(106 days)
Not Found
Yes
The document explicitly states that the device is based on the main technological principle of Artificial Intelligence (AI) software as a medical device and uses artificial intelligence technology to analyze images.
No.
The device is described as a "retinal diagnostic software device" used to automatically detect diabetic retinopathy, indicating a diagnostic rather than therapeutic function.
Yes
Explanation: The "Intended Use / Indications for Use" section states that the device is "indicated for use by health care providers to automatically detect more than mild diabetic retinopathy (mtmDR)", and the "Device Description" explicitly calls it a "retinal diagnostic software device" designed to perform "diagnostic screening for the condition of more-than-mild diabetic retinopathy (mtmDR)".
Yes
The device description explicitly states "AEYE-DS is a retinal diagnostic software device" and details its components as software modules. While it interacts with fundus cameras, the device itself is described solely as software for processing images and providing a diagnostic result.
Based on the provided information, the AEYE-DS device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze samples taken from the human body. This typically includes blood, urine, tissue, etc.
- The AEYE-DS analyzes images of the human body (retinal images). It does not process biological samples.
The AEYE-DS is a software device that uses Artificial Intelligence to analyze medical images for diagnostic screening. This falls under the category of medical devices, but specifically those that process imaging data, not biological samples.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
The AEYE-DS is indicated for use by health care providers to automatically detect more than mild diabetic retinopathy (mtmDR) in adults diagnosed with diabetes who have not been previously diagnosed with diabetic retinopathy. The AEYE-DS is indicated for use with the Topcon NW400 camera and the Optomed Aurora camera.
Product codes
PIB
Device Description
AEYE-DS is a retinal diagnostic software device that incorporates an algorithm to evaluate retinal images for diagnostic screening to identify retinal diseases or conditions. Specifically, the AEYE-DS is designed to perform diagnostic screening for the condition of more-than-mild diabetic retinopathy (mtmDR).
The AEYE-DS is comprised of 5 software components: (1) Client; (2) Service; (3) Analytics; (4) Reporting and Archiving; and (5) System Security.
The AEYE-DS device is based on the main technological principle of Artificial Intelligence (AI) software as a medical device. The software as a medical device uses artificial intelligence technology to analyze specific disease features from fundus retinal images for diagnostic screening of diabetic retinopathy.
The AEYE-DS device is based on the principle of operation, whereby a fundus camera is used to obtain retinal images. The fundus camera is attached to a computer, where the Client module/software is installed. The Client module/software guides the user to acquire the images and enables the user to interact with the server-based analysis software over a secure internet connection. Using the Client module/software, users identify the fundus images per eye to be dispatched to the Service module/software. The Service module/software is installed on a server hosted at a secure datacenter, receives the fundus images and transfers them to the Analytics module/software. The Analytics module/software, which runs alongside the Service module/software, processes the fundus images and returns information on the image quality and the presence or absence of mtmDR to the Service module/software. The Service module/software then returns the results to the Client module/software.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Fundus retinal images, color fundus images
Anatomical Site
Eye
Indicated Patient Age Range
Adults (subjects ≥22 years of age were recruited to the study)
Intended User / Care Setting
Health care providers / Hospitals, Clinics
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
In Study 1 a total of 317 subjects were enrolled in the study. The baseline demographic data and characteristics analysis showed that the mean age was 55 years, 51% were male and 49% were female, 26% were African-American, 47% White and 22% Hispanic or Latino, the remainder were of other racial/ethnic origins. Approximately 94% of the subjects in the study were diagnosed with type 2 diabetes while approximately 6% percent were diagnosed with type 1 diabetes. Average duration of diabetes since was 10 years (SD=8). Mean HbA 1c level was 8.2% (SD=2.1).
In Study 2 a total of 362 subjects were enrolled in the study. The baseline demographic data and characteristics analysis showed that the mean age was 58 years, 45% were male and 55% were female, 19% were African-American, 46% White and 30% Hispanic or Latino, the remainder were of other racial/ethnic origins. Approximately 97% of the subjects in the study were diagnosed with type 2 diabetes while approximately 3% percent were diagnosed with type 1 diabetes. Average duration of diabetes since was 10 years (SD=8). Mean HbA 1c level was 8.3% (SD=2.2).
For both studies: The study populations represented the target population for the use of this device and consisted of stable, visually asymptomatic subjects who were previously diagnosed with diabetes and had no prior diagnosis of DR. Subjects participated in a routine retinal screening test for diabetic retinopathy (DR) in hospitals, primary care clinics or medical research centers. Patients of both genders, all ethnicities and ≥22 years of age were recruited to the study. General patient demographics, medical history, concomitant medications, fundoscopy system used, OCT system used, etc., were obtained for each study subject.
Novice operators, who had not previously performed ocular imaging, obtained fundoscopy images from each eye of the patient, using the Optomed Aurora funduscopic camera. Upon submission of the fundoscopy images to the AEYE-DS client software, a diagnostic result (and PDF diagnostics report) of more than mild DR (mtmDR) detected or more than mild DR not detected was produced. A result of 'insufficient quality' was determined if the novice operator reached a maximum of 6 image submission attempts and one or more of the images was still of insufficient image quality. After the novice operator generated an AEYE-DS diagnostic output, each participant underwent additional retinal imaging captured by a professional ophthalmic photographer, to obtain dilated four widefield color fundus images, lens photography for media opacity assessment and macular optical coherence tomography (OCT) imaging. The professional images were sent to an independent reading center where the severity of retinopathy and clinically significant diabetic macular edema (DME) were determined according to the Early Treatment for Diabetic Retinopathy Study severity (ETDRS) scale. The Reading Center diagnostic results formed the reference standard (ground truth) for the study. As part of the final clinical assessment, each participant was categorized as mtmDR+ or mtmDR-, based on the worst of two eyes. The final clinical assessment based on the worst of two eyes was compared with the AEYE- DS output, at the participant level.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
The AEYE-DS device performance for automated detection of more than mild Diabetic Retinopathy (mtmDR) from digital funduscopic images was demonstrated in several clinical studies. Both studies were prospective, multi-center, single-arm, blinded studies. The studies were conducted at study sites in the United States.
Study 1:
- Sample Size: 317 subjects
- Sensitivity: 92% [C1:79%; 97% ] (fundus and multimodality based)
- Specificity: 94% [CI: 90%; 96%] (fundus and multimodality based)
- Positive Predictive Value (PPV): 68% [CI: 54%; 79%] (fundus and multimodality based)
- Negative Predictive Value (NPV): 99% [CI: 96%; 100%] (fundus and multimodality based)
- Key results: The AEYE-DS device successfully identified all participants with ETDRS level 43 or higher. Further sub-analyses showed that there were no significant effects of age, sex, race/ethnicity, HbA1c, diabetic duration since diagnosis, lens status, positive ETDRS levels, or pharmacological dilation on sensitivity and specificity. 89% of the subjects did not require pharmacologic dilation. Imageability was 99% [98%; 100%].
Study 2:
- Sample Size: 362 subjects
- Sensitivity: 93% [CI:80%; 97%] (fundus based) and 90% [CI:77%; 96%] (multi-modality based)
- Specificity: 89% [CI: 85%; 92%] (fundus based) and 89% [CI: 84%; 92%] (multi-modality based)
- Positive Predictive Value (PPV): 53% [CI: 41%; 64%] (for both modalities)
- Negative Predictive Value (NPV): 99% [CI: 97%; 100%] (fundus based) and 98% [CI: 96%; 99%] (multi-modality based)
- Key results: The AEYE-DS device successfully identified all participants with ETDRS level 43 or higher. Further sub-analyses showed that there were no significant effects of sex, race/ethnicity, HbA1c, diabetic duration since diagnosis, lens status, positive ETDRS levels, or pharmacological dilation on sensitivity and specificity. The only sub-analyses which showed a significant effect was age, with the lower age group (
§ 886.1100 Retinal diagnostic software device.
(a)
Identification. A retinal diagnostic software device is a prescription software device that incorporates an adaptive algorithm to evaluate ophthalmic images for diagnostic screening to identify retinal diseases or conditions.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Software verification and validation documentation, based on a comprehensive hazard analysis, must fulfill the following:
(i) Software documentation must provide a full characterization of technical parameters of the software, including algorithm(s).
(ii) Software documentation must describe the expected impact of applicable image acquisition hardware characteristics on performance and associated minimum specifications.
(iii) Software documentation must include a cybersecurity vulnerability and management process to assure software functionality.
(iv) Software documentation must include mitigation measures to manage failure of any subsystem components with respect to incorrect patient reports and operator failures.
(2) Clinical performance data supporting the indications for use must be provided, including the following:
(i) Clinical performance testing must evaluate sensitivity, specificity, positive predictive value, and negative predictive value for each endpoint reported for the indicated disease or condition across the range of available device outcomes.
(ii) Clinical performance testing must evaluate performance under anticipated conditions of use.
(iii) Statistical methods must include the following:
(A) Where multiple samples from the same patient are used, statistical analysis must not assume statistical independence without adequate justification.
(B) Statistical analysis must provide confidence intervals for each performance metric.
(iv) Clinical data must evaluate the variability in output performance due to both the user and the image acquisition device used.
(3) A training program with instructions on how to acquire and process quality images must be provided.
(4) Human factors validation testing that evaluates the effect of the training program on user performance must be provided.
(5) A protocol must be developed that describes the level of change in device technical specifications that could significantly affect the safety or effectiveness of the device.
(6) Labeling must include:
(i) Instructions for use, including a description of how to obtain quality images and how device performance is affected by user interaction and user training;
(ii) The type of imaging data used, what the device outputs to the user, and whether the output is qualitative or quantitative;
(iii) Warnings regarding image acquisition factors that affect image quality;
(iv) Warnings regarding interpretation of the provided outcomes, including:
(A) A warning that the device is not to be used to screen for the presence of diseases or conditions beyond its indicated uses;
(B) A warning that the device provides a screening diagnosis only and that it is critical that the patient be advised to receive followup care; and
(C) A warning that the device does not treat the screened disease;
(v) A summary of the clinical performance of the device for each output, with confidence intervals; and
(vi) A summary of the clinical performance testing conducted with the device, including a description of the patient population and clinical environment under which it was evaluated.
0
April 23, 2024
Image /page/0/Picture/1 description: The image shows the logos of the Department of Health and Human Services and the Food and Drug Administration (FDA). The Department of Health and Human Services logo is on the left, and the FDA logo is on the right. The FDA logo includes the FDA acronym in a blue square, followed by the words "U.S. Food & Drug Administration" in blue text.
AEYE Health Inc. % Ahava Stein Regulatory Consultant A. Stein - Regulatory Affairs Consulting Ltd. 18 Hata'as Str. Kfar Saba, 4442518 Israel
Re: K240058
Trade/Device Name: Aeye-ds Regulation Number: 21 CFR 886.1100 Regulation Name: Retinal Diagnostic Software Device Regulatory Class: Class II Product Code: PIB Dated: January 8, 2024 Received: March 15, 2024
Dear Ahava Stein:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device"
1
(https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30. Design controls; 21 CFR 820.90. Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review. the OS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely.
Elvin Y. Ng -S
Elvin Ng Assistant Director DHT1A: Division of Ophthalmic Devices OHT1: Office of Ophthalmic, Anesthesia,
2
Respiratory, ENT and Dental Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
3
Indications for Use
510(k) Number (if known) K240058
Device Name AEYE-DS
Indications for Use (Describe)
The AEYE-DS is indicated for use by health care providers to automatically detect more than mild diabetic retinopathy (mtmDR) in adults diagnosed with diabetes who have not been previously diagnosed with diabetic retinopathy. The AEYE-DS is indicated for use with the Topcon NW400 camera and the Optomed Aurora camera.
Type of Use (Select one or both, as applicable)
| Prescription Use (Part 21 CFR 801 Subpart D) | Over-The-Counter Use (21 CFR 801 Subpart C) |
|---|---|
| ---------------------------------------------- | --------------------------------------------- |
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510(K) SUMMARY
AEYE-DS DEVICE
510(k) Number K240058
| Applicant Name: | AEYE Health Inc. |
|---|---|
| Contact Person: | Zack Dvey-Aharon, Ph.D. |
| Contact: | AEYE Health Inc. |
| 1501 Broadway | |
| New York, NY, 10036 USA | |
| E-mail: info@aeyehealth.com | |
| +1 866 262 7343 | |
| Date Prepared: | April 17, 2024 |
| Trade Name: | AEYE-DS |
| Classification Name: | 21 CFR 886.1100; (Product Code PIB) |
| Retinal Diagnostic Software Device | |
| Classification: | Class II |
Predicate Device:
The AEYE-DS device is substantially equivalent to the following predicate device:
| Device | Manufacturer | 510(k) No. |
|---|---|---|
| AEYE-DS | AEYE Health Inc. | K221183 |
Device Description:
AEYE-DS is a retinal diagnostic software device that incorporates an algorithm to evaluate retinal images for diagnostic screening to identify retinal diseases or conditions. Specifically, the AEYE-DS is designed to perform diagnostic screening for the condition of more-than-mild diabetic retinopathy (mtmDR).
The AEYE-DS is comprised of 5 software components: (1) Client; (2) Service; (3) Analytics; (4) Reporting and Archiving; and (5) System Security. The device configuration of these modules is presented in the figure below, indicating which components are local to the user and which are remotely located.
5
Image /page/5/Figure/0 description: This image shows a diagram of a system for healthcare providers to use. The diagram is split into two sections, "Local to User" and "Remote". The "Local to User" section includes a healthcare provider, a user computer, and a client module. The "Remote" section includes secure servers, an analytics module, a service module, a system security module, and a reporting and archiving module. The diagram shows how fundus images are transferred between the different modules.
Figure 1: Device Configuration
The AEYE-DS device is based on the main technological principle of Artificial Intelligence (AI) software as a medical device. The software as a medical device uses artificial intelligence technology to analyze specific disease features from fundus retinal images for diagnostic screening of diabetic retinopathy.
The AEYE-DS device is based on the principle of operation, whereby a fundus camera is used to obtain retinal images. The fundus camera is attached to a computer, where the Client module/software is installed. The Client module/software guides the user to acquire the images and enables the user to interact with the server-based analysis software over a secure internet connection. Using the Client module/software, users identify the fundus images per eye to be dispatched to the Service module/software. The Service module/software is installed on a server hosted at a secure datacenter, receives the fundus images and transfers them to the Analytics module/software. The Analytics module/software, which runs alongside the Service module/software, processes the fundus images and returns information on the image quality and the presence or absence of mtmDR to the Service module/software. The Service module/software then returns the results to the Client module/software.
Intended Use/Indication for Use:
The AEYE-DS is indicated for use by health care providers to automatically detect more than mild diabetic retinopathy (mtmDR) in adults diagnosed with diabetes who have not been
6
previously diagnosed with diabetic retinopathy. The AEYE-DS is indicated for use with the Topcon NW400 camera and the Optomed Aurora camera.
Prescription Use only: Federal law restricts this device for sale by or on the order of a physician.
Performance Standards:
The AEYE-DS device complies with the following FDA recognized consensus standards:
- Software Verification and Validation Testing Software verification and validation . testing were conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The software for this device was considered as requiring "Enhanced Documentation", since a failure or latent flaw in the software could result in serious injury to the patient through incorrect or delayed information or through the action of a care provider.
- IEC 62304 Edition 1.1 2015-06 CONSOLIDATED VERSION Medical device software . - Software life cycle processes
- ISO 14971 Medical devices Application of risk management to medical devices .
Non-Clinical (Bench) Performance Data:
Software validation testing in compliance with FDA guidelines for software validation and IEC 62304 standard requirements was conducted.
The software hazard analysis was performed as part of the system hazard analysis. The hazards of the software influencing the operations of the system, the hardware problems impairing the software's integrity, and the incorrect operations of the system by the user that could affect the software's correct functioning, were handled as part of the system hazard analysis. The risks and the risk reductions are found in the Risk Analysis for the AEYE-DS device.
The cybersecurity requirements for the AEYE-DS device were identified according to the Content of Premarket Submissions for Management of Cybersecurity in Medical Devices. A threat analysis was performed and documented in the Cybersecurity Report.
The results of the performance tests, including software validation, cybersecurity and hazard analysis demonstrated that the AEYE-DS device is substantially equivalent to the predicate devices.
Animal Performance Data / Histology Data:
Not Applicable
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Clinical Performance Data:
The AEYE-DS device performance for automated detection of more than mild Diabetic Retinopathy (mtmDR) from digital funduscopic images was demonstrated in several clinical studies. Both studies were prospective, multi-center, single-arm, blinded studies. The studies were conducted at study sites in the United States. The study populations represented the target population for the use of this device and consisted of stable, visually asymptomatic subjects who were previously diagnosed with diabetes and had no prior diagnosis of DR. Subjects participated in a routine retinal screening test for diabetic retinopathy (DR) in hospitals, primary care clinics or medical research centers. Patients of both genders, all ethnicities and ≥22 years of age were recruited to the study. General patient demographics, medical history, concomitant medications, fundoscopy system used, OCT system used, etc., were obtained for each study subject.
Novice operators, who had not previously performed ocular imaging, obtained fundoscopy images from each eye of the patient, using the Optomed Aurora funduscopic camera. Upon submission of the fundoscopy images to the AEYE-DS client software, a diagnostic result (and PDF diagnostics report) of more than mild DR (mtmDR) detected or more than mild DR not detected was produced. A result of 'insufficient quality' was determined if the novice operator reached a maximum of 6 image submission attempts and one or more of the images was still of insufficient image quality. After the novice operator generated an AEYE-DS diagnostic output, each participant underwent additional retinal imaging captured by a professional ophthalmic photographer, to obtain dilated four widefield color fundus images, lens photography for media opacity assessment and macular optical coherence tomography (OCT) imaging. The professional images were sent to an independent reading center where the severity of retinopathy and clinically significant diabetic macular edema (DME) were determined according to the Early Treatment for Diabetic Retinopathy Study severity (ETDRS) scale. The Reading Center diagnostic results formed the reference standard (ground truth) for the study. As part of the final clinical assessment, each participant was categorized as mtmDR+ or mtmDR-, based on the worst of two eyes. The final clinical assessment based on the worst of two eyes was compared with the AEYE- DS output, at the participant level.
In Study 1 a total of 317 subjects were enrolled in the study. The baseline demographic data and characteristics analysis showed that the mean age was 55 years, 51% were male and 49% were female, 26% were African-American, 47% White and 22% Hispanic or Latino, the remainder were of other racial/ethnic origins. Approximately 94% of the subjects in the study were diagnosed with type 2 diabetes while approximately 6% percent were diagnosed with type 1 diabetes. Average duration of diabetes since was 10 years (SD=8). Mean HbA 1c level was 8.2% (SD=2.1).
The results of sensitivity and specificity in Study 1 based on images obtained from the handheld Optomed Aurora camera were 92% [C1:79%; 97% ] and 94% [CI: 90%; 96%] (fundus and multimodality based), respectively. The AEYE-DS device successfully identified all participants with ETDRS level 43 or higher. Positive Predictive Value (PPV) was 68% [CI: 54%; 79%] and the
8
Negative Predictive Value (NPV) was 99% [CI: 96%; 100%]. Further sub-analyses showed that there were no significant effects of age, sex, race/ethnicity, HbA1c, diabetic duration since diagnosis, lens status, positive ETDRS levels, or pharmacological dilation on sensitivity and specificity. 89% of the subjects did not require pharmacologic dilation.
In Study 2 a total of 362 subjects were enrolled in the study. The baseline demographic data and characteristics analysis showed that the mean age was 58 years, 45% were male and 55% were female, 19% were African-American, 46% White and 30% Hispanic or Latino, the remainder were of other racial/ethnic origins. Approximately 97% of the subjects in the study were diagnosed with type 2 diabetes while approximately 3% percent were diagnosed with type 1 diabetes. Average duration of diabetes since was 10 years (SD=8). Mean HbA 1c level was 8.3% (SD=2.2).
The results in Study 2 based on images obtained from the handheld Optomed Aurora camera were 93% [CI:80%; 97%] (fundus based) and 90% (CI:77%; 96%) (multi-modality based) for sensitivity and 89% [CI: 85%; 92%] (fundus based) and 89% [CI: 84%; 92%] (multi-modality based) for specificity. The AEYE-DS device successfully identified all participants with ETDRS level 43 or higher. Positive Predictive Value (PPV) was 53% [CI: 41%; 64%] (for both modalities) and the Negative Predictive Value (NPV) was 99% [CI: 97%; 100%] (fundus based) and 98% [CI: 96%; 99%] (multi-modality based). Further sub-analyses showed that there were no significant effects of sex, race/ethnicity, HbA1c, diabetic duration since diagnosis, lens status, positive ETDRS levels, or pharmacological dilation on sensitivity and specificity. The only sub-analyses which showed a significant effect was age, with the lower age group (