AIMC Topic: Humans

Clear Filters Showing 8291 to 8300 of 95995 articles

Integrating prior knowledge with deep learning for optimized quality control in corneal images: A multicenter study.

Computer methods and programs in biomedicine
OBJECTIVE: Artificial intelligence (AI) models are effective for analyzing high-quality slit-lamp images but often face challenges in real-world clinical settings due to image variability. This study aims to develop and evaluate a hybrid AI-based ima...

Enhanced glaucoma classification through advanced segmentation by integrating cup-to-disc ratio and neuro-retinal rim features.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Glaucoma is a progressive eye condition caused by high intraocular fluid pressure, damaging the optic nerve, leading to gradual, irreversible vision loss, often without noticeable symptoms. Subtle signs like mild eye redness, slightly blurred vision,...

Structural uncertainty estimation for medical image segmentation.

Medical image analysis
Precise segmentation and uncertainty estimation are crucial for error identification and correction in medical diagnostic assistance. Existing methods mainly rely on pixel-wise uncertainty estimations. They (1) neglect the global context, leading to ...

Comparative examination of the chemistry and biology of AI-driven gold NPs in Theranostics: New insights into biosensing, bioimaging, genomics, diagnostics, and therapy.

Nanomedicine : nanotechnology, biology, and medicine
Integrating artificial intelligence (AI) with nanomedicine is transforming Theranostics, driving advances in biosensing, bioimaging, genomics, diagnostics, and treatment. This review highlights the latest advancements in AI-driven nanomedicine, focus...

Challenges, optimization strategies, and future horizons of advanced deep learning approaches for brain lesion segmentation.

Methods (San Diego, Calif.)
Brain lesion segmentation is challenging in medical image analysis, aiming to delineate lesion regions precisely. Deep learning (DL) techniques have recently demonstrated promising results across various computer vision tasks, including semantic segm...

A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm.

Academic radiology
RATIONALE AND OBJECTIVES: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy and symptom overlap with other n...

Interictal network dysfunction and cognitive impairment in epilepsy.

Nature reviews. Neuroscience
Epilepsy is diagnosed when neural networks become capable of generating excessive or hypersynchronous activity patterns that result in observable seizures. In many cases, epilepsy is associated with cognitive comorbidities that persist between seizur...

Deep learning-assisted detection of meniscus and anterior cruciate ligament combined tears in adult knee magnetic resonance imaging: a crossover study with arthroscopy correlation.

International orthopaedics
AIM: We aimed to compare the diagnostic performance of physicians in the detection of arthroscopically confirmed meniscus and anterior cruciate ligament (ACL) tears on knee magnetic resonance imaging (MRI), with and without assistance from a deep lea...

Tiny Convolutional Neural Network with Supervised Contrastive Learning for Epileptic Seizure Prediction.

International journal of neural systems
Automatic seizure prediction based on ElectroEncephaloGraphy (EEG) ensures the safety of patients with epilepsy and mitigates anxiety. In recent years, significant progress has been made in this field. However, the predictive performance of existing ...