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Machine learning-based interpretation of non-contrast feature tracking strain analysis and T1/T2 mapping for assessing myocardial viability.
Sci Rep
January 2025
Cardiovascular Research Center, Rajaie Cardiovascular, Medical, and Research Center, University of Medical Sciences, Tehran, Iran.
Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindications in patients with renal dysfunction and lengthy scan times. This study investigates the potential of non-contrast CMR techniques-feature tracking strain analysis and T1/T2 mapping-combined with machine learning (ML) models, as an alternative to LGE-CMR for myocardial viability assessment.
View Article and Find Full Text PDFPointer meters recognition method in the wild based on innovative deep learning techniques.
Sci Rep
January 2025
College of Computer and Data Science, Minjiang University, Fuzhou, 350018, China.
This study presents a novel approach to identifying meters and their pointers in modern industrial scenarios using deep learning. We developed a neural network model that can detect gauges and one or more of their pointers on low-quality images. We use an encoder network, jump connections, and a modified Convolutional Block Attention Module (CBAM) to detect gauge panels and pointer keypoints in images.
View Article and Find Full Text PDFDevelopment and validation of machine learning-based model for hospital length of stay in patients undergoing endovascular interventional embolization for intracranial aneurysms.
World Neurosurg
January 2025
Department of Neurology, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou 434000, China. Electronic address:
Objective: This study was to explore the factors associated with prolonged hospital length of stay (LOS) in patients with intracranial aneurysms (IAs) undergoing endovascular interventional embolization and construct prediction model machine learning algorithms.
Methods: Employing a retrospective cohort study design, this study collected patients with ruptured IA who received endovascular treatment at Jingzhou First People's Hospital during the inclusion period from September 2022 to December 2023. The entire dataset was randomly split into training and testing dataset with a 7:3 ratio.
Improving the screening ability of neuron-specific enolase on small cell lung cancer.
Lung Cancer
January 2025
Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China. Electronic address:
Neuron-specific enolase (NSE) is one of the most common biomarkers of small cell lung cancer (SCLC) and is widely used in lung cancer screening. But its specificity is affected by many factors. Using residual correction and machine learning, corrected NSE and its reference range were constructed based on metabolic factors and smoking history affecting NSE in the training set of 48,009 healthy individuals recruited from the First Affiliated Hospital of Nanjing Medical University.
View Article and Find Full Text PDFDevelopment of machine learning models for diagnostic biomarker identification and immune cell infiltration analysis in PCOS.
J Ovarian Res
January 2025
Reproductive Medicine Center, Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Background: Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. It is characterized by symptoms such as hyperandrogenemia, oligo or anovulation and polycystic ovarian, significantly impacting quality of life. However, the practical implementation of machine learning (ML) in PCOS diagnosis is hindered by the limitations related to data size and algorithmic models.
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