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The Lifetek Medical Embryo Glide™ Embryo Transfer Catheter is an assisted reproduction catheter used in IVF assisted reproduction procedures to introduce embryo(s) into the female uterus.

The Embryo Glide™ Embryo Transfer Catheter and accessory stylet (ETC) is used during In-Vitro Fertilization (IVF) procedures to introduce embryo(s) into the female body. The embryo passes through the clear, 5 Fr polyurethane catheter, which is open on the end. The 5 Fr catheter is available in 18 cm and 23 cm lengths. Catheters have color-coded hubs to indicate different lengths. Each ETC comes with a 7 Fr sheath which provides a smooth passage into the uterus. An optional stylet (accessory) is available to aid in the initial placement of the 7 Fr sheath. Both the Embryo Transfer Catheter and stylet accessory (optional) are protected within a polypropylene sheath in a sterile, peel-able pouch.

This submission (K992307) for the Embryo Glide™ Embryo Transfer Catheter and accessory stylet does not contain the kind of detailed information typically found in acceptance criteria and study results for performance claims of AI/ML-enabled medical devices.

Instead, this 510(k) summary focuses on demonstrating substantial equivalence to existing predicate devices under the 21 CFR §884.6110 Assisted Reproduction Catheters (Procode 85 MQF) and conformance with general biocompatibility standards. The "Summary of Testing" section is brief and outlines general safety testing rather than performance metrics related to a specific clinical outcome or the analytical/clinical performance of an algorithm.

Therefore, I cannot provide the requested information in the format of a table of acceptance criteria and reported device performance or details about a study evaluating AI/ML performance because that information is not present in the provided text.

Specifically:

• 1. A table of acceptance criteria and the reported device performance: Not available. The document mentions biocompatibility testing (cytotoxicity, vaginal mucosal and dermal sensitization) and Mouse Embryo Assays as "testing," but it does not specify performance criteria (e.g., cell viability thresholds, irritation scores) or quantitative results from these tests. These are general safety tests, not performance tests in the context of an AI/ML algorithm.
• 2. Sample sized used for the test set and the data provenance: Not applicable. There is no "test set" in the context of AI/ML performance evaluation for this device. The biocompatibility tests would have their own sample sizes (e.g., number of animals for assays), but these are not for algorithm performance.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. There is no ground truth establishment for an AI/ML algorithm.
• 4. Adjudication method for the test set: Not applicable.
• 5. If a multi reader multi case (MRMC) comparative effectiveness study was done: No. This type of study is relevant for AI-assisted diagnostic or interpretative devices, which this catheter is not.
• 6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: No. There is no algorithm.
• 7. The type of ground truth used: Not applicable for performance of an AI/ML algorithm. For biocompatibility, the "ground truth" would be the biological response observed in the assays (e.g., cell death, irritation).
• 8. The sample size for the training set: Not applicable. There is no AI/ML algorithm to train.
• 9. How the ground truth for the training set was established: Not applicable.

In summary, this 510(k) submission pertains to a conventional medical device (an embryo transfer catheter) and focuses on demonstrating its safety and substantial equivalence to legally marketed predicate devices, not on the performance evaluation of an artificial intelligence or machine learning algorithm. The testing mentioned (biocompatibility and Mouse Embryo Assays) are standard safety tests for devices that come into contact with biological tissues and embryos, not performance studies for an AI/ML component.

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