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1.
Front Radiol ; 3: 1190745, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37492393

RESUMO

Background: Chest x-ray (CXR) is widely applied for the detection and diagnosis of children's lung diseases. Lung field segmentation in digital CXR images is a key section of many computer-aided diagnosis systems. Objective: In this study, we propose a method based on deep learning to improve the lung segmentation quality and accuracy of children's multi-center CXR images. Methods: The novelty of the proposed method is the combination of merits of TransUNet and ResUNet. The former can provide a self-attention module improving the feature learning ability of the model, while the latter can avoid the problem of network degradation. Results: Applied on the test set containing multi-center data, our model achieved a Dice score of 0.9822. Conclusions: This novel lung segmentation method proposed in this work based on TransResUNet is better than other existing medical image segmentation networks.

2.
Front Neurorobot ; 16: 1059739, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36506818

RESUMO

Machine learning works similar to the way humans train their brains. In general, previous experiences prepared the brain by firing specific nerve cells in the brain and increasing the weight of the links between them. Machine learning also completes the classification task by constantly changing the weights in the model through training on the training set. It can conduct a much more significant amount of training and achieve higher recognition accuracy in specific fields than the human brain. In this paper, we proposed an active learning framework called variational deep embedding-based active learning (VaDEAL) as a human-centric computing method to improve the accuracy of diagnosing pneumonia. Because active learning (AL) realizes label-efficient learning by labeling the most valuable queries, we propose a new AL strategy that incorporates clustering to improve the sampling quality. Our framework consists of a VaDE module, a task learner, and a sampling calculator. First, the VaDE performs unsupervised reduction and clustering of dimension over the entire data set. The end-to-end task learner obtains the embedding representations of the VaDE-processed sample while training the target classifier of the model. The sampling calculator will calculate the representativeness of the samples by VaDE, the uncertainty of the samples through task learning, and ensure the overall diversity of the samples by calculating the similarity constraints between the current and previous samples. With our novel design, the combination of uncertainty, representativeness, and diversity scores allows us to select the most informative samples for labeling, thus improving overall performance. With extensive experiments and evaluations performed on a large dataset, we demonstrate that our proposed method is superior to the state-of-the-art methods and has the highest accuracy in the diagnosis of pneumonia.

3.
BMJ Open ; 4(2): e004071, 2014 Feb 27.
Artigo em Inglês | MEDLINE | ID: mdl-24578536

RESUMO

OBJECTIVE: To assess the frequency of warfarin use, the achieved international normalised ratio (INR) balance among warfarin users and the primary healthcare outpatient costs of patients with atrial fibrillation (AF). DESIGN: Retrospective, non-interventional registry study. SETTING: Primary healthcare. PARTICIPANTS: All patients with AF (n=2746) treated in one Finnish health centre between October 2010 and March 2012. METHODS: Data on healthcare resource use, warfarin use, individually defined target INR range and INR test results were collected from the primary healthcare database for patients with AF diagnosis. The analysed dataset consisted of a 1-year follow-up. Warfarin treatment balance was estimated with the proportion of time spent in the therapeutic INR range (TTR). The cost of used healthcare resources was valued separately with national and service provider unit costs to estimate the total outpatient treatment costs. The factors potentially impacting the treatment costs were assessed with a generalised linear regression model. RESULTS: Approximately 50% of the patients with AF with CHADS-VASc ≥1 used warfarin. The average TTR was 65.2% but increased to 74.5% among patients using warfarin continuously (ie, without gaps exceeding 56 days between successive INR tests) during follow-up. One-third of the patients had a TTR of below 60%. The average outpatient costs in the patient cohort were €314.44 with the national unit costs and €560.26 with the service provider unit costs. The costs among warfarin users were, on average, €524.11 or €939.54 higher compared with the costs among non-users, depending on the used unit costs. A higher TTR was associated with lower outpatient costs. CONCLUSIONS: The patients in the study centre using warfarin were, on average, well controlled on warfarin, yet one-third of patients had a TTR of below 60%.


Assuntos
Anticoagulantes/uso terapêutico , Fibrilação Atrial/tratamento farmacológico , Padrões de Prática Médica/estatística & dados numéricos , Atenção Primária à Saúde , Varfarina/administração & dosagem , Idoso , Anticoagulantes/economia , Feminino , Finlândia , Custos de Cuidados de Saúde , Humanos , Coeficiente Internacional Normatizado , Masculino , Atenção Primária à Saúde/economia , Sistema de Registros , Estudos Retrospectivos , Varfarina/economia
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