Deep LearningNCT06845462
Application of Artificial Intelligence Algorithm Based on CT Imaging for Muscle Parameter Measurement
An observational study of Deep Learning and Sarcopenia, run by RenJi Hospital at 1 site in China.
Trial summary
To establish an artificial intelligence model for automated diagnosis of sarcopenia based on CT imaging Read more about this study
- Who is it for?
- Ages 18 to 90 yearsHealthy volunteers can take part
- Conditions the study is for
- Deep Learning, Sarcopenia, Computed Tomography
- How many people
- 1,080 participants
- Where
- 1 siteChina
- Study dates
- 5 Sep 2023to 31 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 90 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: 960 inpatients in the geriatric department of Renji Hospital, 20 patients from Ruijin Hospital affiliated to Shanghai Jiaotong University School of Medicine, 20 patients from The First Affiliated Hospital of Zhejiang Medical University, 50 patients from The First Affiliated Hospital of Wenzhou Medical University, and 30 patients from Huangshan People's Hospital.
Who may be able to join
- The population undergoing BIA and abdominal CT examinations;
- Can cooperate to complete human body composition analysis, grip strength measurement, 6m walking time measurement, and questionnaire survey.
Who cannot join
- Age<18 years old;
- Existence of abdominal wall edema;
- History of spinal surgery or vertebral fractures, or vertebral tumor lesions;
- History of neuromuscular disorders.
Trial locations and contacts
1 site in 1 country.
China1 site
Shanghai Jiaotong University School of Medicine, Renji Hospital Ethics Committee
Shanghai, Shanghai Municipality, 2000127
About this study
Official title: Application of Artificial Intelligence Algorithm Based on CT Imaging for Muscle Parameter Measurement in the Diagnosis of Sarcopenia
To establish an artificial intelligence model for automated diagnosis of sarcopenia based on CT imaging
Read the detailed description
With the accelerating aging process, the early identification and diagnosis of sarcopenia, along with the effective prevention of its adverse outcomes, have become a focal point in medical research. However, current methods for assessing and diagnosing sarcopenia still face significant limitations, making the development of more efficient and accurate techniques for muscle mass evaluation an urgent clinical need. Although CT is considered as the most promising method for assessing muscle mass, its practical application is hindered by factors such as reliance on physician expertise and time-consuming procedures, limiting its widespread clinical adoption. In light of these challenges, this study aims to develop an artificial intelligence model for fully automated muscle mass measurement based on abdominal CT imaging and to validate its application value in assisting the diagnosis of sarcopenia.
- Conditions
- Deep Learning, Sarcopenia, Computed Tomography, Body Composition
- Medical subject headings
- Sarcopenia
What is being measured
The primary outcome is the main question the study is designed to answer.
To automatedly and precisely quantify three-dimensional muscle volume and fat volume.
To achieve an automated and precise quantification of three-dimensional muscle volume and fat volume at the L3 vertebral region by deep learning.
Measured: 2020-2023
To establish an artificial intelligence model for diagnosis of sarcopenia.
The validation of artificial intelligence models can assist in the diagnosis of sarcopenia.
Measured: 2020-2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Feb 2025
- Study start5 Sep 2023
- Primary completion31 Dec 2024
- Study completion31 Dec 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Deep LearningStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Enrolment
- 1,080 participants (actual)
Sponsor and investigators
- Lead sponsor
- RenJi Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yaomin Hu, RenJi Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06845462
- Sponsor's protocol ID
- LY2023-150-A
- Status last verified
- Feb 2025
- First submitted
- 20 Feb 2025
- Last updated
- 25 Feb 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 25 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Artificial Intelligence Algorithm Based on CT Imaging for Muscle Parameter Measurement in the Diagnosis of Sarcopenia
- ClinicalTrials.gov ID
- NCT06845462
- Acronym / sponsor ID
- LY2023-150-A
- Conditions
- Deep Learning, Sarcopenia, Computed Tomography, Body Composition
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,080 participants (actual)
- Lead sponsor
- RenJi HospitalUniversity, hospital or other
- Study start
- 5 Sep 2023 (actual)
- Primary completion
- 31 Dec 2024 (actual)
- Study completion
- 31 Dec 2024 (actual)
- First posted
- 25 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
To establish an artificial intelligence model for automated diagnosis of sarcopenia based on CT imaging
Detailed description
With the accelerating aging process, the early identification and diagnosis of sarcopenia, along with the effective prevention of its adverse outcomes, have become a focal point in medical research. However, current methods for assessing and diagnosing sarcopenia still face significant limitations, making the development of more efficient and accurate techniques for muscle mass evaluation an urgent clinical need. Although CT is considered as the most promising method for assessing muscle mass, its practical application is hindered by factors such as reliance on physician expertise and time-consuming procedures, limiting its widespread clinical adoption. In light of these challenges, this study aims to develop an artificial intelligence model for fully automated muscle mass measurement based on abdominal CT imaging and to validate its application value in assisting the diagnosis of sarcopenia.
Study design
- Observational model
- Case only
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- 960 inpatients in the geriatric department of Renji Hospital, 20 patients from Ruijin Hospital affiliated to Shanghai Jiaotong University School of Medicine, 20 patients from The First Affiliated Hospital of Zhejiang Medical University, 50 patients from The First Affiliated Hospital of Wenzhou Medical University, and 30 patients from Huangshan People's Hospital.
Key inclusion criteria (2 listed)
- The population undergoing BIA and abdominal CT examinations;
- Can cooperate to complete human body composition analysis, grip strength measurement, 6m walking time measurement, and questionnaire survey.
Key exclusion criteria (4 listed)
- Age<18 years old;
- Existence of abdominal wall edema;
- History of spinal surgery or vertebral fractures, or vertebral tumor lesions;
- History of neuromuscular disorders.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
To automatedly and precisely quantify three-dimensional muscle volume and fat volume.
Time frame: 2020-2023
To achieve an automated and precise quantification of three-dimensional muscle volume and fat volume at the L3 vertebral region by deep learning.
To establish an artificial intelligence model for diagnosis of sarcopenia.
Time frame: 2020-2023
The validation of artificial intelligence models can assist in the diagnosis of sarcopenia.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Feb 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Deep Learning and Sarcopenia, run by RenJi Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 25 Feb 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To automatedly and precisely quantify three-dimensional muscle volume and fat volume. (time frame: 2020-2023). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Feb 2025.