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The MiNS grasping instruments are a family of minimally invasive devices with the means to penetrate soft tissue to access certain areas of the human anatomy. The devices are used to grasp, hold and manipulate other soft internal tissues as well as items such as hernia mesh.

The MiNS devices are a family of disposable and re-useable devices. Consisting of an integrated, insulated needle/cannula shaft that houses a retractable grasper (NLU) which are inserted into a reusable Handle. The disposable NLU's when inserted into a reusable Handles are used for mobilization and manipulation of soft tissue during general laparoscopic procedures. The system has the ability to directly penetrate soft tissue to access certain areas of the human anatomy without the need for a traditional insertion conduit. The shaft of the instrument can be introduced percutaneously to the surgical site, after which the working portion of the instrument can be deployed to approximate, grasp and manipulate soft tissue.

Here's a summary of the acceptance criteria and the study information for the MiNS Needlescopic Resposable Laparoscopic device system, based on the provided document:

Acceptance Criteria and Reported Device Performance

The document states that "The subject device has been subjected to and passed a variety of bench tests for mechanical and attribute evaluations." However, it does not explicitly list specific quantitative acceptance criteria or corresponding reported performance metrics in a table format for the MiNS device itself. Instead, it relies on substantial equivalence to predicate devices, implying that its performance is acceptable if it is comparable to the predicates without introducing new risks.

The comparison table provided focuses on technological characteristics rather than specific performance metrics for the new device versus a benchmark.

Study Information

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
   The document states the device "has been subjected to and passed a variety of bench tests for mechanical and attribute evaluations." However, it does not specify the sample size for these bench tests, nor does it mention data provenance (country of origin, retrospective/prospective).

2. 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):
   This information is not provided in the document, as the evaluations conducted were "bench tests for mechanical and attribute evaluations," rather than clinical studies requiring expert ground truth for interpretation.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
   This information is not applicable/not provided, as the conducted tests are bench tests, not clinical evaluations requiring adjudication of results from independent reviewers.

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 MRMC comparative effectiveness study was done. This device is a manual surgical instrument, not an AI-powered diagnostic or assistive tool.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
   Not applicable, as this device does not include software or an algorithm.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
   For the mechanical and attribute bench tests, the "ground truth" would be engineering specifications and established test methods to verify physical properties, material compatibility, and functionality. It is not expert consensus, pathology, or outcomes data.

7. The sample size for the training set:
   Not applicable. This device does not have a training set, as it is a manual surgical instrument without software or machine learning components.

8. How the ground truth for the training set was established:
   Not applicable, as there is no training set for this device.

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The MiniLap Technologies family of BiPolar instruments with bipolar cautery have applications in a variety of general, thoracic, gynecologic (except for use in female sterilization), urologic, laparoscopic and endoscopic procedures for manipulation and coagulation of tissue.

The MiniLap Bipolar Electrocautery devices are a family of disposable bipolar electrosurgical graspers that directly penetrate soft tissue to access certain areas of the human anatomy without the need for a traditional insertion conduit. These disposable instruments are used for electrosurgical cauterization during general laparoscopic procedures. Fabricated from a stainless steel alloy, MiniLap Bipolar instruments consist of an integrated 2.4mm (+/- .1mm) insulated needle/cannula shaft that houses a retractable grasper instrument. The shaft can be introduced percutaneously to the surgical site, after which the working portion of the instrument can be deployed to approximate and cauterize soft tissue. Each BiPolar instrument has a male connection provided as an integral part of the instrument that may be utilized for the bipolar connection when attached to standard two plug bipolar cables and their generators. The bipolar connection is compatible with manufacturers of standard dual female bipolar plug cables. The instruments come in multiple lengths from 150mm to 300mm.

The provided 510(k) summary describes a medical device called "MiniLap Bipolar Electrocautery Devices." However, it does not contain any information about acceptance criteria or a study designed to prove the device meets specific performance metrics in the way a diagnostic AI/ML device would.

This document is a submission for a traditional medical device (electrosurgical grasper), not an AI/ML-driven diagnostic tool. The performance claim for such a device focuses on mechanical, electrical, and biocompatibility aspects, and its "acceptance" is typically based on demonstrating substantial equivalence to a predicate device.

Therefore, many of the requested sections about studies, sample sizes, ground truth, and expert-read performance are not applicable to this type of device submission and are not found in the provided text.

Here's how to address the request based on the provided text, highlighting the absence of AI/ML specific information:

Acceptance Criteria and Device Performance

The provided document doesn't explicitly state "acceptance criteria" in the format of performance metrics for an AI/ML device (e.g., sensitivity, specificity, AUC). Instead, the acceptance is based on demonstrating substantial equivalence to predicate devices through a comparison of technological characteristics and compliance with general safety and performance standards for electrosurgical instruments.

The "performance data" section states: "The subject device has been subjected to and passed a variety of mechanical and electrical tests and evaluations. Additionally, the device is composed of biocompatible materials with a history of usage in the medical device industry." This broadly indicates that the device met internal performance benchmarks related to its function as a surgical tool.

Table of Acceptance Criteria and Reported Device Performance (as inferred for a traditional medical device):

• Normal Operating Range: Device: 20-35W, Predicate: 20-35W (Equivalent)
• Safety: Device: Safety Interlock, Insulated shaft/jaws, IEC compliant. Predicate: No safety interlock, Insulated jaws, IEC compliant. (Device has more safety features)
• Sterilization: Device: Sterile, single use. Predicate: Non sterile, reusable. (Difference noted)
• Needle Diameter: Device: 2.4mm ± .1mm. Predicate: 3.5mm, 5mm. (Device has smaller options)
• Length: Device: 150-300mm. Predicate: 320mm, 450mm. (Device has shorter options) |
  | Technological Characteristics Equivalence (to Predicate B - MINI LAP Instruments K070686) | - Needle Diameter: Device: 2.4mm ± .1mm, Predicate: 2.4mm (Equivalent)
• Device Length: Device: 150-300mm, Predicate: 150-300mm (Equivalent)
• Material Composition: Device: Medical Grade Stainless Steel 17-7 SS, 300 SS. Predicate: Medical Grade Stainless Steel 470 SS, 300 SS. (Similar)
• Sterilization: Device: Sterile, single use. Predicate: Sterile, single use. (Equivalent)
• Rotation: Device: 175 degree shaft rotation each direction. Predicate: 360 degree shaft rotation. (Difference noted) |
  | Intended Use Equivalence | "The MiniLap Technologies family of BiPolar instruments with bipolar cautery have applications in a variety of general, thoracic, gynecologic (except for use in female sterilization), urologic, laparoscopic and endoscopic procedures for manipulation and coagulation of tissue." (Compared to predicate indications for open and laparoscopic surgery for grasping, coagulating, and transecting tissue or penetrating soft tissue to access areas and grasp/manipulate tissues). |

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

• Not Applicable. This is a physical medical device (surgical instrument), not an AI/ML diagnostic system. There is no "test set" in the context of image data or clinical scenarios for an algorithm. Testing involved mechanical, electrical, and biocompatibility evaluations of the device itself. The provenance of such "data" would be internal lab testing, not patient data.

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

• Not Applicable. As above, there is no "test set" in the AI/ML sense, and thus no ground truth established by experts for algorithmic performance. The "acceptance" of the device is based on engineers and regulatory experts reviewing its design, testing results, and comparison to predicate devices.

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

• Not Applicable. No adjudiciation method for a clinical test set is relevant here.

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:

• Not Applicable. This is a physical surgical instrument, not an AI-assisted diagnostic tool.

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

• Not Applicable. There is no algorithm to evaluate.

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

• Not Applicable. The "ground truth" for a physical device's performance is typically defined by engineering standards, material specifications, and operational parameters (e.g., electrical impedance, material strength, insulation effectiveness).

7. The sample size for the training set:

• Not Applicable. There is no AI/ML model that requires a training set.

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

• Not Applicable. There is no AI/ML model that requires a training set or its associated ground truth.

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