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The JTL Tissue Processing Device is a sterile medical device intended for the processing of lipoaspirate tissue in medical procedures involving the harvesting, concentrating and transferring of autologous adipose tissue harvested with a legally marketed lipoplasty system. The device is intended for use in the following surgical specialties when the transfer of harvested adipose tissue is desired: orthroscopic surgery, neurosurgery, gastrointestinal and affiliated organ surgery, urological surgery, gynecological surgery, thoracic surgery, laparoscopic surgery, and plastic and reconstructive surgery when aesthetic body contouring is desired. Only legally marketed accessory items, such as syringes, should be used with the system. If harvested fat is to be re-implanted, the harvested fat is only to be used without any additional manipulation.

The JTL-250-01 Tissue Processing Device is a centrifuge tube for processing lipoaspirate fat tissue that is intended for autologous homofunctional implant (lipofilling). The device is a pre-assembled, single-use centrifuge tube composed of a collection upper chamber, intermediate chamber, and a lower chamber. The diameter of the JTL Tissue Processing Device is 4.2 inches at the top ring and 3.6 inches in the main body. It stands 4 inches tall. The device weighs less than 1 pound. The components of the device are made of biocompatible materials. The device is sterilized by radiation and is intended for single use only. The JTL Tissue Processing Device is to be used in a healthcare facility. It is to be used and in contact with patient tissue for less than 24 hours and is made of materials that are biocompatible.

The provided text is a 510(k) summary for the JTL-250-01 Tissue Processing Device. This document focuses on demonstrating substantial equivalence to a predicate device (LipoGems System K161636) and does not contain detailed information about specific acceptance criteria or a study designed to prove the device meets such criteria in terms of analytical or clinical performance of an AI/ML algorithm.

Therefore, many of the requested fields cannot be extracted from this document as they are typically associated with performance studies of diagnostic or prognostic devices, especially those involving AI/ML.

However, I can extract the information related to the device's characteristics and the general performance testing conducted to support its safety and effectiveness.

Here’s what can be extracted based on the provided text:

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

The document does not explicitly state quantitative acceptance criteria for device performance in the context of an AI/ML algorithm's output (e.g., sensitivity, specificity, accuracy). Instead, it lists general performance testing categories.

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 provided in the document. The document refers to "mechanical testing" but does not specify the sample size of devices or the nature of the data (e.g., patient samples) used in these tests, nor their provenance.

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 information is not applicable to the type of device and study described. There is no mention of expert ground truth establishment for a test set, as this is not an AI/ML diagnostic or prognostic device requiring such a process.

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

This information is not applicable. No adjudication method is mentioned as there is no test set involving human interpretation needing adjudication for ground truth.

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

This information is not applicable. No MRMC study was done, as this is not an AI-assisted diagnostic device.

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

This information is not applicable. This device does not involve an algorithm.

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

For mechanical testing, the "ground truth" would likely be based on established engineering specifications, material science standards, and functional performance benchmarks (e.g., expected fat graft volume, oil separation efficiency). The document indicates "Verification testing of the functional design outputs for the device" and "Performance assessment... with respect to production of fat tissue that is viable and able to be administered". This suggests functional benchmarks were used, rather than expert consensus, pathology, or outcomes data in a clinical sense.

8. The sample size for the training set

This information is not applicable. There is no mention of a training set as this is not an AI/ML device.

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

This information is not applicable. There is no training set for an AI/ML algorithm.

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