PO-1483: Evaluation of an artificial intelligence driven planning system for online adaptive radiotherapy

2020 ◽  
Vol 152 ◽  
pp. S797-S798
Author(s):  
L. Calmels ◽  
L. Andersson ◽  
D. Sjöström ◽  
M. Sjölin ◽  
P. Sibolt
Keyword(s):  
Artificial Intelligence ◽  
Adaptive Radiotherapy ◽  
Planning System

scholarly journals Using Artificial Intelligence and Machine Learning to Create a Travel Planning System Based on Users' Preferences and Behaviours

2021 ◽  
Author(s):  
Trang Bui
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Large Datasets ◽  
Planning System ◽  
The Internet ◽  
Travel Planning ◽  
Travel And Tourism ◽  
Mode Of Transportation

Travel and tourism have become a worldwide trend, and the market for this industry continues to grow extensively. Although most people enjoy traveling, planning trips can take hours, weeks or even months to ensure the best place, the best itinerary, and the best price is found. With Artificial Intelligence (AI) and Machine Learning (ML), large datasets can be analyzed and an AI-infused travel system can be utilized to generate highly personalized suggestions. The main purpose of the project is to create a travel planning application to provide an efficient and highly personalized experience for users. The app will help users plan their trips, choose activities, restaurants, mode of transportation and destinations that fit their preferences, budgets, and schedules in minutes without having to spend hours researching on the Internet or downloading multiple travel apps.

Method of Quatitative Evaluation of the Size and Density of the Tumor During Adaptive Radiotherapy Using CT Images

2019 ◽  
Vol 64 (6) ◽  
pp. 64-69
Author(s):  
I. Lebedenko ◽  
B. Gavrikov ◽  
T. Borisova
Keyword(s):  
Cancer Patients ◽  
Quantitative Method ◽  
Biological Tissues ◽  
Adaptive Radiotherapy ◽  
Planning System ◽  
Response To Treatment ◽  
Dynamic Mode ◽  
Horizontal Line ◽  
Physical Density ◽  
Graphic Information

Purpose: Clinically available quantitative method for assessing the dynamics of the size and physical density (in g/cm3) of the tumor in adaptive radiotherapy for cancer patients and any cases of visualization of tumor boundaries including the cases when the border of a tumor is not clearly visualized. Material and methods: A preliminary analysis of the images transmitted over the CT network was carried out in the Eclipse planning system (PS). The radiotherapy treatment planning using electron accelerators with a multi-leaf collimator( Varian (USA)) was carried out at the Eclipse PS. The image quality control of Light Speed RT 16 (manufactured by GE) X-ray computed tomography scanner was performed using the multi-modular phantom Catphan® 504. The assessment of the densitometric characteristics CT imaging made using eight tissue-equivalent a test object with mass densities from 0.03 to 1.37 g/cm3 which corresponding to the density of biological tissues of the human body. To quantify the size and density of the tumor in a dynamic mode, we have written and used our own Matlab program installed on a separate computer. For lossless compression of graphic information, the PNG-image scale (raster graphic information storage format) is used, which is equivalent to the scale of the original DICOM file at the Eclipse PS. A program consists of subroutines that include calibration, contour integration, and integration along a horizontal line. Results: The quantitative information content of the method is shown. The method is used in clinical practice. Conclusions: A clinically available quantitative method for assessing dynamics of the size and physical density of the tumor has been developed and proposed for use in adaptive radiatiotherapy for cancer patients for any cases of visualization of tumor boundaries. When a positive dynamics in the tumor, the integral index is greater than 1 (M > 1), when a negative dynamics (in the absence of response to treatment) M ≤ 1. Quantitative characteristics are objective, do not depend on the subjective assessments of personnel and can serve as a basis for rescheduling exposure plans.

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