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The PhotonBlade® with Adaptive Smoke Evacuation is a monopolar RF device coupled with illumination that is indicated for cutting and coagulation of tissue during general surgical procedures and for removing smoke generated by electrosurgery when used in conjunction with an effective smoke evacuation system.

The PhotonBlade® with Adaptive Smoke Evacuation (PB2SE) is a single use, sterile, electrosurgical device with a light and adaptive smoke evacuation attachment. The device has a monopolar electrode at the distal tip, which delivers Radiofrequency (RF) energy for cutting and coagulation of tissue. The electrode tip is located at the distal end of a rotatable and extendable shaft. The adaptive smoke evacuation attachment connects to the telescoping shaft and the cable of the PhotonBlade. It incorporates a barb connector at the end of the tubing to allow connection to a secondary suction hose connected to a smoke evacuation system. The device handle is integrated with controls for cut, coagulation, and illumination (light). A universal cable attaches the device to a 510(k) cleared electrosurgical unit.

The provided text describes a 510(k) submission for the "PhotonBlade® with Adaptive Smoke Evacuation" device. This is a premarket notification for a medical device seeking substantial equivalence to a legally marketed predicate device, rather than a novel AI/software as a medical device (SaMD) cleared through a de novo or PMA pathway.

Therefore, the document does not contain the information typically sought for a study proving device performance against acceptance criteria for an AI/SaMD. Specifically:

• No acceptance criteria table for AI performance: The document focuses on performance testing for an electrosurgical device (e.g., electrical safety, mechanical/functional testing, biocompatibility, smoke evacuation flow rate).
• No mention of AI/algorithm: The device is described as a monopolar RF device with illumination and adaptive smoke evacuation. There is no indication of an AI component.
• No data provenance, expert ground truth, adjudication, MRMC studies: These elements are characteristic of studies evaluating AI performance in interpreting medical images or other data, which is not applicable here.
• No standalone algorithm performance or training set information: Again, these are specific to AI/machine learning models.

Based on the provided text, the device is an electrosurgical tool, and the "study" described is a set of engineering and bench tests, not a clinical study to prove AI performance.

Therefore, it is not possible to describe the acceptance criteria and study as requested, because the provided input does not pertain to an AI/SaMD.

However, I can extract the acceptance criteria and performance data for the electrosurgical device itself as described in the 510(k) summary:

Acceptance Criteria and Device Performance (for the electrosurgical device, not AI/SaMD)

The document frames the "acceptance criteria" and "performance" in terms of confirming the device meets design specifications, safety, and effectiveness compared to predicates, rather than statistical performance metrics for an AI system.

1. Table of Acceptance Criteria and Reported Device Performance (as inferred from the text):

2. Sample Size Used for the Test Set and Data Provenance:

• Sample Size: Not explicitly stated in numerical terms for each test. The text mentions "conditioned samples" for mechanical and functional testing, and "the material" for biocompatibility. This suggests standard engineering test samples, not patient cohorts.
• Data Provenance: Not applicable in the context of clinical data for AI/SaMD. The studies are bench/engineering tests conducted by the manufacturer. No country of origin for "data" in the sense of patient data is relevant here. The studies are retrospective in the sense that they are performed on manufactured devices to support the submission.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

• Not Applicable. This is an electrosurgical device, not an AI system that interprets medical data. Ground truth in this context refers to engineering specifications and performance standards established through generally accepted methods in medical device testing. There are no human "experts" establishing a clinical ground truth for image interpretation or disease diagnosis.

4. Adjudication Method for the Test Set:

• Not Applicable. As there are no human experts classifying or interpreting data for decision-making (as in AI/SaMD evaluations), there is no adjudication process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:

• No. MRMC studies are specific to evaluating the clinical performance of diagnostic or screening devices, often involving human readers with and without AI assistance. This device is an electrosurgical tool, and such a study is not relevant to its function.

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

• Not Applicable. This device does not have a standalone algorithm for diagnostic or interpretative purposes. "Standalone performance" in this context would refer to the device's electrosurgical and smoke evacuation functions on their own, which are indeed what the performance tests cover (e.g., flow rate, electrical safety).

7. The Type of Ground Truth Used:

• For this device, the "ground truth" is defined by:
  • Engineering Specifications: Defined by the manufacturer's design inputs.
  • International Standards: e.g., IEC 60601 series for electrical safety, ISO 10993 for biocompatibility.
  • Predicate Device Performance: Benchmarking against the previously cleared PhotonBlade® and reference devices for function (e.g., smoke evacuation flow rate).
  • Intended Use/Design Requirements: Verification that the device performs as intended (cutting, coagulation, smoke removal) without affecting electrosurgical function.

8. The Sample Size for the Training Set:

• Not Applicable. This is not an AI/machine learning device; therefore, there is no "training set."

9. How the Ground Truth for the Training Set was Established:

• Not Applicable. As there is no training set for an AI model, there is no ground truth establishment process for it.

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The PhotonBlade is a monopolar RF device coupled with illumination that is indicated for cutting and coagulation of tissue during general surgical procedures.

The PhotonBlade is a single use, sterile, electrosurgical device with a light. The PhotonBlade has an electrode at the distal tip, which delivers RF energy for cutting and coagulation of soft tissue. The electrode tip is located at the distal end of a rotatable and extendable shaft. The device handle is integrated with controls for cut, coagulation, and illumination (light). A universal cable attaches the device to a 510(k) cleared electrosurgical unit.

The provided text describes the PhotonBlade device, an electrosurgical device with illumination, and its comparison to predicate devices for FDA 510(k) clearance. However, it does not contain information about acceptance criteria, a specific study proving the device meets those criteria, sample sizes for test or training sets, data provenance, expert ground truth establishment, adjudication methods, or MRMC studies for AI devices.

The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to legally marketed predicate devices, primarily through engineering and bench testing, rather than clinical trials or AI performance evaluations.

Given the information provided in the text, I can only report what is included:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly present a table of acceptance criteria with corresponding device performance metrics. Instead, it lists various performance tests conducted to demonstrate compliance with product requirements, safety, efficacy, and substantial equivalence to the predicate devices. These tests generally assess the device's capability to perform its stated functions.

Missing Information (Not found in the provided text):

2. Sample size used for the test set and the data provenance:
* Not specified in the provided text. The document refers to "Performance testing was conducted on PhotonBlade devices" but does not detail sample sizes for specific tests or the origin of any data (e.g., country of origin, retrospective/prospective). The illumination output mentioned "n=29" for the PhotonBlade, which refers to the number of devices measured for that specific attribute.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
* Not applicable. This document is for a traditional surgical device, not an AI device requiring expert ground truth for classification/diagnosis.

4. Adjudication method for the test set:
* Not applicable. This document is for a traditional surgical device, not an AI device.

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 document is for a traditional surgical device, not an AI device. No MRMC study was conducted or mentioned.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
* Not applicable. This document is for a traditional surgical device, not an AI device.

7. The type of ground truth used:
* Not applicable in the context of AI algorithms. The "ground truth" for the device's performance relies on engineering specifications, physical measurements, and comparison to the established performance of predicate devices, as demonstrated through bench testing and ex-vivo studies.

8. The sample size for the training set:
* Not applicable. This document is for a traditional surgical device, not an AI device. No training set is mentioned.

9. How the ground truth for the training set was established:
* Not applicable. This document is for a traditional surgical device, not an AI device. No training set or ground truth establishment method for it is mentioned.

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