Semi-Automatic Tracking of Laser Speckle Contrast Images of Microcirculation in Diabetic Foot Ulcers

Foot ulcers are a severe complication of diabetes mellitus. Assessment of the vascular status of diabetic foot ulcers with Laser Speckle Contrast Imaging (LSCI) is a promising approach for diagnosis and prognosis. However, manual assessment during analysis of LSCI limits clinical applicability. Our aim was to develop and validate a fast and robust tracking algorithm for semi-automatic analysis of LSCI data. The feet of 33 participants with diabetic foot ulcers were recorded with LSCI, including at baseline, during the Post-Occlusive Reactive Hyperemia (PORH) test, and during the Buerger's test. Different regions of interest (ROIs) were used to measure microcirculation in different areas of the foot. A tracking algorithm was developed in MATLAB to reposition the ROIs in the LSCI scans. Manual- and algorithm-tracking of all recordings were compared by calculating the Intraclass Correlation Coefficient (ICC). The algorithm was faster in comparison with the manual approach (90 s vs. 15 min). Agreement between manual- and algorithm-tracking was good to excellent during baseline (ICC = 0.896-0.984; p &lt;0.001), the PORH test (ICC = 0.790-0.960; p &lt;0.001), and the Buerger's test (ICC = 0.851-0.978; p &lt;0.001), resulting in a tracking algorithm that delivers assessment of LSCI in diabetic foot ulcers with results comparable to a labor-intensive manual approach, but with a 10-fold workload reduction.</p

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

Laser speckle contrast imaging for assessing microcirculation in diabetic foot disease

Diabetes Mellitus is one of the most common chronic diseases worldwide and it has been estimated that the number of people with diabetes will grow because of our lifestyle changes and longer life-expectancy. This development is disturbing because diabetes has a severe impact on the patient’s life. It can cause serious complications such as blindness, heart attacks, and strokes. Another severe and most frequently recognized complication of diabetes are diabetic foot ulcers. Diabetic foot ulcers are associated with high morbidity, mortality and healthcare costs. These consequences can even be more severe in case of diabetic foot ulcers with critical ischemia. Hence, an early and accurate diagnosis of this health condition is needed. Today, the most common diagnosis of (critical-) ischemia is determined in clinical practice, using non-invasive measurements of blood flow in the feet, by means of assessments of the ankle pressure, toe pressure or transcutaneous oxygen pressure. Yet, these currently used non-invasive measurement techniques have various disadvantages. Therefore, research into improved ways to assess the microcirculation in people with diabetic foot ulcers is needed. This thesis tried to fill this knowledge gap by looking into the potential of novel optical imaging techniques, and in particular in the potential of Laser Speckle Contrast Imaging (LSCI), for the assessment of the microcirculation in the diabetic foot and its applicability in the clinical setting.LSCI shows both similarities and differences with the currently used non-invasive blood pressure measurements, which is an indication that it measures perfusion in a novel and different way than the currently used techniques. However, in our cohort we have not been able to link perfusion as measured with LSCI to clinical outcome parameters such as ulcer healing or successful revascularization. Despite this current lack of applicability, this novel non-invasive optical imaging technique still offer potential to change clinical practice in the field of diabetic foot disease. For this, future research is needed to further investigate how LSCI can best be used to improve outcomes of diabetic foot ulcers.<br/

Morphological foot model for temperature pattern analysis proposed for diabetic foot disorders

Infrared thermography is a non-invasive and accessible tool that maps the surface temperature of a body. This technology is particularly useful for diabetic foot disorders, since it facilitates the identification of higher risk patients by frequent monitoring and therefore limits the incidence of disabling conditions. The aim of this work is to provide a methodology to explore the entire plantar aspects of both feet, based on infrared thermography, for the assessment of diabetic foot anomalies. A non-invasive methodology was established to identify areas of higher risk and track their progress via longitudinal monitoring. A standard morphological model was extracted from a group of healthy subjects, nine females and 13 males, by spatial image registration. This healthy foot model can be taken as a template for the assessment of temperature asymmetry, even in cases in which partial amputations or deformations are present. A pixel-wise comparison of the temperature patterns was carried out by Wilcoxon´s matched-pairs test using the corresponding template. For all the subjects, the left foot was compared to the contralateral foot, the right one, providing a map of statistically significant areas of variation, within the template, among the healthy subjects at different time points. In the female case, the main areas of variability were the boundaries of the feet, whereas for the male, in addition to this, substantial changes that exhibited a clear pattern were observed. A fast and simple monitoring tool is provided to be used for personalized medical diagnosis in patients affected by diabetic foot disorders.This research was funded by the IACTEC Technological Training program, grant number TF INNOVA 2016-2021. This work was completed while Abián Hernández was a beneficiary of a pre-doctoral grant given by the “Agencia Canaria de Investigacion, Innovacion y Sociedad de la Información (ACIISI)” of the “Consejería de Economía, Conocimiento y Empleo” of the “Gobierno de Canarias”, which is partly financed by the European Social Fund (FSE) (POC 2014-2020, Eje 3 Tema Prioritario 74 (85%))

DETECTION OF GRANULATION TISSUE FOR HEALING ASSESSMENT OF CHRONIC ULCERS

Wounds that fail to heal within an expected period develop into ulcers that cause severe pain and expose patients to limb amputation. Ulcer appearance changes gradually as ulcer tissues evolve throughout the healing process. Dermatologists assess the progression of ulcer healing based on visual inspection of ulcer tissues, which is inconsistent and subjective. The ability to measure objectively early stages of ulcer healing is important to improve clinical decisions and enhance the effectiveness of the treatment. Ulcer healing is indicated by the growth of granulation tissue that contains pigment haemoglobin that causes the red colour of the tissue. An approach based on utilising haemoglobin content as an image marker to detect regions of granulation tissue on ulcers surface using colour images of chronic ulcers is investigated in this study. The approach is utilised to develop a system that is able to detect regions of granulation tissue on ulcers surface using colour images of chronic ulcers

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

AI technology for remote clinical assessment and monitoring

Objective: To report the clinical validation of an innovative, artificial intelligence (AI)-powered, portable and non-invasive medical device called Wound Viewer. The AI medical device uses dedicated sensors and AI algorithms to remotely collect objective and precise clinical data, including three-dimensional (3D) wound measurements, tissue composition and wound classification through the internationally recognised Wound Bed Preparation (WBP) protocol; this data can then be shared through a secure General Data Protection Regulation (GDPR)- and Health Insurance Portability and Accountability Act (HIPAA)-compliant data transfer system. This trial aims to test the reliability and precision of the AI medical device and its ability to aid health professionals in clinically evaluating wounds as efficiently remotely as at the bedside. Method: This non-randomised comparative clinical trial was conducted in the Clinica San Luca (Turin, Italy). Patients were divided into three groups: (i) patients with venous and arterial ulcers in the lower limbs; (ii) patients with diabetes and presenting with diabetic foot syndrome; and (iii) patients with pressure ulcers. Each wound was evaluated for area, depth, volume and WBP wound classification. Each patient was examined once and the results, analysed by the AI medical device, were compared against data obtained following visual evaluation by the physician and research team. The area and depth were compared with a Kruskal–Wallis one-way analysis of variations in the obtained distribution (expected p-value>0.1 for both tests). The WBP classification and tissue segmentation were analysed by directly comparing the classification obtained by the AI medical device against that of the testing physician. Results: A total of 150 patients took part in the trial. The results demonstrated that the AI medical device's AI algorithm could acquire objective clinical parameters in a completely automated manner. The AI medical device reached 97% accuracy against the WBP classification and tissue segmentation analysis compared with that performed in person by the physician. Moreover, data regarding the measurements of the wounds, as analysed through the Kruskal–Wallis technique, showed that the data distribution proved comparable with the other methods of measurement previously clinically validated in the literature (p=0.9). Conclusion: These findings indicate that remote wound assessment undertaken by physicians is as effective through the AI medical device as bedside examination, and that the device was able to assess wounds and provide a precise WBP wound classification. Furthermore, there was no need for manual data entry, thereby reducing the risk of human error while preserving high-quality clinical diagnostic data

Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis

Wound management poses a significant challenge, particularly for bedridden patients and the elderly. Accurate diagnostic and healing monitoring can significantly benefit from modern image analysis, providing accurate and precise measurements of wounds. Despite several existing techniques, the shortage of expansive and diverse training datasets remains a significant obstacle to constructing machine learning-based frameworks. This paper introduces Syn3DWound, an open-source dataset of high-fidelity simulated wounds with 2D and 3D annotations. We propose baseline methods and a benchmarking framework for automated 3D morphometry analysis and 2D/3D wound segmentation.Comment: In the IEEE International Symposium on Biomedical Imaging (ISBI) 202