Estimation of heart dose in left breast cancer radiotherapy: Assessment of vDIBH feasibility using the supervised machine learning algorithm.

Background And Objective: The volunteer deep inspiration breath hold (vDIBH) technique is used to reduce the heart dose in left breast cancer radiotherapy. Many times, it is faced that despite rigorous exercise and training, not all patients get benefited as expected. The primary objective of this study was to develop a machine learning program for prediction of mean heart dose before left breast radiotherapy under vDIBH.

Novel Artificial Intelligence Tool for Real-time Patient Identification to Prevent Misidentification in Health Care.

Purpose: Errors in the identification of true patients in a health-care facility may result in the wrong dose or dosage being given to the wrong patient at the wrong site during radiotherapy sessions, radiopharmaceutical administration, radiological scans, etc. The aim of this article is to reduce the error in the identification of correct patients by implementation of the Python deep learning-based real-time patient identification program.

Materials And Methods: The authors utilized and installed Anaconda Prompt (miniconda 3), Python (version 3.

Performance evaluation of Monaco radiotherapy treatment planning system using CIRS Thorax Phantom: Dosimetric assessment of flattened and non-flattened photon beams.

Aim: The present study was undertaken to evaluate the performance of different algorithms for flattening filter-free (FFF) and flattened (FF) photon beams in three different in-homogeneities.

Materials And Method: Computed tomography (CT) image sets of the CIRS phantom maintained in the SAD setup by placing the ionization chamber in the lung, bone, and tissue regions, respectively, were acquired. The treatment planning system (TPS) calculated and the ionization chamber measured the doses at the center of the chamber (in the three mediums) were recorded for the flattened and non-flattened photon beams.