In recent years, progress in artificial intelligence, particularly in the realm of deep learning, has resulted in substantial enhancements in the diagnosis of various medical conditions. This study introduces a framework that leverages multiple light...
BACKGROUND: To develop a deep learning-based model that is capable of automatically segmenting teeth in cone-beam computed tomography (CBCT) images and generating auxiliary diagnostic reports.
BACKGROUND: Splice site prediction in plant genomes poses substantial challenges that can be addressed using deep learning models. U2-type introns are especially useful for such studies given their ubiquity in plant genomes and the availability of ri...
Early diagnosis and personalized intervention for Autism Spectrum Disorder (ASD) in children can potentially improve developmental outcomes, though current methods often lack scalability and adaptability. This study introduces an integrated system co...
Variable physiological [F]FDG uptake patterns and a lack of labelled data make it challenging to automatically distinguish normal from pathological suspicious uptake in whole-body PET/CT imaging. We propose a deep learning method that generates patie...
The classification of malignant versus benign microcalcifications in mammograms remains a critical yet challenging task in breast cancer screening. Deep learning models, particularly convolutional neural networks, have demonstrated promising results;...
Chest X-rays (CXRs) are widely used for diagnosing respiratory diseases, including the recent example of COVID-19. Supervised deep learning techniques can help detect cases faster and monitor disease progression. However, they are usually developed u...
Quantifying structural brain changes is critical for diagnosing and monitoring neurodegenerative diseases. Although magnetic resonance imaging (MRI) is the silver standard, limited accessibility and cost hamper routine use. We developed a deep learni...
Endodontic illnesses affect around 52% of the global population and are projected to rise by 4% by 2030. Endodontic problems necessitate precise classification for treatment planning and clinical decision-making. Radiographs and expert analysis are e...
Recent advancements in deep learning have led to significant improvements in pneumoconiosis diagnosis from chest X-rays (CXR). However, these models typically require large training datasets, which are challenging to collect due to the rarity of the ...
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