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The introducer system is intended for use in percutaneous procedures to introduce catheters and other intravascular devices into the peripheral vasculature.

The finished device is a valved tear-away introducer for use in percutaneous procedures to introduce or position catheters or other interventional devices into the peripheral vasculature. The unmodified predicate introducers are constructed using identical processes and material with the exception of the subject device was modified to add a new valve design and material. The performance characteristics to the current marketed predicate device are unchanged and are consistent with other legally marketed devices.

The provided text is related to a medical device's 510(k) submission, specifically for the Galt Medical Corp. VTI Valved Tearaway Introducer. This document outlines the regulatory approval process and demonstrates substantial equivalence to a predicate device, focusing on functional testing rather than clinical study results involving human readers or AI.

Therefore, many of the requested categories in the prompt regarding AI, human readers, ground truth, and training data are not applicable to this type of document. The information provided is primarily about the physical and functional performance of the device itself.

Here's an analysis based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document refers to "product specifications" and "protocols based on international standards and Galt Medical requirements" but does not explicitly list the specific quantitative acceptance criteria or detailed numerical results for each test. Instead, it states that the device "met product specifications."

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

• Test Set Sample Size: Not explicitly stated. The document mentions "Functional testing on un-aged and 4 year aged product," implying a set of physical devices were tested.
• Data Provenance: Not specified, but generally, such functional and physical tests are conducted in a controlled laboratory environment by the manufacturer (Galt Medical Corp.) in the US (Garland, TX). The study is retrospective in the sense that the testing has already been completed and the results are being submitted for regulatory review, but it's a prospective test of the device's characteristics against its specifications.

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

• Not Applicable. This is a physical device, and the "ground truth" for its performance is established by standardized functional tests and measurements, not by expert interpretation of data like in an AI or diagnostic study.

4. Adjudication method for the test set:

• Not Applicable. As per point 3, there's no "adjudication" in the sense of resolving discrepancies in expert interpretations. The tests have clear pass/fail criteria based on objective measurements.

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:

• Not Applicable. This device is a catheter introducer, not a diagnostic AI system or a system that assists human readers.

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

• Not Applicable. This is a physical medical device, not an algorithm.

7. The type of ground truth used:

• Physical device measurements and established engineering/performance standards. The "ground truth" is whether the physical device can perform its intended functions (e.g., locking, not leaking, ease of use, biocompatibility) according to predefined specifications and regulatory requirements.

8. The sample size for the training set:

• Not Applicable. There is no "training set" in the context of this device's functional testing. This is not an AI/machine learning product.

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

• Not Applicable. Same as point 8.

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The CAS PSI Shoulder is intended to be used as a surgical instrument to construct and transfer a pre-surgical plan to orthopaedic surgical procedures. The CAS PSI Shoulder is indicated, based on patient-specific radiological images with identifiable placement anatomical landmarks, to assist in pre-operative planning and/or intra-operative guiding of surgical instruments for shoulder replacement surgical procedures on patients not otherwise precluded from being radiologically scanned.

The CAS PSI Shoulder is to be used with the Zimmer® Trabecular Metal™ Reverse Shoulder Baseplate in accordance with the implant system's indications and contraindications.

The CAS PSI Shoulder hardware components (jigs and bone model) are intended for single use only.

The CAS PSI Shoulder consists of both software and hardware components and requires the patient to be radiologically scanned. The CAS PSI Shoulder has been developed with the fundamental goals to assist in pre-operative planning (using the CAS PSI Shoulder Software) and to accurately construct and transfer a pre-operative plan to orthopedic surgical procedures (using the CAS PSI Shoulder Hardware). The hardware (jigs and bone model) have features designed to mate with legally marketed instruments to aid in the implantation of legally marketed Class II implant devices. The hardware components are designed to mate with legally marketed instruments and thus indirectly aid in the placement of legally marketed implants. The software is developed in C++ programming language for a windows operating system. The hardware (jigs and bone guide) are made from biocompatible polyamide (Duraform) with press-fit 304 and 17-4 Stainless Steel components.

The provided text describes the "CAS PSI Shoulder" device, a surgical planning and instrument guidance system for shoulder replacement procedures. However, the document does not contain the level of detail requested in the prompt regarding acceptance criteria and a specific study proving the device meets those criteria.

Instead, it provides a general overview of non-clinical performance studies and a high-level conclusion.

Here's a breakdown of what can and cannot be extracted from the given text based on your request:

What can be extracted:

1. A table of acceptance criteria and the reported device performance:

The document states: "Non-clinical testing demonstrated that the CAS PSI Shoulder meets performance requirements as defined by Design Control activities and is substantially equivalent to the predicate device in terms of safety and efficacy."

However, it does not provide a specific table of acceptance criteria nor quantified device performance metrics from these tests. It only lists the types of non-clinical studies conducted.

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 for any of the listed non-clinical studies.
• Data Provenance: Not specified for any of the listed non-clinical studies (e.g., country of origin, retrospective/prospective). The studies are listed as "Simulated Use Testing," "Cadaveric Testing," etc., implying laboratory or cadaver-based testing rather than clinical patient data.

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):

• This information is not provided in the document. The non-clinical studies (Simulated Use, Cadaveric) would likely involve internal experts or engineers evaluating performance, but their number and qualifications are not detailed.

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

• This information is not provided 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:

• No, an MRMC comparative effectiveness study was not done or reported. The document focuses on non-clinical testing and states that "clinical data and conclusions were not needed to demonstrate substantial equivalence." The device assists in planning but the studies listed are not designed to measure human reader improvement with or without AI.

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

• The document describes "Software Verification and Validation" as one of the non-clinical studies. This would likely involve evaluating the algorithm's performance in a standalone capacity internally. However, specific metrics of its standalone performance (e.g., accuracy, precision) are not reported.

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

• For the non-clinical studies:
  • Simulated Use Testing & Cadaveric Testing: The ground truth would likely be based on established anatomical landmarks, surgical protocols, engineering specifications, and possibly measurements of implant or instrument placement accuracy against a predefined ideal.
  • Software Verification and Validation: Ground truth would be based on expected software output given specific inputs, adherence to design specifications, and computational accuracy.
• Specific methodologies for establishing ground truth are not detailed.

8. The sample size for the training set:

• Not applicable/Not provided. The document describes a "Software Verification and Validation" study and does not mention machine learning or AI in a way that suggests a distinct training set for an AI model. The software component aids in pre-operative planning, which is a rules-based or computational task rather than a traditional machine learning model requiring a training set in the context of this document.

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

• Not applicable/Not provided for the same reasons as point 8.

In summary:

The provided 510(k) summary focuses on demonstrating substantial equivalence through non-clinical performance studies (simulated use, cadaveric, biocompatibility, sterilization, dimensional stability, drop testing, and software verification/validation). It explicitly states that clinical data and conclusions were not needed. As such, it lacks the detailed performance metrics, sample sizes for test/training sets, expert qualifications, and ground truth establishment methods typically found in studies evaluating AI diagnostic or prognostic devices against specific acceptance criteria.

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