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BD Perisafe™ Tuohy Epidural Needle and BD Perisafe™ Weiss Epidural Needle are indicated for the administration of anesthesia or analgesia into the epidural space, or to introduce a dedicated catheter for continuous administration of anesthesia or analgesia into the epidural space. They can also be used to introduce a spinal needle to perform a combined spinal and epidural procedure.

These devices are intended for adult and pediatric patients.

BD Perisafe™ Tuohy Epidural Needle and BD Perisafe™ Weiss Epidural Needle are single use, sterile needles which incorporate an ISO 594-1/-2 compliant connector. The needles are also available in bulk, non-sterile configurations (to be sterilized prior to use). Each type of needle consists of a hollow, stainless steel cannula, a translucent, polypropylene hub and a polypropylene shield over the needle. The cannula is bonded to the hub at one end. The BD Perisafe™ Epidural needles also contain a plastic stylet handle and are available in various needle lengths. The needle hubs are ISO 594-1 and ISO 594-2 compliant connectors. The stylet handle for the BD Perisafe™ Epidural needles is color coded and correlate to the gauge size per ISO 6009:2016.

The provided text is a 510(k) summary for the BD Perisafe™ Tuohy Epidural Needle and BD Perisafe™ Weiss Epidural Needle. It describes the subject device, its indications for use, and compares it to a predicate device to demonstrate substantial equivalence.

Based on the information provided, this document deals with the regulatory clearance of a medical device (epidural needles) and not an AI/ML-driven device. Therefore, many of the requested criteria related to AI/ML model performance, such as sample size for test/training sets, ground truth establishment by experts, adjudication methods, MRMC studies, and effect sizes, are not applicable to this submission.

The acceptance criteria and performance data provided are for physical and biological characteristics of the medical device, not for an AI algorithm's diagnostic or predictive performance.

Here's an attempt to answer the questions based only on the provided document, highlighting when the information is not applicable or not present for AI/ML-specific inquiries:

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

The document provides a comparison table between the subject device and the predicate device across various aspects, including functional testing and biocompatibility testing. The "Substantially Equivalent" column indicates that the subject device met the acceptance criteria by demonstrating equivalence or acceptable performance based on established standards or internal requirements.

Notes:

• The asterisks (*) indicate that for "Stylet Pull Force," "Handle/Hub Separation Force," and "Needle Shield/Hub Separation Force," the subject device had internal requirements and performed additional testing because the predicate device did not specify these tests, and there were design differences that necessitated these tests to ensure safety and effectiveness.
• "N/A" in the "Substantial Equivalence" column for some biocompatibility and additional tests indicates that these specific tests were performed by the subject device's manufacturer but were not explicitly specified for the predicate device in the comparison table. This implies the subject device met these criteria, contributing to substantial equivalence based on a broader assessment including materials equivalence.

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 as this is not an AI/ML-enabled device. The testing described focuses on physical and biological performance of the device itself (e.g., fluid leakage, material strength, biocompatibility), not on data-driven performance. The sample sizes for the functional and biocompatibility tests are not explicitly stated in this summary.

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. This is not an AI/ML-enabled device and no ground truth from expert readers/reviewers was established for its performance. The "ground truth" for this device's performance is based on established engineering standards (ISO standards, USP standards) and internal testing protocols.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. This is not an AI/ML-enabled device and no human adjudication process for performance was involved.

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. This is not an AI/ML-enabled device. No MRMC study or assessment of human reader improvement with AI assistance was performed.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. This is not an AI/ML-enabled device. There is no algorithm to test in a standalone manner.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The "ground truth" for the device's performance is derived from:

• International Standards: ISO (International Organization for Standardization) standards (e.g., ISO 594, ISO 9626, ISO 10993, ISO 7864) for functional, mechanical, and biocompatibility properties.
• United States Pharmacopeia (USP) Standards: (e.g., USP, USP, USP) for specific biological and purity tests.
• Internal Requirements: For tests not explicitly covered by external standards but deemed critical by the manufacturer (e.g., Stylet Pull Force, Handle/Hub Separation Force, Needle Shield/Hub Separation Force). The rationale for these internal tests is provided, linking them to specific risks (e.g., user inconvenience, inability to use product, needle-stick injury).

8. The sample size for the training set
Not applicable. This is a physical medical device, not an AI/ML algorithm. There is no concept of a "training set" in this context.

9. How the ground truth for the training set was established
Not applicable. As there is no training set for an AI/ML algorithm, no ground truth was established for it.

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