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The LigaSure Sealer/Divider is a bipolar electrosurgical instrument intended for use in minimally invasive or open surgical procedures where ligation of vessels, tissue bundles, and lymphatics is desired. The LigaSure Sealer/ Divider can be used on vessels (arteries, veins, pulmonary veins) up to and including 7 mm. It is indicated for use in general surgery and surgical specialties as urologic, vascular, thoracic, and gynecologic. Procedures may include, but are not limited to, Nissen fundoplication, cholecystectomy, adhesiolysis, hysterectomy, oophorectomy, etc.

The LigaSure system has not been shown to be effective for tubal coagulation for sterilization procedures. Do not use the LigaSure system for these procedures.

The LigaSure™ Maryland Jaw Thoracic Sealer/Divider One-step Sealing, Nano-coated (LF1930T) device is a sterile, single-use, coated, hand-held bipolar vessel sealing device designed for use with a Covidien electrosurgical generator that include vessel sealing capabilities to ligate (seal) and divide (cut) vessels (arteries, veins, pulmonary arteries, pulmonary veins), tissue bundles, and lymphatics clamped between the jaws using radio frequency (RF) energy. The LF1930T subject can also grasp tissue and perform blunt dissection during general surgical procedures (as indicated).

A hand actuated lever allows the user to open or close the instrument jaws during vessel sealing and cutting, with a single activation button incorporated into the body of the handle. The proposed device has a 30cm length shaft and does not contain software.

The provided document is a 510(k) summary for the LigaSure™ Maryland Jaw Thoracic Sealer/Divider One-step Sealing, Nano-coated (LF1930T). It details the device's technical characteristics, indications for use, and a comparison to predicate devices, along with performance data to support its substantial equivalence.

However, the document does not describe a study in the context of typical AI/ML device evaluations. This submission is for a traditional medical device (an electrosurgical instrument), and therefore the 'acceptance criteria' and 'study' described here are related to hardware performance, electrical safety, and animal studies, not statistical measures of algorithmic performance like accuracy, sensitivity, or specificity. There are no mentions of "test set," "training set," "ground truth experts," or an "AI/ML algorithm" in the provided text.

Therefore, many of the requested fields cannot be answered as they pertain to AI/ML device studies, which is not what this document describes.

Here's an attempt to fill in the table and information based on the provided text, while acknowledging the limitations for AI/ML specific questions:

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)

• Test set sample size: Not applicable in the context of an AI/ML algorithm evaluation. For the acute and chronic animal studies, specific numbers of animals are not provided. The bench testing involved "validated and representative conditions," but specific sample sizes for vessel burst pressure or other tests are not detailed.
• Data Provenance:
  • Bench performance: Ex-vivo (likely animal tissue or synthetic models).
  • Animal Studies: Live animal subjects (acute and chronic). No country of origin is specified.
  • Study Design: These studies would be considered prospective for the specific tests performed.

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

Not applicable. This device is not an AI/ML diagnostic or prognostic tool. Ground truth would be defined by direct physical measurements, observations by veterinary surgeons/pathologists in animal studies, and engineering specifications. The document does not specify expert qualifications beyond the implicit expertise required to conduct and interpret these types of pre-clinical studies.

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

Not applicable. This concept applies to human reader consensus for AI ground truth, which is not relevant here. Decisions about performance in animal studies or bench testing would be based on objective criteria and measurements, likely by a single investigator or team, with standard scientific peer review implicit in the process, but not a formal adjudication akin to expert consensus for image interpretation.

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 device is a surgical instrument, not an AI-assisted diagnostic tool for human readers. No MRMC study was performed as it is irrelevant to the device's function. The study compares the performance of the new device to predicate (existing) devices.

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

Not applicable. This device is a manual electrosurgical instrument requiring human operation. "Standalone" performance in this context would refer to the device's inherent mechanical and electrical function. The "Bench Performance Testing" and "Animal Studies" serve this purpose by evaluating the device's function on its own or in the hands of trained users without comparison to an AI.

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

For the various studies:

• Electrical Safety & EMC: Ground truth established directly from standardized tests and measurements against published limits (e.g., IEC standards).
• Bench Performance: Ground truth established through engineering specifications, direct physical measurements (e.g., burst pressure), and functional tests against design inputs.
• Acute Animal Study: Ground truth based on direct observation of sealing performance and histological assessment of lateral thermal spread.
• Chronic Animal Study: Ground truth based on animal survival, direct observation of hemostasis, and potentially post-mortem pathological examination of sealed vessels.
• Human Factors: Ground truth based on observing user interaction against safety protocols and effectiveness criteria defined by usability engineering principles.

8. The sample size for the training set

Not applicable. This refers to AI/ML model training, which is not described.

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

Not applicable. This refers to AI/ML model training, which is not described.

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