AIMC Topic: Humans

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Neural architecture search with Deep Radon Prior for sparse-view CT image reconstruction.

Medical physics
BACKGROUND: Sparse-view computed tomography (CT) reduces radiation exposure but suffers from severe artifacts caused by insufficient sampling and data scarcity, which compromise image fidelity. Recent advancements in deep learning (DL)-based methods ...

Automatic Joint Lesion Detection by enhancing local feature interaction.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Recently, deep learning models have demonstrated impressive performance in Automatic Joint Lesion Detection (AJLD), yet balancing accuracy and efficiency remains a significant challenge. This paper focuses on achieving end-to-end lesion detection whi...

Exploring the importance of clinical and sociodemographic factors on self-rated health in midlife: A cross-sectional study using machine learning.

International journal of medical informatics
BACKGROUND: Self-rated health (SRH) is influenced by various factors, including clinical and sociodemographic characteristics. However, in the context of Brazil, we still lack a clear understanding of the relative importance of these factors and how ...

Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence.

Molecules and cells
Metal coordination is essential for structural/catalytic functions of metalloproteins that mediate a wide range of biological processes in living organisms. Advances in bioinformatics have significantly enhanced our understanding of metal-binding sit...

Importance of dataset design in developing robust U-Net models for label-free cell morphology evaluation.

Journal of bioscience and bioengineering
Advances in regenerative medicine highlighted the need for label-free cell image analysis to replace conventional microscopic observation for non-invasive cell quality evaluation. Image-based evaluation provides an efficient, quantitative, and automa...

Novel pre-spatial data fusion deep learning approach for multimodal volumetric outcome prediction models in radiotherapy.

Medical physics
BACKGROUND: Given the recent increased emphasis on multimodal neural networks to solve complex modeling tasks, the problem of outcome prediction for a course of treatment can be framed as fundamentally multimodal in nature. A patient's response to tr...

Differentiating Functional Connectivity Patterns in ADHD and Autism Among the Young People: A Machine Learning Solution.

Journal of attention disorders
OBJECTIVE: ADHD and autism are complex and frequently co-occurring neurodevelopmental conditions with shared etiological and pathophysiological elements. In this paper, we attempt to differentiate these conditions among the young people in terms of i...

Feature gene selection and functional validation of SH3KBP1 in infantile hemangioma using machine learning.

Biochemical and biophysical research communications
BACKGROUND: Infantile hemangioma (IH) is a prevalent vascular tumor in infancy with a complex pathogenesis that remains unclear. This study aimed to investigate the underlying mechanisms of IH using comprehensive bioinformatics analyses and in vitro ...

Generative Artificial Intelligence in Academic Surgery: Ethical Implications and Transformative Potential.

The Journal of surgical research
Artificial intelligence (AI) is rapidly being used in medicine due to its advanced capabilities in image and video recognition, clinical decision support, surgical education, and administrative task automation. Large language models such as OpenAI's ...

Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography.

Nature medicine
Developments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data that currently need to be interpreted by human technicians. Here we tested an artificial intelligence (AI) algorithm for direct-to-physician reporting ...