An observational study in Surgical Procedure, Unspecified, sponsored by Shanghai 10th People's Hospital. Status unknown at 1 site in China. Open to participants aged 18 Years to 65 Years. Per ClinicalTrials.gov, last updated 2020-05-12.
Sponsored by Shanghai 10th People's Hospital · Observational
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.
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.
Shanghai 10th People's Hospital is the lead sponsor of 167 studies on the registry; 45 are open to participants now.
Counted across the registry records on this site, refreshed daily.
patients with thin layer spinal CT covering targeted level will be included.
Exclusion Critera:
Thin-layer CT will be manually labeled and used to train, validate and test deep learning algorithm.
Diagnostic Test: deep learning
manually labeled samples will be used to train, validate and test deep learning algorithm, and then realize automatic classification.
classification accuracy
classification accuracy (e.g. area under the curve, etc.)
Time frame: 1 day
segmentation accuracy
segmentation accuracy of multiple structures (e.g. Dice score, etc.)
Time frame: 1 day
Plan to share: Undecided
No publications or documents are linked to this record.
This study is status unknown, as verified in May 2020. You cannot join it, but the record below documents what was studied.
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Shanghai 10th People's Hospital