Latest AI and machine learning research in therapeutic radiology for healthcare professionals.
The chapter explores the extensive integration of artificial intelligence (AI) in healthcare systems, with a specific focus on its application in stereotactic radiosurgery. The rapid evolution of AI technology has led to promising developments in this field, particularly through the utilization of machine learning and deep learning models. The diverse implementation of AI algorithms was developed ...
Segmentation of clinical target volume (CTV) from CT images is critical for cervical cancer brachytherapy, but this task is time-consuming, laborious, and not reproducible. In this work, we aim to propose an end-to-end model to segment CTV for cervical cancer brachytherapy accurately. In this paper, an improved M-Net model (Mnet_IM) is proposed to segment CTV of cervical cancer from CT images. A...
In recent years, Artificial intelligence (AI), specifically deep-learning (DL) based methods, have been employed extensively to solve various problems...
This novel deep-learning (DL) algorithm addresses the challenging task of predicting uterine shape and location when deformed from its natural anatomy...
In this study, we propose a deep learning-based approach to predict Intensity-modulated radiation therapy (IMRT) quality assurance (QA) gamma passing...
We evaluated patients who underwent treatment for prostate cancer and then subsequent robot-assisted radical cystectomy (RARC). Our objective was to ...
The aim of this work is to study the dosimetric effect from generated synthetic computed tomography (sCT) from magnetic resonance (MR) images using a ...
Cervical cancer radiotherapy is often complicated by significant variability in the quality and consistency of treatment plans. Knowledge-based planni...
PURPOSE: To develop and evaluate an automatic intensity-modulated radiation therapy (IMRT) program for cervical cancer, including a Convolution Neural...
The purpose of the study was to compare a 3D convolutional neural network (CNN) with the conventional machine learning method for predicting intensity...
BACKGROUND: It is unclear whether radiomic phenotypes of brain metastases (BM) are related to radiation therapy prognosis. This study assessed whether...
Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-section...
A software platform integrated of radiation treatment planning system and navigation system has been developed for a newly-developed snake robot. This...
BACKGROUND: Glomus tumors are difficult to manage surgically because they are vascular tumors that are topographically associated with important vascu...
Laser interstitial thermal therapy (LITT) is a minimally invasive procedure used to treat a variety of intracranial lesions. Utilization of robotic as...
AIM: To compare survival outcomes for charged particle radiotherapy (CP) and stereotactic body radiotherapy using CyberKnife (CK) in patients who had ...
PURPOSE: Continuous dose delivery in radiation therapy treatments has been shown to decrease total treatment time while improving the dose conformity ...
PURPOSE: It is common practice to perform patient-specific pretreatment verifications to the clinical delivery of IMRT. This process can be time-consu...
PURPOSE: The CyberKnife M6 (CK-M6) Series introduced a multileaf collimator (MLC) for extending its capability from stereotactic radiosurgery/stereota...