Leveraging smart technologies to improve the management of diabetic foot ulcers and extend ulcer-free days in remission

© 2020 John Wiley & Sons Ltd The prevalent and long neglected diabetic foot ulcer (DFU) and its related complications rank among the most debilitating and costly sequelae of diabetes. Management of the DFU is multifaceted and requires constant monitoring from patients, caregivers, and healthcare providers. The alarmingly high rates of recurrence of ulcerations in the diabetic foot requires a change in our approach to care and to the vernacular in the medical literature. Our efforts should be directed not only on healing of open wounds, but also on maximizing ulcer-free days for the patient in diabetic foot remission. The increasing development and use of technology within every aspect of our lives represents an opportunity for creative solutions to prevent or better manage this devastating condition. In particular, recent advances in wearable and mobile health technologies appear to show promise in measuring and modulating dangerous foot pressure and inflammation to extend remission and improve the quality of life for these most complex patients. This review article discusses how harnessing wearables and digital technologies may improve the management and optimize prevention of DFUs by identifying high-risk patients for triage and timely intervention, personalizing prescription of offloading, and improving adherence to protective footwear. While still in their infancy, we envisage a future network of skin-worn, jewellery-worn, and implantable sensors that, if allowed to effectively communicate with one another and the patient, could dramatically impact measuring, personalizing, and managing how we and the patients we serve move through our collective world

Inner Space Preserving Generative Pose Machine

Image-based generative methods, such as generative adversarial networks (GANs) have already been able to generate realistic images with much context control, specially when they are conditioned. However, most successful frameworks share a common procedure which performs an image-to-image translation with pose of figures in the image untouched. When the objective is reposing a figure in an image while preserving the rest of the image, the state-of-the-art mainly assumes a single rigid body with simple background and limited pose shift, which can hardly be extended to the images under normal settings. In this paper, we introduce an image "inner space" preserving model that assigns an interpretable low-dimensional pose descriptor (LDPD) to an articulated figure in the image. Figure reposing is then generated by passing the LDPD and the original image through multi-stage augmented hourglass networks in a conditional GAN structure, called inner space preserving generative pose machine (ISP-GPM). We evaluated ISP-GPM on reposing human figures, which are highly articulated with versatile variations. Test of a state-of-the-art pose estimator on our reposed dataset gave an accuracy over 80% on PCK0.5 metric. The results also elucidated that our ISP-GPM is able to preserve the background with high accuracy while reasonably recovering the area blocked by the figure to be reposed.Comment: http://www.northeastern.edu/ostadabbas/2018/07/23/inner-space-preserving-generative-pose-machine

Efficacy and Safety of Lacosamide in Painful Diabetic Neuropathy

OBJECTIVE: To evaluate efficacy and safety of lacosamide compared with placebo in painful diabetic polyneuropathy. RESEARCH DESIGN AND METHODS: Diabetic patients with at least moderate neuropathic pain were randomized to placebo or lacosamide 400 (in a slow or standard titration) or 600 mg/day over 6-week titration and 12-week maintenance periods. Primary efficacy criterion was intra-individual change in average daily Numeric Pain Rating Scale score from baseline to the last 4 weeks. RESULTS: For the primary end point, pain reduction was numerically but not statistically greater with lacosamide compared with placebo (400 mg/day, P = 0.12; 600 mg/day, P = 0.18). Both doses were significantly more effective compared with placebo over the titration (P = 0.03, P = 0.006), maintenance (P = 0.01, P = 0.005), and entire treatment periods (P = 0.03, P = 0.02). Safety profiles between titration schemes were similar. CONCLUSIONS: Lacosamide reduced neuropathic pain and was well tolerated in diabetic patients, but the primary efficacy criterion was not met, possibly due to an increased placebo response over the last 4 weeks.status: publishe

Automatically Selecting Inference Algorithms for Discrete Energy Minimisation

Minimisation of discrete energies defined over factors is an important problem in computer vision, and a vast number of MAP inference algorithms have been proposed. Different inference algorithms perform better on factor graph models (GMs) from different underlying problem classes, and in general it is difficult to know which algorithm will yield the lowest energy for a given GM. To mitigate this difficulty, survey papers advise the practitioner on what algorithms perform well on what classes of models. We take the next step forward, and present a technique to automatically select the best inference algorithm for an input GM. We validate our method experimentally on an extended version of the OpenGM2 benchmark, containing a diverse set of vision problems. On average, our method selects an inference algorithm yielding labellings with 96% of variables the same as the best available algorithm

2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images

Abstract We present a technique for estimating the spatial layout of humans in still images—the position of the head, torso and arms. The theme we explore is that once a person is localized using an upper body detector, the search for their body parts can be considerably simplified using weak constraints on position and appearance arising from that detection. Our approach is capable of estimating upper body pose in highly challenging uncontrolled images, without prior knowledge of background, clothing, lighting, or the location and scale of the person in the image. People are only required to be upright and seen from the front or the back (not side). We evaluate the stages of our approach experimentally using ground truth layout annotation on a variety of challenging material, such as images from the PASCAL VOC 2008 challenge and video frames from TV shows and feature films. We also propose and evaluate techniques for searching a video dataset for people in a specific pose. To this end, we develop three new pose descriptors and compare their clas

Fast Multi-Aspect 2D Human Detection

Abstract. We address the problem of detecting human figures in images, taking into account that the image of the human figure may be taken from a range of viewpoints. We capture the geometric deformations of the 2D human figure using an extension of the Common Factor Model (CFM) of Lan and Huttenlocher. The key contribution of the paper is an improved iterative message passing inference algorithm that runs faster than the original CFM algorithm. This is based on the insight that messages created using the distance transform are shift invariant and therefore messages can be created once and then shifted for subsequent iterations. Since shifting (O(1) complexity) is faster than computing a distance transform (O(n) complexity), a significant speedup is observed in the experiments. We demonstrate the effectiveness of the new model for the human parsing problem using the Iterative Parsing data set and results are competitive with the state of the art detection algorithm of Andriluka, et al.