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Intended for use by practitioners in a manner identical to similar electrosurgical electrodes of this type presently in commercial distribution. It is intended to provide electrocautery over a large surface.

The Fusion Ball Tip Electrode is a reusable electrode for use in monopolar electrosurgical accessories.

This 510(k) premarket notification for the Fusion Ball Tip Electrode does not contain information about acceptance criteria or a study demonstrating the device meets such criteria in the context of an AI/ML device. The provided text describes a medical device, specifically an electrosurgical electrode, and compares its technological characteristics to a predicate device for the purpose of demonstrating substantial equivalence.

Therefore, the following requested information cannot be extracted or inferred from the provided text:

1. A table of acceptance criteria and the reported device performance: Not applicable. The document focuses on technological characteristics for substantial equivalence, not performance metrics against acceptance criteria.
2. Sample size used for the test set and the data provenance: Not applicable. No test set for performance evaluation is mentioned.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. No test set or ground truth establishment is mentioned.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. No test set or adjudication is mentioned.
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 not an AI/ML device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an AI/ML device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable. No ground truth is mentioned.
8. The sample size for the training set: Not applicable. No training set is mentioned as this is not an AI/ML device.
9. How the ground truth for the training set was established: Not applicable. No training set or ground truth establishment is mentioned.

The document is a traditional 510(k) submission focused on demonstrating substantial equivalence to a legally marketed predicate device based on technological characteristics, indications for use, and material composition, rather than performance against specific acceptance criteria relevant to AI/ML or diagnostic performance.

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The RapiSeal Patch is intended to provide a temporary matrix during the natural tissue repair process, resulting in the additional benefit of hemostatic tamponade.

The Patch is a bioresorbable film containing gelatin. Each Patch is supplied sterile and non-pyrogenic in a single-use package.

This document describes the RapiSeal™ Patch, a surgical patch intended to provide a temporary matrix and hemostatic tamponade.

Here's an analysis of the acceptance criteria and supporting studies as presented in the document:

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

The document does not explicitly state "acceptance criteria" in a tabular format with corresponding performance metrics. However, we can infer the desired performance characteristics (effectiveness and biocompatibility) and extract the reported findings from the "Performance Data" section.

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

• Nonclinical Effectiveness (Splenic Injury Study):
  • Sample Size: 12 pigs (6 untreated control animals, 6 Patch treated animals).
  • Data Provenance: Not explicitly stated, but likely prospective animal study conducted in a controlled lab environment. The country of origin is not specified.
• Clinical Effectiveness (Air Leaks - Lung Study):
  • Sample Size: 48 patients.
  • Data Provenance: "pre-commercial phase of the product." This indicates a prospective human clinical evaluation. The country of origin is not specified.

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

• Nonclinical Effectiveness (Splenic Injury Study): The document mentions "Gross and histological examination." This implies evaluation by experts, likely veterinary pathologists or similar. However, the exact number of experts and their specific qualifications (e.g., years of experience) are not specified.
• Clinical Effectiveness (Air Leaks - Lung Study): The document states "no Patch-related complications were reported" and the study "demonstrated that the Patch was capable of successfully reducing or sealing air leaks intraoperatively." This suggests clinical observation and assessment by surgeons or other medical professionals involved in the procedures. The number and qualifications of these experts are not specified.

4. Adjudication method for the test set

• Nonclinical Effectiveness (Splenic Injury Study): The document does not describe an explicit adjudication method (e.g., 2+1). Assessment was likely based on the findings of gross and histological examinations, presumably by a single or a team of pathologists without a formal adjudication process described for discordance.
• Clinical Effectiveness (Air Leaks - Lung Study): No specific adjudication method is described. Clinical observations and outcomes were reported.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted as this device is a physical surgical patch, not an AI-powered diagnostic or assistive technology for human readers. Therefore, the concept of "human readers improve with AI vs without AI assistance" is not applicable here.

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

Not Applicable. The RapiSeal™ Patch is a medical device, not an algorithm. Therefore, "standalone (algorithm only)" performance is not a relevant concept for this product.

7. The type of ground truth used

• Nonclinical Effectiveness (Splenic Injury Study): The ground truth for effectiveness was established through direct surgical observation of bleeding cessation and wound approximation, followed by histopathology (gross and histological examination) at specific time points.
• Clinical Effectiveness (Air Leaks - Lung Study): The ground truth for effectiveness was established through intraoperative clinical observation of the reduction or sealing of air leaks and the absence of reported Patch-related complications.

8. The sample size for the training set

Not Applicable. As a physical medical device, there is no "training set" in the context of machine learning or AI algorithms. The development of the device likely involved iterative design, testing, and refinement, but this is not analogous to a training set for an algorithm.

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

Not Applicable. (See point 8). The "ground truth" for the device's design and manufacturing would be based on established engineering principles, material science, and pre-clinical physiological understanding.

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The Patch reinforces the soft tissue of the lung thereby sealing or reducing air leaks that occur during pulmonary surgery.
• Reinforce soft tissue of the lung
• Seal or reduce air leaks in thoracic surgery

The Patch is a bioresorbable film containing gelatin. Each Patch is supplied sterile and non-pyrogenic in a single-use package.
Thin film

Here's an analysis of the provided text regarding the acceptance criteria and study for the Fusion Medical Technologies, Inc. "The Patch" device:

The provided text does not explicitly define specific, quantitative acceptance criteria for the device's technical performance. Instead, it offers a general statement about the successful closure of air leaks.

Here's the breakdown based on your requested information, extracting what's available and noting what's not:

Acceptance Criteria and Device Performance

Study Details

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

• Test Set Sample Size: 26 patients. A total of 52 air leaks were treated.
• Data Provenance: Multi-center, open-label study. The country of origin is not specified but is implicitly within the regulatory scope of the FDA given the 510(k) submission. It is a prospective clinical study.

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

The document does not specify the number of experts or their qualifications used to establish the "successful closure" ground truth for the air leaks. It's implied that the clinical investigators participating in the multi-center study made these determinations.

4. Adjudication method for the test set

The document does not specify any explicit adjudication method (e.g., 2+1, 3+1). The assessment of "successful closure" was likely made by the treating physicians/investigators at each study center.

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, an MRMC comparative effectiveness study was not done. This device is a surgical patch, not an AI-assisted diagnostic tool.

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

Not applicable. This is a medical device (surgical patch), not an algorithm.

7. The type of ground truth used

The ground truth for the "successful closure" of air leaks was based on clinical assessment by the investigators/treating physicians during the pulmonary surgery and follow-up.

8. The sample size for the training set

The document does not mention a training set. This is a physical medical device, and the concept of a "training set" as understood in AI/ML contexts does not apply here. The "clinical tests" described are considered the pivotal study for performance evaluation.

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

Not applicable. As above, no training set in the AI/ML sense was used or described.

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