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The Cooper® Prosthetic (polymacon) Soft (hydrophilic) Contact Lens is indicated for daily wear to enhance or alter the apparent color of the eye, including ocular masking, either in sighted or non-sighted eyes that require a prosthetic contact lens for management of conditions such as corneal, iris, or lens abnormalities. The lens may also be prescribed for the correction of refractive ametropia (myopia and hyperopia) in aphakic and not-aphakic persons that may exhibit astigmatism up to 2.00 diopters that does not interfere with visual acuity or for occlusive therapy for conditions such as diplopia, amblyopia or extreme photophobia.

The Cooper Prosthetic Lens (polymacon) Soft (hydrophilic) Contact Lenses are available as spherical lenses. The lens material, polymacon, is a hydrophilic polymer of 2-hydroxyethyl methacrylate (HEMA) which is cross linked with ethyleneglycol dimethacrylate. When hydrated, the lens consists of 62.0% HEMA and 38.0% water by weight when immersed in normal saline. The lenses are made by modifying the uncolored polymacon lens by affixing a colored pigment on that portion of the front surface that corresponds to the iris. The colored pigments consist of carbazole violet, chromium oxide green, dihydrodinaphto brown, dihydrodioxo yellow, phthalocyanine green, iron oxide red, iron oxide brown, iron oxide black, phthalocyanine blue, and titanium oxide. Cooper Prosthetic Lenses are hemispherical shells with the following dimensions: Diameter: 14.0mm to 15.0mm, Base Curve: 8.0mm to 9.5mm, Center Thickness: 0.05mm to 0.40mm (varies with power), Lens Powers: -20.00 to +20.00D. The physical/optical properties of the Cooper Prosthetic Lenses are: Refractive Index: 1.43, Light Transmittance: >90% (open pupil), Surface Character: Hydrophilic, Water Content: 38%, Oxygen Permeability: 8.0 x 10-11 (cm2/sec) (ml O2/ml x mmHg) at 35°C (Fatt method for determination of oxygen permeability).

The provided document is a 510(k) Premarket Notification for the Cooper Prosthetic (polymacon) Soft (hydrophilic) Contact Lens. It does not contain a study that proves the device meets specific acceptance criteria based on performance metrics such as accuracy, sensitivity, or specificity, as would typically be found for AI/imaging-based medical devices.

Instead, this submission seeks to establish substantial equivalence to previously cleared predicate devices. The "acceptance criteria" are implied by demonstrating that the new device's material properties, indications for use, and manufacturing processes are equivalent to those of the predicate devices, and that it does not raise any new questions of safety or effectiveness.

Here's an analysis of the information provided, addressing the requested points where applicable:

Acceptance Criteria and Reported Device Performance

Study Details (Based on Provided Document)

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

   • The document does not report a specific clinical "test set" or data provenance for performance evaluation in the way an AI/imaging device would.
   • For the Monomer Extractable Study, samples were analyzed to determine residual levels, but the sample size is not specified. This was likely a laboratory-based study on manufactured units.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

   • Not applicable. This submission relies on substantial equivalence to predicate devices, not on a clinical performance study with expert-adjudicated ground truth.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • Not applicable. See point 2.

4. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • Not applicable. This is a contact lens, not an AI-powered diagnostic tool. No MRMC study was conducted.

5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:

   • Not applicable. This is a physical medical device (contact lens), not an algorithm.

6. The type of ground truth used (expert concensus, pathology, outcomes data, etc):

   • The "ground truth" for this submission is the established safety and effectiveness of the predicate devices, as determined by their prior FDA clearance (K984259 and K010126). The current device's physical, chemical, and biological properties are compared against these predicates, and toxicology testing confirms non-toxicity and compliance with specifications.

7. The sample size for the training set:

   • Not applicable. There is no "training set" in the context of an AI/machine learning model. The relevant "data" would be the manufacturing controls and material specifications consistently achieved during production, which are compared to the predicate's characteristics.

8. How the ground truth for the training set was established:

   • Not applicable. See point 7.

Conclusion:

This 510(k) submission for the Cooper Prosthetic (polymacon) Soft (hydrophilic) Contact Lens is a substantial equivalence submission. It explicitly states: "It was determined that Clinical Studies were not necessary to establish the safety and efficacy of the Cooper Prosthetic (polymacon) Soft (hydrophilic) Contact Lens. This determination was based on the following: Cooper Prosthetic (polymacon) Soft (hydrophilic) Contact Lens has demonstrated to be substantially equivalent to the predicate Cooper Prosthetic (polymacon) Soft (hydrophilic) Contact Lens (K984259) and (K010126)."

Therefore, the device meets its "acceptance criteria" by demonstrating that its design, materials, manufacturing processes, and intended use are sufficiently similar to legally marketed predicate devices, and that it performs equivalently in terms of physical, optical, and chemical properties, and passes standard toxicology tests. There are no performance outcomes reported in terms of accuracy, sensitivity, or specificity, as those are typical metrics for diagnostic devices, particularly those involving AI.

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