(106 days)
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.
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 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.
Here's a breakdown of the acceptance criteria and the study that proves the AEYE-DS device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document primarily focuses on establishing substantial equivalence to a predicate device (AEYE-DS K221183), rather than explicitly listing pre-defined, quantitative acceptance criteria for each metric in the same way one might find in a clinical trial protocol. However, we can infer the implicitly accepted performance by comparing the subject device's results to the predicate's and demonstrating robust performance across two studies. The table below presents the key performance metrics reported for the subject device (AEYE-DS K240058 with Optomed Aurora camera) and the predicate device (AEYE-DS K221183 with Topcon NW400 camera).
| Metric | Acceptance Criteria (Implied by Predicate Performance) | AEYE-DS Device (K240058) with Optomed Aurora (Study 1) | AEYE-DS Device (K240058) with Optomed Aurora (Study 2) |
|---|---|---|---|
| Sensitivity | ≥ 93% | 92% [79%; 97%] (Fundus-based & Multi-modality-based) | 93% [80%; 97%] (Fundus-based) 90% [77%; 96%] (Multi-modality-based) |
| Specificity | ≥ 91% | 94% [90%; 96%] (Fundus-based & Multi-modality-based) | 89% [85%; 92%] (Fundus-based & Multi-modality-based) |
| Imageability | ≥ 99% | 99% [98%; 100%] | 99% [97%; 100%] |
| PPV | ≥ 60% | 68% [54%; 79%] | 53% [41%; 64%] |
| NPV | ≥ 99% | 99% [96%; 100%] | 99% [97%; 100%] (Fundus-based) 98% [96%; 99%] (Multi-modality-based) |
Note: While PPV in Study 2 (53%) for the subject device is below the predicate's performance (60%), the document attributes this to the actual prevalence of mtmDR+ patients in the study's diabetic population (i.e., 12%), stating that the robustness of the studies is demonstrated by the similar PPV and NPV results across both studies despite this. The overall conclusion is substantial equivalence.
2. Sample Sizes Used for the Test Set and Data Provenance
- Study 1 Sample Size: 317 subjects
- Study 2 Sample Size: 362 subjects
- Data Provenance: Both studies were prospective, multi-center, single-arm, blinded studies conducted at study sites in the United States.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The ground truth was established by an independent reading center. While the exact number of experts (readers) is not specified, their role in determining the severity of retinopathy and clinically significant diabetic macular edema (DME) according to the Early Treatment for Diabetic Retinopathy Study severity (ETDRS) scale implies a high level of expertise, typical of ophthalmic specialists or certified graders.
4. Adjudication Method for the Test Set
The document states that the "Reading Center diagnostic results formed the reference standard (ground truth) for the study." It does not explicitly describe an adjudication method (e.g., 2+1, 3+1) among multiple readers within the reading center. It implies a single, definitive determination by the reading center.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No multi-reader multi-case (MRMC) comparative effectiveness study was done. The studies were designed to evaluate the standalone performance of the AEYE-DS device, not to compare its performance in assisting human readers. The device is intended to "automatically detect" mtmDR.
6. Standalone (Algorithm Only) Performance
Yes, a standalone (algorithm only) performance evaluation was done. The reported sensitivity, specificity, PPV, and NPV values are for the AEYE-DS device's automated detection of mtmDR.
7. Type of Ground Truth Used
The ground truth used was expert consensus / standardized clinical assessment based on:
- Dilation four widefield color fundus images
- Lens photography for media opacity assessment
- Macular optical coherence tomography (OCT) imaging
- Severity determination according to the Early Treatment for Diabetic Retinopathy Study (ETDRS) scale by an independent reading center.
8. Sample Size for the Training Set
The document does not explicitly state the sample size for the training set. The clinical studies (Study 1 and Study 2) are described as the basis for the performance evaluation of the device (i.e., the test set performance). The training of the AI model would have occurred prior to these validation studies.
9. How the Ground Truth for the Training Set Was Established
The document does not explicitly describe how the ground truth for the training set was established. However, it is standard practice for AI models in medical imaging to be trained on large datasets where ground truth is established by experienced clinical experts (e.g., ophthalmologists, retina specialists) thoroughly reviewing and annotating images, often with consensus protocols, similar to the method described for the test set's ground truth (ETDRS grading by a reading center). Given the device's predicate status and the detailed description of the ground truth for the test sets, it is highly probable that a rigorous, expert-based process was applied to the training data as well.
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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"
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(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,
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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) 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 BroadwayNew York, NY, 10036 USAE-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.
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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
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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
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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 (<55 years) showing better results than the higher age group. 75% of the subjects received an AEYE-DS diagnostic output result on the first submission attempt. 90% of subjects did not require pharmacologic dilation.
PPV in both studies was influenced by the actual prevalence of mtmDR+ patients in the studies diabetic population (i.e., 12%), as these were not enriched studies. Furthermore, the PPV and NPV results obtained in both studies were similar, demonstrating the robustness of the studies.
| AEYE-DS DeviceFundus-based Analysis | AEYE-DS DeviceMulti-modality-based Analysis | |||
|---|---|---|---|---|
| AEYE-DS Study 1 | AEYE-DS Study 2 | AEYE-DS Study 1 | AEYE-DS Study 2 | |
| Sensitivity | 92% [79%; 97%] | 93% [80%; 97%] | 92% [79%; 97%] | 90% [77%; 96%] |
| Specificity | 94% [90%; 96%] | 89% [85%; 92%] | 94% [90%; 96%] | 89% [84%; 92%] |
Key results from the clinical studies are summarized in the table below.
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| Imageability | 99% [98%; 100%] | 99% [97%; 100%] | 99% [98%; 100%] | 99% [97%; 100%] |
|---|---|---|---|---|
| PPV | 68% [54%; 79%] | 53% [41%; 64%] | 68% [54%; 79%] | 53% [41%; 64%] |
| NPV | 99% [96%; 100%] | 99% [97%; 100%] | 99% [96%; 100%] | 98% [96%; 99%] |
The imageability results from both Study 1 and Study 2, reflecting data on the usability in the hands of the study novice operators, reported 99% imageability for the AEYE-DS device using the Optomed Aurora fundoscopy camera.
Precision Study
Overall, twenty one (21) participants were included in the final statistical analysis. All 21 participants completed the entire AEYE-DS device imaging protocol and diagnostic output twelve consecutive times, imaged by three different novice operators, using two different Optomed Aurora fundoscopy devices, except for one subject who withdrew consent after 10 image sets (operator #3 did not use camera #2) for a total of 250 image sets/diagnoses in total. Per protocol, operators allowed subjects at least 8 minutes between successive imaging. The results are presented in the following tables.
Precision Tables:
| Intra-Operator Repeatability | Repeat 2 - mtmDR | ||
|---|---|---|---|
| Repeat 1 - mtmDR | mtmDR + | mtmDR - | Insufficient quality |
| mtmDR + | 58 | 0 | 0 |
| mtmDR - | 1 | 66 | 0 |
| Insufficient quality | 0 | 0 | 0 |
| OA | 99% (124/125) [96%; 100%] | ||
| APA | 99% [95%; 100%] | ||
| ANA | 99% [96%; 100%] | ||
| AUA | Not presented as all cases were of sufficient quality |
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| BetweenOperatorReproducibilityOperator 1 | Operator 3 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| mtmDR +Operator 2 | mtmDR -Operator 2 | Insufficient qualityOperator 2 | |||||||
| mtmDR+ | mtmDR- | IQ | mtmDR+ | mtmDR- | IQ | mtmDR+ | mtmDR- | IQ | |
| mtmDR + | 18 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| mtmDR - | 0 | 0 | 0 | 0 | 21 | 0 | 0 | 0 | 0 |
| Insufficientquality | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| OA | 95% (39/41) [84%; 99%] | ||||||||
| APA | 97% [88%; 99%] | ||||||||
| ANA | 97% [90%; 99%] | ||||||||
| AUA | Not presented as all cases were of sufficient quality |
| Between-Device Reproducibility | Device 2 mtmDR | ||
|---|---|---|---|
| Device 1 - mtmDR | mtmDR + | mtmDR - | Insufficient quality |
| mtmDR + | 28 | 1 | 0 |
| mtmDR - | 1 | 32 | 0 |
| Insufficient quality | 0 | 0 | 0 |
| OA | 97% (60/62) [89%; 99% ] | ||
| APA | 97% [88%; 99% ] | ||
| ANA | 97% [90%; 99% ] | ||
| AUA | Not presented as all cases were of sufficient quality |
Human Factors Validation Study
Usability of the AEYE-DS device was also assessed in a human factors validation study with the Optomed Aurora handheld camera, including User Manual comprehension and usability of the device in the hands of potential users. Once the users underwent initial, one-time training and practice, all users stated that the device was easy and straightforward and all were successful in submitting images for diagnosis and obtaining a diagnosis output result and PDF report. All users stated that the user manual was clear and easy to use.
In summary, the AEYE-DS device using images acquired from the Optomed Aurora fundoscopy device demonstrates successful performance, in terms of sensitivity, specificity, PV and NPV, as well as imageability, usability, and precision.
Substantial Equivalence:
The subject AEYE-DS device, manufactured by AEYE Health Inc., is substantially equivalent to the cleared AEYE-DS device (also manufactured by AEYE Health Inc. and the subject of 510(k) K221183).
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| TechnologicalCharacteristic | AEYE-DS Device(K221183) | AEYE-DS Device(K240058) |
|---|---|---|
| Product Code,Class | PIBClass II | Same |
| Indications forUse | The AEYE-DS is indicated for use byhealth care providers to automaticallydetect more than milddiabeticretinopathy (mtmDR) in adultsdiagnosed with diabetes who have notbeen previously diagnosed with diabeticretinopathy. The AEYE-DS is indicatedfor use with the TopconNW400. | AEYE-DS is indicated for use by healthcare providers to automatically detect morethan mild diabetic retinopathy (mtmDR) inadults diagnosed with diabetes who havenot been previously diagnosed withdiabetic retinopathy. AEYE-DS isindicated for use with the Topcon NW400camera and Optomed Aurora camera. |
| Target Population | Adult subjects diagnosed with Diabetes | Adult subjects diagnosed with Diabetes |
| Anatomical Sites | Eye examination | Same |
| EnvironmentUsed | Hospitals, Clinics | Same |
| Energy Used/Delivered | Not Applicable | Same |
| Design: | A fundus camera is attached to acomputer, where the AEYE-DS Clientmodule is installed. The Client moduleallows the user to interact with theserver-based analysis software over asecure internet connection. Using theClient module, users identify one(macular) or two (macular and disc)fundus images per eye to be dispatchedto the Service module. The Service isinstalled on a server hosted at a securedatacenter. The Analytics module,which runs alongside the Servicemodule, processes the fundus imagesand returns information on the imagequality and the presence or absence ofmtmDR to the Service module. TheService then returns the results to theClient module. | A fundus camera is attached to a computer,where the AEYE-DS Client module isinstalled. The Client module allows theuser to interact with the server-basedanalysis software over a secure internetconnection. Using the Client module, usersidentify one (macular) or two (macular anddisc) fundus images per eye to bedispatched to the Service module. Onemacular image per eye is required for theTopcon NW400 camera or the OptomedAurora camera. Two (macular and disc)images per eye are optionally used only forthe Topcon NW400 camera. The Service isinstalled on a server hosted at a securedatacenter. The Analytics module, whichruns alongside the Service module,processes the fundus images and returnsinformation on the image quality and thepresence or absence of mtmDR to theService module. The Service then returnsthe results to the Client module. |
| TechnologicalCharacteristic | AEYE-DS Device(K221183) | AEYE-DS Device(K240058) |
| -Mechanism ofAction | Artificial Intelligence software as amedical device | Same |
| - Components | The AEYE-DS device consists of thefollowing components:- Client software on computerconnected to fundoscopy camera- Server including Service software andAnalytics software | Same |
| - Inputs | Macula and disc centered color fundusimages with at least 45° field of view, 2images per eye; OrMacula centered color fundus imageswith at least 45° field of view, 1 imageper eye. | Macula and disc centered color fundusimages (2 images per eye) with at least 45°field of view (for the Topcon NW400); OrMacula centered color fundus images (1image per eye) with at least 45° field ofview (for the Topcon NW400 camera) orwith at least 50 by 40º field of view (for theOptomed Aurora camera). |
| - Outputs | More than mild diabetic retinopathy(mtmDR) detected, not detected orinsufficient quality | Same |
| - IndicatedCameras | Topcon NW400 camera | Topcon NW400 camera or OptomedAurora camera |
| Performance | Topcon NW400:Sensitivity: 93%Specificity: 91%Imageability: 99%PPV: 60%NPV: 99% | Optomed AuroraStudy 1:Sensitivity: 92%Specificity: 94%Imageability: 99%PPV: 68%NPV: 99%Optomed AuroraStudy 2:Sensitivity: 93%Specificity: 89%Imageability: 99%PPV: 53%NPV: 99% |
| Human Factors | The AEYE-DS device uses the Clientmodule as the user interface. The safeand efficient use of the device wasestablished in a Usability study withnovice operators. | Same |
| Standards Met | IEC 62304 andFDA Guidance for the Content ofPremarket Submissions for SoftwareContained in Medical DevicesISO 14971FDA Guidance - Content of PremarketSubmissions for Management ofCybersecurity in Medical Devices | Same |
| TechnologicalCharacteristic | AEYE-DS Device(K221183) | AEYE-DS Device(K240058) |
| Materials | No patient contacting materials | Same |
| Biocompatibility | Not Applicable | Same |
| CompatibilityWith theEnvironment andOther Devices | The AEYE-DS device is compatible foruse with the Topcon NW400 device.Compatibility with the environment isnot applicable. | The AEYE-DS device is compatible for usewith the Topcon NW400 or OptomedAurora device.Compatibility with the environment isnot applicable. |
| Sterility | Not Applicable | Same |
| Electrical Safety | Not Applicable | Same |
| Mechanical Safety | Not Applicable | Same |
| Chemical Safety | Not Applicable | Same |
| Thermal Safety | Not Applicable | Same |
| Radiation Safety | Not Applicable | Same |
Table 1: Comparison of the AEYE-DS Device to the predicate AEYE-DS Device (K221183)
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Conclusions:
The subject AEYE-DS device, manufactured by AEYE Health Inc., is substantially equivalent to the cleared AEYE-DS device (also manufactured by AEYE Health Inc. and the subject of 510(k) K221183).
The subject AEYE-DS device has the same intended use and indications for use as the cleared AEYE-DS device. The subject device and the cleared AEYE-DS device are similar in terms of their intended prescription use only, suitable for the adult population diagnosed with diabetes, indicated for use in the same anatomical site (i.e., for eye examinations) and to be used in hospital or clinic settings.
The predicate AEYE- DS device is compatible for use with the Topcon NW400 device, whereas the subject AEYE-DS device is compatible for use with the Topcon NW400, as well as the Optomed Aurora camera.
The subject AEYE-DS and cleared AEYE-DS devices are composed of the same components, including a Client module, and a Server including the Service and Analytics modules. The subject AEYE-DS device has the same mechanism of operation and uses the same underlying technology as the predicate AEYE-DS device. That is, a user interface Client module communicates with the Server, where the fundus images (one macular centered image per eye for the Topcon NW400 camera and the Optomed Aurora camera; or one macular centered and one disc centered image per eye for the Topcon NW400 only) are received and analyzed in the Analytics module to provide information regarding the presence of mtmDR, which
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is returned to the Client module. Both the AEYE-DS devices are based on Artificial Intelligence (AI) software as a medical device and use the identical software algorithm for detecting the presence or absence of more than mild Diabetic Retinopathy in diabetic patients.
The performance characteristics, including the sensitivity, specificity, imageability, PPV and NPV of the subject AEYE-DS device using the Optomed Aurora camera are substantially equivalent to the cleared AEYE-DS device using the Topcon NW400 camera, as demonstrated in the clinical studies performed with the AEYE-DS device. The human factors incorporated into the subject AEYE-DS device and the cleared AEYE-DS device are the same. Both devices use the Client module as the user interface and the safe and efficient use of the device was established in a Usability study with novice operators for both devices.
The subject device, as the cleared device, complies with same relevant consensus standards and FDA guidance document requirements, including the special controls, software validation, cybersecurity and risk analysis.
In summary, the subject AEYE-DS device intended use, technological characteristics and clinical performance are substantially equivalent to the predicate AEYE-DS device. Consequently, it can be concluded that the subject AEYE-DS device is substantially equivalent to the predicate AEYE-DS device, cleared in 510(k) K221183 and therefore, the subject AEYE-DS device may be legally marketed in the USA.
§ 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.