AIMC Topic: Aged
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Image quality of virtual monochromatic and material density iodine images for evaluation of head and neck neoplasms using deep learning-based CT image reconstruction - A retrospective observational study.
European journal of radiology
Oct 25, 2024
PURPOSE: To compare the quality of deep learning image reconstructed (DLIR) virtual monochromatic images (VMI) and material density (MD) iodine images from dual-energy computed tomography (DECT) for the evaluation of head and neck neoplasms with CT s...
Deep learning analysis of histopathological images predicts immunotherapy prognosis and reveals tumour microenvironment features in non-small cell lung cancer.
British journal of cancer
Oct 25, 2024
BACKGROUND: Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer mortality worldwide. Immune checkpoint inhibitors (ICIs) have emerged as a crucial treatment option for patients with advanced NSCLC. However, only a subset of pati...
Utilizing graph neural networks for adverse health detection and personalized decision making in sensor-based remote monitoring for dementia care.
Computers in biology and medicine
Oct 25, 2024
BACKGROUND: Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming to prevent hospitalizations and reduce caregiver burden. However, home sensor data is often noisy, o...
Automated Extraction of Stroke Severity From Unstructured Electronic Health Records Using Natural Language Processing.
Journal of the American Heart Association
Oct 25, 2024
BACKGROUND: Multicenter electronic health records can support quality improvement and comparative effectiveness research in stroke. However, limitations of electronic health record-based research include challenges in abstracting key clinical variabl...
Integrating VAI-Assisted Quantified CXRs and Multimodal Data to Assess the Risk of Mortality.
Journal of imaging informatics in medicine
Oct 24, 2024
To address the unmet need for a widely available examination for mortality prediction, this study developed a foundation visual artificial intelligence (VAI) to enhance mortality risk stratification using chest X-rays (CXRs). The VAI employed deep le...
Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model.
Ophthalmology. Retina
Oct 24, 2024
PURPOSE: To test the diagnostic performance of an artificial intelligence algorithm for detecting and segmenting macular neovascularization (MNV) with OCT and OCT angiography (OCTA) in eyes with macular edema from various diagnoses.
Deep Learning
Visual Acuity
Tomography, Optical Coherence
Fundus Oculi
Aged, 80 and over
Cross-Sectional Studies
Male
Middle Aged
Retinal Vein Occlusion
Macular Edema
Fluorescein Angiography
Macula Lutea
Retinal Neovascularization
Humans
Diabetic Retinopathy
Algorithms
Prospective Studies
Female
Aged
A novel endoscopic artificial intelligence system to assist in the diagnosis of autoimmune gastritis: a multicenter study.
Endoscopy
Oct 24, 2024
BACKGROUND:  Autoimmune gastritis (AIG), distinct from Helicobacter pylori-associated atrophic gastritis (HpAG), is underdiagnosed due to limited awareness. This multicenter study aimed to develop a novel endoscopic artificial intelligence (AI) syste...
An explainable machine learning estimated biological age based on morphological parameters of the spine.
GeroScience
Oct 24, 2024
Accurately estimating biological age is beneficial for measuring aging and predicting risk. It is widely accepted that the prevalence of spine compression increases significantly with age. However, biological age based on vertebral morphological data...
A new approach to assess post-mortem interval: A machine learning-assisted label-free ATR-FTIR analysis of human vitreous humor.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Oct 24, 2024
A crucial issue in forensics is determining the post-mortem interval (PMI), the time between death and the finding of a body. Despite various methods already employed for its estimation, only approximate values are currently achievable. Vitreous humo...
Accuracy of distinguishing benign, high-risk lesions and malignancies with inductive machine learning models in BIRADS 4 and BIRADS 5 lesions on breast MR examinations.
European journal of radiology
Oct 24, 2024
RATIONALE AND OBJECTIVES: The aim of this study is to explore the utility of Inductive Decision Tree models (IDTs) in distinguishing between benign, malignant, and high-risk (B3) breast lesions.
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