Osteoporosis is a prevalent metabolic bone disease that frequently remains undiagnosed due to limited access to bone mineral density (BMD) tests, such as Dual-energy X-ray absorptiometry (DXA). To address this issue, recent research explores alternat...
Cancer is a complex and heterogeneous condition marked by the unchecked growth and dissemination of abnormal cells, posing significant challenges in detection, treatment, and patient care. As one of the leading global causes of death, a deep understa...
High-dimensional nuclear magnetic resonance (NMR) spectroscopy can assist in determining protein structure, but it requires time-consuming acquisition. Deep learning enables ultrafast reconstruction but is limited to spectra of up to three dimensions...
Deep learning has shown great promise for improving medical image reconstruction, including positron emission tomography (PET). However, concerns remain about the stability and robustness of these methods, especially when trained on limited data. Thi...
Imagined speech classification involves decoding brain signals to recognize verbalized thoughts or intentions without actual speech production. This technology has significant implications for individuals with speech impairments, offering a means to ...
The CutMix technique is a sophisticated approach for augmenting data in order to train neural network-based image classifiers. Essentially, it involves cutting out a portion of a random image and pasting it into the same location as another image. Ho...
Rapid urbanization and growing traffic volumes have increased the demand for efficient and accurate road damage detection to ensure traffic safety and optimize maintenance. Traditional manual and vehicle-mounted inspection methods are often inefficie...
OBJECTIVE: To develop a predictive framework integrating machine learning and clinical parameters for postoperative pulmonary complications (PPCs) in non-small cell lung cancer (NSCLC) patients undergoing video-assisted thoracic surgery (VATS).
The primary objective of this study is to employ machine learning (ML) algorithms to develop predictive models for renal function recovery in critically ill patients undergoing continuous renal replacement therapy (CRRT) due to acute kidney injury (...
Medical healthcare has advanced substantially due to advancements in Artificial Intelligence (AI) techniques for early disease detection alongside support for clinical decisions. However, a gap exists in widespread adoption of results of these algori...
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