AIMC Topic: Humans

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Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA).

Predicting the Risk of Maxillary Canine Impaction Based on Maxillary Measurements Using Supervised Machine Learning.

Orthodontics & craniofacial research
OBJECTIVES: To predict palatally impacted maxillary canines based on maxilla measurements through supervised machine learning techniques.

A unified multimodal classification framework based on deep metric learning.

Neural networks : the official journal of the International Neural Network Society
Multimodal classification algorithms play an essential role in multimodal machine learning, aiming to categorize distinct data points by analyzing data characteristics from multiple modalities. Extensive research has been conducted on distilling mult...

Clinical Impact of Radiologist's Alert System on Patient Care for High-risk Incidental CT Findings: A Machine Learning-Based Risk Factor Analysis.

Academic radiology
RATIONALE AND OBJECTIVES: Efficient communication between radiologists and clinicians ordering computed tomography (CT) examinations is crucial for managing high-risk incidental CT findings (ICTFs). Herein, we introduced a Radiologist's Alert and Pat...

Assessment of wearable robotics performance in patients with neurological conditions.

Current opinion in neurology
PURPOSE OF REVIEW: While wearable robotics is expanding within clinical settings, particularly for neurological rehabilitation, there is still a lack of consensus on how to effectively assess the performance of these devices. This review focuses on t...

AI in Surgery: Navigating Trends and Managerial Implications Through Bibliometric and Text Mining Odyssey.

Surgical innovation
This research employs bibliometric and text-mining analysis to explore artificial intelligence (AI) advancements within surgical procedures. The growing significance of AI in healthcare underscores the need for healthcare managers to prioritize inves...

Augmenting biomedical named entity recognition with general-domain resources.

Journal of biomedical informatics
OBJECTIVE: Training a neural network-based biomedical named entity recognition (BioNER) model usually requires extensive and costly human annotations. While several studies have employed multi-task learning with multiple BioNER datasets to reduce hum...

Enhancing dietary analysis: Using machine learning for food caloric and health risk assessment.

Journal of food science
In the wake of growing concerns regarding diet-related health issues, this study investigates the application of machine learning methods to estimate the energy content and classify the health risks of foods based on the USDA National Nutrient Databa...

A novel mean shape based post-processing method for enhancing deep learning lower-limb muscle segmentation accuracy.

PloS one
This study aims at improving the lower-limb muscle segmentation accuracy of deep learning approaches based on Magnetic Resonance Imaging (MRI) scans, crucial for the diagnostic and therapeutic processes in musculoskeletal diseases. In general, segmen...

Space Radiology: Emerging Nonsonographic Medical Imaging Techniques and the Potential Applications for Human Spaceflight.

Wilderness & environmental medicine
Space medicine is a multidisciplinary field that requires the integration of medical imaging techniques and expertise in diagnosing and treating a wide range of acute and chronic conditions to maintain astronaut health. Medical imaging within this do...