AIMC Topic: Humans

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ChatExosome: An Artificial Intelligence (AI) Agent Based on Deep Learning of Exosomes Spectroscopy for Hepatocellular Carcinoma (HCC) Diagnosis.

Analytical chemistry
Large language models (LLMs) hold significant promise in the field of medical diagnosis. There are still many challenges in the direct diagnosis of hepatocellular carcinoma (HCC). α-Fetoprotein (AFP) is a commonly used tumor marker for liver cancer. ...

Machine learning for the early prediction of long-term cognitive outcome in autoimmune encephalitis.

Journal of psychosomatic research
BACKGROUND AND OBJECTIVE: Autoimmune encephalitis (AE) is an immune-mediated disease. Some patients experience persistent cognitive deficits despite receiving immunotherapy. We aimed to develop a prediction model for long-term cognitive outcomes in p...

Detection of metastatic breast carcinoma in sentinel lymph node frozen sections using an artificial intelligence-assisted system.

Pathology, research and practice
We developed an automatic method based on a convolutional neural network (CNN) that identifies metastatic lesions in whole slide images (WSI) of intraoperative frozen sections from sentinel lymph nodes in breast cancer. A total of 954 sentinel lymph ...

Deep learning paradigms in lung cancer diagnosis: A methodological review, open challenges, and future directions.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Lung cancer is the leading cause of global cancer-related deaths, which emphasizes the critical importance of early diagnosis in enhancing patient outcomes. Deep learning has demonstrated significant promise in lung cancer diagnosis, excelling in nod...

Review on computational methods for the detection and classification of Parkinson's Disease.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: The worldwide estimates reveal two-fold increase in incidence of Parkinson's disease (PD) over 25 years. The two-fold increased incidence and lack of proper treatment uplifted a compelling solicitude, nagging towards accurat...

Current state and future prospects of Horizontal Gene Transfer detection.

NAR genomics and bioinformatics
Artificial intelligence (AI) has been shown to be beneficial in a wide range of bioinformatics applications. Horizontal Gene Transfer (HGT) is a driving force of evolutionary changes in prokaryotes. It is widely recognized that it contributes to the ...

Risk assessment with the fuzzy Fine-Kinney method in a business operating in the metal industry.

International journal of occupational safety and ergonomics : JOSE
Occupational risk assessment involves examining and ranking the risks and hazards in a production or service facility, focusing on workplace health and safety. This study aims to address the deficiencies of traditional methods by applying a fuzzy log...

Evaluation of factors predicting transition from prediabetes to diabetes among patients residing in underserved communities in the United States - A machine learning approach.

Computers in biology and medicine
INTRODUCTION: Over one-third of the population in the United States (US) has prediabetes. Unfortunately, underserved population in the United States face a higher burden of prediabetes compared to urban areas, increasing the risk of stroke and heart ...

Alzheimer's Disease detection and classification using optimized neural network.

Computers in biology and medicine
Alzheimer's disease (AD) is a degenerative neurological condition characterized by a progressive decline in cognitive abilities, resulting in memory impairment and limitations in performing daily tasks. Timely and precise identification of AD holds p...

Multi-modality medical image classification with ResoMergeNet for cataract, lung cancer, and breast cancer diagnosis.

Computers in biology and medicine
The variability in image modalities presents significant challenges in medical image classification, as traditional deep learning models often struggle to adapt to different image types, leading to suboptimal performance across diverse datasets. This...