AIMC Topic: Adult

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Classification of patients with chronic disease by activation level using machine learning methods.

Health care management science
Patient Activation Measure (PAM) measures the activation level of patients with chronic conditions and correlates well with patient adherence behavior, health outcomes, and healthcare costs. PAM is increasingly used in practice to identify patients n...

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Nature communications
Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value f...

Individualized hemodialysis: Is similar hemodialysis adequacy possible using less water?

Turkish journal of medical sciences
BACKGROUND AND AIM: There are over 60,000 hemodialysis (HD) patients in Türkiye, and the number of patients is increasing yearly. Dialysate flow rate (Qd) is a factor in HD adequacy. Approximately 150 L of water are consumed per session to prepare th...

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks.

Sensors (Basel, Switzerland)
Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hi...

Are medical oncologists ready for the artificial intelligence revolution? Evaluation of the opinions, knowledge, and experiences of medical oncologists about artificial intelligence technologies.

Medical oncology (Northwood, London, England)
The use of artificial intelligence technologies (AIT) in medicine is increasing worldwide. In this study, it was aimed to evaluate the experiences, opinions, and future expectations of medical oncologists on artificial intelligence (AI). After the re...

Global and Regional Deep Learning Models for Multiple Sclerosis Stratification From MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The combination of anatomical MRI and deep learning-based methods such as convolutional neural networks (CNNs) is a promising strategy to build predictive models of multiple sclerosis (MS) prognosis. However, studies assessing the effect ...

The effect of sequential combination of mirror therapy and robot-assisted therapy on motor function, daily function, and self-efficacy after stroke.

Scientific reports
Robot-assisted therapy and mirror therapy are both effective in promoting upper limb function after stroke and combining these two interventions might yield greater therapeutic effects. We aimed to examine whether using mirror therapy as a priming st...

Detection of Vertebral Mass and Diagnosis of Spinal Cord Compression in Computed Tomography With Deep Learning Reconstruction: Comparison With Hybrid Iterative Reconstruction.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
PURPOSE: To compare the impact of deep learning reconstruction (DLR) and hybrid-iterative reconstruction (hybrid-IR) on vertebral mass depiction, detection, and diagnosis of spinal cord compression on computed tomography (CT).

The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: An overview of the systematic reviews.

PloS one
BACKGROUND AND PURPOSE: In comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis...

Eye-Tracking in Physical Human-Robot Interaction: Mental Workload and Performance Prediction.

Human factors
BACKGROUND: In Physical Human-Robot Interaction (pHRI), the need to learn the robot's motor-control dynamics is associated with increased cognitive load. Eye-tracking metrics can help understand the dynamics of fluctuating mental workload over the co...