AIMC Topic: Humans

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Analysis of AI foundation model features decodes the histopathologic landscape of HPV-positive head and neck squamous cell carcinomas.

Oral oncology
OBJECTIVES: Human papillomavirus (HPV) influences the pathobiology of Head and Neck Squamous Cell Carcinomas (HSNCCs). While deep learning shows promise in detecting HPV from hematoxylin and eosin (H&E) stained slides, the histologic features utilize...

Healthcare leaders' perceptions of the contribution of artificial intelligence to person-centred care: An interview study.

Scandinavian journal of public health
AIMS: The aim of this study was to explore healthcare leaders' perceptions of the contribution of artificial intelligence (AI) to person-centred care (PCC).

Enhancing Blood-Brain Barrier Penetration Prediction by Machine Learning-Based Integration of Novel and Existing, In Silico and Experimental Molecular Parameters from a Standardized Database.

Journal of chemical information and modeling
Predicting blood-brain barrier (BBB) penetration is crucial for developing central nervous system (CNS) drugs, representing a significant hurdle in successful clinical phase I studies. One of the most valuable properties for this prediction is the po...

Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images.

Cell reports. Medicine
Large language models have shown efficacy across multiple medical tasks. However, their value in the assessment of longitudinal follow-up computed tomography (CT) images of patients with lung nodules is unclear. In this study, we evaluate the ability...

Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy.

Proceedings of the National Academy of Sciences of the United States of America
Cardiomyocyte hypertrophy is a key clinical predictor of heart failure. High-throughput and AI-driven screens have the potential to identify drugs and downstream pathways that modulate cardiomyocyte hypertrophy. Here, we developed LogiRx, a logic-bas...

AD-VAE: Adversarial Disentangling Variational Autoencoder.

Sensors (Basel, Switzerland)
Face recognition (FR) is a less intrusive biometrics technology with various applications, such as security, surveillance, and access control systems. FR remains challenging, especially when there is only a single image per person as a gallery datase...

Deep Learning-Emerged Grid Cells-Based Bio-Inspired Navigation in Robotics.

Sensors (Basel, Switzerland)
Grid cells in the brain's entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, focusin...

Skeleton Reconstruction Using Generative Adversarial Networks for Human Activity Recognition Under Occlusion.

Sensors (Basel, Switzerland)
Recognizing human activities from motion data is a complex task in computer vision, involving the recognition of human behaviors from sequences of 3D motion data. These activities encompass successive body part movements, interactions with objects, o...

Automated Electrical Detection of Proteins for Oral Squamous Cell Carcinoma in an Integrated Microfluidic Chip Using Multi-Frequency Impedance Cytometry and Machine Learning.

Sensors (Basel, Switzerland)
Proteins can act as suitable biomarkers for the prognosis and diagnosis of certain conditions and can help us gain an understanding of the fundamental processes that occur inside an organism. In this work, we present a fully automated machine learnin...

miRNA-Based Diagnosis of Schizophrenia Using Machine Learning.

International journal of molecular sciences
Diagnostic practices for schizophrenia are unreliable due to the lack of a stable biomarker. However, machine learning holds promise in aiding in the diagnosis of schizophrenia and other neurological disorders. Dysregulated miRNAs were extracted from...