Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT04952233 |
| Other study ID # |
M2020499 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
January 30, 2021 |
| Est. completion date |
July 30, 2021 |
Study information
| Verified date |
June 2021 |
| Source |
Peking University Third Hospital |
| Contact |
huishu yuan |
| Phone |
13521627288 |
| Email |
chunjiewang17[@]126.com |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Compared with the personal experience judgment of physicians, deep learning can identify
something more quickly, efficiently, and accurately The identification and diagnosis of
diseases save the energy of clinical and imaging doctors and achieve an individualized
diagnosis of patients Diagnosis and evaluation are beneficial to the formulation of clinical
surgical methods and the improvement of patients' prognoses.
This study uses deep learning technology, through the big data of cervical spondylosis cases
learn, to explore the use of deep learning The feasibility of identifying and analyzing the
characteristic imaging findings of cervical CT images that may be suggestive of a diagnosis
It is attempted to reach the level of artificial intelligence-assisted diagnosis of cervical
spondylosis.
Description:
Cervical spondylosis is due to cervical discs and intervertebral joints and their secondary
changes to the adjacent spinal cord, nerve roots, and vertebrae Artery and other tissue
structure, causing the corresponding clinical symptoms. Cervical spondylosis is a common and
frequent disease among middle-aged and elderly people. The incidence of cervical spondylosis
is 10 percent in people over 30 years of age and is higher among those who work at a desk.
Two kinds of cervical spondylosis are common: cervical spondylotic radiculopathy and cervical
spondylotic myelopathy. The principles of treatment differ greatly, and the prognosis after
treatment is also different. Precise preoperative imaging and clinical diagnosis are helpful
to accurately estimate the effect of surgery and the prognosis of patients.
Therefore, to achieve early accurate diagnosis is to improve the cervical spine Key to the
curative effect of the disease. Generally referred to as deep learning is a kind of pattern
analysis method, through the combination of simple and nonlinear module for multi-level
"character learning" or "said learning" model of each module to each layer (original input)
data into a higher dimension, more abstract representation, when there are enough of these
transformations, The initial "low level" feature representation can be transformed into "high
level" feature representation, and complex learning tasks such as classification can be
completed.
For the classification task, the features that are important to the classification are
amplified by increasing the number of layers, and the irrelevant features are ignored.
At present, the main research methods include Convolutional Neural Networks (CNN),
self-coding Neural Networks based on multi-layer neurons, and deep confidence Networks. At
present, CNN is mainly used in the field of medical image recognition.
Such as AlexNet, Densenet, VGG16, Googlenet and so on. The motivation for deep learning is to
build neural networks that mimic the human brain for analytical learning. It imitates the
mechanisms of the human brain to interpret data, such as images, sounds, and text and has
made significant progress in solving many problems that are difficult for traditional machine
learning to solve.
Deep learning methods have proven to be very good at discovering complex structures in
high-dimensional data.
In the field of medical and health care, artificial intelligence technology has gradually
solved the problem of data interpretation of various medical images, discovered more
information "hidden" behind the images, such as X-ray, CT, MRI, etc., and will gradually
realize the recognition, screening, and diagnosis of a specific case, reaching the expert
level of diagnosis.
At the same time, the application of artificial intelligence technology can reduce the load
of doctors, reduce medical misdiagnosis, and improve the efficiency of diagnosis and
treatment.
At present, deep learning has been widely used in the diagnosis and prediction of various
diseases in various systems of the body.
Hemke et al. conducted machine learning research after manual segmentation of pelvic CT
images and found that the deep learning model can be used to automatically recognize and
segment a variety of different types of tissues in the pelvic cavity, including muscle, bone,
fat, subcutaneous adipose tissue, etc.
Koichiro Yasaka et al. [8] conducted a deep learning study on the lumbar spine and found that
deep learning can be used to predict the bone density of the lumbar spine and further
evaluate the severity of osteoporosis.
Spampinato et al. conducted a deep learning study on hand X-rays to predict bone age.
Therefore, it is of great significance to apply deep learning, an emerging analytical
technique, to the recognition of imaging features of cervical CT images. The research
significance is as follows:
Accurate and efficient preoperative diagnosis of cervical spondylosis can be realized to
avoid excessive medical treatment or disease delay caused by missed diagnosis and
misdiagnosis. Individualized diagnosis and evaluation can be achieved for patients, which
helps clinically to reasonably specify treatment strategies, and is conducive to the
formulation of clinical surgical methods and the improvement of patient prognosis.
