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The Sonicision™ cordless ultrasonic dissection device is indicated for soft tissue incisions when bleeding control and minimal thermal injury are desired. The device can be used as an adjunct to or substitute for electrosurgery, lasers, and steel scalpels in general, plastic, gynecologic, urologic, exposure to orthopedic structures (such as spine and joint space) and other open and endoscopic procedures. The Sonicision™ cordless ultrasonic dissection device can be used to coagulate isolated vessels up to 5 mm in diameter.

The Sonicision™ 13 cm device is also indicated for use in otorhinolaryngologic (ENT) procedures.

The Sonicision™ Cordless Ultrasonic Dissector is a component of the Sonicision™ Cordless Ultrasonic Dissection Device, which is a hand-held surgical device consisting of three interdependent components that. when assembled, enable ultra-high-frequency mechanical motion (ultrasonic energy) to transect, dissect, and coagulate tissue.

The dissector is a sterile, single-use component to which the Sonicision reusable generators and battery packs attach. This component contains features essential to the control and performance of the assembled device; such as the clamping jaw, active blade, speaker, two-stage energy button, rotation wheel, and jaw lever.

Four configurations are available, differing principally by shaft lengths are 13 cm, 26 cm, 39 cm, and 48 cm; corresponding with catalog numbers SCD13, SCD26, SCD396, and SCD48, respectively. The only difference between SCD391 and SCD396 is the packaging configuration. There is no difference in the design of the device.

The provided text describes a 510(k) premarket notification for the Sonicision™ Cordless Ultrasonic Dissector, a surgical device. While it details performance testing, it does not explicitly state specific "acceptance criteria" numerical values that the device had to meet. Instead, the performance studies aim to demonstrate comparability or non-inferiority to the predicate device.

Therefore, I cannot populate a table with explicit acceptance criteria and corresponding reported performance numerical values as they are not presented in that format in the document. The document describes the type of performance tests conducted and the finding of comparability.

However, I can extract and present the information related to the studies in the requested format, interpreting "acceptance criteria" as the goal of the test (e.g., comparable burst strength) and "reported device performance" as the outcome in relation to the predicate.

Here's the breakdown of the information based on the provided text:

Acceptance Criteria and Device Performance

As explicit numerical acceptance criteria are not provided, the "acceptance criteria" are inferred as demonstrating comparability or non-inferiority to the predicate device for various performance aspects.

Study Details

1. Sample sizes used for the test set and the data provenance:

   • Ex-vivo Burst Testing: Specific sample size not provided, but performed on "blood vessels."
   • Ex-vivo Tissue Testing: Specific sample size not provided, but performed on "mesentery."
   • Acute In-vivo Testing: Specific sample size not provided.
   • Chronic In-vivo Testing: Specific sample size not provided.
   • Human Factors Validation: Performed in a "porcine model and a human cadaver model." Specific number of participants/cadavers not provided.
   • Data Provenance: The studies are described as "bench and animal tests" and human cadaver models, implying controlled laboratory or clinical simulation environments. The country of origin is not specified but is presumably where Covidien (Boulder, Colorado) conducts its R&D or contracted research. The studies are prospective in nature, designed specifically to test the device.

2. 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 or qualifications of experts involved in establishing ground truth for any of these performance tests. For animal and cadaver studies, typically veterinarians, pathologists, and surgeons/anatomists would be involved, but this information is not provided.

3. Adjudication method for the test set:

   • The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for the outcomes of these performance tests. The results seem to be presented as direct measurements or observations.

4. 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 involving human "readers" or AI assistance was not done. This device is a surgical instrument, not an imaging diagnostic device that would typically involve human "readers" or AI for image interpretation. The human factors validation is about user interaction with the device, not diagnostic interpretation.

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

   • This is not applicable as the device is a physical surgical instrument and does not involve an AI algorithm with standalone performance. Its performance is always integrated with human use.

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

   • Burst Strength: Likely measured mechanically (physics-based ground truth).
   • Temperatures/Cool Down Times: Measured using temperature sensors (physics-based ground truth).
   • Hemostasis/Lateral Thermal Spread: Likely assessed visually, histologically (pathology-based), or through direct measurement during animal studies (outcomes/observational ground truth).
   • Hemostasis Retention: Assessed through long-term observation in chronic animal studies (outcomes data).
   • Human Factors Validation: Established by objective task completion metrics (e.g., successful dissection, coagulation, no user errors) and subjective user feedback, compared against pre-defined user needs and FDA expectations for usability.

7. The sample size for the training set:

   • This concept (training set) is not applicable as the device is a physical surgical instrument, not a machine learning/AI model. Therefore, there is no "training set" in the conventional sense. The development of the device would involve engineering, design, and iterative testing, but not data-driven "training" like an AI.

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

   • As there is no "training set" for an AI model, this question is not applicable to this device submission.

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The Sonicision™ reusable sterilization tray is intended to provide storage for the Sonicision™ reusable generator and Sonicision™ reusable battery insertion guides during sterilization, storage, and transportation within the hospital environment. The Sonicision™ reusable sterilization tray is only intended to maintain sterility of the enclosed components during autoclave sterilization when used in conjunction with an FDA-cleared sterilization wrap or rigid container (validated with Aesculap™ rigid container model JN741/JK789). The sterilization tray has only been evaluated for a non-stacked configuration.

The Sonicision Sterilization Tray is a reusable, optional accessory to the Sonicision System (cleared through K180149). This device is specially made to enclose up to one Sonicision Reusable Generator and up to two Sonicision Reusable Battery Insertion Guides in order to provide storage during sterilization, storage, and transportation within the hospital environment. The tray does not provide a sterile barrier on its own: it can only maintain sterility using an FDA-cleared sterilization wrap or rigid container.

The document primarily focuses on the Sonicision™ Reusable Sterilization Tray and its substantial equivalence to a predicate device, focusing on its ability to maintain sterility after specific sterilization cycles. It describes performance data related to cleaning, sterilization, mechanical reliability, and human factors. However, the provided text does not contain the detailed quantitative acceptance criteria and study results for assessing device performance in a typical medical device AI/software context.

Specifically, the document discusses a sterilization tray, which is a physical accessory, not an AI or software device. Therefore, the requested information regarding AI/software performance criteria (like sample size for test/training sets, data provenance, expert ground truth, adjudication methods, MRMC studies, or standalone algorithm performance) is not applicable to this physical device submission.

Here's a breakdown of what the document does provide in relation to your request, adapted for a physical device, and what it does not provide:

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

• Acceptance Criteria (Implied for Sterilization Efficacy): The primary acceptance criterion for the sterilization tray is its ability to maintain sterility of the enclosed components after specific steam sterilization cycles when used with FDA-cleared sterilization wrap or rigid containers. The specific cycle parameters (temperature, exposure time, dry time) listed are the validated parameters that the device must withstand and enable sterility for.
• Reported Device Performance: The document states, "The performance data support that the Sonicision Reusable Sterilization Tray is as safe, as effective, and performs as well or better than the legally marketed predicate device." However, it does not provide quantitative results such as sterility assurance levels (SAL) or other specific metrics from the cleaning and sterilization testing. It only states that the testing was performed "in accordance with" relevant standards and guidance.

Table based on the provided information (focused on relevant criteria for a sterilization tray):

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

• Not Applicable / Not Provided: The document does not specify sample sizes for cleaning, sterilization, mechanical, or human factors testing in terms of "test sets" as one would for AI/software. It does not mention data provenance (country, retrospective/prospective). This information is typically found in detailed validation reports, not in the 510(k) summary provided.

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 criterion is for AI/software accuracy validation. For a physical sterilization tray, "ground truth" relates to the successful outcome of sterilization (e.g., lack of microbial growth), which is assessed through laboratory methods, not expert consensus in the way a medical image is.

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

• Not Applicable: This is an AI/software specific term for resolving discrepancies in expert 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

• Not Applicable: MRMC studies are for evaluating human performance (e.g., radiologists reading images) with and without AI assistance. This device is a physical sterilization tray.

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

• Not Applicable: This refers to the performance of an AI algorithm alone.

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

• Implied Ground Truth (for a physical device): For the sterilization aspects, the ground truth would typically be established through microbiology testing (e.g., using biological indicators or test coupons) to demonstrate the absence of viable microorganisms after the sterilization cycles. For mechanical testing, it would be adherence to engineering specifications and performance under stress. For human factors, it would be observed safe and correct use by intended users. The document does not detail the specific ground truth methods or results.

8. The sample size for the training set

• Not Applicable: This (training set) is a concept for machine learning models.

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

• Not Applicable: This (ground truth for training set) is a concept for machine learning models.

In summary, the provided FDA 510(k) letter and summary describe a physical medical device (a sterilization tray) and its regulatory clearance based on demonstrating substantial equivalence to a predicate device through various performance testing (cleaning, sterilization, mechanical, human factors). It does not involve AI or software, and therefore, most of your questions are not directly applicable to the content provided.

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