AIMC Topic: Humans

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Deep learning, data ramping, and uncertainty estimation for detecting artifacts in large, imbalanced databases of MRI images.

Medical image analysis
Magnetic resonance imaging (MRI) is increasingly being used to delineate morphological changes underlying neurological disorders. Successfully detecting these changes depends on the MRI data quality. Unfortunately, image artifacts frequently compromi...

Testing the performance, adequacy, and applicability of an artificial intelligence model for pediatric pneumonia diagnosis.

Computer methods and programs in biomedicine
BACKGROUND: Community-acquired Pneumonia (CAP) is a common childhood infectious disease. Deep learning models show promise in X-ray interpretation and diagnosis, but their validation should be extended due to limitations in the current validation wor...

COVID-19 diagnosis using clinical markers and multiple explainable artificial intelligence approaches: A case study from Ecuador.

SLAS technology
The COVID-19 pandemic erupted at the beginning of 2020 and proved fatal, causing many casualties worldwide. Immediate and precise screening of affected patients is critical for disease control. COVID-19 is often confused with various other respirator...

Domain and Histopathology Adaptations-Based Classification for Malignancy Grading System.

The American journal of pathology
Accurate proliferation rate quantification can be used to devise an appropriate treatment for breast cancer. Pathologists use breast tissue biopsy glass slides stained with hematoxylin and eosin to obtain grading information. However, this manual eva...

Visual modalities-based multimodal fusion for surgical phase recognition.

Computers in biology and medicine
Surgical workflow analysis is essential to help optimize surgery by encouraging efficient communication and the use of resources. However, the performance of phase recognition is limited by the use of information related to the presence of surgical i...

STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering.

Computers in biology and medicine
BACKGROUND: Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morpholog...

Using a dual-stream attention neural network to characterize mild cognitive impairment based on retinal images.

Computers in biology and medicine
Mild cognitive impairment (MCI) is a critical transitional stage between normal cognition and dementia, for which early detection is crucial for timely intervention. Retinal imaging has been shown as a promising potential biomarker for MCI. This stud...

Deep learning for automated measurement of CSA related acromion morphological parameters on anteroposterior radiographs.

European journal of radiology
BACKGROUND: The Critical Shoulder Angle Related Acromion Morphological Parameter (CSA- RAMP) is a valuable tool in the analyzing the etiology of the rotator cuff tears (RCTs). However, its clinical application has been limited by the time-consuming a...

Predictive learning by a burst-dependent learning rule.

Neurobiology of learning and memory
Humans and other animals are able to quickly generalize latent dynamics of spatiotemporal sequences, often from a minimal number of previous experiences. Additionally, internal representations of external stimuli must remain stable, even in the prese...