AIMC Topic: Humans

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A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images.

Sensors (Basel, Switzerland)
Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of...

Optimization of the ASPIRE Spherical Parallel Rehabilitation Robot Based on Its Clinical Evaluation.

International journal of environmental research and public health
The paper presents the design optimization of the ASPIRE spherical parallel robot for shoulder rehabilitation following clinical evaluation and clinicians' feedback. After the development of the robotic structure and the implementation of the control...

Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer.

Scientific reports
This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy...

Stakeholders' views on the organisational factors affecting application of artificial intelligence in healthcare: a scoping review protocol.

BMJ open
INTRODUCTION: Artificial intelligence (AI) offers great potential for transforming healthcare delivery leading to better patient-outcomes and more efficient care delivery. However, despite these advantages, integration of AI in healthcare has not kep...

Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer.

Radiation oncology (London, England)
BACKGROUND AND PURPOSE: To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer.

Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach.

JMIR mHealth and uHealth
BACKGROUND: Mental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a pr...

Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System.

Journal of healthcare engineering
Multilabel recognition of morphological images and detection of cancerous areas are difficult to locate in the scenario of the image redundancy and less resolution. Cancerous tissues are incredibly tiny in various scenarios. Therefore, for automatic ...

Artificial intelligence in cardiovascular CT: Current status and future implications.

Journal of cardiovascular computed tomography
Artificial intelligence (AI) refers to the use of computational techniques to mimic human thought processes and learning capacity. The past decade has seen a rapid proliferation of AI developments for cardiovascular computed tomography (CT). These al...

A New Argument for No-Fault Compensation in Health Care: The Introduction of Artificial Intelligence Systems.

Health care analysis : HCA : journal of health philosophy and policy
Artificial intelligence (AI) systems advising healthcare professionals will be widely introduced into healthcare settings within the next 5-10 years. This paper considers how this will sit with tort/negligence based legal approaches to compensation f...

Deep learning approach to skin layers segmentation in inflammatory dermatoses.

Ultrasonics
Monitoring skin layers with medical imaging is critical to diagnosing and treating patients with chronic inflammatory skin diseases. The high-frequency ultrasound (HFUS) makes it possible to monitor skin condition in different dermatoses. Accurate an...