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510(k) Data Aggregation
(80 days)
AURORA DIAGNOSTIC WORKSTATION
The intended use of the Aurora Diagnostic Workstation is identical to other commercially available medical imaging workstations.
The Aurora Medical Imaging Workstation consists of a personal computer, a "Processor "board set for decompression of tiled, stored images; up to eight high resolution (1024 x 1280) monochrome CRT monitors; up to eight video controller boards to display and store the image; a retrieval monitor; and proprietary software for retrieval, image handling and the user interface.
Here's an analysis of the provided 510(k) summary regarding acceptance criteria and study information.
It's important to note that this 510(k) (K962589) is from 1996 for the Aurora™ Diagnostic Workstation. Medical device regulations and best practices, especially concerning AI/ML and performance studies, have evolved significantly since then. The information provided reflects the standards of that era.
1. Table of Acceptance Criteria and Reported Device Performance
Based on the provided text, the submission does not contain specific quantitative acceptance criteria or detailed performance metrics in the way modern AI/ML device submissions would. The evaluation strategy is primarily a direct comparison to a predicate device based on "substantial equivalence" of characteristics.
Therefore, the table below reflects the characteristics compared and the qualitative assessment of "performance" as presented in the document. No numerical performance metrics are provided for the Aurora device itself.
| Category | Acceptance Criteria (Implied) | Reported Device Performance (Aurora Workstation) |
|---|---|---|
| Product Labeling | Substantially equivalent to predicate. | Substantially equivalent |
| Intended Use | Identical to predicate. | Identical |
| Physical Characteristics | Substantially equivalent to predicate. | Substantially equivalent |
| Anatomical Sites | N/A (Not applicable, as it's a general imaging workstation). | N/A |
| Target Population | Identical to predicate. | Identical |
| Performance Testing | Demonstrate performance substantially equivalent to predicate. | Substantially equivalent (Note: No specific test results are detailed in the provided text.) |
| Safety Characteristics | Substantially equivalent to predicate. | Substantially equivalent |
| Device Design & Function | Substantially equivalent to predicate. | Substantially equivalent |
2. Sample Size Used for the Test Set and Data Provenance
The provided text does not mention a test set with a specific sample size or data provenance. The submission focuses on comparing the design, function, and intended use to a predicate device rather than conducting a new performance study on a specific dataset.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
Not applicable. No test set with ground truth established by experts is described in the provided text.
4. Adjudication Method for the Test Set
Not applicable. No test set requiring an adjudication method is described.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No. The provided text does not mention an MRMC study or any study evaluating human reader performance with or without AI assistance. The Aurora device is an "imaging workstation" to display images, not an AI diagnostic aid in the modern sense.
6. Standalone (Algorithm Only) Performance Study
No. The K962589 submission does not describe a standalone performance study. The device is a workstation for displaying medical images, not an AI algorithm performing a specific diagnostic task. Its "performance" is implicitly tied to its ability to display images accurately and efficiently, which is assessed through substantial equivalence to a predicate system.
7. Type of Ground Truth Used
Not applicable. Since no specific performance study on a dataset is mentioned, there is no discussion of ground truth in the context of diagnostic accuracy. The "ground truth" for the submission is the predicate device's established performance and safety.
8. Sample Size for the Training Set
Not applicable. The Aurora Diagnostic Workstation is a hardware and software system for displaying images. It does not utilize machine learning or AI algorithms that require a "training set" in the contemporary sense. The "proprietary software" mentioned would have been developed using traditional software engineering methods, not statistical learning from data.
9. How the Ground Truth for the Training Set Was Established
Not applicable. As there is no machine learning or AI component requiring a training set, this question is not relevant to the provided submission.
Summary of the Study that Proves the Device Meets Acceptance Criteria:
The study proving the device meets its acceptance criteria, as presented in this 510(k), is a "Substantial Equivalence" comparison to a legally marketed predicate device (Eltrax Systems, Inc. user retrieval and image viewing stations).
The "study" documented here focuses on:
- Qualitative comparison of characteristics: Product labeling, intended use, physical characteristics, target population, performance testing (in a general sense), and safety characteristics.
- Statement of equivalence: Aurora Technology asserted that the Aurora workstation components are "substantially equivalent" in device design, function, and intended use to the predicate.
- Nonclinical tests summary: It states that the devices used are "not new or significantly improved" and that components (except proprietary boards and software) are "commercially available," implying their established reliability and safety.
Essentially, the "proof" is the argument and documentation of substantial equivalence to a device already cleared by the FDA, rather than a de novo performance study with quantitative metrics against a defined acceptance criterion.
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