4,703 research outputs found
Efficient refinements on YOLOv3 for real-time detection and assessment of diabetic foot Wagner grades
Currently, the screening of Wagner grades of diabetic feet (DF) still relies
on professional podiatrists. However, in less-developed countries, podiatrists
are scarce, which led to the majority of undiagnosed patients. In this study,
we proposed the real-time detection and location method for Wagner grades of DF
based on refinements on YOLOv3. We collected 2,688 data samples and implemented
several methods, such as a visual coherent image mixup, label smoothing, and
training scheduler revamping, based on the ablation study. The experimental
results suggested that the refinements on YOLOv3 achieved an accuracy of 91.95%
and the inference speed of a single picture reaches 31ms with the NVIDIA Tesla
V100. To test the performance of the model on a smartphone, we deployed the
refinements on YOLOv3 models on an Android 9 system smartphone. This work has
the potential to lead to a paradigm shift for clinical treatment of the DF in
the future, to provide an effective healthcare solution for DF tissue analysis
and healing status.Comment: 11 pages with 11 figure
Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach
The global burden of acute and chronic wounds presents a compelling case for
enhancing wound classification methods, a vital step in diagnosing and
determining optimal treatments. Recognizing this need, we introduce an
innovative multi-modal network based on a deep convolutional neural network for
categorizing wounds into four categories: diabetic, pressure, surgical, and
venous ulcers. Our multi-modal network uses wound images and their
corresponding body locations for more precise classification. A unique aspect
of our methodology is incorporating a body map system that facilitates accurate
wound location tagging, improving upon traditional wound image classification
techniques. A distinctive feature of our approach is the integration of models
such as VGG16, ResNet152, and EfficientNet within a novel architecture. This
architecture includes elements like spatial and channel-wise
Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated
Multi-Layer Perceptron, providing a robust foundation for classification. Our
multi-modal network was trained and evaluated on two distinct datasets
comprising relevant images and corresponding location information. Notably, our
proposed network outperformed traditional methods, reaching an accuracy range
of 74.79% to 100% for Region of Interest (ROI) without location
classifications, 73.98% to 100% for ROI with location classifications, and
78.10% to 100% for whole image classifications. This marks a significant
enhancement over previously reported performance metrics in the literature. Our
results indicate the potential of our multi-modal network as an effective
decision-support tool for wound image classification, paving the way for its
application in various clinical contexts
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Applications of Machine Learning in Medical Prognosis Using Electronic Medical Records
Approximately 84 % of hospitals are adopting electronic medical records (EMR) In the United States. EMR is a vital resource to help clinicians diagnose the onset or predict the future condition of a specific disease. With machine learning advances, many research projects attempt to extract medically relevant and actionable data from massive EMR databases using machine learning algorithms. However, collecting patients\u27 prognosis factors from Electronic EMR is challenging due to privacy, sensitivity, and confidentiality. In this study, we developed medical generative adversarial networks (GANs) to generate synthetic EMR prognosis factors using minimal information collected during routine care in specialized healthcare facilities. The generated prognosis variables used in developing predictive models for (1) chronic wound healing in patients diagnosed with Venous Leg Ulcers (VLUs) and (2) antibiotic resistance in patients diagnosed with Skin and soft tissue infections (SSTIs). Our proposed medical GANs, EMR-TCWGAN and DermaGAN, can produce both continuous and categorical features from EMR. We utilized conditional training strategies to enhance training and generate classified data regarding healing vs. non-healing in EMR-TCWGAN and susceptibility vs. resistance in DermGAN. The ability of the proposed GAN models to generate realistic EMR data was evaluated by TSTR (test on the synthetic, train on the real), discriminative accuracy, and visualization. We analyzed the synthetic data augmentation technique\u27s practicality in improving the wound healing prognostic model and antibiotic resistance classifier. We achieved the area under the curve (AUC) of 0.875 in the wound healing prognosis model and an average AUC of 0.830 in the antibiotic resistance classifier by using the synthetic samples generated by GANs in the training process. These results suggest that GANs can be considered a data augmentation method to generate realistic EMR data
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
Long‐Term Imaging of Wound Angiogenesis with Large Scale Optoacoustic Microscopy
Wound healing is a well-coordinated process, necessitating efficient formation of new blood vessels. Vascularization defects are therefore a major risk factor for chronic, non-healing wounds. The dynamics of mammalian tissue revascularization, vessel maturation, and remodeling remain poorly understood due to lack of suitable in vivo imaging tools. A label-free large-scale optoacoustic microscopy (LSOM) approach is developed for rapid, non-invasive, volumetric imaging of tissue regeneration over large areas spanning up to 50 mm with a depth penetration of 1.5 mm. Vascular networks in dorsal mouse skin and full-thickness excisional wounds are imaged with capillary resolution during the course of healing, revealing previously undocumented views of the angiogenesis process in an unperturbed wound environment. Development of an automatic analysis framework enables the identification of key features of wound angiogenesis, including vessel length, diameter, tortuosity, and angular alignment. The approach offers a versatile tool for preclinical research in tissue engineering and regenerative medicine, empowering label-free, longitudinal, high-throughput, and quantitative studies of the microcirculation in processes associated with normal and impaired vascular remodeling, and analysis of vascular responses to pharmacological interventions in vivo
Mobile Wound Assessment and 3D Modeling from a Single Image
The prevalence of camera-enabled mobile phones have made mobile wound assessment a viable treatment option for millions of previously difficult to reach patients. We have designed a complete mobile wound assessment platform to ameliorate the many challenges related to chronic wound care. Chronic wounds and infections are the most severe, costly and fatal types of wounds, placing them at the center of mobile wound assessment. Wound physicians assess thousands of single-view wound images from all over the world, and it may be difficult to determine the location of the wound on the body, for example, if the wound is taken at close range. In our solution, end-users capture an image of the wound by taking a picture with their mobile camera. The wound image is segmented and classified using modern convolution neural networks, and is stored securely in the cloud for remote tracking. We use an interactive semi-automated approach to allow users to specify the location of the wound on the body. To accomplish this we have created, to the best our knowledge, the first 3D human surface anatomy labeling system, based off the current NYU and Anatomy Mapper labeling systems. To interactively view wounds in 3D, we have presented an efficient projective texture mapping algorithm for texturing wounds onto a 3D human anatomy model. In so doing, we have demonstrated an approach to 3D wound reconstruction that works even for a single wound image
A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-rays
Accurate teeth segmentation and orientation are fundamental in modern oral
healthcare, enabling precise diagnosis, treatment planning, and dental implant
design. In this study, we present a comprehensive approach to teeth
segmentation and orientation from panoramic X-ray images, leveraging deep
learning techniques. We build our model based on FUSegNet, a popular model
originally developed for wound segmentation, and introduce modifications by
incorporating grid-based attention gates into the skip connections. We
introduce oriented bounding box (OBB) generation through principal component
analysis (PCA) for precise tooth orientation estimation. Evaluating our
approach on the publicly available DNS dataset, comprising 543 panoramic X-ray
images, we achieve the highest Intersection-over-Union (IoU) score of 82.43%
and Dice Similarity Coefficient (DSC) score of 90.37% among compared models in
teeth instance segmentation. In OBB analysis, we obtain the Rotated IoU (RIoU)
score of 82.82%. We also conduct detailed analyses of individual tooth labels
and categorical performance, shedding light on strengths and weaknesses. The
proposed model's accuracy and versatility offer promising prospects for
improving dental diagnoses, treatment planning, and personalized healthcare in
the oral domain. Our generated OBB coordinates and codes are available at
https://github.com/mrinal054/Instance_teeth_segmentation
- …