Search Results

SINC Support Catheters are intended to be used in conjunction with steerable guidewires to access discrete regions of the peripheral vasculature. They may be used to facilitate placement and exchange of guidewires and other interventional devices, and provide a conduit for delivery of saline solutions or diagnostic/ therapeutics agents.

The SINC™ (Selective Interventional Navigation Catheter) Support Catheters are single lumen catheters designed to access peripheral vasculature. These catheters are available in a variety of lengths and offer a side port. Each configuration has a braided support matrix and hydrophilic coating on the distal segment of the catheter. The distal tip is clearly distinguished by a radiopaque gold coated tip.

The SINC Support Catheters will also allow for exchange of guidewires and other interventional devices and provide a conduit for delivery of saline solutions or diagnostic contrast.

The provided text is a 510(k) summary for the Reflow Medical SINC Support Catheter. It outlines the device description, indications for use, and a comparison to a predicate device. However, it does not include information about a study proving the device meets acceptance criteria related to a standalone AI algorithm's performance, human-in-the-loop performance, or the nuances of AI model development (training/test sets, ground truth establishment, expert adjudication).

The document focuses on the physical and mechanical performance of the catheter itself, demonstrating substantial equivalence to a predicate device through non-clinical laboratory testing. The types of tests mentioned are:

• Simulated Use Testing
• Dimensional Verification
• Leak Testing
• Bond Integrity
• Component Integrity
• Corrosion Testing
• Kink Resistance
• Torque Testing
• Particulate
• Catheter Flow Rate and Burst Testing
• Lubricity Characterization
• Packaging Testing
• Design Validation

These tests are standard for medical devices like catheters to ensure their mechanical safety and functionality, not for AI/ML performance.

Therefore, I cannot provide accurate answers to most of your questions as they pertain to an AI/ML device study, which is not described in the given text.

However, I can extract information relevant to the device's mechanical performance to show what "acceptance criteria" and "performance" are mentioned for this type of device:

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

The document states: "The SINC™ Support Catheter met all specified criteria and did not raise new safety and performance questions." While specific quantitative acceptance criteria and reported values for each test (e.g., "Kink Resistance: Passed 1N force without kinking") are not detailed in this summary, the general statement indicates successful performance against pre-defined criteria for each test listed.

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

This information is not applicable to the non-clinical, mechanical testing described. The "test set" here refers to the physical units of the SINC Support Catheter used for the various lab tests (e.g., a certain number of catheters were subjected to torque testing, burst testing, etc.). The document does not specify the number of units tested for each category, nor is "data provenance" (country/retrospective/prospective) relevant to lab-based mechanical tests.

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 is not applicable. "Ground truth" in this context refers to the successful completion or failure of mechanical tests according to engineering specifications, not expert interpretation of medical images or patient outcomes. The "experts" would be the engineers and technicians conducting and evaluating the specified physical/mechanical tests according to established international standards (like ISO 10555-1, referenced in the document). Their qualifications would be in engineering, materials science, or related fields, not medical imaging.

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

Not applicable. Adjudication methods like 2+1 or 3+1 are used for establishing consensus on medical image interpretations or clinical outcomes, typically when ground truth is subjective or challenging to obtain. For mechanical tests, the results (e.g., pass/fail, measurable physical properties) are typically objective and determined by the test protocol itself.

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. No MRMC study was performed as this is a medical device (catheter), not an AI-assisted diagnostic tool.

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

Not applicable. No standalone algorithm performance was assessed as this is a medical device (catheter), not an AI algorithm.

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

For the mechanical tests listed, the "ground truth" is defined by the pass/fail criteria of the specified engineering and performance tests, often based on international standards (like ISO 10555-1 mentioned in the document) and the device's design specifications. For example, a "kink resistance" test would have an objective threshold for what constitutes a "kink" and the maximum force the catheter must withstand without kinking.

8. The sample size for the training set

Not applicable. This device does not involve a "training set" in the machine learning sense. The "training" for such a device involves its design, manufacturing processes, and quality control.

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

Not applicable.

In summary, the provided document details the regulatory clearance of a physical medical device (a catheter) and its mechanical performance testing. It does not describe an AI/ML device or a study involving AI performance metrics or human-AI interaction.

Ask a specific question about this device