K223357 · Eyenuk, Inc. · PIB · Jun 16, 2023 · Ophthalmic
Device Facts
Record ID
K223357
Device Name
EyeArt v2.2.0
Applicant
Eyenuk, Inc.
Product Code
PIB · Ophthalmic
Decision Date
Jun 16, 2023
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 886.1100
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K223357 · Jun 16, 2023
EyeArt v2.2.0
Eyenuk, Inc.
Retrospective analysis of clinical data from pivotal study (Protocol EN-01)
The retrospective study evaluated the clinical performance (sensitivity, specificity, imageability) of the modified EyeArt v2.2.0 software using existing data from 1,310 eyes of 655 participants enrolled in a prior pivotal study.
Prospective, multi-center clinical study (Protocol EN-01b): 336 eyes of 171 participants
>1 (certified graders)
Indications for Use
EyeArt is indicated for use by healthcare providers to automatically detect more than mild diabetic retinopathy and vision-threatening diabetic retinopathy (severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy and/or diabetic macular edema) in eyes of adults diagnosed with diabetes who have not been previously diagnosed with diabetic retinopathy. EyeArt is indicated for use with Canon CR-2 AF, Canon CR-2 Plus AF, and Topcon NW400 cameras.
Device Story
EyeArt is a software-as-a-medical-device (SaMD) for automated diabetic retinopathy (DR) screening. It processes two retinal fundus images per eye (macula-centered and optic nerve head-centered) captured by specific fundus cameras (Canon CR-2 AF/Plus AF, Topcon NW400). The system comprises a local Client (GUI for image capture/transfer), a Server (secure data handling), and a remote Analysis Computation Engine. The engine uses AI/deep learning to assess image quality and detect DR. The Client provides live image quality feedback to operators to ensure successful capture. Results are returned to the healthcare provider to assist in clinical decision-making regarding patient referral or follow-up. The device is intended for use in clinical settings by healthcare providers.
Clinical Evidence
Prospective multi-center study (N=246) evaluated diagnostic accuracy and precision using Canon and Topcon cameras. Primary endpoints were sensitivity and specificity for mtmDR and vtDR detection against a clinical reference standard (WRC grading). For mtmDR, sensitivity was 95.9% (Canon) and 94.4% (Topcon); specificity was 86.4% (Canon) and 91.1% (Topcon). For vtDR, sensitivity was 96.8% (both cameras); specificity was 91.7% (Canon) and 91.6% (Topcon). Retrospective analysis of 1310 eyes confirmed performance consistency. Precision (repeatability/reproducibility) was established for the Topcon NW400.
Technological Characteristics
AI-based software as a medical device (SaMD). Inputs: 1.69 MP+ retinal fundus images (45° FOV, 2 per eye). Architecture: Client-Server-Analysis Computation Engine. Connectivity: Internet-required for remote analysis. Cybersecurity: Encryption at rest/transit, authentication/authorization protocols. Software level of concern: Major.
Indications for Use
Indicated for adults with diabetes, not previously diagnosed with diabetic retinopathy, to detect more than mild or vision-threatening diabetic retinopathy.
Regulatory Classification
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.
Special Controls
In combination with the general controls of the FD&C Act, the retinal diagnostic software device is subject to the following special controls:
*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.
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June 16, 2023
Eyenuk, Inc. Kaushal Solanki, PhD CEO 5850 Canoga Ave, Suite 250 Los Angeles, California 91367
Re: K223357
Trade/Device Name: EyeArt v2.2.0 Regulation Number: 21 CFR 886.1100 Regulation Name: Retinal Diagnostic Software Device Regulatory Class: Class II Product Code: PIB Dated: May 5, 2023 Received: May 5, 2023
Dear Dr. Kaushal Solanki:
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 (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 located 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.
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
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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 803) for devices or postmarketing safety reporting (21 CFR 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 (OS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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, Respiratory, ENT and Dental Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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# Indications for Use
510(k) Number (if known) K223357
Device Name EyeArt
### Indications for Use (Describe)
EyeArt is indicated for use by healthcare providers to automatically detect more than mild diabetic retinopathy and visionthreatening diabetic retinopathy (severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy and/or diabetic macular edema) in eyes of adults diabetes who have not been previously diagnosed with diabetic retinopathy. EyeArt is indicated for use with Canon CR-2 Plus AF, and Topcon NW400 caneras.
Type of Use (Select one or both, as applicable)
| <div style="display:flex; align-items:center;"> <div style="margin-right:5px;">☑</div> <div>Prescription Use (Part 21 CFR 801 Subpart D)</div> </div> | <div style="display:flex; align-items:center;"> <div style="margin-right:5px;">☐</div> <div>Over-The-Counter Use (21 CFR 801 Subpart C)</div> </div> |
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## 510(k) SUMMARY Eyenuk's EyeArt
### I. Submitter
Eyenuk, Inc. 5850 Canoga Ave., Suite 250 Los Angeles, CA, 91367 Phone: +1 (818) 835-3585
Contact Person: Kaushal Solanki
Date Prepared: 15 June 2023
#### Device II.
Name of Device: EyeArt v2.2.0 Classification Name: Retinal diagnostic software device Regulatory Class: Class II Regulation: 21 CFR 886.1100 Product Code: PIB
#### III. Predicate device
Trade name of the device: EyeArt Manufacturer's Name: Eyenuk, Inc. Premarket notification number: K200667
#### IV. Device Description
EyeArt is a software as a medical device that consists of three components - Client, Server, and Analysis Computation Engine (Figure 1).
Image /page/3/Figure/12 description: The image shows a diagram of a system for analyzing fundus images. The system is divided into two parts: local to the user and remote. The local part includes a patient, a camera, and a user computer with the EyeArt client. The remote part includes a secure virtual private network with the EyeArt server and the EyeArt analysis computation engine.
Figure 1: EyeArt components: Client, Server, and Analysis Compute Engine.
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A retinal fundus camera, used to capture retinal fundus images of the patient, is connected to a computer where the EyeArt Client software is installed. The EyeArt Client software provides a graphical user interface (GUI) that allows the EyeArt operator to transfer the appropriate fundus images to and receive results from the remote EyeArt Analysis Computation Engine through the EyeArt Server. The EyeArt Analysis Computation Engine is installed on remote computer(s) in a secure data center and uses artificial intelligence algorithms to analyze the fundus images and return results. EyeArt is intended to be used with retinal fundus images of resolution 1.69 megapixels or higher captured using one of the indicated retinal fundus cameras (Canon CR-2 AF, Canon CR-2 Plus AF, and Topcon NW400) with 45 degrees field of view. EyeArt is specified for use with two retinal fundus images per eye: optic nerve head (ONH) centered and macula centered. For each patient eye, the EyeArt results separately indicate whether "more than mild diabetic retinopathy (mtmDR)" and "vision-threatening diabetic retinopathy (vtDR)" are detected. "More than mild diabetic retinopathy" is defined as the presence of moderate non-proliferative diabetic retinopathy or worse on the International Clinical Diabetic Retinopathy (ICDR) severity scale and/or the presence of diabetic macular edema. "Vision-threatening diabetic retinopathy" is defined as the presence of severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy on the ICDR severity scale and/or the presence of diabetic macular edema. Description of EyeArt components is provided below.
- . EyeArt Client: This component is installed on the computer connected to a fundus camera and used by the EyeArt operator. It allows the operator to transfer images to the EyeArt Analysis Computation Engine and receive results. Its functioning requires an internet connection. If images from a patient encounter cannot be analyzed, due to poor image quality or due to lack of all required image fields, live image quality feedback is provided to the operator to help successfully obtain results upon image recapture.
- . EyeArt Server: This component provides an interface that securely handles incoming requests and securely stores user information including images and results. It enables the EyeArt Client to use the EyeArt Analysis Computation Engine through an application programming interface (API).
- EyeArt Analysis Computation Engine: This component analyzes the images to determine exam . quality and detect mtmDR and vtDR. It consists of an ensemble of clinically aligned machine learning (including deep learning) algorithms.
#### V. Intended Use / Indications for Use
The EyeArt v2.2.0 has the same intended use as the predicate device and all other devices regulated under 21 CFR 886.1100, which is "to evaluate ophthalmic images for diagnostic screening to identify retinal diseases or conditions." The Indication for Use (IFU) statement for EyeArt v2.2.0 is the following:
EyeArt is indicated for use by healthcare providers to automatically detect more than mild diabetic retinopathy and vision-threatening diabetic retinopathy (severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy and/or diabetic macular edema) in eyes of adults
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diagnosed with diabetes who have not been previously diagnosed with diabetic retinopathy. EyeArt is indicated for use with Canon CR-2 AF, Canon CR-2 Plus AF, and Topcon NW400 cameras.
The IFU statement for EyeArt v2.2.0 is not substantially different from that of the predicate device and the differences do not alter the intended use. The main difference in the IFU statement for EyeArt v2.2.0 versus that of the predicate is the inclusion of an additional fundus camera, the Topcon NW400. This difference is supported with non-clinical performance testing.
### VI. Purpose of this submission
The purpose of this 510(k) notification is to modify EyeArt (software version 2.1.0) and update it to software version 2.2.0. The technological differences between the subject and predicate device are summarized as follows:
- Updated image quality assessment module in the EyeArt Analysis Computation Engine;
- Support for additional Topcon NW400 camera model, in addition to previously cleared Canon CR-2 AF and CR-2 Plus AF;
- Live image quality feedback to provide quality feedback to the EyeArt operator as they capture ● images:
- Ability to save a patient encounter entry and retry imaging after pupil dilation.
#### VII. Comparison of the technological characteristics with the predicate
The main technological principle for the subject and predicate devices is artificial intelligence (AI)based technology to analyze specific features from retinal images. Both include the same general components: the EyeArt Client installed on the computer used by the EyeArt operator to help capture good quality retinal images, transfer the images for analyses, and receive results; EyeArt Analysis Computation Engine to analyze images and determine exam quality and detect DR; and EyeArt Server to provide a secure interface between the Client and Analysis Computation Engine.
| EyeArt v2.2.0<br>(Subject device) | EyeArt v2.1.0<br>(K200667, Predicate device) | Discussion |
|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------|
| EyeArt is indicated for use by healthcare<br>providers to automatically detect more than<br>mild diabetic retinopathy and vision-<br>threatening diabetic retinopathy (severe non-<br>proliferative diabetic retinopathy or<br>proliferative diabetic retinopathy and/or<br>diabetic macular edema) in eyes of adults<br>diagnosed with diabetes who have not been<br>previously diagnosed with diabetic<br>retinopathy. | EyeArt is indicated for use by healthcare<br>providers to automatically detect more than<br>mild diabetic retinopathy and vision-<br>threatening diabetic retinopathy (severe non-<br>proliferative diabetic retinopathy or<br>proliferative diabetic retinopathy and/or<br>diabetic macular edema) in eyes of adults<br>diagnosed with diabetes who have not been<br>previously diagnosed with more than mild<br>diabetic retinopathy. | Main<br>difference is<br>the addition<br>of the Topcon<br>NW400<br>fundus<br>camera,<br>supported by<br>non-clinical<br>and clinical |
### Table 1: Comparison of the IFU statements of the EyeArt subject device and the predicate device
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# EyeArt 510(k) Summary (K223357)
| EyeArt v2.2.0<br>(Subject device) | | EyeArt v2.1.0<br>(K200667, Predicate device) | Discussion |
|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------|------------|
| EyeArt is indicated for use with Canon CR-2<br>AF, Canon CR-2 Plus AF, and Topcon NW400<br>cameras. | EyeArt is indicated for use with Canon CR-2<br>AF and Canon CR-2 Plus AF cameras in both<br>primary care and eye care settings. | performance<br>testing. | |
## Table 2: Comparison of other technological elements of the EyeArt device and the predicate device.
| | EyeArt v2.2.0 | EyeArt v2.1.0 | Discussion |
|---------------|-------------------------------|-------------------------------|-------------------------------|
| | (Subject device) | (K200667, Predicate device) | |
| Technological | Artificial Intelligence | Artificial Intelligence | Equivalent |
| principle | software as a medical device | software as a medical device | |
| Inputs | Macula and disc centered | Macula and disc centered | Equivalent |
| | retinal fundus images with | retinal fundus images with | |
| | 45° field of view, 2 per eye | 45° field of view, 2 per eye | |
| Main outputs | Detection of diabetic | Detection of diabetic | Equivalent |
| | retinopathy in each eye: | retinopathy in each eye: | |
| | More than mild diabetic | More than mild diabetic | |
| | retinopathy (mtmDR): one | retinopathy (mtmDR): one | |
| | of negative, positive, or | of negative for mtmDR, | |
| | ungradable. | mtmDR detected, or | |
| | | ungradable. | |
| | Vision-threatening diabetic | | |
| | retinopathy (vtDR): one of | Vision-threatening diabetic | |
| | negative, positive, or | retinopathy (vtDR): one of | |
| | ungradable. | negative for vtDR, vtDR | |
| | | detected, or ungradable. | |
| | | | |
| | | | |
| | | | |
| | | | |
| Architecture | Client software (user facing) | Client software (user facing) | Equivalent |
| | transfers images to and | transfers images to and | |
| | receives results from | receives results from | |
| | Analysis Computation | Analysis Computation | |
| | Engine through Server. | Engine through Server. | |
| Indicated | Canon CR-2 AF, Canon CR- | Canon CR-2 AF and Canon | The Canon CR-2 AF and |
| Cameras | 2 Plus AF, and Topcon | CR-2 Plus AF | Canon CR-2 Plus AF |
| | NW400 | | cameras are used to capture |
| | | | macula and disc centered |
| | | | retinal images with 45° field |
| | | | of view (2 per eye) for both |
| | | | the subject and the predicate |
| | | | device. The clinical |
| | | | performance data support the |
| | | | use of EyeArt with the |
| | | | additional indicated Topcon |
| | | | NW400 camera. |
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## VIII. Performance data: non-clinical testing
EyeArt (software version v2.2.0) was identified as having a major level of concern as defined in the FDA guidance document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices."
Verification and validation activities at unit, integration, and system level were performed. In all instances. EveArt functioned as intended and results observed were as expected (i.e., all specifications were met).
Comprehensive risk analysis has been conducted for EyeArt with identification and detailed characterization of the hazards including their causes and severity. Adequate risk control measures have been designed and implemented to mitigate all identified hazards to acceptable levels. EyeArt also implements comprehensive cybersecurity measures for data confidentiality, data integrity, and data and service availability. Designed to meet industry standard cybersecurity best practices, EyeArt ensures that data remains secure (with encryption during transit and at rest) and private (with authentication and authorization protocols enabling access). EyeArt has been designed to provide results that are aligned with the clinical practice recommendations for the ophthalmic care of patients with diabetes and has been developed in a clinically aligned framework.
#### IX. Performance testing: clinical testing
## A. Prospective clinical study
A prospective, multi-center clinical study (Protocol EN-01b) was conducted to evaluate the diagnostic accuracy and precision of EyeArt v2.2.0 with the Topcon NW400 and Canon CR-2 AF cameras. Eligible adults age 22 or older with known diagnosis of diabetes mellitus (DM) who provided informed consent were enrolled across six sites that did not contribute data used for training or development of EyeArt. Main exclusion criteria were persistent visual impairment in one or both eyes, contraindication to fundus photography or pharmacologic mydriasis, and/or history of retinal vascular occlusion, ocular injections, laser treatments to the retina, or prior intraocular surgery other than uncomplicated cataract extraction. Participants underwent EyeArt imaging with the study cameras. Multiple EyeArt imaging sessions were performed by multiple operators on multiple cameras to collect data for estimation of precision. Pupils were pharmacologically dilated as needed per instructions in the User Manual. Participants then underwent dilated 4-widefield stereo fundus imaging by a certified photographer. The clinical reference standard (CRS) was determined by experienced and certified graders at the University of Wisconsin Reading Center (WRC) per the Early Treatment for Diabetic Retinopathy Study (ETDRS) severity scale on the dilated 4-wide field stereo fundus images. Graders and WRC study staff were masked to participant history and EyeArt results. The WRC grading was used to determine the "ground truth" for study eyes, which was determined as follows:
- . Clinical reference for more than mild DR (mtmDR):
- o positive if ETDRS level was 35 or greater (but not equal to 90) or DME grade was present
- negative if ETDRS levels were 10-20 and DME grade was absent o
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- ungradable if ETDRS level was 90, or DME grade was questionable or ungradable with o ETDRS level 10-20
- Clinical reference for vision-threatening DR (vtDR): .
- o positive if ETDRS level was 53 or greater (but not equal to 90) or DME grade was present
- negative if ETDRS levels were 10-47 and DME grade was absent o
- ungradable if ETDRS level was 90, or DME grade was questionable or ungradable with o ETDRS level 10-47
246 participants were enrolled and four participants either withdrew consent (N=1) or did not meet eligibility criteria (N=3). Data from 336 eyes of 171 participants were included in the accuracy and agreement analyses and data from 264 eyes from 132 participants were included in the precision analyses. In the accuracy analysis population, the mean age was 58.8±13.7 years (range 23-83). 46.2% were women (79/171) and 53.8% (92/171) were men. 86% (147/171) of the cohort was White, 10.5% (18/171) Black, 0.6% (1/171) Asian, 2.3% (4/171) other race, 0.6% (1/171) Native Hawaiian or other Pacific Islander. 11.7% (20/171) were Hispanic/Latino. 70.8% (121/171) have type 2 DM and 29.2% (50/171) have type 1 DM. Mean DM duration was 16.4±11.8 years (range zero - 53 years) and mean hemoglobin A1c (HbA1c) level from the prior three months was 7.7%±1.5% (range 5.6% -14.1%). 22.0% (74/336) and 9.2% (31/336) of study eyes were mtmDR+ and vtDR+ by CRS grading, respectively. 3.0% (10/336) and 3.6% (12/336) of study eyes were designated ungradable for mtmDR and vtDR (respectively) by CRS grading.
Canon CR-2 AF/Plus AF and Topcon NW400 images from 90.4% of participants received EyeArt analysis results without pupil dilation. After dilation as needed, 99.0% of participants with Canon CR-2 AF/Plus AF images and 98.9% with Topcon NW400 images yielded in EyeArt analysis results.
Table 3 provides the sensitivity, specificity, positive value (PPV), and negative predictive value (PPV) using EyeArt v2.2.0 with the Canon CR-2 AF/Plus AF and Topcon NW400 cameras. Best-case (images unanalyzable by EyeArt are imputed as being in agreement with the CRS determination) and worst-case (images unanalyzable by EyeArt are imputed as being in disagreement with the CRS determination) sensitivity and specificity are also shown.
| | mtmDR | | vtDR | |
|-----------------------------------------|-------------------------------------------------|-------------------------------------|-------------------------------------------------|--------------------------------------|
| EN-01b entire<br>analysis<br>population | EyeArt v2.2.0 with<br>Canon CR-2 AF<br>/Plus AF | EyeArt v2.2.0 with<br>Topcon NW400 | EyeArt v2.2.0 with<br>Canon CR-2 AF<br>/Plus AF | EyeArt v2.2.0 with<br>Topcon NW400 |
| Sensitivity | 95.9%<br>[90.4% - 100%]<br>(70/73) | 94.4%<br>[88.3% - 98.8%]<br>(68/72) | 96.8%<br>[90.0% - 100%]<br>(30/31) | 96.8%<br>[89.5% - 100%]<br>(30/31) |
| Best-case<br>sensitivity | 95.9%<br>[90.5% - 100.0%]<br>(71/74) | 94.6%<br>[88.6% - 98.8%]<br>(70/74) | 96.8%<br>[90.9% - 100.0%]<br>(30/31) | 96.8%<br>[89.5% - 100.0%]<br>(30/31) |
| Worst-case<br>sensitivity | 94.6%<br>[88.9% - 98.8%]<br>(70/74) | 91.9%<br>[84.8% - 97.3%]<br>(68/74) | 96.8%<br>[90.0% - 100.0%]<br>(30/31) | 96.8%<br>[89.5% - 100.0%]<br>(30/31) |
Table 3: Key performance measures of EyeArt with Canon CR-2 AF/Plus AF and Topcon NW400 for mtmDR and vtDR detection.
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| | mtmDR | | vtDR | |
|------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------|----------------------------------------|-------------------------------------------------|---------------------------------------|
| EN-01b entire<br>analysis<br>population | EyeArt v2.2.0 with<br>Canon CR-2 AF<br>/Plus AF | EyeArt v2.2.0 with<br>Topcon NW400 | EyeArt v2.2.0 with<br>Canon CR-2 AF<br>/Plus AF | EyeArt v2.2.0 with<br>Topcon NW400 |
| Specificity | 86.4%<br>[81.2% - 91.1%]<br>(216/250) | 91.1%<br>[86.8% - 94.8%]<br>(226/248) | 91.7%<br>[87.7% - 95.2%]<br>(266/290) | 91.6%<br>[87.5% - 95.1%]<br>(263/287) |
| Best-case<br>specificity | 86.5%<br>[81.3% - 91.2%]<br>(218/252) | 91.3%<br>[87.1% - 94.9%]<br>(230/252) | 91.8%<br>[87.9% - 95.3%]<br>(269/293) | 91.8%<br>[87.8% - 95.1%]<br>(269/293) |
| Worst-case<br>specificity | 85.7%<br>[80.6% - 90.3%]<br>(216/252) | 89.7%<br>[85.05% - 93.3%]<br>(226/252) | 90.8%<br>[86.7% - 94.7%]<br>(266/293) | 89.8%<br>[85.7% - 93.6%]<br>(263/293) |
| PPV – Positive<br>Predictive Value | 67.3%<br>[55.9% - 77.4%]<br>(70/104) | 75.6%<br>[64.6% - 85.4%]<br>(68/90) | 55.6%<br>[39.2% - 72.0%]<br>(30/54) | 55.6%<br>[38.0% - 72.1%]<br>(30/54) |
| NPV - Negative<br>Predictive Value | 98.6%<br>[96.9% - 100%]<br>(216/219) | 98.3%<br>[96.4% - 99.6%]<br>(226/230) | 99.6%<br>[98.8% - 100%]<br>(266/267) | 99.6%<br>[98.5% - 100%]<br>(263/264) |
| All the 95% confidence intervals (95% CI, [xx.x% - xx.x%]) are computed using the clustered bootstrap method that takes into | | | | |
consideration the correlation between eyes of the same participant.
Agreement between cameras - For mtmDR detection, the average positive agreement (APA) between EyeArt results with the Topcon NW400 camera and those with the Canon CR-2 AF/Plus AF cameras was 97.1% [95% CI: 93.8% - 99.7%] among reference standard positives and the average negative agreement (ANA) was 95.0% [95% CI: 92.7% - 97.1%] among reference standard negatives. For vtDR detection, APA was 100.0% [95% CI: 94.0% - 100.0%] among reference standard positives and ANA was 96.4% [95% CI: 94.6% -98.0%] among reference standard negatives.
Estimates of precision - Intra-operator repeatability data was collected from multiple sites by having each participant in the repeatability cohorts undergo at least three EyeArt operations performed by the same EyeArt operator with the same Topcon NW400 camera unit. The inter-operator reproducibility data was collected by having each participant in the reproducibility cohorts undergo at least one EyeArt operation performed by three distinct operators each operating a distinct Topcon NW400 camera unit. The precision results of EyeArt with the Topcon NW400 cameras is provided in Table 4 for mtmDR and vtDR detection.
| | | | | Table 4: Kev precision measures of EveArt with the Topcon NW400 camera for mtmDR and vtDR detection | |
|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------|---------------------------------|-----------------------------------|-----------------------------------------------------------------------------------------------------|-----------------------------------|
| | | | | | |
| | EyeArt with<br>Topcon NW400 | mtmDR | vtDR | | |
| | | Intra-operator<br>Repeatability | Inter-operator<br>Reproducibility | Intra-operator<br>Repeatability | Inter-operator<br>Reproducibility |
| APA among reference<br>standard positives | | 100.0%<br>[98.2% - 100.0%] | 100.0%<br>[96.4% - 100.0%] | 100.0%<br>[94.9% - 100.0%] | 100.0%<br>[75.8% - 100.0%] |
| ANA among reference<br>standard negatives | | 98.9%<br>[97.6% - 99.7%] | 97.2%<br>[92.3% - 100.0%] | 99.5%<br>[98.8% - 100.0%] | 93.3%<br>[87.3% - 97.7%] |
| All the 95% confidence intervals (95%CI, [xx.x% - xx.x%]) are computed using the clustered bootstrap method that takes into<br>consideration the correlation between eyes of the same participant.<br>For cases with proportion of 100%, the 95% CIs using clustered bootstrap are [100% - 100%], hence the Wilson method is used,<br>which however is not designed to consider eye correlation. | | | | | |
{10}------------------------------------------------
The results of this prospective study support a determination of substantial equivalence between EyeArt v2.2.0 and EyeArt v2.1.0 and support the addition of the Topcon NW400 camera to the IFU statement.
- B. Retrospective study
The clinical performance of EyeArt v2.2.0 was evaluated in a retrospective clinical study utilizing the data already collected in the EyeArt pivotal multi-center clinical study (Protocol EN-01). The primary endpoints were sensitivity and specificity, and the secondary endpoints were imageability (the proportion of evaluated participant eyes that received EyeArt disease detection results), positive predictive value (PPV), and negative predictive value (NPV). The modified version of EyeArt (v2.2.0) was evaluated on data from 1310 eyes of 655 participants that were enrolled in the pivotal study.
88.4% of undilated eyes received EyeArt results with EyeArt v2.2.0 vs. 86.6% with EyeArt v2.1.0. ("first-submission imageability," Table 5). "Final-submission imageability" after dilation as needed was 99.1% with EyeArt v2.2.0 and 97.3% with EyeArt v2.1.0.
Table 5: Imageability of EyeArt for mtmDR and vtDR detection for undilated (first submission) and dilate-ifneeded (final submission) images.
| EN-01 entire<br>analysis<br>population | mtmDR | | vtDR | |
|------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------|-----------------------------------------|-----------------------------------------|-----------------------------------------|
| | EyeArt v2.1.0<br>(predicate device) | EyeArt v2.2.0<br>(subject device) | EyeArt v2.1.0<br>(predicate device) | EyeArt v2.2.0<br>(subject device) |
| Imageability<br>(dilate-if-needed,<br>final submissions) | 97.3%<br>[96.2% - 98.4%]<br>(1246/1281) | 99.1%<br>[98.4% - 99.5%]<br>(1269/1281) | 97.3%<br>[96.0% - 98.4%]<br>(1230/1264) | 99.1%<br>[98.4% - 99.6%]<br>(1252/1264) |
| Imageability<br>(undilated, first<br>submissions) | 86.6%<br>[83.7% - 89.1%]<br>(1109/1281) | 88.4%<br>[86.2% - 90.6%]<br>(1132/1281) | 86.9%<br>[84.1% - 89.4%]<br>(1098/1264) | 88.6%<br>[86.4% - 90.8%]<br>(1120/1264) |
| All the 95% confidence intervals (95% CI, [xx.x% - xx.x%]) are computed using the clustered bootstrap method that takes into | | | | |
consideration the correlation between eyes of the same participant.
The main results are summarized in Tables 6 and 7.
Table 6: Key EyeArt performance measures for mtmDR and vtDR detection using "first-submission" images (undilated submissions)
| EN-01 entire<br>analysis<br>population | mtmDR | | vtDR | |
|-----------------------------------------------------------------------------------------------------------------------------|---------------------------------------|---------------------------------------|----------------------------------------|----------------------------------------|
| | EyeArt v2.1.0<br>(predicate device) | EyeArt v2.2.0<br>(subject device) | EyeArt v2.1.0<br>(predicate device) | EyeArt v2.2.0<br>(subject device) |
| Sensitivity | 94.4%<br>[90.6% - 97.5%]<br>(151/160) | 94.6%<br>[91.0% - 97.9%]<br>(159/168) | 92.3%<br>[83.6% - 100.0%]<br>(36/39) | 92.7%<br>[83.7% - 100%]<br>(38/41) |
| Specificity | 86.1%<br>[83.7% - 88.9%]<br>(817/949) | 85.9%<br>[83.5% - 88.1%]<br>(828/964) | 92.4%<br>[90.3% - 94.4%]<br>(979/1059) | 92.4%<br>[90.7% - 94.2%]<br>(997/1079) |
| PPV – Positive<br>Predictive Value | 53.4%<br>[46.0% - 60.2%]<br>(151/283) | 53.9%<br>[47.3% - 59.9%]<br>(159/295) | 31.0%<br>[21.2% - 40.3%]<br>(36/116) | 31.7%<br>[22.2% - 40.5%]<br>(38/120) |
| NPV – Negative<br>Predictive Value | 98.9%<br>[98.2% - 99.5%]<br>(817/826) | 98.9%<br>[98.1% - 99.6%]<br>(828/837) | 99.7%<br>[99.4% - 100.0%]<br>(979/982) | 99.7%<br>[99.3% - 100%]<br>(997/1000) |
| All the 95% confidence intervals (95%CI, [xx.x% - xx.x%]) are computed using the clustered bootstrap method that takes into | | | | |
| consideration the correlation between eyes of the same participant. | | | | |
{11}------------------------------------------------
| EN-01 entire<br>analysis<br>population | mtmDR | | vtDR | |
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------|----------------------------------------|------------------------------------------|------------------------------------------|
| | EyeArt v2.1.0<br>(predicate device) | EyeArt v2.2.0<br>(subject device) | EyeArt v2.1.0<br>(predicate device) | EyeArt v2.2.0<br>(subject device) |
| Sensitivity | 94.4%<br>[90.8% - 97.6%]<br>(168/178) | 95.1%<br>[91.7% - 98.2%]<br>(174/183) | 92.7%<br>[85.1% - 100.0%]<br>(38/41) | 95.5%<br>[89.8% - 100.0%]<br>(42/44) |
| Specificity | 86.3%<br>[83.8% - 88.7%]<br>(922/1068) | 86.1%<br>[83.8% - 88.5%]<br>(920/1068) | 92.7%<br>[90.9% - 94.5%]<br>(1102/1189) | 92.4%<br>[90.7% - 94.2%]<br>(1100/1190) |
| PPV – Positive<br>Predictive Value | 53.5%<br>[46.0% - 60.2%]<br>(168/314) | 54.0%<br>[47.4% - 60.3%]<br>(174/322) | 30.4%<br>[22.0% - 40.0%]<br>(38/125) | 31.8%<br>[22.5% - 40.2%]<br>(42/132) |
| NPV - Negative<br>Predictive Value | 98.9%<br>[98.3% - 99.6%]<br>(922/932) | 99.0%<br>[98.4% - 99.7%]<br>(920/929) | 99.7%<br>[99.4% - 100.0%]<br>(1102/1105) | 99.8%<br>[99.5% - 100.0%]<br>(1100/1102) |
| All the 95% confidence intervals (95%CI, [xx.x% - xx.x%]) are computed using the clustered bootstrap method that takes into<br>consideration the correlation between eyes of the same participant. | | | | |
Table 7: Key EyeArt performance measures for mtmDR and vtDR detection using "final-submission" images (dilate-if-needed submissions)
Worst-case and best-case performance is shown in Table 8. The worst-case imputation assumes that EyeArt ungradable submissions all disagree with the clinical reference standard, and the best-case imputation assumes that the EyeArt ungradable submissions all agree with the clinical reference standard.
| EN-01 entire<br>analysis<br>population | mtmDR | | vtDR | |
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------|----------------------------------------|------------------------------------------|------------------------------------------|
| | EyeArt v2.2.0<br>(worst-case) | EyeArt v2.2.0<br>(best-case) | EyeArt v2.2.0<br>(worst-case) | EyeArt v2.2.0<br>(best-case) |
| Sensitivity | 94.7%<br>[91.3% - 97.6%]<br>(178/188) | 95.2%<br>[91.7% - 98.0%]<br>(179/188) | 95.6%<br>[89.5% - 100.0%]<br>(43/45) | 95.6%<br>[89.5% - 100.0%]<br>(43/45) |
| Specificity | 85.4%<br>[82.7% - 87.7%]<br>(933/1093) | 86.4%<br>[83.8% - 88.7%]<br>(944/1093) | 91.5%<br>[89.6% - 93.2%]<br>(1115/1219) | 92.5%<br>[90.8% - 94.3%]<br>(1127/1219) |
| PPV – Positive<br>Predictive Value | 52.7%<br>[46.0% - 58.9%]<br>(178/338) | 54.6%<br>[48.2% - 60.8%]<br>(179/328) | 29.3%<br>[20.3% - 37.5%]<br>(43/147) | 31.9%<br>[23.2% - 41.2%]<br>(43/135) |
| NPV - Negative<br>Predictive Value | 98.9%<br>[98.2% - 99.5%]<br>(933/943) | 99.1%<br>[98.4% - 99.6%]<br>(944/953) | 99.8%<br>[99.5% - 100.0%]<br>(1115/1117) | 99.8%<br>[99.6% - 100.0%]<br>(1127/1129) |
| All the 95% confidence intervals (95% CI, [xx.x% - xx.x%]) are computed using the clustered bootstrap method that takes into<br>consideration the correlation between eyes of the same participant | | | | |
Table 8: Worst-case and best-case EyeArt v2.2.0 performance for mtmDR and vtDR detection
The results of this retrospective study support a determination of substantial equivalence between EyeArt v2.2.0 and EyeArt v2.1.0.
{12}------------------------------------------------
### X. Human Factors Validation Testing
The human factors data support the safety and effectiveness of the EyeArt Client user interface and the indicated camera models. The human factors validation testing for EyeArt v2.2.0 was conducted as per the current human factors guidance document. The critical task for using EyeArt is the ability to capture four images of sufficient quality to produce EyeArt gradable results.
### XI. Conclusions
EyeArt v2.2.0 with the Canon CR-2 AF, Canon CR-2 Plus AF, and Topcon NW400 cameras is substantially equivalent to the predicate device, EyeArt v2.1.0. They have the same intended use. The technological differences between EyeArt v2.2.0 and the predicate device do not raise new types of questions of safety or effectiveness. Performance data support the substantial equivalence of EyeArt to the predicate device.
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Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.