Artificial Intelligence to detect and classify tumors
Principal Investigator
Name
Daniel Meyer
Degrees
BA, MBA
Institution
Sphera, Inc.
Position Title
President
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-215
Initial CDAS Request Approval
May 12, 2016
Title
Artificial Intelligence to detect and classify tumors
Summary
Lung cancer is one of the most significant health problems as measured by worldwide deaths, impact on quality of life and economic burden. Early stage detection of lung cancer holds the opportunity to improve long-term survival rates. Artificial Intelligence (AI) techniques have the ability to support research, clinical development, diagnosis and management of disease by providing additional information to clinical radiologists and other stakeholders.
A key aim of Sphera is to develop, evaluate and compare AI techniques to improve the detection and classification of lung cancer. We will evaluate existing approaches and develop novel AI methodologies. We also aim to investigate AI methods to improve segmentation of normal tissues.
A key aim of Sphera is to develop, evaluate and compare AI techniques to improve the detection and classification of lung cancer. We will evaluate existing approaches and develop novel AI methodologies. We also aim to investigate AI methods to improve segmentation of normal tissues.
Aims
1) Evaluate AI methods to segment normal tissues.
We will develop and evaluate a suite of technologies to accurately segment normal tissues within the chest.
2) Evaluate AI methods to detect lung nodules.
We will develop AI methods specifically to detect lung nodules. For this we will use normal tissue segmentations developed in Aim1.
3) Evaluate AI methods to classify lung nodules.
We will develop AI methods to classify the detected nodules of Aim2.
Collaborators
none