AIMC Topic: Algorithms

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Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals.

Scientific reports
This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analy...

A preliminary study of a photon dose calculation algorithm using a convolutional neural network.

Physics in medicine and biology
The aim of dose calculation algorithm research is to improve the calculation accuracy while maximizing the calculation efficiency. In this study, the three-dimensional distribution of total energy release per unit mass (TERMA) and the electron densit...

Deep Learning With Electronic Health Records for Short-Term Fracture Risk Identification: Crystal Bone Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Fractures as a result of osteoporosis and low bone mass are common and give rise to significant clinical, personal, and economic burden. Even after a fracture occurs, high fracture risk remains widely underdiagnosed and undertreated. Comm...

Machine learning discriminates a movement disorder in a zebrafish model of Parkinson's disease.

Disease models & mechanisms
Animal models of human disease provide an system that can reveal molecular mechanisms by which mutations cause pathology, and, moreover, have the potential to provide a valuable tool for drug development. Here, we have developed a zebrafish model of...

An Investigation of Speech Features, Plant System Alarms, and Operator-System Interaction for the Classification of Operator Cognitive Workload During Dynamic Work.

Human factors
OBJECTIVE: To investigate speech features, human-machine alarms, and operator-system interaction for the estimation of cognitive workload in full-scale realistic simulated scenarios.

CT iterative vs deep learning reconstruction: comparison of noise and sharpness.

European radiology
OBJECTIVES: To compare image noise and sharpness of vessels, liver, and muscle in lower extremity CT angiography between "adaptive statistical iterative reconstruction-V" (ASIR-V) and deep learning reconstruction "TrueFidelity" (TFI).

Deep learning-based method for reducing residual motion effects in diffusion parameter estimation.

Magnetic resonance in medicine
PURPOSE: Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects.

CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tediou...

Extracting Lungs from CT Images via Deep Convolutional Neural Network Based Segmentation and Two-Pass Contour Refinement.

Journal of digital imaging
Lung segmentation is a key step of thoracic computed tomography (CT) image processing, and it plays an important role in computer-aided pulmonary disease diagnostics. However, the presence of image noises, pathologies, vessels, individual anatomical ...

Self-contained deep learning-based boosting of 4D cone-beam CT reconstruction.

Medical physics
PURPOSE: Four-dimensional cone-beam computed tomography (4D CBCT) imaging has been suggested as a solution to account for interfraction motion variability of moving targets like lung and liver during radiotherapy (RT) of moving targets. However, due ...