AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Radiometry

Showing 51 to 60 of 117 articles

Clear Filters

A semantic database for integrated management of image and dosimetric data in low radiation dose research in medical imaging.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Medical ionizing radiation procedures and especially medical imaging are a non negligible source of exposure to patients. Whereas the biological effects of high absorbed doses are relatively well known, the effects of low absorbed doses are still deb...

[Not Available].

Zeitschrift fur medizinische Physik
BACKGROUND: Currently there is an ever increasing interest in Lu-177 targeted radionuclide therapies, which target neuro-endocrine and prostate tumours. For a patient-specific treatment, an individual dosimetry based on SPECT/CT imaging is necessary....

Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy.

Medical physics
PURPOSE: We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient-specific quality assurance (QA) for intensity-modulated radiation therapy...

CT Dosimetry: What Has Been Achieved and What Remains to Be Done.

Investigative radiology
Radiation dose in computed tomography (CT) has become a hot topic due to an upward trend in the number of CT procedures worldwide and the relatively high doses associated with these procedures. The main aim of this review article is to provide an ove...

Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery.

Radiation oncology (London, England)
BACKGROUND: In breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) segmentat...

Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery.

Scientific reports
Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensiv...

Deep learning-augmented radiotherapy visualization with a cylindrical radioluminescence system.

Physics in medicine and biology
This study aims to demonstrate a low-cost camera-based radioluminescence imaging system (CRIS) for high-quality beam visualization that encourages accurate pre-treatment verifications on radiation delivery in external beam radiotherapy. To ameliorate...

Computation of epistemic uncertainty due to limited data samples in small field dosimetry using Fuzzy Set Theory.

The British journal of radiology
OBJECTIVE: To estimate the epistemic (or fuzzy) uncertainty, arising due to limited data samples in the measurement of the output factors (OFs) of the small fields using Fuzzy Set Theory (FST).

Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network.

Scientific reports
Exposure to appropriate doses of UV radiation provides enormously health and medical treatment benefits including psoriasis. Typical hospital-based phototherapy cabinets contain a bunch of artificial lamps, either broad-band (main emission spectrum 2...

Radiation dose calculation in 3D heterogeneous media using artificial neural networks.

Medical physics
PURPOSE: External beam radiotherapy (EBRT) treatment planning requires a fast and accurate method of calculating the dose delivered by a clinical treatment plan. However, existing methods of calculating dose distributions have limitations. Monte Carl...