AEYE-DS

K240058

AEYE Health Inc. · cleared 2024-04-23 · product code PIB · Ophthalmic

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“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.”
AlgorithmArtificial Intelligence (AI) software as a medical device
source quote (p.6)
“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.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedYes
source quote (p.7)
“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.”

Validation studies (5)

Bench

sample size not stated

standards: 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, FDA Guidance for Industry and FDA Staff, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices., Content of Premarket Submissions for Management of Cybersecurity in Medical Devices

Prospective clinical

n=317 patients

endpoints: automated detection of more than mild Diabetic Retinopathy (mtmDR)

Prospective clinical

n=362 patients

endpoints: automated detection of more than mild Diabetic Retinopathy (mtmDR)

Standalone

n=21 patients

endpoints: Intra-Operator Repeatability; Between-Device Reproducibility

Standalone

sample size not stated

endpoints: User Manual comprehension; usability of the device

Reported performance (4 observations)

sensitivity0.92CI 79%; 97%
source quote (p.8)
“The results of sensitivity and specificity in Study 1 based on images obtained from the handheld Optomed Aurora camera were 92% [CI:79%; 97%] and 94% [CI: 90%; 96%] (fundus and multi-modality based), respectively.”
specificity0.94CI 90%; 96%
source quote (p.8)
“The results of sensitivity and specificity in Study 1 based on images obtained from the handheld Optomed Aurora camera were 92% [CI:79%; 97%] and 94% [CI: 90%; 96%] (fundus and multi-modality based), respectively.”
ppvas written: “Positive Predictive Value (PPV)”0.68CI 54%; 79%
source quote (p.8)
“Positive Predictive Value (PPV) was 68% [CI: 54%; 79%] and the Negative Predictive Value (NPV) was 99% [CI: 96%; 100%].”
npvas written: “Negative Predictive Value (NPV)”0.99CI 96%; 100%
source quote (p.8)
“Positive Predictive Value (PPV) was 68% [CI: 54%; 79%] and the Negative Predictive Value (NPV) was 99% [CI: 96%; 100%].”

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

0
recalls in product code, 24mo
0
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
0
drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device); a recall is shown as device-attributed only when the recall record itself lists this clearance number. Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Ophthalmic panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K240058