Search Results

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 and veins) up to and including 7 mm. It is indicated for use in gerry and such surgical specialties as urologic, vascular, thoracic, and gynecologic. Procedures may include, but are not limited to, Nissen fundoplication, colectomy, 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 Sealer/Divider One-step Sealing, Nano-coated (LF19XX) devices are sterile, single-use, coated, hand-held bipolar vessel sealing devices designed for use with Covidien electrosurgical generators that include vessel sealing capabilities to ligate (seal) and divide (cut) vessels, tissue bundles, and lymphatics clamped between the jaws, grasp tissue, and perform blunt dissection during general surgical procedures (as indicated) using radio frequency (RF) energy.

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 devices come in three shaft lengths (23, 37, and 44 cm) and do not contain software.

This document, a 510(k) summary for the LigaSure™ Maryland Jaw Sealer/Divider One-step Sealing, Nano-coated, does not describe a study involving an AI/ML algorithm or human readers. Instead, it focuses on demonstrating substantial equivalence to a predicate device through pre-clinical performance data, primarily related to the physical and functional characteristics of an electrosurgical device.

Therefore, many of the requested details regarding AI/ML-specific study design (such as data provenance, expert adjudication, MRMC studies, standalone algorithm performance, and training set details) are not applicable to the information provided in this 510(k) summary.

However, I can extract the acceptance criteria as demonstrated by the device performance tests outlined in the document, which primarily focus on a traditional medical device's safety and efficacy.

Here's a breakdown of the information that can be extracted or inferred from the provided text, and where the requested information is not applicable:

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

The document doesn't present explicit numerical "acceptance criteria" in a table format with corresponding "reported device performance" in the same way one might for an AI/ML model's metrics (e.g., sensitivity/specificity thresholds). Instead, it lists types of performance tests conducted and generally states that the device "performed as expected" or "demonstrated that the subject devices are as safe and effective as the predicate devices."

Implied Acceptance Criteria and Reported Performance (based on tests conducted):

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

• Test Set Sample Size:
  • Ex-vivo Vessel Burst Pressure: "excised fresh porcine renal arteries and lymphatics" (Number not specified)
  • Acute Animal Study: "porcine underwent various procedures" (Number not specified)
  • Chronic Animal Study: "All animals" (Number not specified, but this refers to a continuous assessment over 21 days on the animals used in the chronic study).
• Data Provenance:
  • The animal studies indicate data from in vivo porcine models, which are animal data, not human patient data.
  • The ex-vivo studies indicate in vitro data using excised porcine tissue.
  • The document does not specify a country of origin for the data, but the submitter is based in Boulder, CO, USA.
• Retrospective or Prospective: These are laboratory/pre-clinical studies, so the terms "retrospective" or "prospective" as applied to clinical trials are not directly applicable. They were conducted specifically for this submission.

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 a physical electrosurgical instrument, not an AI/ML diagnostic device requiring expert interpretation of images or patient data to establish "ground truth." The "ground truth" for its performance is established through direct measurements (e.g., burst pressure), physiological outcomes (e.g., hemostasis in animal models), and adherence to safety standards.

4. Adjudication method for the test set:

• Not Applicable. See point 3. The outcomes are objective measurements or direct observations in animal models.

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. This product is an electrosurgical device, not an image analysis or diagnostic AI/ML system. Therefore, MRMC studies involving human readers are not relevant.

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

• Not Applicable. No algorithm is involved in the operation of this device.

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

• For this electrosurgical device, the "ground truth" is established by:
  • Physical and Electrical Standards: Adherence to defined industry and regulatory standards (e.g., ISO 10993-1, ANSI AAMI ES 60601-1, IEC 60601-2-2, IEC 60601-1-2) for material properties and electrical safety.
  • Direct Objective Measurement: Mechanical properties (jaw force, gap), electrical characteristics (resistance, capacitance, inductance).
  • Physiological Outcomes in Animal Models: Successful vessel sealing (demonstrated by burst pressure, lack of acute bleeding, chronic hemostasis) and acceptable thermal damage in live porcine models.
  • Functional Performance: The device's ability to ligate, divide, grasp, and perform blunt dissection as intended.

8. The sample size for the training set:

• Not Applicable. No AI/ML model with a "training set" is described for this device.

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

• Not Applicable. No AI/ML model or training set.

In summary, the provided document details the pre-clinical validation of a traditional electrosurgical device, not an AI/ML-enabled medical device. Therefore, the information pertains to physical, electrical, and biological performance characteristics rather than data-driven AI model performance metrics.

Ask a specific question about this device