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The Langston dual lumen catheter is indicated for delivery of contrast medium in angiographic studies and for simultaneous pressure measurement from two sites. This type of pressurement is useful in determining transvalvular, intravascular, and intraventricular pressure gradients.

The Langston dual lumen catheter consists of a coaxial tube (outer lumen) mounted over a braided catheter shaft (inner lumen) and an extension line with a 3-way stopcock. The extension line with stopcock connects to the outer lumen. The outer lumen, and extension line are joined by an over molded manifold. The manifold also includes a luer that connects to the inner lumen. The manifold is printed with the Langston catheter length, French size, maximum guidewire diameter, and product logo ("Langston"). The Langston dual lumen catheter tip terminates in either a pigtail or multipurpose tip configuration.

The provided text describes a medical device, the Langston Dual Lumen Catheter, and its substantial equivalence to predicate devices, but it does not contain details about acceptance criteria or a study designed to prove the device meets specific performance criteria in the context of an AI/ML model for diagnosis or prediction.

The document is a 510(k) summary for a medical device (a catheter) seeking FDA clearance, demonstrating substantial equivalence to already cleared predicate devices. The "studies" mentioned are bench tests and biocompatibility tests to show that the new device's modifications (e.g., in manufacturing, materials) do not negatively impact its safety and performance compared to the previously cleared versions. These are not clinical studies in the sense of evaluating diagnostic accuracy or predictive performance through human reader evaluations or ground truth comparisons.

Therefore, I cannot provide the requested information about acceptance criteria for an AI/ML model's performance, sample sizes for test sets, expert qualifications, adjudication methods, MRMC studies, standalone performance, or ground truth establishment for training sets, because this information is not present in the provided text.

The closest relevant information from the document is related to the performance verification of the physical medical device, not an AI component.

Here's a breakdown of what is available in the document, framed in the context of device performance, but noting its irrelevance to AI/ML model evaluation:

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

The document broadly states: "The results of the verification tests met the specified acceptance criteria and did not raise new safety or performance issues." It does not provide a table with specific quantitative acceptance criteria or detailed reported performance figures for each test. Instead, it lists the types of tests performed.

The following points are explicitly NOT present in the provided text, as they relate to AI/ML model evaluation, which is not the subject of this 510(k) summary:

2. Sample size used for the test set and the data provenance: Not applicable, no AI/ML test set mentioned.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable, no AI/ML ground truth mentioned.
4. Adjudication method: Not applicable.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done: "Clinical testing was not performed to validate the performance of the subject device." Therefore, no MRMC study for AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable, no algorithm.
7. The type of ground truth used: Not applicable, no AI/ML ground truth mentioned.
8. The sample size for the training set: Not applicable, no AI/ML training set mentioned.
9. How the ground truth for the training set was established: Not applicable, no AI/ML training set mentioned.

In summary, the provided document details the regulatory clearance process for a physical medical catheter through non-clinical bench and biocompatibility testing, not the evaluation of an AI-powered diagnostic or predictive device.

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