AIMC Topic: Humans

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Dialogue with a conversational agent promotes children's story comprehension via enhancing engagement.

Child development
Dialogic reading, when children are read a storybook and engaged in relevant conversation, is a powerful strategy for fostering language development. With the development of artificial intelligence, conversational agents can engage children in elemen...

Review of automated performance metrics to assess surgical technical skills in robot-assisted laparoscopy.

Surgical endoscopy
INTRODUCTION: Robot-assisted laparoscopy is a safe surgical approach with several studies suggesting correlations between complication rates and the surgeon's technical skills. Surgical skills are usually assessed by questionnaires completed by an ex...

Evaluation of a custom-designed human-robot collaboration control system for dental implant robot.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The purpose of this study is to develop a methodology to better control a human-robot collaboration for robotic dental implant placement. We have designed a human-robot collaborative implant system (HRCDIS) which is based on a zero-force ...

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.

Circulation
BACKGROUND: Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predi...

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature.

Circulation
BACKGROUND: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health and tumorigenesis. The retinal fundus is a window for human in vivo noninvasive assessment of the microvasculature. Large-scale com...

3D spatial priors for semi-supervised organ segmentation with deep convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Fully Convolutional neural Networks (FCNs) are the most popular models for medical image segmentation. However, they do not explicitly integrate spatial organ positions, which can be crucial for proper labeling in challenging contexts.

A deep-learning toolkit for visualization and interpretation of segmented medical images.

Cell reports methods
Generalizability of deep-learning (DL) model performance is not well understood and uses anecdotal assumptions for increasing training data to improve segmentation of medical images. We report statistical methods for visual interpretation of DL model...

Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment.

Sensors (Basel, Switzerland)
Concussion injuries remain a significant public health challenge. A significant unmet clinical need remains for tools that allow related physiological impairments and longer-term health risks to be identified earlier, better quantified, and more easi...

A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers.

Sensors (Basel, Switzerland)
With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to b...