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The T-Line Hernia Mesh is indicated for the reinforcement of soft tissue where weakness exists for the repair of ventral hernias performed via an open onlay or sub lay approach in adults (greater than 21 years of age).

The T-Line Hernia Mesh is manufactured by knitting and heat pressing standard medical grade polypropylene monofilament yarn using well-established standard processes that are used to manufacture other commercially available hernia meshes. Mesh extensions are used to apply the device to the abdominal wall. The extensions of the T-Line Hernia Meshare incorporated directly into the mesh body. The mesh design incorporates continuous, uninterrupted, seamless extensions from the mesh body to facilitate mesh securement to tissue. After knitting, needles are swaged onto the ends of the extensions to allow the extensions to be sewn into the abdominal fascia by surgeons akin to how sutures are sewn into fascia.

This FDA 510(k) summary does not describe a study involving a medical device with acceptance criteria and performance results in the way one would typically expect for a diagnostic or AI-driven device. Instead, it concerns a surgical mesh (T-Line Hernia Mesh) and its substantial equivalence to previously cleared versions of the same product.

The primary goal of this submission (K232924) was to add a smaller size of the T-Line Hernia Mesh to its product line, based on customer feedback. Therefore, the "study" described is a performance evaluation to demonstrate that this new size meets the same safety and effectiveness standards as the existing, cleared mesh products.

Here's the breakdown based on your request, with an emphasis on how it applies to this specific submission:

1. Table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size: The document does not specify a distinct "test set" in the context of clinical data for the new, smaller mesh size. The performance evaluation likely involved bench testing of the new mesh size. The "sample size" for such tests would refer to the number of physical mesh samples subjected to various mechanical and material property tests. This information is not provided in this summary.
• Data Provenance: Not applicable in the context of clinical trial data. The device is manufactured by Deep Blue Medical Advances, Inc. in Durham, NC, USA. The performance data would originate from their internal testing.
• Retrospective or Prospective: Not applicable for a device modification of this nature, which primarily relies on bench testing and comparison to previously cleared devices.

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 a diagnostic device where expert ground truth on medical images or patient conditions is required. The assessment is based on engineering and material science principles, and comparison to existing, cleared products.

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

• Not applicable. This is not a study requiring adjudication of expert interpretations. The evaluation focuses on physical and material properties and risk analysis.

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

• No. This is not an AI-driven or diagnostic device. Therefore, no MRMC study, human reader improvement, or AI assistance is relevant.

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

• No. This is not an algorithm or software device.

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

• The "ground truth" here is implied by the established performance specifications and material properties of the previously cleared predicate T-Line Hernia Mesh devices (K230227, K221556, K193144), as well as relevant external standards for surgical mesh. The new, smaller mesh size must demonstrate that it meets these same pre-determined physical, chemical, and mechanical requirements.

8. The sample size for the training set

• Not applicable. This is not a machine learning or AI device that requires a training set.

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

• Not applicable. As above, there is no training set for this type of device submission.

In summary: K232924 is a submission for a minor modification (addition of a smaller size) to an already cleared surgical mesh. The "study" involves performance testing and risk analysis to confirm that the new size maintains the same safety and effectiveness profile as the predicate devices, rather than a clinical study establishing new efficacy or diagnostic performance. The acceptance criteria are essentially that the modified device's performance, material characteristics, and risk profile are equivalent to its previously cleared versions and comply with relevant standards.

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The T-Line Hernia Mesh is indicated for the reinforcement of soft tissue where weakness exists for the repair of ventral hernias performed via an open onlay or sublay approach in adults (greater than 21 years of age).

The T-Line® Hernia Mesh is manufactured by knitting and heat pressing standard medical grade polypropylene monofilament yarn using wellestablished standard processes that are used to manufacture other commercially available hernia meshes. Mesh extensions are used to apply the device to the abdominal wall. The extensions of the T-Line® Hernia Meshare incorporated directly into the mesh body. The mesh design incorporates continuous, uninterrupted, seamless extensions from the mesh body to facilitate mesh securement to tissue. After knitting, needles are swaged onto the ends of the extensions to allow the extensions to be sewn into the abdominal fascia by surgeons akin to how sutures are sewn into fascia.

I am sorry, but the provided text from the FDA 510(k) notification does not contain information about acceptance criteria or a study that proves the device meets those criteria, such as a multi-reader multi-case (MRMC) comparative effectiveness study, standalone algorithm performance, details on ground truth establishment, or sample sizes for training and test sets.

The document is a substantial equivalence determination for a surgical mesh, not an AI-powered medical device. It focuses on demonstrating that the T-Line Hernia Mesh is substantially equivalent to previously cleared predicate devices, primarily due to a change in contract manufacturer. The performance data mentioned refers to non-clinical testing to ensure the modified mesh continues to meet functional and performance requirements, not the kind of clinical study details typically associated with AI/ML device evaluations.

Therefore, I cannot provide the requested information based on the given text.

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The T-Line® Hernia Mesh is indicated for the reinforcement of soft tissue where weakness exists for the repair of ventral hernias performed via an open onlay or sublay approach in adults (greater than 21 years of age).

The T-Line® Hernia Mesh is manufactured by knitting and heat pressing standard medical grade polypropylene monofilament yarn using wellestablished standard processes that are used to manufacture other commercially available hernia meshes. Mesh extensions are used to apply the device to the abdominal wall. The extensions of the T-Line® Hernia Meshare incorporated directly into the mesh body. The mesh design incorporatescontinuous, uninterrupted, seamless extensions from the mesh body to facilitate mesh securement to tissue. After knitting, needles are swaged onto the ends of the extensions to allow the extensions to be sewn into the abdominal fascia by surgeons akin to how sutures are sewn into fascia.

The provided text describes a 510(k) premarket notification for the T-Line Hernia Mesh and does not include information about an AI/ML powered medical device. Therefore, I cannot answer the questions about acceptance criteria, study details, expert qualifications, or comparative effectiveness as these are not relevant to the provided content.

The document discusses the substantial equivalence of the T-Line Hernia Mesh to a predicate device, focusing on an expansion in its Indications for Use to include an open sublay approach. It details performance testing conducted on an animal model to support this expanded indication for use.

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T-LINE™ HERNIA MESH is indicated for the reinforcement of soft tissue where weakness exists for the repair of ventral hernias performed via an open onlay approach.

The T-Line Hernia Mesh is manufactured by knitting and heat pressing standard medical grade polypropylene monofilament yarn using wellestablished standard processes that are used to manufacture other commercially available hernia meshes. Mesh extensions are used to apply the device to the abdominal wall. The extensions of the T-Line Hernia Mesh are incorporated directly into the mesh body. The mesh design incorporates continuous, uninterrupted, seamless extensions from the mesh body to facilitate mesh securement to tissue. After knitting, needles are swaqed onto the ends of the extensions to allow the extensions to be sewn into the abdominal fascia by surgeons akin to how sutures are sewn into fascia.

This is an FDA 510(k) summary for the T-Line Hernia Mesh, which is a medical device. As such, the concept of "acceptance criteria" and "study that proves the device meets the acceptance criteria" in the context of an AI/ML device focusing on metrics like sensitivity, specificity, AUC, and expert adjudication, does not directly apply here. This document describes the equivalence of a conventional medical device (surgical mesh) to a predicate device, based on material properties, design, and biological response in animal models, not on AI/ML performance.

However, I can interpret your request in the context of device performance and safety acceptance for this type of medical device, and extract the relevant information provided:

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

2. Sample size used for the test set and the data provenance:

• Sample Size: The document states "All animals" were evaluated, referring to the porcine model. A specific number of animals is not provided.
• Data Provenance: In vivo testing performed in a "simulated use porcine model." The country of origin is not specified but it's an animal model, not human data. This was a prospective study to evaluate the device in an animal model.

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

• This information is not explicitly provided. Histological evaluations and scoring would typically be performed by trained pathologists or veterinary surgeons, but the number or specific qualifications are absent.

4. Adjudication method for the test set:

• Not applicable/Not specified for this type of device and study. The evaluation likely involved comparative histological assessment and observation, not a human consensus process for classification.

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:

• No. This is not an AI/ML device. Therefore, an MRMC study comparing human readers with and without AI assistance was not conducted.

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

• No. This is not an AI/ML device.

7. The type of ground truth used:

• The "ground truth" for the performance evaluation in this context was established through histological evaluations (inflammation, bioincorporation, fibrosis, cellular populations), gross findings (in a simulated use porcine model), and clinical observations (body weights, clinical pathology parameters) over a 6-month post-operative period, compared against a predicate device.

8. The sample size for the training set:

• Not applicable. This is not an AI/ML device, so there is no "training set" in that sense. The device design was informed by "design inputs; including procedural characteristics and requirements, clinical input from physicians, review of predicate device characteristics and labeling, user interface considerations, relevant external standards, and outputs from the company's risk management process."

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

• Not applicable. As noted above, there isn't a "training set" for an AI/ML model. The design and specifications were derived from various inputs, including existing predicate device characteristics, clinical needs, and risk management rather than a labeled dataset in the AI sense.

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