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The Vu-Path Ultrasound System with associated probe and accessories provide ultrasound guidance for the placement of needles and catheters in vascular structures adults aged 22 and older. Ultrasound imaging of vascular structures and surrounding tissue may also be performed. The ultrasound probe is only intended to be placed on the skin and should not be advanced percutaneously. The device provides B-Mode and Color Doppler modes of operation.

The Vu-Path Ultrasound system needle guidance technology is indicated for use by appropriately-trained healtheare professionals to provide them with visual tools for passive tracking of a needle with respect to ultrasound image data.

The Vu-Path Ultrasound System employs the same core scientific technology as the other ultrasound devices: digital acquisition, processing, analysis and display capability. Piezoelectric material in the transducer is used as an ultrasound source to transmit sound waves into the body. Sound waves are reflected back to the transducer and converted to electrical signals that are processed and shown on the device display. The Vu-Path is designed primarily to assist physicians in gaining vascular access to major veins and arteries. The system consists of a beamformer and a transducer that operates in two modes, B-Mode and Color Doppler. A single USB connection transfers the information between the beamformer and a user provided computer with display. The application software is installed on the user provided computer. The Vu-Path needle guide attaches over the sheathed transducer and has an integral needle channel that provides a path for a needle to be introduced into the target location.

The provided text is a 510(k) summary for the Vu-Path Ultrasound System (K203657). It outlines the device's purpose, comparison to a predicate device, and the non-clinical performance data submitted to demonstrate substantial equivalence.

Critically, the document explicitly states: "The subject of this premarket submission, the Vu-Path Ultrasound System, did not require clinical studies to support substantial equivalence."

This means that the information requested regarding acceptance criteria, study details (sample size, data provenance, expert ground truth, MRMC study, standalone performance, training set details), as it pertains to clinical performance studies, is explicitly not applicable to this submission. The substantial equivalence was established through non-clinical bench testing and software validation.

Therefore, I cannot provide a table of acceptance criteria for clinical performance or details about a clinical study, as none were conducted or required for this 510(k) clearance.

However, I can extract the information regarding the non-clinical testing and general regulatory compliance:

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

Based on the provided text, the acceptance criteria for the non-clinical tests were that the "predetermined acceptance criteria were met" and that "all risk mitigations were satisfactorily verified and validated." The reported performance is summarized as follows:

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):

The document mentions "Performance testing with a statistically significant sample size was conducted," but does not specify the exact sample sizes for any of the non-clinical tests (e.g., how many units were tested for electrical safety, how many cycles for software testing, etc.).
The data provenance is implied to be from internal company testing (Crystalline Medical) as part of their Design Control process in compliance with 21 CFR Part 820.30. No country of origin or retrospective/prospective nature is specified for test data, as these are bench tests, not clinical studies.

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):

Not applicable. Ground truth as typically established by experts (e.g., radiologists, pathologists) is relevant for clinical validation studies, which were not required or performed. Non-clinical tests establish "ground truth" based on engineering specifications and compliance with recognized standards.

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

Not applicable, as no clinical study or expert consensus for ground truth was performed or required.

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. The submission explicitly states, "The subject of this premarket submission, the Vu-Path Ultrasound System, did not require clinical studies to support substantial equivalence." Therefore, no MRMC study, AI assistance performance, or human reader improvement was assessed or reported.

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

Not explicitly detailed for software performance, but the software testing was done in compliance with IEC 62304. The "needle guidance technology... provides them with visual tools for passive tracking of a needle," implying it is an assistance tool, not a fully standalone diagnostic algorithm. However, the clearance was based on demonstrating substantial equivalence to a predicate, not on demonstrating standalone diagnostic performance improvements.

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

For non-clinical testing, the "ground truth" is typically defined by:

• Engineering Specifications: The device met pre-defined technical specifications (e.g., output power, image resolution, accuracy of needle tracking mechanism within a phantoms/bench setup).
• Compliance with Industry Standards: The device's performance was measured against established criteria in recognized standards (e.g., IEC 60601 series for safety and performance, IEC 62304 for software lifecycle processes).

8. The sample size for the training set:

Not applicable, as this device's clearance was based on substantial equivalence to a predicate device through non-clinical testing, not on the performance of a machine learning model that would require a distinct training set for clinical performance validation. While there is software on the device, the summary does not indicate that it is a deep learning or AI model in the sense that would require a large "training set" for its core functionality validation. The software validation followed IEC 62304 standards.

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

Not applicable, as no described training set was used for a machine learning model.

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