AIMC Topic: Humans

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Interpretable Machine Learning Model for Pulmonary Hypertension Risk Prediction: Retrospective Cohort Study.

JMIR medical informatics
BACKGROUND: Pulmonary hypertension (PH) is a progressive disorder characterized by elevated pulmonary artery pressure and increased pulmonary vascular resistance, ultimately leading to right heart failure. Early detection is critical for improving pa...

Exploratory Research With a Health Consumer Group on Social Robot Use Among Older Adults: Qualitative Study.

JMIR human factors
BACKGROUND: There is an increased focus on involving members of the public in health research. These types of groups, such as "health consumer groups," bring different expertise to inform the design of a research study. There is a growing general con...

Accelerating discovery in natural science laboratories with AI and robotics: Perspectives and challenges.

Science robotics
Science laboratory automation enables accelerated discovery in life sciences and materials. However, it requires interdisciplinary collaboration to address challenges such as robust and flexible autonomy, reproducibility, throughput, standardization,...

Online reinforcement learning of state representation in recurrent network supported by the power of random feedback and biological constraints.

eLife
Representation of external and internal states in the brain plays a critical role in enabling suitable behavior. Recent studies suggest that state representation and state value can be simultaneously learned through Temporal-Difference-Reinforcement-...

Automated Resectability Classification of Pancreatic Cancer CT Reports with Privacy-Preserving Open-Weight Large Language Models: A Multicenter Study.

Journal of medical systems
 To evaluate the effectiveness of open-weight large language models (LLMs) in extracting key radiological features and determining National Comprehensive Cancer Network (NCCN) resectability status from free-text radiology reports for pancreatic ducta...

A novel hybrid deep learning model for segmentation and uzzy Res-LeNet based classification for Alzheimer's disease.

Neurogenetics
Alzheimer's disease (AD) is a progressive illness that can cause behavioural abnormalities, personality changes, and memory loss. Early detection helps with future planning for both the affected person and caregivers. Thus, an innovative hybrid Deep ...

Micro- and nanoscale biosensing technologies for early diagnosis of Parkinson's disease.

Mikrochimica acta
Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, remains challenging to diagnose at its earliest stages due to the absence of definitive biomarkers and overlapping clinical features with other synucleinopathies, thereby...

DCDSN: dual-color domain siamese network for multi-classification of pathological artifacts.

Biomedical physics & engineering express
Pathological images are prone to artifacts during scanning and preparation, which can compromise diagnostic accuracy. Therefore, robust artifact detection is essential for improving image quality and ensuring reliable pathological assessments. Howeve...

Cognitive impairment assessment using eye-tracking: multilevel saccade paradigms with differential analysis and attention-based neural networks.

Physiological measurement
. The accurate assessment of cognitive impairment plays a vital role in more targeted treatments for Dementia. Eye movement analysis is a non-invasive and objective method that offers fine-grained insight into cognitive functioning, complementing con...

An artificial intelligence-enhanced early ovarian cancer diagnosis biosensor.

Journal of materials chemistry. B
In early cancer diagnosis, extracellular vesicles (EVs) are more advantageous than circulating tumor cells due to their smaller size, greater stability, and enhanced tissue penetration. These qualities lead to higher EV concentrations in body fluids,...