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ARGOS is a non-invasive, non-contact biometer based on swept-source optical coherence tomography (SS-OCT). The device is intended to acquire ocular measurements as well as perform calculations to determine the appropriate intraocular lens (IOL) power and type for implantation during intraocular lens placement. ARGOS measures the following 9 parameters: Axial Length, Corneal Thickness, Anterior Clamber Depth, Lens Thickness, K-values (Radii of flattest and steepest meridians), Astigmatism, White (corneal diameter) and Pupil Size. The Reference Image functionality is intended for use as a preoperative and postoperative image capture tool.

It is intended for use by ophthalmologists, physicians, and other eye-care professionals and may only be used under the supervision of a physician.

The ARGOS is substantially equivalent to the predicate device identified previously:

• the ARGOS (Santec Corporation) that was cleared by the FDA on October 2nd, 2015(K150754) as the primary predicate as it has the most similar intended use and characteristics
• the IOLMaster 700 (Carl Zeiss Meditec) that was cleared by the FDA on June 29, ● 2015 (K143275, K170171) as an additional (secondary) predicate to support a new feature of reference image capture on the version of ARGOS in this submission.

The predicate devices are Class 2 devices to premarket notification, as defined per regulation number 21 CFR 886.1850. In addition, the predicate devices have product codes of MXK(ARGOS), and HJO(IOLMaster700).

The version of ARGOS in this submission is a modified version of the Argos cleared under K150754 which is substantially equivalent with regard to intended use, operating principle, function, materials, and energy source. The differences from the predicate ARGOS (K150754) that are subject of this 510(k) submission are:

• An additional feature of reference image capture function ●
• Labeling change including change in the intended use adding the feature of reference . image capture.
• . This image can be transferred to image guided devices in order to support the execution of preoperative plan.

The changes described in this submission do not affect how the hardware is used to acquire measurements as a biometer, nor do these changes affect the principle of operation of the device.

The provided document describes the ARGOS device, a non-invasive, non-contact biometer based on swept-source optical coherence tomography (SS-OCT), and its 510(k) submission for clearance. The document focuses on demonstrating substantial equivalence to predicate devices, particularly regarding the ARGOS ver1.5 which includes a new "Reference Image functionality."

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Acceptance Criteria and Reported Device Performance

The document does not explicitly present a "table of acceptance criteria and reported device performance" in terms of specific quantitative thresholds that the new ARGOS ver1.5 needs to meet for its added functionality. Instead, it argues for substantial equivalence primarily by:

• Comparing technological characteristics of the new ARGOS (ver1.5) with its primary predicate (ARGOS K150754) and a secondary predicate (IOLMaster 700 K143275, K170171).
• Stating that the new feature (reference image capture) is similar to a feature already present in a legally marketed predicate (IOLMaster 700).
• Referring to compliance with recognized consensus standards for performance and safety.
• Highlighting that the core biometry measurement capabilities did not change from the primary predicate. The in-vivo repeatability specifications for measured parameters (Axial Length, Corneal Thickness, Anterior Chamber Depth, Lens Thickness, Keratometry, Astigmatism, Pupil Size, White-to-White) are listed as identical to the primary predicate, implying these performance metrics were already accepted.

Implied Acceptance Criteria and Reported Performance (from comparison tables):

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

The document does not specify the sample size for any clinical test set in terms of patient data. It primarily relies on bench testing and software verification/validation. The comparison of in-vivo repeatability values references the existing performance of the predicate device, not new clinical data for the ARGOS ver1.5 to prove equivalent clinical performance.

• Sample Size for Test Set: Not explicitly stated for patient data. The "Performance Testing" section refers to "bench tests" and "software verification and validation," which typically involve internal testing and simulations rather than patient samples for the new features. The existing in-vivo repeatability data cited appears to be from the primary predicate, not new testing on the modified device regarding its core measurement functions.
• Data Provenance: The document does not provide details on the country of origin of data or whether it was retrospective or prospective. Given the focus on substantial equivalence through design and testing against standards rather than new clinical trials for the added feature, such details are not expected to be prominent.

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

N/A. The document does not describe a clinical study involving human readers or expert-established ground truth for a test set, especially pertaining to the new "Reference Image functionality." The changes are assessed through engineering and software validation, and comparison to existing, already-cleared devices.

4. Adjudication Method for the Test Set

N/A. No clinical study with human readers and adjudication is described.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

N/A. No MRMC study is described. The device is not an AI/CADe device that assists human readers in diagnosis. It's a measurement device with an added image capture utility.

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

The device itself is a standalone measurement instrument. Its "performance" is its ability to accurately measure ocular parameters and capture images. The "Performance Testing" section (12.1 and 12.2) effectively describes this standalone performance evaluation, focusing on:

• Bench tests: ISO standards, hardware specifications (axial/lateral distance, SNR, depth attenuation).
• Software verification and validation: According to FDA guidance and ISO standards.
• Cybersecurity review.

These tests demonstrate the device's capability to function as intended without human intervention for the measurement process itself, or for the image capture function. The usability evaluation ensures the human-device interaction is acceptable.

7. The Type of Ground Truth Used

• For biometric measurements: The "ground truth" for proving performance (cited from the predicate) would typically be established based on highly accurate reference devices or physical models, although the document doesn't detail the predicate's original ground truth methodology. For the new device, the claim is that its measurement capabilities have not changed and are thus equivalent to the predicate.
• For reference image functionality: The ground truth for this new feature would be its ability to capture and transfer an image. This is validated by functional testing and comparison to the secondary predicate (IOLMaster 700), which already has this function. There isn't a "ground truth" in the clinical diagnostic sense for an image capture tool beyond its intended function of capturing an image.
• For safety and performance standards: The "ground truth" is compliance with the specifications and limits defined by the international and national consensus standards (e.g., ISO, IEC, ANSI/AAMI).

8. The Sample Size for the Training Set

N/A. This is not an AI/ML device that requires a training set in the typical sense of machine learning. It's a measurement instrument. The "Vision Planner software package" and "Argos UI software" are traditional software applications that undergo verification and validation, not model training.

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

N/A. No training set is involved.

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