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The Easy Vision Legs option is intended for visualization, registration, and measurement of the lower-limb skeletal geometry.

The Legs Option uses a series of images of the skeletal leg anatomy generated with a digital fluoroscopic X-ray system and reconstructs these images as a single composite image of the complete skeletal anatomy of the lower limbs.

Here's an analysis of the provided text regarding the Philips Easy Vision Family Workstation Legs Option, focusing on the acceptance criteria and study details.

Important Note: The provided text is a 510(k) summary and FDA clearance letter from 1999. These documents typically focus on demonstrating substantial equivalence to a predicate device rather than presenting detailed performance studies with acceptance criteria in the way a modern AI/ML device submission would. Therefore, much of the requested information, particularly regarding specific performance metrics, sample sizes for training/test sets, ground truth establishment, and MRMC studies, is not explicitly present in this type of submission from that era.

Acceptance Criteria and Device Performance

The provided document does not explicitly state quantitative acceptance criteria or detailed device performance metrics in a tabular format as would be expected for a modern AI/ML device. The "performance" described is largely functional and qualitative, focusing on the features and capabilities of the device in relation to its intended use and comparison to a predicate device.

Table 1: Acceptance Criteria and Reported Device Performance (Inferred)

Additional Study Details

Given the nature of a 1999 510(k) for a workstation option, the depth of study information for AI/ML performance is not available.

1. Sample Size used for the test set and the data provenance:

   • Not explicitly stated. The document is a 510(k) summary, not a detailed clinical study report. It implies functional testing and comparison to predicate device capabilities but does not specify a "test set" of images or patients for performance evaluation in the way a modern AI/ML submission would. Data provenance is not mentioned.

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

   • Not applicable/Not stated. This submission pre-dates the common methodologies for establishing ground truth for AI/ML performance. The "truth" would have likely been clinical observations by radiologists using the system or comparison of measurements against established methods.

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

   • Not applicable/Not stated. Adjudication methods for ground truth in AI/ML performance studies were not common practice for this type of device submission in 1999.

4. 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 mentioned or implied. This device is an "option" for a workstation, providing tools for visualization and measurement, not an AI-assisted diagnostic algorithm in the modern sense. The focus is on the availability of these tools, not on how they improve human reader performance through AI integration.

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

   • No, not in the AI/ML sense. The device is a "Workstation Legs Option," implying human interaction and control. It supports visualization, registration, and measurement, which are functionalities the user performs or guides, not fully automated, standalone algorithmic decisions.

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

   • Not explicitly stated for a "test set." For evaluating the functionality of a measurement tool, the ground truth would typically be derived from established clinical measurement techniques or phantoms, with human expertise (e.g., orthopedic surgeons or radiologists) making the measurements or assessments. It's unlikely that pathology or outcomes data would be directly used for validating the measurement accuracy of this type of image processing tool.

7. The sample size for the training set:

   • Not applicable/Not stated. This is not an AI/ML algorithm that undergoes a "training" phase with a large dataset. The "development" would have involved programming and engineering to implement the image reconstruction, registration, and measurement functionalities.

8. How the ground truth for the training set was established:

   • Not applicable/Not stated. As it's not an AI/ML device in the modern context, there wouldn't be a "training set" with ground truth in that specific manner. The "ground truth" for development would be the accurate mathematical and anatomical principles underlying the image processing and measurement algorithms.

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