AIMC Topic: Humans

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Radiation Dosimetry, Artificial Intelligence and Digital Twins: Old Dog, New Tricks.

Seminars in nuclear medicine
Developments in artificial intelligence, particularly convolutional neural networks and deep learning, have the potential for problem solving that has previously confounded human intelligence. Accurate prediction of radiation dosimetry pre-treatment ...

Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI.

European radiology
OBJECTIVES: Prostate volume (PV) in combination with prostate specific antigen (PSA) yields PSA density which is an increasingly important biomarker. Calculating PV from MRI is a time-consuming, radiologist-dependent task. The aim of this study was t...

The impact of artificial intelligence on radiography as a profession: A narrative review.

Journal of medical imaging and radiation sciences
BACKGROUND AND PURPOSE: Artificial intelligence (AI) algorithms, particularly deep learning, have made significant strides in image recognition and classification, providing remarkable diagnostic accuracy to various diseases. This domain of AI has be...

A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning.

Clinical imaging
This survey aims to identify commonly used methods, datasets, future trends, knowledge gaps, constraints, and limitations in the field to provide an overview of current solutions used in medical image analysis in parallel with the rapid developments ...

SD-CNN: A static-dynamic convolutional neural network for functional brain networks.

Medical image analysis
Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology o...

P-ResUnet: Segmentation of brain tissue with Purified Residual Unet.

Computers in biology and medicine
Brain tissue of Magnetic Resonance Imaging is precisely segmented and quantified, which aids in the diagnosis of neurological diseases such as epilepsy, Alzheimer's, and multiple sclerosis. Recently, UNet-like architectures are widely used for medica...

Application of machine and deep learning algorithms in optical microscopic detection of Plasmodium: A malaria diagnostic tool for the future.

Photodiagnosis and photodynamic therapy
Machine and deep learning techniques are prevalent in the medical discipline due to their high level of accuracy in disease diagnosis. One such disease is malaria caused by Plasmodium falciparum and transmitted by the female anopheles mosquito. Accor...

Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Deep Learning (DL) technique has shown great potential but still has limited success in online contouring for MR-guided adaptive radiotherapy (MRgART). This study proposed a patient-specific DL auto-segmentation (DLAS) strateg...

Dataset with Tactile and Kinesthetic Information from a Human Forearm and Its Application to Deep Learning.

Sensors (Basel, Switzerland)
There are physical Human-Robot Interaction (pHRI) applications where the robot has to grab the human body, such as rescue or assistive robotics. Being able to precisely estimate the grasping location when grabbing a human limb is crucial to perform a...

Telepresence Robot System for People with Speech or Mobility Disabilities.

Sensors (Basel, Switzerland)
Due to an increase in the number of disabled people around the world, inclusive solutions are becoming a priority. People with disabilities may encounter many problems and may not be able to easily participate in various activities due to physical ba...