Artificial Intelligence-Powered Chronic Wound Management System: Towards Human Digital Twins

Artificial Intelligence (AI) has witnessed increased application and widespread adoption over the past decade. AI applications to medical images have the potential to assist caregivers in deciding on a proper chronic wound treatment plan by helping them to understand wound and tissue classification and border segmentation, as well as visual image synthesis. This dissertation explores chronic wound management using AI methods, such as Generative Adversarial Networks (GAN) and Explainable AI (XAI) techniques. The wound images are collected, grouped, and processed. One primary objective of this research is to develop a series of AI models, not only to present the potential of AI in wound management but also to develop the building blocks of human digital twins. First of all, motivations, contributions, and the dissertation outline are summarized to introduce the aim and scope of the dissertation. The first contribution of this study is to build a chronic wound classification and its explanation utilizing XAI. This model also benefits from a transfer learning methodology to improve performance. Then a novel model is developed that achieves wound border segmentation and tissue classification tasks simultaneously. A Deep Learning (DL) architecture, i.e., the GAN, is proposed to realize these tasks. Another novel model is developed for creating lifelike wounds. The output of the previously proposed model is used as an input for this model, which generates new chronic wound images. Any tissue distribution could be converted to lifelike wounds, preserving the shape of the original wound. The aforementioned research is extended to build a digital twin for chronic wound management. Chronic wounds, enabling technologies for wound care digital twins, are examined, and a general framework for chronic wound management using the digital twin concept is investigated. The last contribution of this dissertation includes a chronic wound healing prediction model using DL techniques. It utilizes the previously developed AI models to build a chronic wound management framework using the digital twin concept. Lastly, the overall conclusions are drawn. Future challenges and further developments in chronic wound management are discussed by utilizing emerging technologies

WG\u3csup\u3e2\u3c/sup\u3eAN: Synthetic Wound Image Generation Using Generative Adversarial Network

In part due to its ability to mimic any data distribution, Generative Adversarial Network (GAN) algorithms have been successfully applied to many applications, such as data augmentation, text-to-image translation, image-to-image translation, and image inpainting. Learning from data without crafting loss functions for each application provides broader applicability of the GAN algorithm. Medical image synthesis is also another field that the GAN algorithm has great potential to assist clinician training. This paper proposes a synthetic wound image generation model based on GAN architecture to increase the quality of clinical training. The proposed model is trained on chronic wound datasets with various sizes taken from real hospital environments. Hyperparameters such as epoch count and dataset size for training tasks are studied to find optimum training conditions as well. The performance of the developed model was evaluated through a mean squared error (MSE) metric to determine the similarity between generated and actual wounds. Visual inspection is performed to examine generated wound images. The results show that the proposed synthetic wound image generation (WG2AN) model has great potential to be used in medical training and performs well in producing synthetic wound images with a 1000-image training dataset and 200 epochs of training

Simultaneous Wound Border Segmentation and Tissue Classification Using a Conditional Generative Adversarial Network

Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images as well as the number of epochs using GAN for wound border segmentation and tissue classification. The results show that the proposed GAN model performs efficiently for wound border segmentation and tissue classification tasks with a set of 2000 images at 200 epochs

A Comparison of Deep Learning Algorithms on Image Data for Detecting Floodwater on Roadways

Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask Region-Based Convolutional Neural Networks (Mask-R-CNN) and Generative Adversarial Networks (GAN) algorithms. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performances of the algorithms are assessed in accurately detecting the floodwater captured in images. The results show that the proposed Mask-R-CNN-based floodwater detection and segmentation outperform previous studies, whereas the GAN-based model has a straightforward implementation compared to other models

Change in the geographic distribution of human resources for health in Turkey, 2002-2016

Introduction: Turkey, which suffers from both undersupply of physicians, nurses and midwives and imbalanced distribution of healthcare personnel, has been developing and implementing various policies to solve these problems. The Ministry of Health launched the Health Transformation Program in 2003 for effective, efficient and fair provision of healthcare services for all people. This study aimed to take a closer look at the impact of policies implemented to reduce the imbalance of the distribution of human resources for health for the past 15 years in Turkey. Methods: Data for the distributional imbalance obtained from Ministry of Health registries was analysed by using Lorenz curves and Gini coefficient for the years 2002, 2005, 2008, 2012 and 2016. Results: Geographical imbalances for healthcare professions decreased distinguishably during the 15 years. Gini coefficient was 0.33 for specialist distribution in 2002, and decreased gradually to 0.26 in 2008 and finally 0.21 in 2016. Similarly, Gini coefficients were 0.18, 0.20 and 0.25 for general practitioners, nurses and midwives, respectively, in 2002. In 2012, Gini coefficients for the same professionals were calculated as 0.09, 0.11 and 0.19, respectively. Conclusion: The findings indicate that the policies targeting the distribution of healthcare personnel in Turkey have yielded positive results. Yet it is evident that these results are not due to a single action. It is essential to improve existing implementations, identify the instruments and factors that satisfy and motivate healthcare personnel, and continue developing and implementing comprehensive policies

BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models

Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. The training data is disseminated across numerous machines in federated learning, and the learning process is collaborative. There are numerous privacy attacks on deep learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in medical data applications. Homomorphic encryption-based model security from the adversarial collaborator is one of the answers to this challenge. Using homomorphic encryption, this research presents a privacy-preserving federated learning system for medical data. The proposed technique employs a secure multi-party computation protocol to safeguard the deep learning model from adversaries. The proposed approach is tested in terms of model performance using a real-world medical dataset in this paper

The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification

Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades. AI algorithms are, in large part, built on weights calculated as a result of large matrix multiplications. It is typically hard to interpret and debug the computationally intensive processes. Explainable Artificial Intelligence (XAI) aims to solve black-box and hard-to-debug approaches through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies chronic wounds through the use of transfer learning and fully connected layers. Classified chronic wound images serve as input to the XAI model for an explanation. Interpretable results can help shed new perspectives to clinicians during the diagnostic phase. The proposed method successfully provides chronic wound classification and its associated explanation to extract additional knowledge that can also be interpreted by non-data-science experts, such as medical scientists and physicians. This hybrid approach is shown to aid with the interpretation and understanding of AI decision-making processes

Development of a Data Science Curriculum for an Engineering Technology Program

Data science has gained the attention of various industries, educators, parents, and students thinking about their future careers. Statistics departments have traditionally offered data science courses for a long time. The main objective of these courses is to examine the fundamental concepts and theories. However, teaching data science courses has also expanded to other disciplines due to the vast amount of data being collected by numerous modern applications. Also, someone needs to learn how to collect and process data, especially from industrial devices, because of the recent development of Internet of Things (IoT) technologies. Hence, integrating data science into the curricula of different engineering branches becomes a matter of relating the statistics background to the specific discipline. There are several reasons for this transition. Firstly, as the increased computational power and massive availability of the data make the use of statistical theories possible in more engineering applications, there is a growing need for engineering students to build knowledge in data science concepts. Secondly, the wide availability of libraries and models allows for the implementation of diverse solutions to engineering problems. This paper will discuss introducing a new data science curriculum in an Engineering Technology (ET) program with a focus on Electrical Engineering Technology (EET) program

An Initial Look into the Computer Science and Cybersecurity Pathways Project for Career and Technical Education Curricula

Computer science and cybersecurity have gained the attention of various stakeholders, industry representatives, educators, parents and students who are thinking about their future careers. Teaching computer science courses has moved into K-12 education, no longer introduced in the college classroom. There are various reasons for this trend. One is that in this way more children have access to the curriculum that integrates computer science principles, not just those undergraduate students in specific STEM majors. Other industries need different levels of computer science and cybersecurity education. There are various programs across the nation that are focusing on introducing these topics as early as elementary school through various outreach programs or even in the regular curriculum. In 2014, Governor Terry McAuliffe (Commonwealth of Virginia) established the “Cyber Virginia and the Virginia Cyber Security Commission” with recommendations that a cybersecurity workforce pipeline should start in K-12 education and that various pathways should be developed and implemented across the Commonwealth. This paper will provide an initial look into a project funded by the Department of Education that is focused on the Career and Technical Education (CTE) pathways in Computer Science and Cybersecurity. It is the first year of implementation