418 research outputs found
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
Diabetic Foot Ulcers Classification using a fine-tuned CNNs Ensemble
Diabetic Foot Ulcers (DFU) are lesions in the foot region caused by diabetes mellitus. It is essential to define the appropriate treatment in the early stages of the disease once late treatment may result in amputation. This article proposes an ensemble approach composed of five modified convolutional neural networks (CNNs) - VGG-16, VGG-19, Resnet50, InceptionV3, and Densenet-201 - to classify DFU images. To define the parameters, we fine-tuned the CNNs, evaluated different configurations of fully connected layers, and used batch normalization and dropout operations. The modified CNNs were well suited to the problem; however, we observed that the union of the five CNNs significantly increased the success rates. We performed tests using 8,250 images with different resolution, contrast, color, and texture characteristics and included data augmentation operations to expand the training dataset. 5-fold cross-validation led to an average accuracy of 95.04%, resulting in a Kappa index greater than 91.85%, considered Excellent
Risk prediction analysis for post-surgical complications in cardiothoracic surgery
Cardiothoracic surgery patients have the risk of developing surgical site infections
(SSIs), which causes hospital readmissions, increases healthcare costs and may lead to
mortality. The first 30 days after hospital discharge are crucial for preventing these
kind of infections. As an alternative to a hospital-based diagnosis, an automatic digital
monitoring system can help with the early detection of SSIs by analyzing daily images
of patient’s wounds. However, analyzing a wound automatically is one of the biggest
challenges in medical image analysis.
The proposed system is integrated into a research project called CardioFollowAI,
which developed a digital telemonitoring service to follow-up the recovery of cardiothoracic
surgery patients. This present work aims to tackle the problem of SSIs by predicting
the existence of worrying alterations in wound images taken by patients, with the help of
machine learning and deep learning algorithms. The developed system is divided into a
segmentation model which detects the wound region area and categorizes the wound type,
and a classification model which predicts the occurrence of alterations in the wounds.
The dataset consists of 1337 images with chest wounds (WC), drainage wounds (WD)
and leg wounds (WL) from 34 cardiothoracic surgery patients. For segmenting the images,
an architecture with a Mobilenet encoder and an Unet decoder was used to obtain
the regions of interest (ROI) and attribute the wound class. The following model was
divided into three sub-classifiers for each wound type, in order to improve the model’s
performance. Color and textural features were extracted from the wound’s ROIs to feed
one of the three machine learning classifiers (random Forest, support vector machine and
K-nearest neighbors), that predict the final output.
The segmentation model achieved a final mean IoU of 89.9%, a dice coefficient of
94.6% and a mean average precision of 90.1%, showing good results. As for the algorithms
that performed classification, the WL classifier exhibited the best results with a
87.6% recall and 52.6% precision, while WC classifier achieved a 71.4% recall and 36.0%
precision. The WD had the worst performance with a 68.4% recall and 33.2% precision.
The obtained results demonstrate the feasibility of this solution, which can be a start for
preventing SSIs through image analysis with artificial intelligence.Os pacientes submetidos a uma cirurgia cardiotorácica tem o risco de desenvolver
infeções no local da ferida cirúrgica, o que pode consequentemente levar a readmissões
hospitalares, ao aumento dos custos na saúde e à mortalidade. Os primeiros 30 dias
após a alta hospitalar são cruciais na prevenção destas infecções. Assim, como alternativa
ao diagnóstico no hospital, a utilização diária de um sistema digital e automático de
monotorização em imagens de feridas cirúrgicas pode ajudar na precoce deteção destas
infeções. No entanto, a análise automática de feridas é um dos grandes desafios em análise
de imagens médicas.
O sistema proposto integra um projeto de investigação designado CardioFollow.AI,
que desenvolveu um serviço digital de telemonitorização para realizar o follow-up da recuperação
dos pacientes de cirurgia cardiotorácica. Neste trabalho, o problema da infeção
de feridas cirúrgicas é abordado, através da deteção de alterações preocupantes na ferida
com ajuda de algoritmos de aprendizagem automática. O sistema desenvolvido divide-se
num modelo de segmentação, que deteta a região da ferida e a categoriza consoante o seu
tipo, e num modelo de classificação que prevê a existência de alterações na ferida.
O conjunto de dados consistiu em 1337 imagens de feridas do peito (WC), feridas
dos tubos de drenagem (WD) e feridas da perna (WL), provenientes de 34 pacientes de
cirurgia cardiotorácica. A segmentação de imagem foi realizada através da combinação
de Mobilenet como codificador e Unet como decodificador, de forma a obter-se as regiões
de interesse e atribuir a classe da ferida. O modelo seguinte foi dividido em três subclassificadores
para cada tipo de ferida, de forma a melhorar a performance do modelo.
CaraterÃsticas de cor e textura foram extraÃdas da região da ferida para serem introduzidas
num dos modelos de aprendizagem automática de forma a prever a classificação final
(Random Forest, Support Vector Machine and K-Nearest Neighbors).
O modelo de segmentação demonstrou bons resultados ao obter um IoU médio final
de 89.9%, um dice de 94.6% e uma média de precisão de 90.1%. Relativamente aos algoritmos
que realizaram a classificação, o classificador WL exibiu os melhores resultados
com 87.6% de recall e 62.6% de precisão, enquanto o classificador das WC conseguiu um recall de 71.4% e 36.0% de precisão. Por fim, o classificador das WD teve a pior performance
com um recall de 68.4% e 33.2% de precisão. Os resultados obtidos demonstram
a viabilidade desta solução, que constitui o inÃcio da prevenção de infeções em feridas
cirúrgica a partir da análise de imagem, com recurso a inteligência artificial
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
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
Novel Computerised Techniques for Recognition and Analysis of Diabetic Foot Ulcers
Diabetic Foot Ulcers (DFU) that affect the lower extremities are a major complication
of Diabetes Mellitus (DM). It has been estimated that patients with
diabetes have a lifetime risk of 15% to 25% in developing DFU contributing up
to 85% of the lower limb amputation due to failure to recognise and treat DFU
properly. Current practice for DFU screening involves manual inspection of the
foot by podiatrists and further medical tests such as vascular and blood tests are
used to determine the presence of ischemia and infection in DFU. A comprehensive
review of computerized techniques for recognition of DFU has been performed
to identify the work done so far in this field. During this stage, it became clear
that computerized analysis of DFU is relatively emerging field that is why related
literature and research works are limited. There is also a lack of standardised
public database of DFU and other wound-related pathologies.
We have received approximately 1500 DFU images through the ethical approval
with Lancashire Teaching Hospitals. In this work, we standardised both
DFU dataset and expert annotations to perform different computer vision tasks
such as classification, segmentation and localization on popular deep learning
frameworks. The main focus of this thesis is to develop automatic computer vision methods that can recognise the DFU of different stages and grades. Firstly, we used machine learning algorithms to classify the DFU patches against normal skin
patches of the foot region to determine the possible misclassified cases of both
classes. Secondly, we used fully convolutional networks for the segmentation of
DFU and surrounding skin in full foot images with high specificity and sensitivity.
Finally, we used robust and lightweight deep localisation methods in mobile devices
to detect the DFU on foot images for remote monitoring. Despite receiving
very good performance for the recognition of DFU, these algorithms were not able
to detect pre-ulcer conditions and very subtle DFU.
Although recognition of DFU by computer vision algorithms is a valuable
study, we performed the further analysis of DFU on foot images to determine
factors that predict the risk of amputation such as the presence of infection and
ischemia in DFU. The complete DFU diagnosis system with these computer vision
algorithms have the potential to deliver a paradigm shift in diabetic foot care
among diabetic patients, which represent a cost-effective, remote and convenient
healthcare solution with more data and expert annotations
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