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iCare ALTIUS CW is a Medical Device Software indicated for the review, processing and analysis of ophthalmic medical images, for the review of video, clinical and diagnostic data, measurements and reports, generated by ophthalmic medical devices or documentation system through computerized networks, to support trained healthcare professionals in the diagnosis and monitoring of several eye pathologies.

iCare ALTIUS CW is a cloud-based software application with a web-based interface able to:

• review medical ophthalmic images, including videos,
• digitally process images,
• review diagnostic data, clinical information and reports,
  from ophthalmic diagnostic instruments. CW does not perform automated image analysis but provides advanced imaging manipulation tools.
  CW allows to review and process diagnostic data and multiple images with different formats (e.g. PDFS, JPEG, ...) and provides the following features:
• image manipulation filters such as zooming, changing brightness and contrast and gamma, RGB filtering,
• side-by-side image comparison (detached or synchronized mode) with different layouts,
• advanced imaging tools, such as flicker between different pictures and mosaics of several images,
• review and print reports generated by ophthalmic devices.
  CW integrates with PACS software systems, which provide the medical images and reports, to be analysed by the CW. The patient data and medical images exchange between CW and PACS is done through computerized networks using secured network communication.
  The web-based interface of CW is designed to be used through a desktop PC or a laptop using keyboard and mouse (further details in the technical requirements section).
  The User Interface is available in the languages required by the applicable regulatory requirement of the country where the device is placed on the market.

The iCare ALTIUS CW device is a Medical Device Software indicated for the review, processing, and analysis of ophthalmic medical images, video, clinical and diagnostic data, measurements, and reports generated by ophthalmic medical devices or documentation systems. It aims to support trained healthcare professionals in the diagnosis and monitoring of various eye pathologies.

The provided text does not contain detailed acceptance criteria or a comprehensive study report with specific performance metrics and statistical results. It describes the device, its intended use, and states that "Software Verification and Validation Testing" was conducted, and "documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, 'Content of Premarket Submissions for Device Software Functions.'" However, it does not specify what those acceptance criteria were, what the reported device performance against those criteria was, or provide the specifics of the study methodology (e.g., sample sizes, ground truth establishment, expert qualifications, etc.).

Therefore, I cannot fully answer your request based on the provided input.

However, I can extract the available information and highlight what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample size for test set: Not specified.
• Data provenance: Not specified.

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

• Number of experts: Not specified.
• Qualifications of experts: Not specified.

4. Adjudication method for the test set

• Adjudication method: Not specified.

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. The device description states, "CW does not perform automated image analysis but provides advanced imaging manipulation tools," suggesting it's primarily a viewing and processing platform rather than an AI-driven diagnostic tool in the typical sense that would necessitate an MRMC study comparing AI-assisted vs. unassisted human performance in diagnosis or detection. The capabilities listed (zooming, brightness, contrast, comparison, flickering, mosaic, cup-to-disc ratio annotation) are image manipulation and viewing tools.

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

• Standalone study: Not specified. As noted above, the device is described as a tool to "support trained healthcare professionals" and "does not perform automated image analysis." Thus, a standalone algorithm performance evaluation would not be applicable in the same way it would for an autonomous AI diagnostic system.

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

• Type of ground truth: Not specified.

8. The sample size for the training set

• Sample size for training set: Not specified. Given that the device "does not perform automated image analysis," it's unlikely to have a "training set" in the context of a machine learning algorithm.

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

• Method for establishing ground truth: Not applicable based on the device description.

Summary of what is known:

• Device Name: iCare ALTIUS CW
• Regulatory Status: K234076, Class II, Product Code NFJ
• Indications for Use: Review, process, and analyze ophthalmic medical images, video, clinical and diagnostic data, measurements, and reports to support healthcare professionals in diagnosis and monitoring of eye pathologies.
• Key Features: Image manipulation (zoom, pan, brightness, contrast, gamma, RGB filtering), side-by-side comparison, advanced imaging tools (flicker, mosaic), review/print reports, cup-to-disc ratio annotation.
• Core Functionality: Cloud-based software providing advanced imaging manipulation tools; it does not perform automated image analysis.
• Performance Data Provided: "Software Verification and Validation Testing" was conducted, and documentation complied with FDA guidance and IEC 62304.
• Conclusion: The device is substantially equivalent to the predicate (FORUM, K213527), and differences (absence of purely database features, measurements only in dimensionless units for cup-to-disc, mosaic and flickering features, different system architecture) have no effect on safety and effectiveness.

What is explicitly missing from the provided text to fully answer the request:

• Specific quantitative or qualitative acceptance criteria.
• Detailed results of the verification and validation testing against those criteria.
• Any information regarding clinical studies, test set sizes, ground truth establishment, expert qualifications, or adjudication methods.
• Information about training sets or AI performance metrics, as the device explicitly states it does not perform automated image analysis.

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