Search Results
Found 1 results
510(k) Data Aggregation
(229 days)
The CXI TriForce is intended to be percutaneously introduced into blood vessels and support a wire guide while performing percutaneous peripheral intervention. The product is also intended for injection of radiopaque contrast media for the purpose of angiography.
The CXI™ TriForce Peripheral Crossing Set is an introducer set, supplied with a 5.0 French Flexor introducer sheath, a 4.0 French CXI Support Catheter and a peel-away sheath. The sets are compatible with the supplied 0.035" wire guide. The sets are supplied sterile and intended for one-time use.
The provided text is a 510(k) summary for the CXI TriForce Peripheral Crossing Set. It describes the device, its intended use, and the testing performed to demonstrate substantial equivalence to a predicate device. However, it does not contain the level of detail requested for a comprehensive acceptance criteria and study description as would be found for a novel AI/ML-based medical device.
Specifically, the document focuses on the mechanical and material performance of a physical medical device (a catheter and introducer set), not the performance of an AI/ML algorithm. Therefore, many of the requested points, such as "effect size of how much human readers improve with AI vs without AI assistance," "standalone (i.e. algorithm only without human-in-the-loop performance)," "number of experts used to establish ground truth," and "sample size for training set," are not applicable to this type of traditional medical device submission.
The document states that:
- "Acute Performance - Testing performed verified that performance parameters were acceptable for clinical use. The predetermined acceptance criterion was met."
- "Check-Flo valve liquid leakage testing -- Testing verified that the Check-Flo valve will not experience excessive leakage when utilized according to the device's intended use. The predetermined acceptance criteria were met."
- "Flow Rate Testing - Flow rates through the device were measured at designated injection pressures using saline and contrast."
- "Biocompatibility testing... The predetermined acceptance criteria were met."
Without specific numerical values for the acceptance criteria and performance (e.g., "leakage rate Y mL/sec"), and the exact methodology and sample sizes for these tests, it is impossible to construct the tables and detailed study information requested for a robust AI/ML device evaluation.
Therefore, I will explain why this document does not contain the requested information and provide a general structure of what such a response would look like if the relevant information were available.
Explanation of Missing Information and Why:
The provided document (K170931) is a 510(k) summary for a physical medical device (CXI TriForce Peripheral Crossing Set), which is a catheter and introducer set. It is not an AI/ML-driven device. The regulatory submission for such a device focuses on:
- Mechanical performance: How well the device functions physically (e.g., acute performance, leakage, flow rates).
- Biocompatibility: Ensuring the materials are safe for contact with the human body.
- Sterility: Ensuring the device is free from microorganisms.
- Material compatibility: With other fluids or devices.
Consequently, the types of "acceptance criteria" and "study data" presented are related to these physical and material characteristics, and not to diagnostic accuracy, sensitivity, specificity, reader performance, or multi-reader multi-case studies, which are pertinent to AI/ML devices.
What a complete answer for an AI/ML device would look like (if the information were available):
Based on the provided document, the device is a physical medical device (catheter and introducer set), not an AI/ML-driven device. Therefore, the detailed criteria related to AI/ML algorithm performance, such as sensitivity, specificity, inter-reader variability, human reader improvement with AI assistance, and specific data provenances for AI model training/testing, are not applicable to this submission and are not present in the provided text.
The document generally states that "predetermined acceptance criteria were met" for various physical and material tests, but it does not provide the specific numerical acceptance criteria or the reported performance values.
Below is a template of how such an answer would be structured if the device were an AI/ML product and the necessary information was available.
Acceptance Criteria and Device Performance (Hypothetical for an AI/ML Device):
(1) A table of acceptance criteria and the reported device performance
| Performance Metric | Acceptance Criterion (e.g., AI/Human Combined) | Reported Device Performance (e.g., AI/Human Combined) |
|---|---|---|
| Primary Endpoint | (e.g., Specificity ≥ 85%) | (e.g., 87.2%) |
| Secondary Endpoints | ||
| Sensitivity | (e.g., Sensitivity ≥ 90%) | (e.g., 91.5%) |
| Accuracy | (e.g., Accuracy ≥ 88%) | (e.g., 89.0%) |
| AUC | (e.g., AUC ≥ 0.90) | (e.g., 0.92) |
| Workflow Improvement (e.g., time to diagnosis) | (e.g., Reduction of 15% in read time) | (e.g., 18% reduction) |
| Agreement with Ground Truth | (e.g., Kappa coefficient ≥ 0.70) | (e.g., 0.75) |
Study Details (Hypothetical for an AI/ML Device):
(2) Sample size used for the test set and the data provenance
- Test Set Sample Size: [e.g., 500 cases (images/studies)]
- Data Provenance: [e.g., Multi-center retrospective study from hospitals in the USA, UK, and Germany. Data collected between 2018-2022.]
(3) Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of Experts: [e.g., 3-5 independent experts]
- Qualifications: [e.g., Board-certified Radiologists with >10 years of experience in [specific domain, e.g., chest radiography, mammography, etc.], specializing in [specific sub-specialty if applicable].]
(4) Adjudication method for the test set
- Adjudication Method: [e.g., 2+1 (two experts review independently, and if they disagree, a third senior expert adjudicates). OR Majority vote (if >3 experts). OR Consensus meeting. OR None (if a single definitive ground truth like pathology was used).]
(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
- MRMC Study Done: [e.g., Yes]
- Effect Size: Human readers improved [e.g., sensitivity by X%, specificity by Y%, or overall accuracy by Z%] when assisted by the AI device compared to reading without AI assistance. [e.g., Mean AUC for readers increased from 0.85 to 0.91 when using AI, a statistically significant improvement (p
Ask a specific question about this device
Page 1 of 1