effect-change-direction-cod-movement-on-plantar-pressure-foot-balance

Effect of Change of Direction (COD) Movement on Plantar Pressure and Foot Balance in Bilateral Limbs

This protocol establishes a standardized approach to test whether a 45&#176; change-of-direction (COD) task elicits asymmetrical plantar loading between ...

Ningbo University

To investigate clinical and computed tomography (CT) radiomics nomogram for preoperative differentiation of lung adenocarcinoma (LAC) from lung tuberculoma (LTB) in patients with pulmonary solitary solid nodule (PSSN).

Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule.

Computed tomography (CT) signs of lung nodules play an important role in indicating lung nodules' malignancy, and accurate automatic classification of these signs can help doctors improve their diagnostic efficiency. However, few relevant studies targeting multilabel classification (MLC) of nodule signs have been conducted. Moreover, difficulty in obtaining labeled data also restricts this avenue of research to a large extent. To address these problems, a multilabel automatic classification system for nodule signs is proposed, which consists of a 3-dimensional (3D) convolutional neural network (CNN) and an efficient new semi-supervised learning (SSL) framework.

An uncertainty-aware self-training framework with consistency regularization for the multilabel classification of common computed tomography signs in lung nodules.

Broad generalization of radiomics-assisted models may be impeded by concerns about variability. This study aimed to evaluate the merit of combatting batch effect (ComBat) harmonization in reducing the variability of voxel size-related radiomics in both phantom and clinical study in comparison with image resampling correction method.

Optimization and validation of voxel size-related radiomics variability by combatting batch effect harmonization in pulmonary nodules: a phantom and clinical study.

Accurate diagnosis of pneumonia is vital for effective disease management and mortality reduction, but it can be easily confused with other conditions on chest computed tomography (CT) due to an overlap in imaging features. We aimed to develop and validate a deep learning (DL) model based on chest CT for accurate classification of viral pneumonia (VP), bacterial pneumonia (BP), fungal pneumonia (FP), pulmonary tuberculosis (PTB), and no pneumonia (NP) conditions.

Development and validation of a deep learning model for multicategory pneumonia classification on chest computed tomography: a multicenter and multireader study.

Article	Year
Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule. Frontiers in oncology\| PubMed ID: 34712605	2021
An uncertainty-aware self-training framework with consistency regularization for the multilabel classification of common computed tomography signs in lung nodules. Quantitative imaging in medicine and surgery\| PubMed ID: 37711798	2023
Optimization and validation of voxel size-related radiomics variability by combatting batch effect harmonization in pulmonary nodules: a phantom and clinical study. Quantitative imaging in medicine and surgery\| PubMed ID: 37711807	2023
Development and validation of a deep learning model for multicategory pneumonia classification on chest computed tomography: a multicenter and multireader study. Quantitative imaging in medicine and surgery\| PubMed ID: 38106268	2023

Yi Zhan

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