AIMC Topic: Deep Learning

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Recent trends in diabetes mellitus diagnosis: an in-depth review of artificial intelligence-based techniques.

Diabetes research and clinical practice
Diabetes mellitus (DM) is a highly prevalent chronic condition with significant health and economic impacts; therefore, an accurate diagnosis is essential for the effective management and prevention of its complications. This review explores the late...

Inter-hospital transferability of AI: A case study on phase recognition in cholecystectomy.

Computers in biology and medicine
BACKGROUND: Identifying surgical phases is a crucial component of surgical workflow analysis, facilitating the automated evaluation of surgical procedures' performance and efficiency. A significant challenge in developing neural networks for surgical...

Deep learning-based MRI reconstruction with Artificial Fourier Transform Network (AFTNet).

Computers in biology and medicine
Deep complex-valued neural networks (CVNNs) provide a powerful way to leverage complex number operations and representations and have succeeded in several phase-based applications. However, previous networks have not fully explored the impact of comp...

InclusiViz : Visual Analytics of Human Mobility Data for Understanding and Mitigating Urban Segregation.

IEEE transactions on visualization and computer graphics
Urban segregation refers to the physical and social division of people, often driving inequalities within cities and exacerbating socioeconomic and racial tensions. While most studies focus on residential spaces, they often neglect segregation across...

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle.

Molecular biology of the cell
The life cycle of eukaryotic microorganisms involves complex transitions between states such as dormancy, mating, meiosis, and cell division, which are often studied independently from each other. Therefore, most microbial life cycles are theoretical...

A transformer-based framework for temporal health event prediction with graph-enhanced representations.

Journal of biomedical informatics
OBJECTIVE: Deep learning approaches have demonstrated significant potential in predicting temporal health events in recent years. However, existing methods have not fully leveraged the complex interactions among comorbidities and have overlooked imba...

IR-MBiTCN: Computational prediction of insulin receptor using deep learning: A multi-information fusion approach with multiscale bidirectional temporal convolutional network.

International journal of biological macromolecules
The insulin receptor (IR) is a transmembrane protein that controls glucose homeostasis and is highly associated with chronic diseases including cancer and neurological. Traditional experimental methods have provided essential insights into IR structu...

NFR-EDL: Non-linear fuzzy rank-based ensemble deep learning for accurate diagnosis of oral and dental diseases using RGB color photography.

Computers in biology and medicine
BACKGROUND: Oral health plays a vital role in our daily lives, affecting essential activities like eating, speaking, and smiling. Poor oral health can lead to significant social, psychological, and physical consequences, which makes early and accurat...

Artificial intelligence in fetal brain imaging: Advancements, challenges, and multimodal approaches for biometric and structural analysis.

Computers in biology and medicine
Artificial intelligence (AI) is transforming fetal brain imaging by addressing key challenges in diagnostic accuracy, efficiency, and data integration in prenatal care. This review explores AI's application in enhancing fetal brain imaging through ul...