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The Contura Lumen Marker is an accessory to the Contura MLB Applicator intended to be used to identify treatment lumens for radiation therapy dose planning.

Re-usable devices that aid in the identification of lumens within the Contura MLB Applicator. Each marker is 237 mm in length.

The provided document describes the Contura Lumen Marker, an accessory for the Contura MLB Applicator, used to identify treatment lumens for radiation therapy dose planning. This device is not an AI/ML powered device, and therefore the majority of the requested information regarding AI/ML studies is not applicable.

Here's an analysis of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a non-AI/ML device, the concept of "acceptance criteria" is typically tied to functional performance and safety, rather than diagnostic accuracy metrics. The document summarizes "preclinical testing" rather than formal performance studies with explicit acceptance criteria and corresponding reported device performance values as would be seen for a diagnostic or AI/ML device.

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

Not applicable for this type of device. The document mentions "preclinical testing" but does not specify a "test set" in the context of diagnostic performance. The testing would have involved prototypes of the marker and applicator.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. "Ground truth" in the context of diagnostic accuracy is not relevant for this device. The assessment of compatibility, CT visibility, and durability would likely be conducted by engineers and medical physicists, but these are not "experts" establishing a diagnostic ground truth.

4. Adjudication Method

Not applicable. This is not a diagnostic device requiring adjudication of different expert opinions.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. This device is not an AI/ML system and does not involve human readers interpreting images.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

No. This is a physical medical device, not an algorithm.

7. Type of Ground Truth Used

Not applicable. The "performance" of this device is assessed by its physical characteristics and functionality, not by its ability to accurately identify or diagnose a condition. The "ground truth" would be established by direct observation and measurement of its physical properties and interactions with the Contura MLB Applicator and CT imaging.

8. Sample Size for the Training Set

Not applicable. This is a physical medical device, not an AI/ML algorithm that requires a training set.

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

Not applicable.

Summary of Device and Evidence Presented:

The K082264 submission for the Contura Lumen Marker is focused on demonstrating substantial equivalence to predicate devices rather than establishing novel performance metrics through extensive clinical or diagnostic studies. The "Summary of substantial equivalence" section highlights the "preclinical testing" conducted, which assessed:

• Marker compatibility with the Contura MLB Applicator: This would involve testing if the marker fits and functions correctly within the applicator.
• CT visibility: This would involve imaging the marker within the applicator using a CT scanner to ensure it is clearly visible for radiation therapy dose planning.
• Durability: This would involve testing the marker's ability to withstand repeated use or conditions it is expected to encounter.

The document states that the Contura Lumen Marker "performed as intended" in these preclinical tests. The primary argument for substantial equivalence is based on the device having the "Same intended use; Same design; Same materials; Same operating principle; Same technological characteristics" as its predicate devices.

Conclusion:

The provided document describes a physical medical device (Contura Lumen Marker) that aids in radiation therapy planning. The regulatory submission (510(k)) focuses on demonstrating substantial equivalence to existing devices through preclinical testing of its physical and functional properties, rather than diagnostic accuracy studies or AI/ML performance evaluations. Therefore, most of the requested information regarding AI/ML study components is not pertinent to this device.

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