KeratitisNCT05538793
Deep Learning for the Discrimination Among Different Types of Keratits: a Nationwide Study
An observational study of Keratitis and Automatic Judgement, run by Ningbo Eye Hospital at 2 sites in China.
Trial summary
Detecting the cause of keratitis fast is the premise of providing targeted therapy for reducing vision loss and preventing severe complications. Due to overlapping inflammatory features, even expert cornea specialists have relatively poor performance in the identification of causative pathogen of infectious keraitis. Read more about this study
- Who is it for?
- Ages 1 week to 100 years
- Conditions the study is for
- Keratitis, Automatic Judgement, Image
- How many people
- 10,369 participants
- Where
- 2 sitesChina
- Study dates
- 1 Jul 2020to 20 Oct 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 1 week to 100 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Child, Adult, Older Adult
Who may be able to join
- Slit-lamp images with sufficient diagnostic certainty and showing keratitis at the active phase.
Who cannot join
- Poor-quality images
- Images presenting mixed infections (i.e., cornea infected by two or more causative pathogens)
Trial locations and contacts
2 sites in 1 country.
China2 sites
Ningbo Eye Hospital
Ningbo, Zhejiang
Eye Hospital of Wenzhou Medical University
Wenzhou
About this study
Official title: Deep Learning for the Discrimination Among Bacterial, Fungal, Viral, Amebic and Noninfectious Keratitis: a Nationwide Study
Detecting the cause of keratitis fast is the premise of providing targeted therapy for reducing vision loss and preventing severe complications. Due to overlapping inflammatory features, even expert cornea specialists have relatively poor performance in the identification of causative pathogen of infectious keraitis. In this project, the investigators aim to develop an automated and accurate deep learning system to discriminate among bacterial, fungal, viral, amebic and noninfectious keratitis based on slit-lamp images and evaluated this system using the datasets obtained from mutiple independent clinical centers across China.
- Conditions
- Keratitis, Automatic Judgement, Image
- Keywords
- Keratitis, Slit-lamp Image, Deep Learning
- Medical subject headings
- Keratitis
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
Measured: 2020-2022
Secondary outcomes (3)
Accuracy of the deep learning system
Measured: 2020-2022
Sensitivity of the deep learning system
Measured: 2020-2022
Specificity of the deep learning system
Measured: 2020-2022
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Sep 2022
- Study start1 Jul 2020
- Primary completion30 Sep 2023
- Study completion20 Oct 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for KeratitisStudies 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
- Other
- Time perspective
- Other
- Enrolment
- 10,369 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ningbo Eye Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05538793
- Sponsor's protocol ID
- NEH2022091015
- Status last verified
- Oct 2023
- First submitted
- 11 Sep 2022
- Last updated
- 27 Oct 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 27 Oct 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning for the Discrimination Among Bacterial, Fungal, Viral, Amebic and Noninfectious Keratitis: a Nationwide Study
- ClinicalTrials.gov ID
- NCT05538793
- Acronym / sponsor ID
- NEH2022091015
- Conditions
- Keratitis, Automatic Judgement, Image
- Keywords
- Keratitis, Slit-lamp Image, Deep Learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 10,369 participants (actual)
- Lead sponsor
- Ningbo Eye HospitalUniversity, hospital or other
- Study start
- 1 Jul 2020 (actual)
- Primary completion
- 30 Sep 2023 (actual)
- Study completion
- 20 Oct 2023 (actual)
- First posted
- 14 Sep 2022 (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.
Detecting the cause of keratitis fast is the premise of providing targeted therapy for reducing vision loss and preventing severe complications. Due to overlapping inflammatory features, even expert cornea specialists have relatively poor performance in the identification of causative pathogen of infectious keraitis. In this project, the investigators aim to develop an automated and accurate deep learning system to discriminate among bacterial, fungal, viral, amebic and noninfectious keratitis based on slit-lamp images and evaluated this system using the datasets obtained from mutiple independent clinical centers across China.
Study design
- Observational model
- Other
- Time perspective
- Other
Who may qualify
- Ages
- 1 week to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Child, Adult, Older Adult
Key inclusion criteria (1 listed)
- Slit-lamp images with sufficient diagnostic certainty and showing keratitis at the active phase.
Key exclusion criteria (2 listed)
- Poor-quality images
- Images presenting mixed infections (i.e., cornea infected by two or more causative pathogens)
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: 2020-2022
3 secondary outcomes
Accuracy of the deep learning system
Time frame: 2020-2022
Sensitivity of the deep learning system
Time frame: 2020-2022
Specificity of the deep learning system
Time frame: 2020-2022
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 27 Oct 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.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Keratitis and Automatic Judgement, run by Ningbo Eye Hospital at 2 sites 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 2 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 27 Oct 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites 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?
Area under the receiver operating characteristic curve of the deep learning system (time frame: 2020-2022). 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 27 Oct 2023.