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The Pentacam® is designed to take photos of the anterior segment of the cornea, pupil, anterior chamber and lens of the eye. To evaluate:

• · corneal shape,
• · analyze condition of the lens (opaque crystalline lens),
• · analyze the anterior chamber angle,
• · analyze anterior chamber depth,
• · analyze the volume of the anterior chamber,
• · analyze anterior or posterior cortical opacity.
• · analyze the location of cataracts (nuclear, sub capsular and or cortical), using cross slit imaging with densitometry,
• · corneal thickness,
• · axial length,
• · white-to-white distance.

The Pentacam® AXL also performs calculations to assist physicians in determining the power of the intractular lens for implantation.

The Pentacam AXL is designed to take photos of the anterior segment of the eye to measures eye components such as Axial length, Corneal thickness, Anterior chamber depth, Corneal curvature, Corneal cylinder, Corneal cylinder axis and White-to-white-distance. The measured parameters can be used by physicians to calculate the power of the intraocular lens (IOL) implanted during a cataract surgery.

While rotating around the eye. the Pentacam® AXL captures Scheimpflug images of the anterior eye segment through varying axes. The Scheimpflug images created during an examination are transmitted to the connected PC. The axial length of the eye is measured by interferometry.

Scheimpflug images can be captured within maximum two seconds. Up to 138,000 genuine height values are measured and analyzed from the Scheimpflug images.

The Scheimpflug images are the basis for the height data which are used to calculate a mathematical 3D model of the anterior eye segment.

The mathematical 3D model, corrected for eye movements, provides the basis for all subsequent analysis.

The provided text describes the acceptance criteria and study proving that the Pentacam AXL device meets these criteria by demonstrating substantial equivalence to predicate devices (IOL Master 500 and Pentacam).

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria (as implied by comparison to predicate devices) and Reported Device Performance:

The acceptance criteria for the Pentacam AXL are implicitly defined by its agreement with the predicate devices (IOL Master 500 and Pentacam) on various ophthalmic measurements. The study evaluated agreement using mean differences and 95% Limits of Agreement (LoA). The table below summarizes the reported performance for the overall eye population in comparison to IOL Master 500, which serves as the primary benchmark for the new functionalities (like axial length). Similar data exists for other eye populations and for comparison with the original Pentacam for parameters related to Scheimpflug imaging.

Note: The acceptance criteria are interpreted as the demonstrated agreement (Limits of Agreement) with the predicate devices, indicating that the Pentacam AXL performs within acceptable ranges compared to established devices. A separate de novo set of acceptance criteria is not explicitly stated, but the study design aims to show non-inferiority/agreement.

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

• Test Set 1 (Pentacam AXL vs. IOL Master 500):

  • Sample Size: 80 subject eyes (20 eyes in each of four pre-defined eye populations).
  • Data Provenance: Not explicitly stated as country of origin, but described as a clinical study comparing the Pentacam AXL with the IOL Master 500. This implies prospective collection for the purpose of this comparison.
  • Four Eye Populations: Normal eyes (phakic, no cataracts/corneal disease), eyes with cataracts, eyes with abnormal corneal shape (post-keratorefractive surgery), and eyes without a natural lens (aphakic/pseudophakic).

• Test Set 2 (Pentacam AXL vs. Pentacam):

  • Sample Size: 138 eyes of 138 patients.
  • Data Provenance: Not explicitly stated as country of origin, but described as a second agreement study. This implies prospective collection for the purpose of this comparison.

• Test Set 3 (In-house Precision Testing):

  • Sample Size: 40 eyes of 40 subjects.
  • Data Provenance: Described as "In-house Precision Testing," implying internal data collection.

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

This information is not provided in the document. The study design is a comparison between two devices (Pentacam AXL and predicate devices), rather than an evaluation against a manually established ground truth by experts. The "ground truth" for the test set is effectively the measurements obtained by the predicate devices.

4. Adjudication Method for the Test Set:

This information is not provided. As the study is a direct comparison between device measurements, traditional adjudication methods involving expert review of images for diagnosis or measurement might not be directly applicable in the same way as, for example, a diagnostic AI study. The "adjudication" is implicitly the statistical comparison of measurements from two automated devices.

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:

A multi-reader multi-case (MRMC) comparative effectiveness study was not conducted as described in the provided text. The study focused on the agreement between the Pentacam AXL and existing devices (IOL Master 500 and Pentacam) for quantitative ophthalmic measurements. It did not involve assessing human reader performance, either with or without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

Yes, the studies described are essentially standalone performance evaluations. The Pentacam AXL, an ophthalmic device, directly measures various eye parameters. The evaluations compare these direct device measurements against those from predicate devices or against repeat measurements from the same device (precision study). There is no "human-in-the-loop" aspect to these performance assessments; they assess the device's inherent measurement capabilities.

7. The Type of Ground Truth Used:

The type of ground truth used is comparison to legally marketed predicate devices.

• For axial length, corneal curvature, corneal cylinder, anterior chamber depth, and white-to-white distance, the IOL Master 500 served as the predicate/reference device.
• For central corneal thickness, and corneal shape parameters, the Pentacam Scheimpflug Camera served as the predicate/reference device.
• For the precision study, repeat measurements from the Pentacam AXL itself served as the reference for repeatability and reproducibility.

8. The Sample Size for the Training Set:

The document describes studies for device validation (performance testing), not for training a machine learning algorithm. Therefore, there is no "training set" mentioned or implied for an AI/algorithm in the context of device development as presented here. The Pentacam AXL is a measurement device, and the Scheimpflug images capture process and subsequent analysis are part of its inherent design, not machine learning that requires a separate training set.

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

As there is no training set for an AI/algorithm described in this submission, this question is not applicable.

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The ZEISS Cataract Suite markerless workflow uses a preoperative image capture tool from the IOLMaster 500 that permits visualization and guidance during cataract surgery using CALLISTO eye. The ZEISS Cataract Suite markerless utilizes an ophthalmic surgical microscope and the following medical devices:

IOLMaster 500: The IOLMaster is intended for the biometric determination of ocular measurements of axial length, anterior chamber depth, corneal radius, white-to-white (WTW), and for the measurement of pupil size and deviation of the visual axis from the center of the pupil. For patients who are candidates for intraocular lens (IOL) implantation, the device also performs calculations to assist physicians in determining the appropriate IOL power and type for implantation.

CALLISTO eye: CALLISTO eye is an assistance system that provides non-diagnostic video documentation and image capture for ophthalmic surgeries. The system allows the remote control of the surgical microscope. The graphical guidance tools, as displayed on the CALLISTO eye Panel PC or microscope eye piece, aid the surgeon to insert, align, position, and register an artificial lens. These tools are intended for anterior segment ophthalmic surgical procedures, including positioning and angular alignment of toric intraocular lenses, limbal relaxing incisions and capsulorhexis. The system utilizes surgeon information for position of graphical guidance tools.

The markerless toric IOL alignment workflow provided with the ZEISS Cataract Suite markerless is a series of medical devices integrated into one workflow for use by a cataract surgeon. The workflow comprises an optical biometric device with green LED image capture from the IOLMaster 500 (K122418) with an optional accessory (Option Reference Image). The captured image is transferred to a cataract surgery assistance system, the CALLISTO eye (K123464), which operates in conjunction with ophthalmic surgical microscopes. Integrated or external data injection systems (IDIS or EDIS) are capable of displaying the graphical templates of the CALLISTO eye in the right ocular of the ophthalmic surgical microscopes.

The provided text describes the regulatory clearance of the ZEISS Cataract Suite markerless, which is a system comprising the IOLMaster 500, CALLISTO eye, and ophthalmic surgical microscopes, intended to aid in cataract surgery, particularly for toric IOL alignment.

However, the document is a 510(k) summary for regulatory clearance and does not contain the detailed study information as requested. It focuses on demonstrating substantial equivalence to a predicate device and outlines general software and safety testing, but lacks specific performance data, acceptance criteria, study methodologies, or ground truth establishment details.

Therefore, I cannot fulfill all the requested information. Below is what can be inferred or explicitly stated from the provided text, and where information is missing.

Missing Information Disclaimer:

The provided document is a 510(k) summary, which typically focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than presenting detailed standalone performance studies with specific acceptance criteria and statistical analysis as would be found in a clinical trial report. As such, much of the requested information (e.g., specific acceptance criteria values, reported device performance metrics against those criteria, sample sizes for test/training sets, details of ground truth establishment, expert qualifications, adjudication methods, MRMC study results, or standalone algorithm performance) is not present in this document.

1. Table of acceptance criteria and the reported device performance

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

• Sample Size for Test Set: Not specified in the document.
• Data Provenance: Not specified. The document mentions "appropriate bench testing" and software testing. It does not indicate the nature, origin, or type (retrospective/prospective) of any clinical data used for performance evaluation, if any.

3. 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)

• Number of Experts: Not specified in the document.
• Qualifications of Experts: Not specified in the document.

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

• Adjudication Method: Not specified in the document.

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

• MRMC Study: Not specified in the document. The document describes the CALLISTO eye as an "assistance system" and states its graphical tools "aid the surgeon." However, it does not provide details of any MRMC study comparing surgeon performance with and without the system, nor an effect size.

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

• Standalone Performance: The document does not describe a standalone performance study of the algorithm (e.g., a specific "AI" component's accuracy without a human surgeon's input beyond providing the initial images). The system is presented as an "assistance system" for human surgeons.

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

• Type of Ground Truth: Not specified in the document. The document focuses on software verification and validation, and safety testing, which typically involve testing against predefined specifications or known correct outputs, rather than clinical ground truth like pathology or outcomes data.

8. The sample size for the training set

• Sample Size for Training Set: Not specified in the document. It's unclear if machine learning models requiring training sets were central to the "AI" component, but even if so, no such details are provided.

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

• Ground Truth Establishment for Training Set: Not specified in the document.

Summary of what is known from the document:

• Device: ZEISS Cataract Suite markerless, comprising IOLMaster 500, CALLISTO eye, and ophthalmic surgical microscopes.
• Purpose: Markerless toric IOL alignment workflow, visualization, and guidance during cataract surgery.
• Regulatory Basis: Substantial equivalence to predicate device (TrueVision 3D Visualization and Guidance System K101861).
• Testing Mentioned: Software testing (according to FDA Guidance for Software in Medical Devices and IEC 62304:2006), electromagnetic compatibility, safety testing, and "appropriate bench testing."
• Conclusion: The device is deemed "safe and effective with respect to their Indications for Use" based on the mentioned testing and comparison to the predicate.

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