AIMC Topic: Humans

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Classification of knee osteoarthritis severity using markerless motion capture and long short-term memory fully convolutional network.

Computers in biology and medicine
This study explored the integration of markerless motion capture and deep learning to classify knee osteoarthritis severity based on gait kinematics, providing an alternative to traditional assessment methods. We employed a Long Short-Term Memory Ful...

Early detection of Alzheimer's disease using small RNAs. Results from the EPAD cohort.

The journal of prevention of Alzheimer's disease
BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia, and early diagnosis is crucial to enable effective interventions. Currently, Alzheimer's disease is diagnosed through cognitive assessments, brain imaging and fluid biomarkers ...

DeepEM Playground: Bringing deep learning to electron microscopy labs.

Journal of microscopy
Deep learning (DL) has transformed image analysis, enabling breakthroughs in segmentation, object detection, and classification. However, a gap persists between cutting-edge DL research and its practical adoption in electron microscopy (EM) labs. Thi...

Predicting adolescents' environmental action: From individual to national-level factors using an explainable machine learning approach.

Journal of environmental management
As a key force in future environmental actions, youth play a crucial role in driving societal transformation. However, the factors influencing youth environmental actions have not been fully validated, and the role of national-level influences is oft...

In-silico guided identification and studies of potential FFAR4 agonists for type 2 diabetes mellitus therapy.

Expert opinion on drug discovery
BACKGROUND: The activation of free fatty acid receptor 4 (FFAR4) enhances insulin sensitivity and glucose uptake while mitigating inflammation. It is a promising therapeutic approach for managing type 2 diabetes mellitus (T2DM).

DNA-Mediated Bioinspired MXene Gas Sensor Array with Machine Learning for Noninvasive Cancer Recognition.

ACS nano
Noninvasive odor sensing is important in environmental monitoring and medical diagnosis. The two-dimensional material MXene is widely used due to its unique sensing properties but has limitations in specifically recognizing a certain gas. This study ...

Artificial intelligence meets brain theory (again).

Biological cybernetics
After noting the cybernetic origins of Kybernetik/ Biological Cybernetics, we respond to the Editorial by Fellous et al. (2025) and then analyze talks from the NIH BRAIN NeuroAI 2024 Workshop to get one "snapshot" of the state of the conversation bet...

Machine Learning Based Multi-Class Classification and Grading of Squamous Cell Carcinoma in Optical Microscopy.

Microscopy research and technique
Histopathological tissue grading is critical for disease diagnosis and treatment, but manual grading is labor-intensive and time-consuming, requiring expert pathologists. This study presents an efficient analysis of squamous cell carcinoma (SCC) hist...

Artificial intelligence - based approaches based on random forest algorithm for signal analysis: Potential applications in detection of chemico - biological interactions.

Chemico-biological interactions
Random Forest (RF) is a powerful ensemble-based supervised machine learning technique that builds multiple decision trees using bootstrap aggregating and random feature selection to improve classification and regression accuracy while reducing overfi...

The diagnostic model from semi-supervised cross modality transformation improved the distinguished ability of X-rays for pulmonary tuberculosis.

Clinical radiology
BACKGROUND: Early diagnosis of tuberculosis is particularly difficult in resource-poor areas. Traditional chest X-rays (CXR) have limited accuracy, while CT scans are costly and involve radiation exposure. The study aims to improve the diagnostic acc...