AI Medical Compendium Topic

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

Radiotherapy, Intensity-Modulated

Showing 271 to 280 of 281 articles

Clear Filters

Deep Learning Based Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Rapid esophageal radiation treatment planning is often obstructed by manually adjusting optimization parameters. The adjustment process is commonly guided by the dose-volume histogram (DVH), which evaluates dosimetry at planning target volume (PTV) a...

Can the Student Outperform the Master? A Plan Comparison Between Pinnacle Auto-Planning and Eclipse knowledge-Based RapidPlan Following a Prostate-Bed Plan Competition.

Technology in cancer research & treatment
PURPOSE: Pinnacle Auto-Planning and Eclipse RapidPlan are 2 major commercial automated planning engines that are fundamentally different: Auto-Planning mimics real planners in the iterative optimization, while RapidPlan generates static dose objectiv...

[Impact of DVH Outliers Registered in Knowledge-based Planning on Volumetric Modulated Arc Therapy Treatment Planning for Prostate Cancer].

Nihon Hoshasen Gijutsu Gakkai zasshi
RapidPlan, a knowledge-based planning software, uses a model library containing the dose-volume histogram (DVH) of previous treatment plans, and it automatically provides optimization objectives based on a trained model to future patients for volumet...

[Prediction of three-dimensional dose distribution in intensity-modulated radiation therapy based on neural network learning].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: To establish the association between the geometric anatomical characteristics of the patients and the corresponding three-dimensional (3D) dose distribution of radiotherapy plan via feed-forward back-propagation neural network for clinical...

Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

Medical physics
PURPOSE: Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs ...

Robotic intrafractional US guidance for liver SABR: System design, beam avoidance, and clinical imaging.

Medical physics
PURPOSE: To present a system for robotic 4D ultrasound (US) imaging concurrent with radiotherapy beam delivery and estimate the proportion of liver stereotactic ablative body radiotherapy (SABR) cases in which robotic US image guidance can be deploye...

A mathematical framework for virtual IMRT QA using machine learning.

Medical physics
PURPOSE: It is common practice to perform patient-specific pretreatment verifications to the clinical delivery of IMRT. This process can be time-consuming and not altogether instructive due to the myriad sources that may produce a failing result. The...

Automatic learning-based beam angle selection for thoracic IMRT.

Medical physics
PURPOSE: The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Int...