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The WaveLight FS200 Patient Interface 1515 is indicated to be used only with the WaveLight FS200 Laser System, consistent with the cleared Indications for Use for this ophthalmic surgical laser.

The WaveLight® FS200 Patient Interface 1515 is a patient consumable (disposable) consisting of a tubing system with an integrated suction ring and an applanation cone. The flat bottom of the cone is used as an applanation plate for the patient's cornea.

This document is a 510(k) Premarket Notification for the WaveLight FS200 Patient Interface 1515. It is a Class II medical device (product codes GEX/HNO) intended for use with the WaveLight FS200 Laser System in ophthalmic surgical procedures.

The device is a sterile, disposable consumable consisting of a tubing system with an integrated suction ring and an applanation cone. The application being considered here is for a Patient Interface device, which connects a patient's eye to a laser system. This is a physical component, not a software or AI-driven diagnostic device. Therefore, the typical "acceptance criteria" and "study" questions related to AI/software performance (e.g., sensitivity, specificity, human reader improvement with AI assistance, ground truth establishment by experts) are not applicable in this context.

Instead, the acceptance criteria and supporting studies for this type of device focus on manufacturing, material, sterility, packaging, and functional performance, demonstrating equivalence to an existing (predicate) device.

Here's the information extracted and formatted as requested, with "N/A" for sections not relevant to this device type:

Acceptance Criteria and Study for WaveLight FS200 Patient Interface 1515

The acceptance criteria for this device are demonstrated through non-clinical testing to ensure its safety, performance, and substantial equivalence to a predicate device, the WaveLight FS200 Patient Interface 1505. The studies performed are primarily engineering and laboratory-based tests.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and the Data Provenance

• Sample Size: Not explicitly stated as a number of devices/units, but functional testing was performed on units after sterilization, transportation, and aging (accelerated and real-time). The specific number of units tested for each non-clinical test (biocompatibility, sterility, packaging, transport stability, functional testing) is not provided in this summary. These are typically engineering validation tests with defined sample sizes based on statistical methods/standards rather than patient data cohorts.
• Data Provenance: The studies are non-clinical, meaning they are laboratory and engineering tests conducted by the manufacturer (Alcon Research, Ltd.). The data originates from these controlled testing environments. Given this is a 510(k) submission to the US FDA, the tests would be expected to meet international and US-specific standards. This is retrospective in the sense that the studies detailed were completed prior to submitting the 510(k) application.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

• N/A. This device is a physical consumable, not an AI/diagnostic imaging device that requires expert review for "ground truth" establishment in a clinical performance study context. Ground truth is established through validated testing methodologies and adherence to recognized consensus standards (e.g., ISO for sterility, biocompatibility, packaging; ASTM for aging).

4. Adjudication Method for the Test Set

• N/A. Adjudication methods (e.g., 2+1, 3+1) refer to procedures for resolving discrepancies in expert clinical assessments (e.g., image interpretations). This is not relevant to non-clinical engineering and laboratory validation tests.

5. 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

• N/A. This is a physical medical device, not an AI or software product. MRMC studies are not applicable.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done

• N/A. This is a physical medical device, not an algorithm. Standalone performance as typically described for AI/software is not applicable.

7. The Type of Ground Truth Used

• Engineering and Laboratory Standards / Test Results: The "ground truth" for this device's performance is established by demonstrating adherence to recognized consensus standards (e.g., ISO 10993, ISO 11135, ISO 11607, ASTM F1980) and by comparative functional testing against the predicate device. For example, sterility "ground truth" is meeting a 10⁻⁶ SAL, biocompatibility "ground truth" is passing specific ISO 10993 tests, and functional "ground truth" is equivalence to the predicate device's measured performance.

8. The Sample Size for the Training Set

• N/A. This device does not involve a "training set" in the context of machine learning or AI models.

9. How the Ground Truth for the Training Set Was Established

• N/A. As above, no training set for AI/ML is involved.

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The WaveLight® FS200 Patient Interface 1505 is indicated to be used with the WaveLight® FS200 Laser System, consistent with the cleared Indications for Use for this ophthalmic surgical laser.

The WaveLight® FS200 Patient Interface 1505 is a patient contact consumable (disposable) consisting of a Tubing System with integrated Suction Ring and an Applanation Cone. The flat bottom of the cone is used as an applanation plate for the patient's cornea.

The provided 510(k) summary for the WaveLight® FS200 Patient Interface 1505 describes a device that is essentially a consumable component (patient interface) for an existing laser surgical system (WaveLight® FS200 Laser System). The focus of the submission is to demonstrate substantial equivalence to a predicate device (WaveLight® FS200 Patient Interface 1504), rather than proving the performance of a novel diagnostic or therapeutic algorithm.

Therefore, the typical acceptance criteria and study designs associated with AI/ML-driven medical devices (e.g., sensitivity, specificity, MRMC studies, ground truth establishment by experts) are not applicable to this submission. This device does not involve an AI algorithm, a diagnostic output, or a performance metric like accuracy that would require human expert adjudication of ground truth or a comparative effectiveness study with human readers.

Instead, the acceptance criteria and supporting studies for this device relate to its safety and functional equivalence as a consumable physical component.

Here's a breakdown based on the provided text, addressing the points where applicable and explaining why others are not relevant:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Biocompatibility: No specific sample size provided, but tests were "evaluations of materials coming into contact with the patient or fluid path."
• Sterility: Validated for the "product." No specific sample size mentioned, but validation typically involves multiple batches/units for statistical confidence.
• Packaging & Shelf-life: Validated for the "product." No specific sample size mentioned.
• Function and Performance (Flap cutting tests): No specific sample size (number of cuts or units tested) is provided in the summary. Data provenance is implied to be laboratory testing conducted by the manufacturer, not patient data from a specific country or retrospective/prospective collection.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not Applicable. This device is a physical consumable component for a surgical laser, not a diagnostic or AI-powered device requiring expert-established ground truth. The "ground truth" here is objective physical and chemical properties and functional equivalence to the predicate device.

4. Adjudication method for the test set

• Not Applicable. As this is not a diagnostic device requiring interpretation, no adjudication method (e.g., 2+1, 3+1) was used. Testing relies on objective measurements and established standards.

5. 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

• No. An MRMC study is not relevant as this device is a physical consumable for a surgical laser, not an AI-assisted diagnostic or therapeutic tool that involves human readers interpreting output.

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

• Not Applicable. This device does not contain an algorithm or AI component; it is a passive, sterile, disposable physical interface.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Objective Measurements and Predicate Equivalence: The "ground truth" for this device's performance relies on:
  • Compliance with recognized international standards (ISO 10993 for biocompatibility, AAMJ/ISO 11135 for sterility, ISO 11607-1 for packaging) which define acceptable limits for parameters.
  • Functional comparison to the legally marketed predicate device (WaveLight® FS200 Patient Interface 1504) for "flap cutting results," demonstrating equivalence rather than an absolute ideal "ground truth."
  • System validation against internal development and risk management procedures (21 CFR 820, ISO 14971).

8. The sample size for the training set

• Not Applicable. This product does not involve an AI/ML algorithm that requires a training set.

9. How the ground truth for the training set was established

• Not Applicable. This product does not involve an AI/ML algorithm that requires a training set or its associated ground truth establishment.

In summary, this 510(k) submission successfully demonstrated substantial equivalence of a consumable device based on rigorous non-clinical testing (biocompatibility, sterility, packaging, functional performance, and system validation) against established standards and direct comparison to a predicate device, rather than patient-level clinical performance data or AI algorithm validation.

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