OphthalmologyNCT05682105
Detection of Jaundice From Ocular Images Via Deep Learning
An observational study of Ophthalmology and Artificial Intelligence, run by Sun Yat-sen University at 1 site in China.
Trial summary
Our study presents a detection model predicting a diagnosis of jaundice (clinical jaundice and occult jaundice) trained on prospective cohort data from slit-lamp photos and smartphone photos, demonstrating the model's validity and assisting clinical workers in identifying patient underlying hepatobiliary diseases. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Ophthalmology, Artificial Intelligence, Hepatobiliary Disease
- How many people
- 1,633 participants
- Where
- 1 siteChina
- Study dates
- 1 Dec 2018to 30 Jun 2023
- 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 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: This prospective, multicentre, observational study was led by Zhongshan Ophthalmic Centre (ZOC), Sun Yat-sen University, and conducted in three phases to collect data from participants from three surgery departments and three medical examination centres, including the Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University (HTH; Guangzhou, China), the Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University (ITH; Guangzhou, China), the Department of Infectious Diseases, the Affiliated Huadu Hospital of Southern Medical University (HDH; Guangzhou, China), the Medical Centre of the Third Affiliated Hospital of Sun Yat-sen University(MCH; Guangzhou, China), Nantian Medical Centre of Aikang Health Care (NMC), and Huanshidong Medical Centre of Aikang Health Care (HMC).
Who may be able to join
- The quality of slit-lamp images should be clinical acceptable. More than 90% of the slit-lamp image area, including three central regions (sclera, pupil, and lens) are easy to read and discriminate.
Who cannot join
- Images with light leakage (>10% of the area), spots from lens flares or stains, and overexposure were excluded from further analysis
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Detection of Jaundice From Ocular Images Via Deep Learning : a Prospective, Multicenter Cohort Study
Our study presents a detection model predicting a diagnosis of jaundice (clinical jaundice and occult jaundice) trained on prospective cohort data from slit-lamp photos and smartphone photos, demonstrating the model's validity and assisting clinical workers in identifying patient underlying hepatobiliary diseases.
Read the detailed description
This study demonstrated that deep learning models could detect jaundice using ocular images in blood levels with reasonable accuracy, providing a non-invasive method for jaundice detection and recognition. This algorithm can assist clinical surgeons with daily follow-up visits and provide referral advice. It also highlights the algorithm's potential smartphone application in sizeable real-world population-based disease-detecting or telemedicine programs.
- Keywords
- Ophthalmology, Artificial Intelligence, ocualr image, Jaundice
- Medical subject headings
- Digestive System Diseases, Jaundice
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Development dataset | Slit-lamp images collected from the Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University(HTH)， Affiliated Huadu Hospital of Southern Medical University(HDH)， and Nantian Medical Centre of Aikang Health Care (NMC). | |
| Testing dataset | Slit-lamp and smartphone images collected from the Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University（ITH）， Huanshidong Medical Centre of Aikang Health Care， the Medical Centre of the Third Affiliated Hospital of Sun Yat-sen University(MCH). |
What is being measured
The primary outcome is the main question the study is designed to answer.
area under the receiver operating characteristic curve of the deep learning system
The investigators will calculate the area under the receiver operating characteristic curve of deep learning system
Measured: baseline
Secondary outcomes (1)
sensitivity and specificity of the deep learning system
The investigators will calculate the sensitivity and specifity of deep learning system
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Jan 2023
- Study start1 Dec 2018
- Primary completion30 Oct 2022
- Study completion30 Jun 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for OphthalmologyStudies of the same condition, open and under way.
- More trials run by Sun Yat-sen UniversityThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,633 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Collaborators
- Third Affiliated Hospital, Sun Yat-Sen University, Affiliated Huadu Hospital of Southern Medical University, Aikang Health Care
- Responsible party
- Principal investigator, Haotian Lin, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05682105
- Sponsor's protocol ID
- AEHD-2022
- Status last verified
- Jan 2023
- First submitted
- 26 Dec 2022
- Last updated
- 12 Jan 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Jan 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Detection of Jaundice From Ocular Images Via Deep Learning : a Prospective, Multicenter Cohort Study
- ClinicalTrials.gov ID
- NCT05682105
- Acronym / sponsor ID
- AEHD-2022
- Keywords
- Ophthalmology, Artificial Intelligence, ocualr image, Jaundice
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,633 participants (actual)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- Third Affiliated Hospital, Sun Yat-Sen University, Affiliated Huadu Hospital of Southern Medical University, Aikang Health Care
- Study start
- 1 Dec 2018 (actual)
- Primary completion
- 30 Oct 2022 (actual)
- Study completion
- 30 Jun 2023 (estimated)
- First posted
- 12 Jan 2023 (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.
Our study presents a detection model predicting a diagnosis of jaundice (clinical jaundice and occult jaundice) trained on prospective cohort data from slit-lamp photos and smartphone photos, demonstrating the model's validity and assisting clinical workers in identifying patient underlying hepatobiliary diseases.
Detailed description
This study demonstrated that deep learning models could detect jaundice using ocular images in blood levels with reasonable accuracy, providing a non-invasive method for jaundice detection and recognition. This algorithm can assist clinical surgeons with daily follow-up visits and provide referral advice. It also highlights the algorithm's potential smartphone application in sizeable real-world population-based disease-detecting or telemedicine programs.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Development dataset | – | –Slit-lamp images collected from the Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University(HTH)， Affiliated Huadu Hospital of Southern Medical University(HDH)， and Nantian Medical Centre of Aikang Health Care (NMC). |
| Testing dataset | – | –Slit-lamp and smartphone images collected from the Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University（ITH）， Huanshidong Medical Centre of Aikang Health Care， the Medical Centre of the Third Affiliated Hospital of Sun Yat-sen University(MCH). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- This prospective, multicentre, observational study was led by Zhongshan Ophthalmic Centre (ZOC), Sun Yat-sen University, and conducted in three phases to collect data from participants from three surgery departments and three medical examination centres, including the Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University (HTH; Guangzhou, China), the Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University (ITH; Guangzhou, China), the Department of Infectious Diseases, the Affiliated Huadu Hospital of Southern Medical University (HDH; Guangzhou, China), the Medical Centre of the Third Affiliated Hospital of Sun Yat-sen University(MCH; Guangzhou, China), Nantian Medical Centre of Aikang Health Care (NMC), and Huanshidong Medical Centre of Aikang Health Care (HMC).
Key inclusion criteria (1 listed)
- The quality of slit-lamp images should be clinical acceptable. More than 90% of the slit-lamp image area, including three central regions (sclera, pupil, and lens) are easy to read and discriminate.
Key exclusion criteria (1 listed)
- Images with light leakage (>10% of the area), spots from lens flares or stains, and overexposure were excluded from further analysis
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
area under the receiver operating characteristic curve of the deep learning system
Time frame: baseline
The investigators will calculate the area under the receiver operating characteristic curve of deep learning system
1 secondary outcome
sensitivity and specificity of the deep learning system
Time frame: baseline
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Jan 2023.
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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 Ophthalmology and Artificial Intelligence, run by Sun Yat-sen University 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 1 inclusion and 1 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: status not confirmed, as of the sponsor's last update on 12 Jan 2023. 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?
Results are not available in the source record.
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?
area under the receiver operating characteristic curve of the deep learning system (time frame: baseline). 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 12 Jan 2023.