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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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The IOLMaster 700 is intended for biometric measurements and visualization of ocular structures. The measurements and visualization assist in the determination of the appropriate power and type of intraocular lens. The IOLMaster 700 measures:

• · Lens thickness
• · Corneal curvature and thickness
• · Axial length
• · Anterior chamber depth
• · Pupil diameter
• · White-to-white distance (WTW)

The IOLMaster 700 is a non-invasive optical biometry instrument for visualization and measurement of ocular structures. The IOLMaster 700 is the latest generation device in the IOLMaster series. The version of the IOLMaster 700 that is the subject of this submission is a modified version of the IOLMaster 700 cleared under K170171.

Here's a breakdown of the acceptance criteria and study details for the IOLMaster 700 device, based on the provided FDA 510(k) summary.

It's important to note that this document is for a 510(k) submission, which primarily aims to demonstrate substantial equivalence to a predicate device. Therefore, the "acceptance criteria" discussed are largely about demonstrating comparability or non-inferiority to the predicate device and established clinical methods, rather than setting absolute performance thresholds for a novel device. The study design reflects this goal.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a 510(k) for a modified device, the "acceptance criteria" are not explicitly stated as numerical targets in the same way they might be for a de novo device. Instead, the performance data aims to demonstrate that the new features (Total Keratometry and Posterior Corneal Surface measurements) are either:

• Interchangeable with conventional methods for normal eyes.
• Perform better than or comparably to existing history-free approximation methods (like Haigis-L) for post-LVC eyes, especially when historical data is unavailable.
• And that repeatability and reproducibility are comparable to the conventional keratometry.

The reported performance is summarized in the "Results" sections of the clinical studies. For the purpose of this table, I will infer the acceptance criteria from the conclusions drawn by the manufacturer regarding comparability and suitability.

2. Sample Sizes and Data Provenance

• Test Set (Clinical Data):

  • Normal Eyes (Study IOLM71): 142 normal eyes (without previous surgery or pathologies except cataract), 738 measurements. (Provenance: Raw data collected prospectively, non-significant risk clinical study at three sites, described as "normal eyes = without prior Laser Vision Correction").
  • Normal Eyes (Study IOLMaster 2017-01909): 32 non-cataract eyes (281 measurements) and 31 cataract eyes (278 measurements). (Provenance: Prospective, monocentric, non-significant risk clinical R&R study, one eye per patient).
  • Post-LVC Eyes (Study HamburgLVC): 30 eyes, 60 measurements (one pre- and one post-operative measurement for each eye). 29 myopic LASIK, 1 hyperopic LASIK. (Provenance: Prospective, single-site clinical study, one eye per patient).
  • Post-LVC Eyes (Study IOLMaster 2017-01909): 30 post-LVC eyes, 267 measurements. (Provenance: Prospective, monocentric, non-significant risk clinical R&R study, one eye per patient).
  • Country of Origin: Not explicitly stated, but the mention of "HamburgLVC" suggests Germany for at least one study site. The applicant "Carl Zeiss Meditec AG" is based in Germany.

• Training Set: Not explicitly mentioned in this 510(k) summary, as the device improvements are primarily related to algorithms for new measurement calculations (Total Keratometry, Posterior Cornea Surface) derived from existing OCT technology, rather than an AI/ML model that requires explicit "training" in the traditional sense. The software verification and validation are for the overall product, and bench testing with "test targets of known curvatures" was used for accuracy and repeatability of the new measurement calculations.

3. Number of Experts and their Qualifications for Ground Truth

• Not applicable in the context of this 510(k). This device is a measurement instrument. The ground truth for the performance of the measurements is based on:
  • Bench testing with "test targets of known curvatures."
  • Comparison to existing, established clinical measurement methods (conventional keratometry, Gullstrand model, Clinical History Method for post-LVC eyes).
  • The "experts" involved would be the clinicians conducting the clinical studies and presumably validating the established methods used for comparison. The document does not specify the number or qualifications of these clinicians beyond them being study site personnel.

4. Adjudication Method for the Test Set

• Not applicable. This study is focused on the performance of a measurement device. There is no subjective interpretation being adjudicated. The measurements are quantitative.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No. This is not an imaging device where human readers interpret and then AI assists in that interpretation. It is a biometric measurement device. The studies compare the device's measurements to established measurement methods.

6. Standalone (Algorithm Only) Performance

• Yes, implicitly. The device itself performs the measurements for Total Keratometry and Posterior Corneal Surface (via its software algorithm). The performance data (Table 1, Figure 1, Figure 2, Tables 2, 3) represent the output of the device's algorithms. There isn't a human-in-the-loop component for these specific measurements; the device generates the numbers. The clinical data then validates these algorithm outputs against established clinical practices.

7. Type of Ground Truth Used

• For Accuracy/Deviation:
  • Known Reference Standards: Bench testing used "test targets of known curvatures."
  • Established Clinical Methods/Models:
    • Conventional keratometry and the Gullstrand model (for normal eyes).
    • Clinical History Method (CHM) for post-LVC eyes, which is considered the "gold standard" when historical data is available.
• For Repeatability & Reproducibility:
  • Multiple measurements on the same patients/eyes using the device itself across different scans, and sometimes different devices/operators.

8. Sample Size for the Training Set

• Not applicable / Not stated. This 510(k) describes a device that utilizes "Spectral domain interferometry (OCT principle)" and "Swept source laser" to obtain biometric measurements. The improvements are primarily algorithmic enhancements to interpret these optical measurements for new parameters (TK, PCS). It's not described as a machine learning model that undergoes a distinct "training set" phase in the typical AI/ML sense. Bench testing and clinical data validate the performance of these algorithms.

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

• Not applicable / Not stated as there is no explicitly defined "training set" for an AI/ML model. The underlying physics and algorithms are based on established optical principles (OCT, interferometry). The validation data compares the device's output to established clinical measurement techniques and physical standards.

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