AIMC Topic: Aged

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Predicting rapid progression in knee osteoarthritis: a novel and interpretable automated machine learning approach, with specific focus on young patients and early disease.

Annals of the rheumatic diseases
OBJECTIVES: To facilitate the stratification of patients with osteoarthritis (OA) for new treatment development and clinical trial recruitment, we created an automated machine learning (autoML) tool predicting the rapid progression of knee OA over a ...

Identification of key gene signatures for predicting chemo-immunotherapy efficacy in extensive-stage small-cell lung cancer using machine learning.

Lung cancer (Amsterdam, Netherlands)
OBJECTIVES: The lack of definitive biomarkers presents a significant challenge for chemo-immunotherapy in extensive-stage small-cell lung cancer (ES-SCLC). We aimed to identify key genes associated with chemo-immunotherapy efficacy in ES-SCLC through...

International multicenter validation of AI-driven ultrasound detection of ovarian cancer.

Nature medicine
Ovarian lesions are common and often incidentally detected. A critical shortage of expert ultrasound examiners has raised concerns of unnecessary interventions and delayed cancer diagnoses. Deep learning has shown promising results in the detection o...

Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.

IEEE transactions on medical imaging
The interconnection between brain regions in neurological disease encodes vital information for the advancement of biomarkers and diagnostics. Although graph convolutional networks are widely applied for discovering brain connection patterns that poi...

Iodine Density of Lymphoma, Metastatic SCCA, and Normal Cervical lymph nodes: A Comparative Analysis Based on DLSCT.

F1000Research
OBJECTIVE: To compare iodine density (ID) and contrast-enhanced attenuation value (CEAV) from dual-layer spectral computed tomography (DLSCT) scans of lymphomatous, metastatic squamous cell carcinoma (SCCA), and normal cervical lymph nodes.

Towards the automatic detection of activities of daily living using eye-movement and accelerometer data with neural networks.

Computers in biology and medicine
Early diagnosis of neurodegenerative diseases, such as Alzheimer's disease, improves treatment and care outcomes for patients. Early signs of cognitive decline can be detected using functional scales, which are written records completed by a clinicia...

Identifying proteomic prognostic markers for Alzheimer's disease with survival machine learning: The Framingham Heart Study.

The journal of prevention of Alzheimer's disease
BACKGROUND: Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is c...

Prognostic Features for Overall Survival in Male Diabetic Patients Undergoing Hemodialysis Using Elastic Net Penalized Cox Regression; A Machine Learning Approach.

Archives of Iranian medicine
BACKGROUND: Diabetics constitute a significant percentage of hemodialysis (HD) patients with higher mortality, especially among male patients. A machine learning algorithm was used to optimize the prediction of time to death in male diabetic hemodial...

F-18 FDG PET/CT based Preoperative Machine Learning Prediction Models for Evaluating Regional Lymph Node Metastasis Status of Patients with Colon Cancer.

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: This study aimed to develop a simple machine-learning model incorporating lymph node metastasis status with F-18 Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) and clinical information for predicting regio...

Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.

The journal of prevention of Alzheimer's disease
BACKGROUND: Investigators conducting clinical trials have an ethical, scientific, and regulatory obligation to protect the safety of trial participants. Traditionally, safety monitoring includes manual review and coding of adverse event data by exper...