AIMC Topic: Humans

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Robotics and cybersurgery in ophthalmology: a current perspective.

Journal of robotic surgery
Ophthalmology is one of the most enriched fields, allowing the domain of artificial intelligence to be part of its point of interest in scientific research. The requirement of specialized microscopes and visualization systems presents a challenge to ...

Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Celiac disease with its endoscopic manifestation of villous atrophy (VA) is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of VA at routine EGD may improve diagnostic perfo...

Review of Machine Learning and Artificial Intelligence (ML/AI) for the Pediatric Neurologist.

Pediatric neurology
Artificial intelligence (AI) and a popular branch of AI known as machine learning (ML) are increasingly being utilized in medicine and to inform medical research. This review provides an overview of AI and ML (AI/ML), including definitions of common ...

Histogram of Oriented Gradients meet deep learning: A novel multi-task deep network for 2D surgical image semantic segmentation.

Medical image analysis
We present our novel deep multi-task learning method for medical image segmentation. Existing multi-task methods demand ground truth annotations for both the primary and auxiliary tasks. Contrary to it, we propose to generate the pseudo-labels of an ...

Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions.

Medical image analysis
Deep learning prediction of diffusion MRI (DMRI) data relies on the utilization of effective loss functions. Existing losses typically measure the signal-wise differences between the predicted and target DMRI data without considering the quality of d...

Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans.

Neurology
BACKGROUND AND OBJECTIVES: In medical imaging, a limited number of trained deep learning algorithms have been externally validated and released publicly. We hypothesized that a deep learning algorithm can be trained to identify and localize subarachn...

Cross-task cognitive workload recognition using a dynamic residual network with attention mechanism based on neurophysiological signals.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Evaluation of human cognitive workload (CW) helps improve the user experience of human-centered systems. To provide a continuous estimation of the CW, we built a CW recognizer that maps human electroencephalograms (EEGs) to ...

Prediction of body weight from chest radiographs using deep learning with a convolutional neural network.

Radiological physics and technology
Accurate body weights are not necessarily available in routine clinical practice. This study aimed to investigate whether body weight can be predicted from chest radiographs using deep learning. Deep-learning models with a convolutional neural networ...

Software-related recalls in computer-assisted hip and knee arthroplasty.

International orthopaedics
PURPOSE: Computer-assisted arthroplasty supports the surgeons in planning, simulating, and performing the replacement procedure, using robotic or navigation technologies. However, the safety of the technology has not been widely ascertained. Food and...