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Explainability of CNN-based Alzheimer's disease detection from online handwriting.
Scientific reports
Sep 27, 2024
With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer's disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning technique...
Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer.
Scientific reports
Sep 27, 2024
Prophylactic dissection of paratracheal lymph nodes in clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC) remains controversial. This study aims to integrate preoperative and intraoperative variables to compare traditional nomogra...
Predictive modeling of arginine vasopressin deficiency after transsphenoidal pituitary adenoma resection by using multiple machine learning algorithms.
Scientific reports
Sep 27, 2024
This study aimed to predict arginine vasopressin deficiency (AVP-D) following transsphenoidal pituitary adenoma surgery using machine learning algorithms. We reviewed 452 cases from December 2013 to December 2023, analyzing clinical and imaging data....
Developing machine learning models for personalized treatment strategies in early breast cancer patients undergoing neoadjuvant systemic therapy based on SEER database.
Scientific reports
Sep 27, 2024
This study aimed to compare the long-term outcomes of breast-conserving surgery plus radiotherapy (BCS + RT) and mastectomy in early breast cancer (EBC) patients who received neoadjuvant systemic therapy (NST), and sought to construct and authenticat...
Deep-learning model accurately classifies multi-label lung ultrasound findings, enhancing diagnostic accuracy and inter-reader agreement.
Scientific reports
Sep 27, 2024
Despite the increasing use of lung ultrasound (LUS) in the evaluation of respiratory disease, operators' competence constrains its effectiveness. We developed a deep-learning (DL) model for multi-label classification using LUS and validated its perfo...
Detection of cardiovascular disease cases using advanced tree-based machine learning algorithms.
Scientific reports
Sep 27, 2024
Cardiovascular disease (CVD) can often lead to serious consequences such as death or disability. This study aims to identify a tree-based machine learning method with the best performance criteria for the detection of CVD. This study analyzed data co...
Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks.
Nature communications
Sep 27, 2024
The standard method for identifying active Brown Adipose Tissue (BAT) is [F]-Fluorodeoxyglucose ([F]-FDG) PET/CT imaging, which is costly and exposes patients to radiation, making it impractical for population studies. These issues can be addressed w...
Identification of immune patterns in idiopathic pulmonary fibrosis patients driven by PLA2G7-positive macrophages using an integrated machine learning survival framework.
Scientific reports
Sep 27, 2024
Patients with advanced idiopathic pulmonary fibrosis (IPF), a complex and incurable lung disease with an elusive pathology, are nearly exclusive candidates for lung transplantation. Improved identification of patient subtypes can enhance early diagno...
Comparative study of machine learning and statistical survival models for enhancing cervical cancer prognosis and risk factor assessment using SEER data.
Scientific reports
Sep 27, 2024
Cervical cancer is a common malignant tumor of the female reproductive system and the leading cause of death among women worldwide. The survival prediction method can be used to effectively analyze the time to event, which is essential in any clinica...
Early Detection of Parkinson's Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 27, 2024
With the development of digital medical technology, ubiquitous smartphones are emerging as valuable tools for the detection of complex and elusive diseases. This paper exploits smartphone walking recording for early detection of Parkinson's disease (...
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