NCT03790930

Deep-learning Based Classification of Spine CT

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 500 (estimated)
• Sponsor: Shanghai 10th People's Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 65 Years
• Healthy volunteers: Not accepted

Summary

It is time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal CT, which may also be correlated with high inter-observer variance. With the development of computer science, deep learning has emerged as a promising technique to classify images from individual level to pixel level. The main of the study is to automatically identify and classify the lesions, or segment targeted structures on spinal CT with deep learning.

Detailed description

Computer tomography (CT) is one of the most important imaging tool to assist the diagnostic and treatment of spinal disease. Classification of specific targets (e.g. individuals, lesions, etc.) is one of the most common mission of medical image analysis. However, it is time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal CT, which may also be correlated with high inter-observer variance. With the development of computer science, deep learning has emerged as a promising technique to classify images from individual level to pixel level. The main of the study is to automatically identify and classify the lesions, or segment targeted structures on spinal CT with deep learning.

Conditions

• Surgical Procedure, Unspecified

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	deep learning	manually labeled samples will be used to train, validate and test deep learning algorithm, and then realize automatic classification.

Timeline

• Start date: 2019-02-22
• Primary completion: 2020-05-01
• Completion: 2020-05-01
• First posted: 2019-01-02
• Last updated: 2020-05-12

Locations

1 site across 1 country: China