Search Results
Found 2 results
510(k) Data Aggregation
(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.
Ask a specific question about this device
(199 days)
The AEYE-DS device 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.
AEYE-DS is a retinal diagnostic software device that incorporates an algorithm to evaluate ophthalmic images for diagnostic screening to identify retinal diseases or conditions. Specifically, the AEYE-DS is designed to perform diagnostic screening for the condition of more-than-mild diabetic retinopathy (mtmDR).
The AEYE-DS is comprised of 5 software components: (1) Client; (2) Service; (3) Analytics; (4) Reporting and Archiving; and (5) System Security.
The AEYE-DS device is based on the main technological principle of Artificial Intelligence (AI) software as a medical device. The software as a medical device uses artificial intelligence technology to analyze specific disease features from fundus retinal images for diagnostic screening of diabetic retinopathy.
The AEYE-DS device is based on the principle of operation, whereby a fundus camera is used to obtain retinal images. The fundus camera is attached to a computer, where the Client module/software is installed. The Client module/software guides the user to acquire the images and enables the user to interact with the server-based analysis software over a secure internet connection. Using the Client module/software, users identify the fundus images per eye to be dispatched to the Service module/software. The Service module/software is installed on a server hosted at a secure datacenter, receives the fundus images and transfers them to the Analytics module/software. The Analytics module/software, which runs alongside the Service module/software, processes the fundus images and returns information on the image quality and the presence or absence of mtmDR to the Service module/software. The Service module/software then returns the results to the Client module/software.
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 pivotal clinical study evaluated two configurations: 1 image per eye (macula-centered) and 2 images per eye (macula-centered and optic disc-centered). The acceptance criteria for both sensitivity and specificity were pre-defined performance goals.
Acceptance Criteria and Performance (1 Image Per Eye)
| Metric | Acceptance Criteria (Lower One-Sided 97.5% CI Bound) | Reported Device Performance (Lower One-Sided 97.5% CI Bound) | Met? |
|---|---|---|---|
| Sensitivity | ≥ 82% | 83.3% | Yes |
| Specificity | ≥ 87% | 88.22% | Yes |
Acceptance Criteria and Performance (2 Images Per Eye)
| Metric | Acceptance Criteria (Lower One-Sided 97.5% CI Bound) | Reported Device Performance (Lower One-Sided 97.5% CI Bound) | Met? |
|---|---|---|---|
| Sensitivity | ≥ 82% | 85.63% | Yes |
| Specificity | ≥ 87% | 85.18% | No |
Additional Performance Metrics (for both 1 and 2 images per eye)
| Metric | 1 Image Per Eye Performance | 2 Images Per Eye Performance |
|---|---|---|
| Imageability | 99.1% [CI: 97.8%; 99.7%] | 99.1% [CI: 97.8%; 99.7%] |
| PPV | 60.23% [CI: 49.78%; 69.82%] | 54% [CI: 44.26%; 63.44%] |
| NPV | 98.93% [CI: 97.28%; 99.58%] | 99.17% [CI: 97.59%; 99.72%] |
Note: While the specificity for 2 images per eye was slightly below the pre-defined performance goal, the document states that this "does not involve any risks" as sensitivity was high and mtmDR+ subjects would not be missed.
2. Sample Size and Data Provenance
- Test Set Sample Size:
- Pivotal Clinical Study: 531 subjects screened and enrolled.
- For the 1 image per eye analysis, there were 57 mtmDR+ and 405 mtmDR- fully analyzable subjects. The total number of fully analyzable subjects is 462.
- For the 2 images per eye analysis, the exact number of fully analyzable subjects is not explicitly stated in the summary, but the sensitivity and specificity values are provided for a certain number of images, suggesting the same or a very similar subject pool.
- Precision Study: 22 participants.
- Pivotal Clinical Study: 531 subjects screened and enrolled.
- Data Provenance: Prospective, multi-center, single-arm, blinded study conducted at 8 study sites in the United States (7 sites) and Israel (1 site). Enrollment from October 2020 through November 2021.
3. Number of Experts and Qualifications for Ground Truth
- Number of Experts: Not explicitly stated as a number of individual experts. The professional images (dilated four widefield stereo color fundus images, lens photography, and macular OCT) were sent to an "independent reading center."
- Qualifications of Experts: The reading center determined the severity of retinopathy and diabetic macular edema (DME) according to the Early Treatment for Diabetic Retinopathy Study (ETDRS) severity scale. This implies that the experts were highly qualified in retinal imaging and diabetic retinopathy grading, typically ophthalmologists or trained graders with specific expertise in ETDRS.
4. Adjudication Method for the Test Set
The document does not explicitly describe an adjudication method like 2+1 or 3+1. It states that "The Reading Center diagnostic results formed the reference standard (ground truth) for the study." This suggests that the Reading Center's determination was considered the definitive ground truth, implying a consensus or expert-driven process within the center to establish this standard.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The study focused on the standalone performance of the AEYE-DS device against an expert-determined ground truth, not on how human readers' performance might improve with AI assistance.
6. Standalone Performance Study (Algorithm Only)
Yes, a standalone (algorithm only) performance study was conducted. The "Clinical Performance Data" section describes how the AEYE-DS device automatically processed fundoscopy images and produced a diagnostic result ("more than mild DR (mtmDR) detected" or "more than mild DR not detected"). These results were then compared to the "reference standard (ground truth)" established by the independent reading center, directly assessing the algorithm's performance without human intervention in the diagnosis.
7. Type of Ground Truth Used
The ground truth used was expert consensus / expert reading of multi-modal imaging data. Specifically, it was established by an independent reading center based on:
- Dilated four widefield stereo color fundus images.
- Lens photography for media opacity assessment.
- Macular optical coherence tomography (OCT) imaging.
- Severity of retinopathy and DME determined according to the Early Treatment for Diabetic Retinopathy Study (ETDRS) severity scale.
8. Sample Size for the Training Set
The document does not explicitly state the sample size for the training set. The clinical study described is the pivotal clinical study for validation, not the training of the AI model.
9. How the Ground Truth for the Training Set Was Established
The document does not explicitly state how the ground truth for the training set was established, as it focuses on the performance claims from the pivotal clinical study. However, given that it's an AI/ML device, it can be inferred that a similar process of expert grading of images would have been used for the training data, likely by ophthalmologists or trained graders applying recognized clinical standards (e.g., ETDRS).
Ask a specific question about this device
Page 1 of 1