardiomyopathy Questionnaire), and weight were assessed at baseline and final visits. RESULTS: Patients (N=28 patients; intervention: n=15; control: n=13) with heart failure (with reduced ejection fraction: 15/28, 54%; male: 20/28, 71%, female: 8/28, 29%; median age 63 years) were enrolled, and 82% of patients (N=23; intervention: 12/15, 80%; control: 11/13, 85%) completed both baseline and final visits (median follow up 60 days). In the intervention group, 2 out of the 12 patients who completed the study did not use the app after study onboarding due to illnesses and hospitalizations. Of the remaining 10 patients who used the app, 5 patients logged ≥1 interaction with the app per day on average, and 2 patients logged an interaction with the app every other day on average. The intervention group averaged 403 screen views (per patient) in 56 distinct sessions, 5-minute session durations, and 22 weight entries per patient. There was a direct correlation between duration of app use and improvement in heart failure knowledge (Atlanta Heart Failure Knowledge Test score; ρ=0.59, P=.04) and quality of life (Kansas City Cardiomyopathy Questionnaire score; ρ=0.63, P=.03). The correlation between app use and weight change was ρ=-0.40 (P=.19). Only 1 out of 11 patients in the control group retained education material by the follow-up visit. CONCLUSIONS: The Habits Heart App with a Bluetooth-linked scale is a feasible way to engage patients in heart failure management, and barriers to app engagement were identified. A larger multicenter study may be warranted to evaluate the effectiveness of the app. TRIAL REGISTRATION: ClinicalTrials.gov NCT03238729; http://clinicaltrials.gov/ct2/show/NCT03238729.

Towards Effcient Label Fusion by Pre-Alignment of Training Data.

Label fusion is a multi-atlas segmentation approach that explicitly maintains and exploits the entire training dataset, rather than a parametric summary of it. Recent empirical evidence suggests that label fusion can achieve significantly better segmentation accuracy over classical parametric atlas methods that utilize a single coordinate frame. However, this performance gain typically comes at an increased computational cost due to the many pairwise registrations between the novel image and training images. In this work, we present a modified label fusion method that approximates these pairwise warps by first pre-registering the training images via a diffeomorphic groupwise registration algorithm. The novel image is then only registered once, to the template image that represents the average training subject. The pairwise spatial correspondences between the novel image and training images are then computed via concatenation of appropriate transformations. Our experiments on cardiac MR data suggest that this strategy for nonparametric segmentation dramatically improves computational efficiency, while producing segmentation results that are statistically indistinguishable from those obtained with regular label fusion. These results suggest that the key benefit of label fusion approaches is the underlying nonparametric inference algorithm, and not the multiple pairwise registrations.

Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation.

Automatic segmentation of the heart's left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. However, the high anatomical variability of the left atrium presents significant challenges for atlas-guided segmentation. In this paper, we demonstrate an automatic method for left atrium segmentation using weighted voting label fusion and a variant of the demons registration algorithm adapted to handle images with different intensity distributions. We achieve accurate automatic segmentation that is robust to the high anatomical variations in the shape of the left atrium in a clinical dataset of MRA images.