(56 days)
Peri-Strips Staple Line Reinforcement is intended for use as a prosthesis for the surgical repair of soft tissue deficiencies using surgical staplers when staple line reinforcement is needed.
Peri-Strips can be used for reinforcement of staple lines during lung and bronchus resections and during bariatric surgical procedures.
Peri-Strips can be used for reinforcement of staple lines during gastric, small bowel, mesentery, colon, and colorectal procedures.
Peri-Strips is intended to be used for reinforcement of suturelines and staple-lines (i.e., occlusion of the left atrial appendage during open chest procedures) during cardiac surgery.
An implantable surgical patch comprised of crosslinked bovine pericardium
The provided text is a 510(k) summary for a medical device called "Peri-Strips Staple Line Reinforcement." It describes the device, its intended use, and its substantial equivalence to a predicate device. However, it does not include acceptance criteria, details of a study that proves the device meets specific performance criteria, or any of the detailed information requested in points 2-9 of your prompt (sample sizes, ground truth establishment, expert qualifications, adjudication methods, MRMC studies, standalone performance, or training set details).
Medical devices like the Peri-Strips Staple Line Reinforcement, when cleared via the 510(k) pathway, typically demonstrate substantial equivalence to a legally marketed predicate device. This process often relies on showing that the new device has the same technological characteristics and similar indications for use as the predicate, and does not raise new questions of safety and efficacy. This usually involves engineering testing (e.g., tensile strength, burst pressure) and biocompatibility testing, rather than clinical efficacy studies with the detailed metrics you've asked for that are more common for AI/ML-based devices.
Therefore, I cannot populate the table or answer the specific questions about acceptance criteria, study details, expert involvement, or AI/ML performance because the provided document does not contain this information.
Based on the provided text, the device is cleared on the basis of substantial equivalence to a predicate device, and the submission does not detail efficacy studies with specific performance metrics or the involvement of AI/ML.
Here is what can be inferred or explicitly stated from the document regarding comparative testing, but it does not fit the format of your requested table for acceptance criteria and device performance as those specific values are not provided.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Not Explicitly Stated for Performance) | Reported Device Performance (Not Explicitly Stated as Numerical Metrics) |
|---|---|
| Substantial equivalence to predicate device | "Peri-Strips® Staple Line Reinforcement is substantially equivalent to the predicate device, having the same technological characteristics." |
| - | "Peri-Strips® Staple Line Reinforcement is substantially equivalent to the predicate device in term of testing and indications for use." |
No specific numerical performance criteria (e.g., accuracy, sensitivity, specificity, or device-specific mechanical performance thresholds) or corresponding reported values are provided in the document. The basis for clearance is "substantial equivalence" to a predicate device, implying that its performance is presumed to be similar without needing to meet new, explicit, quantitative acceptance criteria for efficacy or exact performance metrics publicly disclosed in this summary. Engineering and biocompatibility testing would have been submitted, but the detailed results are not in this public summary.
Regarding the other questions:
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Sample sizes used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Not provided. The document refers to "testing" as part of establishing substantial equivalence, but gives no details about sample sizes, data provenance, or study design (retrospective/prospective). This often indicates that the testing was primarily mechanical, chemical, or biocompatibility-related, rather than clinical efficacy studies with patient data.
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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 / Not provided. The device is a "Surgical Mesh" (an implantable patch/reinforcement), not an imaging or diagnostic device that typically relies on expert interpretation to establish ground truth for performance evaluation in the way an AI/ML diagnostic tool would. Expert review would be part of the design and risk assessment, but not typically in establishing "ground truth" for a performance test set as envisioned for AI.
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Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not applicable / Not provided. Since there's no mention of a test set requiring expert ground truth or interpretation, an adjudication method isn't relevant to the information provided.
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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. The device is "Peri-Strips Staple Line Reinforcement," a physical surgical implant, not an AI/ML-based diagnostic or assistive device that would involve human readers or AI assistance.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not applicable. This is a physical surgical device, not an algorithm.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Not applicable / Not provided. For a physical device like this, "ground truth" in the AI/ML context doesn't apply. Performance would be assessed through established physical/mechanical testing standards (e.g., burst strength, suture retention), biocompatibility, and potentially animal or limited human clinical data to assess safety and gross functional equivalence. These details are not in the summary.
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The sample size for the training set
- Not applicable. This is not an AI/ML device, so there is no "training set."
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How the ground truth for the training set was established
- Not applicable. This is not an AI/ML device, so there is no "training set" or associated ground truth establishment.
§ 870.3470 Intracardiac patch or pledget made of polypropylene, polyethylene terephthalate, or polytetrafluoroethylene.
(a)
Identification. An intracardiac patch or pledget made of polypropylene, polyethylene terephthalate, or polytetrafluoroethylene is a fabric device placed in the heart that is used to repair septal defects, for patch grafting, to repair tissue, and to buttress sutures.(b)
Classification. Class II (performance standards).