AIMC Topic: Humans

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EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks.

Disease markers
Diabetic retinopathy (DR) is a common chronic fundus disease, which has four different kinds of microvessel structure and microvascular lesions: microaneurysms (MAs), hemorrhages (HEs), hard exudates, and soft exudates. Accurate detection and countin...

Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.

PloS one
Gadolinium-enhancing lesions reflect active disease and are critical for in-patient monitoring in multiple sclerosis (MS). In this work, we have developed the first fully automated method to segment and count the gadolinium-enhancing lesions from rou...

Stimuli-Aware Visual Emotion Analysis.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Visual emotion analysis (VEA) has attracted great attention recently, due to the increasing tendency of expressing and understanding emotions through images on social networks. Different from traditional vision tasks, VEA is inherently more challengi...

When is Psychology Research Useful in Artificial Intelligence? A Case for Reducing Computational Complexity in Problem Solving.

Topics in cognitive science
A problem is a situation in which an agent seeks to attain a given goal without knowing how to achieve it. Human problem solving is typically studied as a search in a problem space composed of states (information about the environment) and operators ...

Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Quantitative ultrasound (QUS) imaging is a safe, reliable, inexpensive, and real-time technique to extract physically descriptive parameters for assessing pathologies. Compared with other major imaging modalities such as computed tomography and magne...

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.

Ophthalmology
IMPORTANCE: The development of artificial intelligence (AI) and other machine diagnostic systems, also known as software as a medical device, and its recent introduction into clinical practice requires a deeply rooted foundation in bioethics for cons...

Deep learning-based model for diagnosing Alzheimer's disease and tauopathies.

Neuropathology and applied neurobiology
AIMS: This study aimed to develop a deep learning-based model for differentiating tauopathies, including Alzheimer's disease (AD), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD) and Pick's disease (PiD), based on tau-immunostai...

Predicting Future Service Use in Dutch Mental Healthcare: A Machine Learning Approach.

Administration and policy in mental health
A mental healthcare system in which the scarce resources are equitably and efficiently allocated, benefits from a predictive model about expected service use. The skewness in service use is a challenge for such models. In this study, we applied a mac...

Diagnostic performance of endoscopic ultrasound-artificial intelligence using deep learning analysis of gallbladder polypoid lesions.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Endoscopic ultrasound (EUS) is the most accurate diagnostic modality for polypoid lesions of the gallbladder (GB), but is limited by subjective interpretation. Deep learning-based artificial intelligence (AI) algorithms are under ...

A combination of support vector machine and voxel-based morphometry in adult male alcohol use disorder patients with cognitive deficits.

Brain research
Cognitive performance deteriorates with drinking. However, the neural basis of cognitive deficits in alcohol use disorder (AUD) is still incompletely understood. Here we examined the relationship between overall drinking, brain structural alterations...