Deep-learned generation of renal dual-energy CT from a single-energy scan

Sheikhi, M.; Sina, S.; Karimipourfard, M.

doi:10.1016/j.crad.2023.09.021

Clinical Radiology

2024

DOI: 10.1016/j.crad.2023.09.021

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Deep-learned generation of renal dual-energy CT from a single-energy scan

M. Sheikhi,

S. Sina,

M. Karimipourfard

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Cited by 2 publications

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Artificial Intelligence in Revolutionizing Kidney Care and Beyond: Kid-AI Revolution

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Recent advancements in artificial intelligence (AI) have significantly impacted the diagnosis and treatment of kidney diseases, offering novel approaches for precise quantitative assessments of nephropathology. The collaboration between computer engineers, renal specialists, and nephropathologists has led to the development of AI- assisted technology, presenting promising avenues for renal pathology diagnoses, disease prediction, treatment effectiveness assessment, and outcome prediction. This review provides a comprehensive overview of AI applications in renal pathology, focusing on computer vision algorithms for kidney structure segmentation, specific pathological changes, diagnosis, treatment, and prognosis prediction based on images along with the role of machine learning (ML) and deep learning (DL) in addressing global public health issues related to various nephrological conditions. Despite the transformative potential, the review acknowledges challenges such as data privacy, interpretability of AI models, the imperative need for trust in AI-driven recommendations for broad applicability, external validation, and improved clinical decision-making. Overall, the ongoing integration of AI technologies in nephrology paves the newer way for more precise diagnostics, personalized treatments, and improved patient care outcome.

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Deep-learned generation of renal dual-energy CT from a single-energy scan

Cited by 2 publications

References 21 publications

Interpretable deep learning analysis on correction of motion blur X-ray images to ensure the efficiency and reliability of clinical decisions

Interpretable deep learning analysis on correction of motion blur X-ray images to ensure the efficiency and reliability of clinical decisions

Artificial Intelligence in Revolutionizing Kidney Care and Beyond: Kid-AI Revolution

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