Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test ...results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians' daily procedure, especially in developing countries. Electronic Health Records systems have been designed to keep the patients' history and reduce the time spent analyzing the patient's data. However, better tools to support decision-making are still needed. In this article, we propose ClinicalPath, a visualization tool for users to track a patient's clinical path through a series of tests and data, which can aid in treatments and diagnoses. Our proposal is focused on patient's data analysis, presenting the test results and clinical history longitudinally. Both the visualization design and the system functionality were developed in close collaboration with experts in the medical domain to ensure a right fit of the technical solutions and the real needs of the professionals. We validated the proposed visualization based on case studies and user assessments through tasks based on the physician's daily activities. Our results show that our proposed system improves the physicians' experience in decision-making tasks, made with more confidence and better usage of the physicians' time, allowing them to take other needed care for the patients.
•Wound area measurement assists healthcare professionals in patients’ follow-up.•Convolutional neural networks accurately segment skin ulcer wounds.•The detection of measurement tools allows ...measuring the pixel density of the images.•The proposed framework estimates the wound area in real-world units.
Background and objectives:Bedridden patients presenting chronic skin ulcers often need to be examined at home. Healthcare professionals follow the evolution of the patients’ condition by regularly taking pictures of the wounds, as different aspects of the wound can indicate the healing stages of the ulcer, including depth, location, and size. The manual measurement of the wounds’ size is often inaccurate, time-consuming, and can also cause discomfort to the patient. In this work, we propose the Automatic Skin Ulcer Region Assessment ASURA framework to accurately segment the wound and automatically measure its size.
Methods:ASURA uses an encoder/decoder deep neural network to perform the segmentation, which detects the measurement ruler/tape present in the image and estimates its pixel density.
Results:Experimental results show that ASURA outperforms the state-of-the-art methods by up to 16% regarding the Dice score, being able to correctly segment the wound with a Dice score higher than 90%. ASURA automatically estimates the pixel density of the images with a relative error of 5%. When using a semi-automatic approach, ASURA was able to estimate the area of the wound in square centimeters with a relative error of 14%.
Conclusions:The results show that ASURA is well-suited for the problem of segmenting and automatically measuring skin ulcers.
Accurate and efficient segmenting of vertebral bodies, muscles, and discs is crucial for analyzing various spinal diseases. However, traditional methods are either laborious and time-consuming ...(manual segmentation) or require extensive training data (fully automatic segmentation). FastCleverSeg, our proposed semi-automatic segmentation approach, addresses those limitations by significantly reducing user interaction while maintaining high accuracy. First, we reduce user interaction by requiring the manual annotation of only two or three slices. Next, we automatically Estimate the Annotation on Intermediary Slices (EANIS) using traditional computer vision/graphics concepts. Finally, our proposed method leverages improved voxel weight balancing to achieve fast and precise volumetric segmentation in the segmentation process. Experimental evaluations on our assembled diverse MRI databases comprising 179 patients (60 male, 119 female), demonstrate a remarkable 25 ms (30 ms standard deviation) processing time and a significant reduction in user interaction compared to existing approaches. Importantly, FastCleverSeg maintains or surpasses the segmentation quality of competing methods, achieving a Dice score of 94%. This invaluable tool empowers physicians to efficiently generate reliable ground truths, expediting the segmentation process and paving the way for future integration with deep learning approaches. In turn, this opens exciting possibilities for future fully automated spine segmentation.
Integrating Internet technologies with traditional healthcare systems has enabled the emergence of cloud healthcare systems. These systems aim to optimize the balance between online diagnosis and ...offline treatment to effectively reduce patients’ waiting times and improve the utilization of idle medical resources. In this paper, a distributed genetic algorithm (DGA) is proposed as a means to optimize the balance of patient assignment (PA) in cloud healthcare systems. The proposed DGA utilizes individuals as solutions for the PA optimization problem and generates better solutions through the execution of crossover, mutation, and selection operators. Besides, the distributed framework in the DGA is proposed to improve its population diversity and scalability. Experimental results demonstrate the effectiveness of the proposed DGA in optimizing the PA problem within the cloud healthcare systems.
Social networks have become another resource for supporting mental health specialists in making inferences and finding indications of mental disorders, such as depression. This paper addresses the ...state-of-the-art regarding studies on recognition of depressive mood disorders in social networks through approaches and techniques of sentiment and emotion analysis. The systematic research conducted focused on social networks, social media, and the most employed techniques, feelings, and emotions were analyzed to find predecessors of a depressive disorder. Discussions on the research gaps identified aimed at improving the effectiveness of the analysis process, bringing the analysis close to the user’s reality. Twitter, Facebook, Blogs and Forums, Reddit, Live Journal, and Instagram are the most employed social networks regarding the identification of depressive mood disorders, and the most used information was text, followed by emoticons, user log information, and images. The selected studies usually employ classic off-the-shelf classifiers for the analysis of the available information, combined with lexicons such as NRC Word-Emoticon Association Lexicon, WordNet-Affect, Anew, and LIWC tool. The challenges include the analysis of temporal information and a combination of different types of information.
•A new proposal based on superpixels and deep-learning models for wound segmentation is proposed.•Distinct parameterizations for the approach are discussed and evaluated experimentally.•The proposed ...method provides tissue segmentation and wounded area quantification more accurately than compared approaches.
The image-based identification of distinct tissues within dermatological wounds enhances patients’ care since it requires no intrusive evaluations. This manuscript presents an approach, we named QTDU, that combines deep learning models with superpixel-driven segmentation methods for assessing the quality of tissues from dermatological ulcers.
QTDU consists of a three-stage pipeline for the obtaining of ulcer segmentation, tissues’ labeling, and wounded area quantification. We set up our approach by using a real and annotated set of dermatological ulcers for training several deep learning models to the identification of ulcered superpixels.
Empirical evaluations on 179,572 superpixels divided into four classes showed QTDU accurately spot wounded tissues (AUC = 0.986, sensitivity = 0.97, and specificity = 0.974) and outperformed machine-learning approaches in up to 8.2% regarding F1-Score through fine-tuning of a ResNet-based model. Last, but not least, experimental evaluations also showed QTDU correctly quantified wounded tissue areas within a 0.089 Mean Absolute Error ratio.
Results indicate QTDU effectiveness for both tissue segmentation and wounded area quantification tasks. When compared to existing machine-learning approaches, the combination of superpixels and deep learning models outperformed the competitors within strong significant levels.
The amount and variety of digital data currently being generated, stored and analyzed, including images, videos, and time series, have brought challenges to data administrators, analysts and ...developers, who struggle to comply with the expectations of both data owners and end users. The majority of the applications demand searching complex data by taking advantage of queries that analyze different aspects of the data, and need the answers in a timely manner. Content-based similarity retrieval techniques are well-suited to handle large databases, because they enable performing queries and analyses using features automatically extracted from the data, without users’ intervention. In this paper, we review and discuss the challenges posed to the database and related communities in order to provide techniques and tools that can meet the variety and veracity characteristics of big and complex data, while also considering the aspects of semantical preservation and completeness of the data. Examples and results obtained over a two-decade-long experience with real applications are presented and discussed.
•A preprocessing technique to speed up the outliers removal from matched point is proposed.•Almost all inliers are kept while a high portion of the outliers are filtered.•The performance of ...traditional consensus methods are kept.•The running time gain is considerable better, about 3 times faster.
Outliers removal from matched points is a crucial step in image matching/mosaicing. A single outlier might lead to incorrect adjustments. In order to deal with this issue, graph-based approaches were proposed in the literature, but usually they are very time consuming. Aimed at minimizing the processing time and keeping the matching accuracy, we developed a preprocessing technique called FOMP, which reduces the number of outliers in the initial set of matched points. FOMP filters out vertices based on the difference of the edge weights. In this paper, we detail and present an improved version (FOMP*) by enhancing the stop criteria, taking into account the average weights of the vertices. We have conducted exhaustive experiments and summarized the results obtained over 5 different image datasets, where four of them present several conditions (camera movements, matching ambiguities, deformable objects and affine transformations). To test our proposed method in real-cases, the fifth image dataset is composed of frames from a video sequence acquired by a drone. The experimental results show that the precision of traditional methods are kept, while processing time speeds up, in average, by two times. FOMP* removes, in average, 92% of the outliers, while keeping 99% of the inliers (considering a reasonable range of outliers, from 5% to 65%).
Similarity search is fundamental to store and retrieve large volumes of complex data required by many real world applications. A useful mechanism for such concept is the query-by-similarity. Based on ...their topological properties, metric similarity functions can be used to index sets of data which can be queried effectively and efficiently by the so-called metric access methods. However, data produced by various application domains and the varying data types handled often lead to missing data, hence, they do not follow the metric similarity requirements. As a consequence, missing data cause distortions in the index structure and yield bias in the query answer. In this paper, we propose the
Hollow-tree
, a novel access method aimed at successfully retrieving data with missing attribute values. It employs new strategies for indexing and searching data elements, capable of handling the missing data issues when the cause of missingness is ignorable. The indexing strategy is based on a family of distance functions that allow measuring the distance between elements with missing values, along with a set of policies able to organize the elements in the index without causing distortions to its internal structure. The searching strategy employs fractal dimension property of the data to achieve accurate query answer while considering data with missing values part of the response. Results from experiments performed on a variety of real and synthetic data sets showed that, while other metric access methods deteriorate with small amounts of missing values, the
Hollow-tree
maintains a remarkable performance with almost 100% of precision and recall for range queries and more than 90% for
k
-nearest neighbor queries, for up to 40% of missing values.
Storing, querying, predicting, and interpolating trajectories of moving objects is a topic which the database community has studied for decades. We study a new variant of this problem in this ...article: We deal with a set of moving objects which do not have an identity, i.e., one does not know whether an object is identical to one observed earlier at another position. Our use case is a stream of images of cells of developing embryos. There exist so-called tracking tools. They match cells in such image sequences, to build trajectory vectors. However, these trackers have certain weaknesses, including counter-intuitive parameters and the expectation of users manually correcting trajectories. In this paper, we propose fully automatic tracking algorithms. They rely on space partitioning heuristics to match cells. This gives way to much cheaper data-analysis pipelines, as we will explain. We also propose two algorithms predicting the next positions of cells, given earlier ones. Experiments over 12 datasets show that our new approaches reduce the execution time by up to 7.8 times for tracking and 6.2 times for prediction. Prediction quality increases by up to 5.6% over the best tracker.
•Cells can be modeled as moving objects without identity that move under uncertainty.•Predictors establish cell motion accurately based on observed cell positions.•Cell prediction avoids computationally costly steps of the tracking pipeline.
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