scholarly journals An Artificial Intelligence-Driven Agent for Rapid Head-and-Neck IMRT Plan Generation

2020 ◽  
Vol 108 (3) ◽  
pp. e263
Author(s):  
C. Wang ◽  
X. Li ◽  
J. Zhang ◽  
Y. Sheng ◽  
F.F. Yin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Head And Neck ◽  
Imrt Plan ◽  
Plan Generation

scholarly journals 195 The Application of Radiomics in Laryngeal Cancer: The Forgotten Cohort

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
A Rajgor ◽  
A McQueen ◽  
T Ali ◽  
E Aboagye ◽  
B Obara ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Head And Neck ◽  
Laryngeal Cancer ◽  
Prognostic Value ◽  
Head And Neck Cancers ◽  
High Dimensional ◽  
Naked Eye ◽  
Online Databases ◽  
Novel Method

Abstract Background Radiomics is a novel method of extracting data from medical images that is difficult to visualise through the naked eye. This technique transforms digital images that hold information on pathology into high-dimensional-data for analysis. Radiomics has the potential to enhance laryngeal cancer care and to date, has shown promise in various other specialties. Aim The aim of this review is to summarise the applications of this technique to laryngeal cancer and potential future benefits. Method A comprehensive systematic review-informed search of the MEDLINE and EMBASE online databases was undertaken. Keywords ‘laryngeal cancer’ OR ‘larynx’ OR ‘larynx cancer’ OR ‘head and neck cancer’ were combined with ‘radiomic’ OR ‘signature’ OR ‘machine learning’ OR ‘artificial intelligence’. Additional articles were obtained from bibliographies using the ‘snowball method’. Results Seventeen articles were identified that evaluated the role of radiomics in laryngeal cancer. Two studies affirmed the value of radiomics in improving the accuracy of staging, whilst fifteen studies highlighted the potential prognostic value of radiomics in laryngeal cancer. Twelve (of thirteen) studies incorporated an array of different head and neck cancers in the analysis and only one study assessed laryngeal cancer exclusively. Conclusions Literature to date has various limitations including, small and heterogeneous cohorts incorporating patients with head and neck cancers of distinct anatomical subsites and stages. The lack of uniform data on solely laryngeal cancer and radiomics means drawing conclusions is difficult, although these studies have affirmed its value. Further large prospective studies exclusively in laryngeal cancer are required to unlock its true potential.

Artificial Intelligence in Head and Neck Imaging

2020 ◽  
Vol 30 (3) ◽  
pp. 359-368
Author(s):  
Kyle Werth ◽  
Luke Ledbetter
Keyword(s):  
Artificial Intelligence ◽  
Head And Neck ◽  
Head And Neck Imaging

SU-GG-T-07: Adaptive IMRT Plan Modification for Head-And-Neck Cancer Patients

2010 ◽  
Vol 37 (6Part15) ◽  
pp. 3184-3184
Author(s):  
Y Feng ◽  
N Li ◽  
K Sun ◽  
X Liang ◽  
C Yu
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Imrt Plan ◽  
Plan Modification

scholarly journals Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Fan Fu ◽  
Jianyong Wei ◽  
Miao Zhang ◽  
Fan Yu ◽  
Yueting Xiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Head And Neck ◽  
Healthcare Services ◽  
Reconstruction Accuracy ◽  
Testing Dataset ◽  
Tertiary Hospitals ◽  
Qualification Rate ◽  
Independent Testing Dataset

Abstract The computed tomography angiography (CTA) postprocessing manually recognized by technologists is extremely labor intensive and error prone. We propose an artificial intelligence reconstruction system supported by an optimized physiological anatomical-based 3D convolutional neural network that can automatically achieve CTA reconstruction in healthcare services. This system is trained and tested with 18,766 head and neck CTA scans from 5 tertiary hospitals in China collected between June 2017 and November 2018. The overall reconstruction accuracy of the independent testing dataset is 0.931. It is clinically applicable due to its consistency with manually processed images, which achieves a qualification rate of 92.1%. This system reduces the time consumed from 14.22 ± 3.64 min to 4.94 ± 0.36 min, the number of clicks from 115.87 ± 25.9 to 4 and the labor force from 3 to 1 technologist after five months application. Thus, the system facilitates clinical workflows and provides an opportunity for clinical technologists to improve humanistic patient care.

scholarly journals An Integrative Framework for Artificial Intelligence Education

2019 ◽  
Vol 33 ◽  
pp. 9670-9677 ◽  
Author(s):  
Pat Langley
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
System Integration ◽  
Intelligent Systems ◽  
Control Plan ◽  
Integrative Framework ◽  
Plan Generation ◽  
Knowledge Based ◽  
Intelligent Behavior ◽  
Intelligence Education

Modern introductory courses on AI do not train students to create intelligent systems or provide broad coverage of this complex field. In this paper, we identify problems with common approaches to teaching artificial intelligence and suggest alternative principles that courses should adopt instead. We illustrate these principles in a proposed course that teaches students not only about component methods, such as pattern matching and decision making, but also about their combination into higher-level abilities for reasoning, sequential control, plan generation, and integrated intelligent agents. We also present a curriculum that instantiates this organization, including sample programming exercises and a project that requires system integration. Participants also gain experience building knowledge-based agents that use their software to produce intelligent behavior.

scholarly journals The application of artificial intelligence in the IMRT planning process for head and neck cancer

Oral Oncology ◽  
2018 ◽  
Vol 87 ◽  
pp. 111-116 ◽  
Author(s):  
Vasant Kearney ◽  
Jason W. Chan ◽  
Gilmer Valdes ◽  
Timothy D. Solberg ◽  
Sue S. Yom
Keyword(s):  
Artificial Intelligence ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Planning Process ◽  
Imrt Planning

Fast and fuzzy multi-objective radiotherapy treatment plan generation for head and neck cancer patients with the lexicographic reference point method (LRPM)

2017 ◽  
Vol 62 (11) ◽  
pp. 4318-4332 ◽  
Author(s):  
Rens van Haveren ◽  
Włodzimierz Ogryczak ◽  
Gerda M Verduijn ◽  
Marleen Keijzer ◽  
Ben J M Heijmen ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Reference Point ◽  
Treatment Plan ◽  
Reference Point Method ◽  
Multi Objective ◽  
Plan Generation ◽  
Radiotherapy Treatment
Sign in / Sign up

Export Citation Format

Share Document