Imparting 3D representations to artificial intelligence for a full assessment of pressure injuries.

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning methods. The main objective of this dissertation is to prove the efficiency of Deep Learning techniques in tackling one of the important health issues we are facing in our society, through medical imaging. Pressure injuries are a dermatology related health issue associated with increased morbidity and health care costs. Managing pressure injuries appropriately is increasingly important for all the professionals in wound care. Using 2D photographs and 3D meshes of these wounds, collected from collaborating hospitals, our mission is to create intelligent systems for a full non-intrusive assessment of these wounds. Five main tasks have been achieved in this study: a literature review of wound imaging methods using machine learning techniques, the classification and segmentation of the tissue types inside the pressure injury, the segmentation of these wounds and the design of an end-to-end system which measures all the necessary quantitative information from 3D meshes for an efficient assessment of PIs, and the integration of the assessment imaging techniques in a web-based application

A Review of Wearable Sensor Systems to Monitor Plantar Loading in the Assessment of Diabetic Foot Ulcers

Diabetes is highly prevalent throughout the world and imposes a high economic cost on countries at all income levels. Foot ulceration is one devastating consequence of diabetes, which can lead to amputation and mortality. Clinical assessment of diabetic foot ulcer (DFU) is currently subjective and limited, impeding effective diagnosis, treatment and prevention. Studies have shown that pressure and shear stress at the plantar surface of the foot plays an important role in the development of DFUs. Quantification of these could provide an improved means of assessment of the risk of developing DFUs. However, commercially-available sensing technology can only measure plantar pressures, neglecting shear stresses and thus limiting their clinical utility. Research into new sensor systems which can measure both plantar pressure and shear stresses are thus critical. Our aim in this paper is to provide the reader with an overview of recent advances in plantar pressure and stress sensing and offer insights into future needs in this critical area of healthcare. Firstly, we use current clinical understanding as the basis to define requirements for wearable sensor systems capable of assessing DFU. Secondly, we review the fundamental sensing technologies employed in this field and investigate the capabilities of the resultant wearable systems, including both commercial and research-grade equipment. Finally, we discuss research trends, ongoing challenges and future opportunities for improved sensing technologies to monitor plantar loading in the diabetic foot

A Mobile App for Wound Localization using Deep Learning

We present an automated wound localizer from 2D wound and ulcer images by using deep neural network, as the first step towards building an automated and complete wound diagnostic system. The wound localizer has been developed by using YOLOv3 model, which is then turned into an iOS mobile application. The developed localizer can detect the wound and its surrounding tissues and isolate the localized wounded region from images, which would be very helpful for future processing such as wound segmentation and classification due to the removal of unnecessary regions from wound images. For Mobile App development with video processing, a lighter version of YOLOv3 named tiny-YOLOv3 has been used. The model is trained and tested on our own image dataset in collaboration with AZH Wound and Vascular Center, Milwaukee, Wisconsin. The YOLOv3 model is compared with SSD model, showing that YOLOv3 gives a mAP value of 93.9%, which is much better than the SSD model (86.4%). The robustness and reliability of these models are also tested on a publicly available dataset named Medetec and shows a very good performance as well.Comment: 8 pages, 5 figures, 1 tabl

Prediction of diabetic foot ulceration: The value of using microclimate sensor arrays

Background: Accurately predicting the risk of diabetic foot ulceration (DFU) could dramatically reduce the enormous burden of chronic wound management and amputation. Yet, current prognostic models are unable to precisely predict DFU events. Typically, efforts have focused on individual factors like temperature, pressure or shear rather than the overall foot microclimate. Method: A systematic review was conducted by searching PubMed reports with no restrictions on start date covering literature published until 20 February 2019 using relevant keywords, including temperature, pressure, shear and relative humidity. We review the use of these variables as predictors of DFU, highlighting gaps in our current understanding and suggesting which specific features should be combined to develop a real-time microclimate prognostic model. Results: Current prognostic models rely either solely on contralateral temperature, pressure or shear measurement; these parameters, however, rarely reach 50% specificity in relation to DFU. There is also considerable variation in methodological investigation, anatomical sensor configuration and resting time prior to temperature measurements (5-20 minutes). Few studies have considered relative humidity and mean skin resistance. Conclusions: Very limited evidence supports the use of single clinical parameters in predicting the risk of DFU. We suggest the microclimate as a whole should be considered to predict DFU more effectively and suggest nine specific features which appear to be implicated for further investigation. Technology supports real-time inshoe data collection and wireless transmission, providing a potentially rich source of data to better predict risk of DFU

Correlating the Effect of Dynamic Variability in the Sensor Environment on Sensor Design

This dissertation studies the effect of biofluid dynamics on the electrochemical response of a wearable sensor for monitoring of chronic wounds. The research investigates various dynamic in vivo parameters and correlates them with experimentally measured behavior with wound monitoring as a use case. Wearable electrochemical biosensors suffer from several unaddressed challenges, like stability and sensitivity, that need to be resolved for obtaining accurate data. One of the major challenges in the use of these sensors is continuous variation in biofluid composition. Wound healing is a dynamic process with wound composition changing continuously. This dissertation investigates the effects of several in vivo biochemical and environmental parameters on the sensor response to establish actionable correlations. Real-time assessment of wound healing was carried out through longitudinal monitoring of uric acid and other wound fluid characteristics. A textile sensor was designed using a simple fabrication approach combining conductive inks with a polymeric substrate, for conformal contact with the wound bed. A −1 cm−2, establishing the applicability of the sensor for measurements in the physiologically relevant range. The sensor was also found to be stable for a period of 3 days when subjected to physiological and elevated temperatures (37oC and 40oC) confirming its relevance for long-term monitoring. A direct correlation between sensor response and the dynamic parameters was seen, with the results showing a ~20% deviation from the accurate UA reading. The results confirmed that as a consequence of these parameters temporally changing in the wound environment, the sensor response will be altered. The work develops mathematical models correlating this effect on sensor response to allow for real-time sensor calibration. The clinical validation studies established the feasibility of UA measurement by the developed electrochemical sensor and derive correlations between the wound chronicity and UA levels. The protocols developed in this work for the design, fabrication, and calibration of the sensor to correct for the dynamic in vivo behavior can be extended to any wearable sensor for improved accuracy

NONINVASIVE MULTIMODAL DIFFUSE OPTICAL IMAGING OF VULNERABLE TISSUE HEMODYNAMICS

Measurement of tissue hemodynamics provides vital information for the assessment of tissue viability. This thesis reports three noninvasive near-infrared diffuse optical systems for spectroscopic measurements and tomographic imaging of tissue hemodynamics in vulnerable tissues with the goal of disease diagnosis and treatment monitoring. A hybrid near-infrared spectroscopy/diffuse correlation spectroscopy (NIRS/DCS) instrument with a contact fiber-optic probe was developed and utilized for simultaneous and continuous monitoring of blood flow (BF), blood oxygenation, and oxidative metabolism in exercising gastrocnemius. Results measured by the hybrid NIRS/DCS instrument in 37 subjects (mean age: 67 ± 6) indicated that vitamin D supplement plus aerobic training improved muscle metabolic function in older population. To reduce the interference and potential infection risk on vulnerable tissues caused by the contact measurement, a noncontact diffuse correlation spectroscopy/tomography (ncDCS/ncDCT) system was then developed. The ncDCS/ncDCT system employed optical lenses to project limited numbers of sources and detectors on the tissue surface. A motor-driven noncontact probe scanned over a region of interest to collect boundary data for three dimensional (3D) tomographic imaging of blood flow distribution. The ncDCS was tested for BF measurements in mastectomy skin flaps. Nineteen (19) patients underwent mastectomy and implant-based breast reconstruction were measured before and immediately after mastectomy. The BF index after mastectomy in each patient was normalized to its baseline value before surgery to get relative BF (rBF). Since rBF values in the patients with necrosis (n = 4) were significantly lower than those without necrosis (n = 15), rBF levels can be used to predict mastectomy skin flap necrosis. The ncDCT was tested for 3D imaging of BF distributions in chronic wounds of 5 patients. Spatial variations in BF contrasts over the wounded tissues were observed, indicating the capability of ncDCT in detecting tissue hemodynamic heterogeneities. To improve temporal/spatial resolution and avoid motion artifacts due to a long mechanical scanning of ncDCT, an electron-multiplying charge-coupled device based noncontact speckle contrast diffuse correlation tomography (scDCT) was developed. Validation of scDCT was done by imaging both high and low BF contrasts in tissue-like phantoms and human forearms. In a wound imaging study using scDCT, significant lower BF values were observed in the burned areas/volumes compared to surrounding normal tissues in two patients with burn. One limitation in this study was the potential influence of other unknown tissue optical properties such as tissue absorption coefficient (µa) on BF measurements. A new algorithm was then developed to extract both µa and BF using light intensities and speckle contrasts measured by scDCT at multiple source-detector distances. The new algorithm was validated using tissue-like liquid phantoms with varied values of µa and BF index. In-vivo validation and application of the innovative scDCT technique with the new algorithm is the subject of future work

A deep learning approach for pressure ulcer prevention using wearable computing

Abstract In recent years, statistics have confirmed that the number of elderly people is increasing. Aging always has a strong impact on the health of a human being; from a biological of point view, this process usually leads to several types of diseases mainly due to the impairment of the organism. In such a context, healthcare plays an important role in the healing process, trying to address these problems. One of the consequences of aging is the formation of pressure ulcers (PUs), which have a negative impact on the life quality of patients in the hospital, not only from a healthiness perspective but also psychologically. In this sense, e-health proposes several approaches to deal with this problem, however, these are not always very accurate and capable to prevent issues of this kind efficiently. Moreover, the proposed solutions are usually expensive and invasive. In this paper we were able to collect data coming from inertial sensors with the aim, in line with the Human-centric Computing (HC) paradigm, to design and implement a non-invasive system of wearable sensors for the prevention of PUs through deep learning techniques. In particular, using inertial sensors we are able to estimate the positions of the patients, and send an alert signal when he/she remains in the same position for too long a period of time. To train our system we built a dataset by monitoring the positions of a set of patients during their period of hospitalization, and we show here the results, demonstrating the feasibility of this technique and the level of accuracy we were able to reach, comparing our model with other popular machine learning approaches

Prevention and healing of diabetes-related foot ulcers:Motivational interviewing, objective adherence and offloading

With this thesis the knowledge and understanding of the prevention and healing of foot ulcers in people with diabetes in clinical practice are expanded. First, it was shown that adherence to wearing orthopedic shoes is suboptimal in most people at moderate-to-high risk of foot ulceration. People with a consistent wearing pattern showed higher daily wearing times than those with an inconsistent pattern. Besides, orthopedic shoes were worn less during weekend days compared to weekdays. Secondly, following the triangulation of the qualitative and quantitative results of the application of MI it can be concluded that after a basic MI-training, podiatrists can effectively apply MI in daily clinical practice at a solid beginner level. Furthermore, the findings support implementation of MI in practice and encourage MI training in the primary podiatrist training and maintenance training for daily clinical practice. Thirdly, one podiatrist-led MI-consultation did not contribute to improving adherence to wearing orthopedic shoes in people with diabetes at low-to-high risk of foot ulceration. Finally, if a wound occurs due to low adherence or due to another reason, a knee-high and non-removable device ensures the best plantar pressure reduction. Overall, it can be concluded that there seems to be no simple standalone solution to prevent and heal diabetes-related foot ulcers and that improved communication of the whole multidisciplinary team with the patient is necessary to help patients at risk as good as possible. We hope that the findings of this thesis support researchers and clinicians in further investigating strategies to prevent and heal foot ulcers in people with diabetes

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain