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Sydney IVF Sperm Cryopreservation Buffer is intended for use as a buffer to prevent damage to sperm samples during cryopreservation and thawing.

Sydney IVF Sperm Cryopreservation Buffer consists of an aqueous solution intended for use as a buffer to prevent damage to sperm samples during cryopreservation and thawing. The IVF technician will use the buffer to cryopreserve washed spermatozoa, including MESA (microsurgical epididymal sperm aspiration) and TESA (testicular sperm extraction) samples. Sydney IVF Sperm Cryopreservation Buffer contains glycerol as a cryoprotectant, and HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) as a buffer. This product is used to cryopreserve washed sperm and store them for future use.

Sydney IVF Sperm Cryopreservation Buffer contains Human Serum Albumin (HSA) (4 mg/mL) and Gentamicin (0.01 mg/mL). The device is available as a 20 mL fill only.

The Sydney IVF Sperm Cryopreservation Buffer is provided in glass vials with Fluorotec coated rubber stoppers held in place with a tamper evident seal. These products are single use, sterile (aseptic filtration) devices.

I am sorry, but the provided text does not contain the information required to describe the acceptance criteria and the study that proves a device meets the acceptance criteria in the context of an AI-powered medical device or diagnostic.

The document is a 510(k) clearance letter from the FDA for a product called "Sydney IVF Sperm Cryopreservation Buffer." This product is a chemical solution, not an AI-powered device or a device that uses algorithms and data analysis in the way implied by the questions (e.g., test sets, training sets, ground truth from experts, MRMC studies).

Therefore, I cannot populate the table or answer the specific questions about the study design because the document describes a chemical product's regulatory clearance based on "substantial equivalence" to a predicate device, focusing on its formulation, shelf-life, and biological performance (e.g., sperm survival, embryo assay), rather than the performance of an AI algorithm.

To answer your prompt, I would need a document describing the validation of an AI-powered medical device or a related diagnostic algorithm.

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Sydney IVF Sperm Cryopreservation Buffer is intended for use as a buffer to prevent damage to sperm samples during cryopreservation and thawing.

Sydney IVF Sperm Cryopreservation Buffer consists of an aqueous solution intended for use as a buffer to prevent damage to sperm samples during cryopreservation and thawing. The IVF technician will use the buffer to cryopreserve washed spermatozoa, including MESA (microsurgical epididymal sperm aspiration) and TESA (testicular sperm extraction) samples.

The provided text describes a 510(k) submission for the Sydney IVF Sperm Cryopreservation Buffer, focusing on demonstrating its substantial equivalence to a predicate device. This submission is for a medical device rather than an algorithm or AI-driven device. As such, many of the requested categories in the prompt, which are typically relevant for AI/ML device evaluations (e.g., sample size for test set, number of experts for ground truth, MRMC studies, standalone performance, training set details), are not directly applicable or discussed in the provided documentation.

However, I can extract the acceptance criteria for the device's performance based on the specific tests mentioned.

Here's an analysis of the provided text in the context of your request:

1. Table of acceptance criteria and the reported device performance

2. Sample sized used for the test set and the data provenance

• Sample Size: Not specified in the provided document. The text states "Sydney IVF Sperm Cryopreservation Buffer was subjected to the following tests," implying that samples of the buffer were tested, but no number of batches or units is given.
• Data Provenance: Not specified. The tests would have been performed by Cook Urological Incorporated or a contracted lab.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not applicable. This device is a chemical buffer, not an AI or diagnostic tool where expert ground truth on data is typically established. The "ground truth" for this device is its physical and biological performance against established scientific and regulatory standards (e.g., pH limits, osmolality ranges, MEA success rates, endotoxin levels), which are determined through laboratory tests.

4. Adjudication method for the test set

• Not applicable. Adjudication methods (like 2+1, 3+1) are used for resolving disagreements among multiple human readers or between human readers and AI outputs in diagnostic imaging or similar fields. For a chemical buffer, the "adjudication" is based on the objective results of the lab tests conforming to predefined specifications.

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 a medical device (a buffer solution), not an AI algorithm. Therefore, MRMC studies are not relevant.

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

• Not applicable. This is not an algorithm. The "standalone performance" is the intrinsic performance of the buffer itself as demonstrated by the specified tests.

7. The type of ground truth used

• The "ground truth" for this device's performance is established by objective laboratory test results that meet predefined specifications for pH, osmolality, embryo toxicity (MEA), and endotoxin levels. These specifications are based on regulatory requirements and scientific understanding for medical devices of this type. It's not based on expert consensus, pathology, or outcomes data in the sense typically applied to diagnostic algorithms.

8. The sample size for the training set

• Not applicable. There is no "training set" as this is not an AI/ML device. The manufacturing process of the buffer is validated, and batches are tested for quality control.

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

• Not applicable. As there is no training set for an AI model, this question is not relevant.

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