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| http://dx.doi.org/10.4274/balkanmedj.galenos.2024.2024.060324 | DOI Listing |
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Intra- and interobserver agreement of proposed objective transvaginal ultrasound image-quality scoring system for use in artificial intelligence algorithm development.
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Robinson Research Institute, University of Adelaide, Adelaide, Australia.
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View Article and Find Full Text PDFThe Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection.
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Laboratory of Automation and Manufacturing Engineering, Department of Industrial Engineering, Batna 2 University, Batna 05000, Algeria.
Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming and error-prone. The rise of deep learning has led to advanced models for automated brain tumor feature extraction, segmentation, and classification.
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Department of Cardiovascular Surgery, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake, Toyoake 470-1192, Aichi, Japan.
Fractional flow reserve (FFR) has been well validated as a modality for evaluating myocardial ischemia, demonstrating the superiority of FFR-guided percutaneous coronary intervention (PCI) over conventional angiography-guided PCI. As a result, the strategy for coronary artery bypass grafting (CABG) is shifting toward FFR guidance. However, the advantage of FFR-guided CABG over angiography-guided CABG remains unclear.
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