Artifical IntelligenceLENSNCT07634913
Development of a Mobile Terminal-Based Intelligent Detection System for Multiple Anterior Segment Diseases of the Eye
An observational study of Artifical Intelligence and Cataract, run by Zhongshan Ophthalmic Center, Sun Yat-sen University at 1 site in China.
Trial summary
This is a multi-center, cross-sectional study evaluating a smartphone-based artificial intelligence (AI) system for anterior segment eye disease screening. The system is designed to identify 16 clinically important anterior segment conditions from images captured using a standard Android smartphone. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Artifical Intelligence, Cataract, Pterygium
- What is being tested?
- Smartphone-based on-device artificial intelligence system for anterior segment eye disease screening
- Type of treatment
- Medical device
- How many people
- 3,000 participants
- Where
- 1 siteChina
- Study dates
- 12 Dec 2023to Dec 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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: Participants are adults aged 18 years or older enrolled from four source populations: consecutive outpatients at a tertiary ophthalmic center and community hospitals; non-medical adults with no ophthalmic training recruited from a tertiary clinic for usability evaluation; community residents aged 60 years or older from two low-resource sites in western and southwestern China for field deployment evaluation; and board-certified ophthalmologists of varying seniority serving as diagnostic raters for AI benchmarking.
Who may be able to join
- Adults aged 18 years or older;
- Willing to participate and able to provide written informed consent prior to enrollment.
Who cannot join
- Unable to cooperate with anterior segment image capture (including smartphone-based photography or slit-lamp biomicroscopy).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Longhui Li contact
China1 site, 1 recruiting
- Recruiting
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, 510060
Haotian Lin, +86 13802793086, haot.lin@hotmail.com
About this study
This is a multi-center, cross-sectional study evaluating a smartphone-based artificial intelligence (AI) system for anterior segment eye disease screening. The system is designed to identify 16 clinically important anterior segment conditions from images captured using a standard Android smartphone. A core design feature of the system is that all image analysis is performed entirely on the smartphone itself, without requiring internet connectivity or cloud-based server infrastructure.
The study is motivated by a structural challenge in the deployment of medical AI: systems that depend on cloud infrastructure for inference are non-functional in settings without reliable internet access, which disproportionately excludes populations in low-resource regions where the burden of preventable eye disease is highest. This study evaluates whether an on-device AI system, designed with operational constraints as a primary engineering objective, can deliver clinically acceptable diagnostic performance while remaining operable under real-world connectivity limitations.
Read more
The study comprises five evaluation components. First, the diagnostic performance of the AI system is benchmarked against board-certified ophthalmologists of varying seniority on a standardized set of smartphone-captured anterior segment images. Second, the usability of the system is evaluated among non-medical users who perform self-administered screening with minimal instruction, with per-screening time recorded across consecutive attempts to characterize the learning curve. Third, a head-to-head field trial directly compares the on-device AI system against a functionally equivalent cloud-based deployment of the same model architecture across key operational dimensions including screening duration, diagnostic performance, and user acceptability. Fourth, population-level screening is conducted among consecutively enrolled community residents at two low-resource sites, with per-disease sensitivity and specificity calculated against reference-standard slit-lamp examinations. Fifth, pre-specified health-economic and environmental analyses compare the two deployment modalities in terms of per-person screening cost, cost-effectiveness, per-inference electricity consumption, and projected carbon emissions at scale.
The reference standard for all diagnostic comparisons is slit-lamp biomicroscopic examination performed by board-certified ophthalmologists. The study is designed and reported in accordance with the DECIDE-AI reporting guideline for early-stage clinical evaluation of AI-driven decision-support systems.
- Conditions
- Artifical Intelligence, Cataract, Pterygium, Keratopathy, Subconjunctival Hemorrhage, Conjunctivitis
- Being tested
- Smartphone-based on-device artificial intelligence system for anterior segment eye disease screening
- Keywords
- Artificial Intelligence, Standalone Deployment, Smartphone, Eye Disease Screening
- Medical subject headings
- Cataract, Pterygium, Conjunctivitis, Hordeolum, Blepharitis, Entropion, Ectropion, Exophthalmos
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Smartphone-based on-device artificial intelligence system for anterior segment eye disease screeningDevice
A structured-pruned one-stage object-detection model deployed as a standalone Android application, performing all image inference on-device without internet connectivity, designed to detect 16 anterior segment eye diseases from smartphone-captured images.
| Study group | Role | What they receive |
|---|---|---|
| On-Device Deployment Group | Participants screened using a smartphone-based AI system that performs all image inference locally on the device without requiring internet connectivity. The AI system analyzes smartphone-captured anterior segment images and generates diagnostic outputs entirely on the smartphone hardware, independent of cloud infrastructure or network access. Receives: Device: Smartphone-based on-device artificial intelligence system for anterior segment eye disease screening | |
| Cloud-Based Deployment Group | Participants screened using a functionally equivalent deployment of the same AI model architecture, in which smartphone-captured anterior segment images are transmitted to a remote server for inference, with diagnostic outputs returned via internet connection. This group serves as the comparator to evaluate the operational differences attributable solely to deployment modality, as the underlying model architecture is identical between the two groups. Receives: Device: Smartphone-based on-device artificial intelligence system for anterior segment eye disease screening |
What is being measured
The primary outcome is the main question the study is designed to answer.
Case-level diagnostic accuracy of the AI system compared with board-certified ophthalmologists
Case-level accuracy is defined as the proportion of images with fully correct diagnostic labels concordant with the reference standard. The AI system and board-certified ophthalmologists stratified by clinical seniority (junior: fewer than 5 years of independent practice; intermediate: 5 to 15 years; senior: more than 15 years) independently evaluate the same standardized set of smartphone-captured anterior segment images, sampled to ensure balanced representation of all 16 disease categories and normal eyes. The reference standard for each image is established by a senior ophthalmologist with more than 30 years of clinical experience who does not participate in the benchmarking exercise. Clinicians are masked to the AI system output and to each other's assessments throughout.
Measured: Day 1
Diagnostic accuracy of the AI system when operated by non-medical users
Diagnostic accuracy is defined as the proportion of images correctly classified by non-medical users operating the AI system independently. Non-medical users, including patients and their family members attending the outpatient clinic of Zhongshan Ophthalmic Center, are instructed to install the AI application on their own smartphones, follow the in-app guidelines, and capture anterior segment images of an accompanying person to receive a screening result. All inference is performed on-device without internet connectivity. AI-generated diagnostic outputs are compared with reference-standard diagnoses obtained from subsequent slit-lamp examinations performed by ophthalmologists at the same clinic.
Measured: Day 1
Sensitivity of the on-device AI system in population-level community screening
Sensitivity is defined as the proportion of participants with a given anterior segment disease who are correctly identified as positive by the on-device AI system (true positives divided by the sum of true positives and false negatives), calculated separately for each target disease category. Village staff without medical training use the on-device AI system to screen consecutively enrolled local residents. The reference standard is established through slit-lamp examinations performed by ophthalmologists.
Measured: Day 1
Incremental cost-effectiveness ratio of on-device versus cloud-based screening
The incremental cost-effectiveness ratio (ICER) is defined as the difference in lifetime costs between on-device and cloud-based screening divided by the difference in quality-adjusted life-years (QALYs) between the two strategies, estimated from a pre-specified decision-analytic model comprising a decision tree with a downstream Markov state-transition structure applied to a simulated cohort of 100,000 individuals.
Measured: Day 1
Per-inference electricity consumption of on-device versus cloud-based deployment
Electricity consumption in joules per inference cycle is measured over 10,000 inference cycles on both the on-device smartphone and a standardized cloud-server configuration (Intel Xeon Gold 6248 CPU, NVIDIA Tesla V100 GPU) using the Experiment Impact Tracker toolkit. Cloud-server measurements are averaged across low-traffic and high-traffic server conditions. Results are reported separately for the on-device and cloud-based deployment modalities and compared as a ratio.
Measured: Day 1
Secondary outcomes (6)
Total image evaluation time of the AI system compared with board-certified ophthalmologists
Total time in seconds required to complete independent evaluation of all images in the standardized benchmarking set, recorded separately for the AI system and for the board-certified ophthalmologists. Reported as mean with standard deviation for both the AI system and the clinician group.
Measured: Day 1
Per-screening time learning curve among non-medical operators
The duration of each screening attempt in seconds is recorded for analysis. Participants repeat the screening process and self-evaluate their operating proficiency after each attempt. The procedure is concluded once a participant deems himself or herself proficient for three consecutive attempts. The learning curve is characterized by the change in mean per-screening duration across consecutive attempts.
Measured: Day 1
Screening duration comparing on-device and cloud-based deployment in resource-limited field settings
Mean screening duration in seconds per participant, recorded for each of the two deployment modalities: on-device inference and cloud-based inference using a functionally equivalent deployment of the identical model architecture with inference performed on a remote server. Local residents are divided into two equal groups and screened by village staff without medical training using either the on-device or the cloud-based system.
Measured: Day 1
User acceptability comparing on-device and cloud-based deployment in resource-limited field settings
User acceptability is assessed using a questionnaire investigating patient satisfaction, privacy concerns, and willingness to recommend the AI system, administered to participants in both the on-device and cloud-based deployment groups. Local residents are divided into two equal groups and screened by village staff without medical training using either the on-device or the cloud-based system. Questionnaire responses are compared between the two deployment groups.
Measured: Day 1
Diagnostic accuracy of the on-device AI system in population-level community screening
Diagnostic accuracy is defined as the proportion of participants for whom the AI system assigns a correct diagnostic label concordant with the reference standard. Village staff without medical training use the on-device AI system to screen consecutively enrolled local residents. The reference standard is established through slit-lamp examinations performed by ophthalmologists.
Measured: Day 1
Specificity of the on-device AI system in population-level community screening
Specificity is defined as the proportion of participants without a given anterior segment disease who are correctly identified as negative by the on-device AI system (true negatives divided by the sum of true negatives and false positives), calculated separately for each target disease category. Village staff without medical training use the on-device AI system to screen consecutively enrolled local residents. The reference standard is established through slit-lamp examinations performed by ophthalmologists.
Measured: Day 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Jun 2026
- Study start12 Dec 2023
- Primary completionMay 2028 (estimated)
- Study completionDec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artifical IntelligenceStudies 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
- Prospective
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Zhongshan Ophthalmic Center, Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Longhui Li, Principal investigator, Zhongshan Ophthalmic Center, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07634913
- Sponsor's protocol ID
- 2023KYPJ274
- Acronym
- LENS
- Status last verified
- May 2026
- First submitted
- 28 May 2026
- Last updated
- 9 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a Mobile Terminal-Based Intelligent Detection System for Multiple Anterior Segment Diseases of the Eye
- ClinicalTrials.gov ID
- NCT07634913
- Acronym / sponsor ID
- LENS / 2023KYPJ274
- Conditions
- Artifical Intelligence, Cataract, Pterygium, Keratopathy, Subconjunctival Hemorrhage, Conjunctivitis
- Keywords
- Artificial Intelligence, Standalone Deployment, Smartphone, Eye Disease Screening
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Zhongshan Ophthalmic Center, Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- 12 Dec 2023 (actual)
- Primary completion
- May 2028 (estimated)
- Study completion
- Dec 2028 (estimated)
- First posted
- 9 Jun 2026 (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.
This is a multi-center, cross-sectional study evaluating a smartphone-based artificial intelligence (AI) system for anterior segment eye disease screening. The system is designed to identify 16 clinically important anterior segment conditions from images captured using a standard Android smartphone. A core design feature of the system is that all image analysis is performed entirely on the smartphone itself, without requiring internet connectivity or cloud-based server infrastructure.
The study is motivated by a structural challenge in the deployment of medical AI: systems that depend on cloud infrastructure for inference are non-functional in settings without reliable internet access, which disproportionately excludes populations in low-resource regions where the burden of preventable eye disease is highest. This study evaluates whether an on-device AI system, designed with operational constraints as a primary engineering objective, can deliver clinically acceptable diagnostic performance while remaining operable under real-world connectivity limitations.
The study comprises five evaluation components. First, the diagnostic performance of the AI system is benchmarked against board-certified ophthalmologists of varying seniority on a standardized set of smartphone-captured anterior segment images. Second, the usability of the system is evaluated among non-medical users who perform self-administered screening with minimal instruction, with per-screening time recorded across consecutive attempts to characterize the learning curve. Third, a head-to-head field trial directly compares the on-device AI system against a functionally equivalent cloud-based deployment of the same model architecture across key operational dimensions including screening duration, diagnostic performance, and user acceptability. Fourth, population-level screening is conducted among consecutively enrolled community residents at two low-resource sites, with per-disease sensitivity and specificity calculated against reference-standard slit-lamp examinations. Fifth, pre-specified health-economic and environmental analyses compare the two deployment modalities in terms of per-person screening cost, cost-effectiveness, per-inference electricity consumption, and projected carbon emissions at scale.
The reference standard for all diagnostic comparisons is slit-lamp biomicroscopic examination performed by board-certified ophthalmologists. The study is designed and reported in accordance with the DECIDE-AI reporting guideline for early-stage clinical evaluation of AI-driven decision-support systems.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| On-Device Deployment Group | – | Device: Smartphone-based on-device artificial intelligence system for anterior segment eye disease screeningParticipants screened using a smartphone-based AI system that performs all image inference locally on the device without requiring internet connectivity. The AI system analyzes smartphone-captured anterior segment images and generates diagnostic outputs entirely on the smartphone hardware, independent of cloud infrastructure or network access. |
| Cloud-Based Deployment Group | – | Device: Smartphone-based on-device artificial intelligence system for anterior segment eye disease screeningParticipants screened using a functionally equivalent deployment of the same AI model architecture, in which smartphone-captured anterior segment images are transmitted to a remote server for inference, with diagnostic outputs returned via internet connection. This group serves as the comparator to evaluate the operational differences attributable solely to deployment modality, as the underlying model architecture is identical between the two groups. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Participants are adults aged 18 years or older enrolled from four source populations: consecutive outpatients at a tertiary ophthalmic center and community hospitals; non-medical adults with no ophthalmic training recruited from a tertiary clinic for usability evaluation; community residents aged 60 years or older from two low-resource sites in western and southwestern China for field deployment evaluation; and board-certified ophthalmologists of varying seniority serving as diagnostic raters for AI benchmarking.
Key inclusion criteria (2 listed)
- Adults aged 18 years or older;
- Willing to participate and able to provide written informed consent prior to enrollment.
Key exclusion criteria (1 listed)
- Unable to cooperate with anterior segment image capture (including smartphone-based photography or slit-lamp biomicroscopy).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Smartphone-based on-device artificial intelligence system for anterior segment eye disease screeningA structured-pruned one-stage object-detection model deployed as a standalone Android application, performing all image inference on-device without internet connectivity, designed to detect 16 anterior segment eye diseases from smartphone-captured images. | Device | Cloud-Based Deployment Group, On-Device Deployment Group |
Primary outcome
Case-level diagnostic accuracy of the AI system compared with board-certified ophthalmologists
Time frame: Day 1
Case-level accuracy is defined as the proportion of images with fully correct diagnostic labels concordant with the reference standard. The AI system and board-certified ophthalmologists stratified by clinical seniority (junior: fewer than 5 years of independent practice; intermediate: 5 to 15 years; senior: more than 15 years) independently evaluate the same standardized set of smartphone-captured anterior segment images, sampled to ensure balanced representation of all 16 disease categories and normal eyes. The reference standard for each image is established by a senior ophthalmologist with more than 30 years of clinical experience who does not participate in the benchmarking exercise. Clinicians are masked to the AI system output and to each other's assessments throughout.
Diagnostic accuracy of the AI system when operated by non-medical users
Time frame: Day 1
Diagnostic accuracy is defined as the proportion of images correctly classified by non-medical users operating the AI system independently. Non-medical users, including patients and their family members attending the outpatient clinic of Zhongshan Ophthalmic Center, are instructed to install the AI application on their own smartphones, follow the in-app guidelines, and capture anterior segment images of an accompanying person to receive a screening result. All inference is performed on-device without internet connectivity. AI-generated diagnostic outputs are compared with reference-standard diagnoses obtained from subsequent slit-lamp examinations performed by ophthalmologists at the same clinic.
Sensitivity of the on-device AI system in population-level community screening
Time frame: Day 1
Sensitivity is defined as the proportion of participants with a given anterior segment disease who are correctly identified as positive by the on-device AI system (true positives divided by the sum of true positives and false negatives), calculated separately for each target disease category. Village staff without medical training use the on-device AI system to screen consecutively enrolled local residents. The reference standard is established through slit-lamp examinations performed by ophthalmologists.
Incremental cost-effectiveness ratio of on-device versus cloud-based screening
Time frame: Day 1
The incremental cost-effectiveness ratio (ICER) is defined as the difference in lifetime costs between on-device and cloud-based screening divided by the difference in quality-adjusted life-years (QALYs) between the two strategies, estimated from a pre-specified decision-analytic model comprising a decision tree with a downstream Markov state-transition structure applied to a simulated cohort of 100,000 individuals.
Per-inference electricity consumption of on-device versus cloud-based deployment
Time frame: Day 1
Electricity consumption in joules per inference cycle is measured over 10,000 inference cycles on both the on-device smartphone and a standardized cloud-server configuration (Intel Xeon Gold 6248 CPU, NVIDIA Tesla V100 GPU) using the Experiment Impact Tracker toolkit. Cloud-server measurements are averaged across low-traffic and high-traffic server conditions. Results are reported separately for the on-device and cloud-based deployment modalities and compared as a ratio.
6 secondary outcomes
Total image evaluation time of the AI system compared with board-certified ophthalmologists
Time frame: Day 1
Per-screening time learning curve among non-medical operators
Time frame: Day 1
Screening duration comparing on-device and cloud-based deployment in resource-limited field settings
Time frame: Day 1
User acceptability comparing on-device and cloud-based deployment in resource-limited field settings
Time frame: Day 1
Diagnostic accuracy of the on-device AI system in population-level community screening
Time frame: Day 1
Specificity of the on-device AI system in population-level community screening
Time frame: Day 1
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Jun 2026.
- Individual participant dataSharing planned: No
Related trials for Artifical Intelligence
Not yet recruiting
AI-Driven Video Avatars vs. Customized GPTs: Professional English Learning Among Nursing Students
Artifical Intelligence, Generative Artificial Intelligence
Not yet recruiting
Chronic Pain Assessment Via AI-Powered Game
Chronic Pain
Not yet recruiting
Technology Use, Safe Health Information, and Artificial Intelligence Among Older Adults
Older Adults, Nursing Homes
Not yet recruitingObservational study
MRI-Based Artificial Intelligence Prediction of Fluoroscopic C-Arm Angles for Lumbar Transforaminal Epidural Steroid Injections
Lumbar Radicular Pain, Artifical Intelligence
RecruitingObservational study
Benchmarking Large Language Models Against Tumour Boards for Oncology Treatment Recommendations
Breast Neoplasms, Lung Neoplasms
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.
Longhui Li contact
Longhui Li principal investigator
Zhongshan Ophthalmic Center, Sun Yat-sen University
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 Artifical Intelligence and Cataract, run by Zhongshan Ophthalmic Center, 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 2 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: looking for participants now, as of the sponsor's last update on 9 Jun 2026. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Haotian Lin (+86 13802793086, haot.lin@hotmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Case-level diagnostic accuracy of the AI system compared with board-certified ophthalmologists (time frame: Day 1). The study lists 5 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 9 Jun 2026.