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The HandX is intended to assist in the control of Human Xtensions laparoscopic instruments including needle holder, grasper and monopolar instruments, for endoscopic manipulation of tissue, including grasping, approximation, ligation, suturing, cutting and/or coagulation, during laparoscopic surgical procedures. The HandX monopolar instruments are connected by a standard cable to a standard electrosurgical power source. It is intended to be used by trained physicians in an operating room environment in accordance with its Instructions for Use.

The HandX™ Monopolar Spatula is a single-use sterile electrosurqical electrode for use with the HandX™ device. The Monopolar Spatula is connected to the HandX™ device and transmits the device motors' rotation to articulate the movement of the end effector of the Monopolar Spatula. It is designed to address surgeons' needs relating to the application of monopolar diathermy for various surgical purposes.

The Monopolar Spatula is connected to a standard electrosurgical unit via a standard generator cable.

This document is a 510(k) summary for the HandX Monopolar Spatula, a medical device. It describes the device's intended use, comparison to a predicate device, and performance data.

Here's an analysis of the provided text in relation to acceptance criteria and study proving device meets those:

The provided text describes a 510(k) premarket notification for a medical device called the "HandX Monopolar Spatula". This document primarily focuses on demonstrating substantial equivalence to a previously cleared predicate device, rather than establishing acceptance criteria and conducting a study to prove the device meets those criteria in the context of an Artificial Intelligence (AI) or machine learning device.

The HandX Monopolar Spatula is an electrosurgical electrode, not an AI or machine learning device. Therefore, many of the typical criteria and study elements associated with AI device performance validation (like sensitivity, specificity, MRMC studies, ground truth establishment with experts, training set size, etc.) are not applicable to this submission.

The "acceptance criteria" for this device are primarily related to its physical, electrical, and biological performance, demonstrating it's as safe and effective as existing similar devices.

However, I will extract relevant information from the document that touches upon "acceptance criteria" and "studies" as best as possible, interpreting them within the context of a traditional medical device submission.

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided text, the "acceptance criteria" are implied by successful completion of various tests, demonstrating compliance with standards and equivalence to the predicate. The "reported device performance" is the statement that these tests were passed successfully.

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

• Sample Size: Not explicitly stated for most tests. For Thermal Effects Testing, it mentions a "porcine model," implying animal subjects, but no specific number of animals or trials. For Human Factors/Usability, a "summative evaluation study" was done, but the number of participants is not provided.
• Data Provenance: The document does not specify the country of origin for the data. The studies appear to be pre-market validation studies conducted by the manufacturer, rather than retrospective or prospective clinical data from a specific patient population.

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

• Not Applicable in the context of an AI device. For this physical device, "ground truth" generally refers to design specifications, validated measurement techniques, and established safety standards. There is no mention of "experts" establishing a "ground truth" as would be done for diagnostic AI. The "Human Factors/Usability" study would likely involve trained medical professionals, but their role is to test usability, not establish a diagnostic ground truth.

4. Adjudication Method for the Test Set

• Not Applicable. As there is no "ground truth" being established by multiple readers/experts in a diagnostic sense, no adjudication method (like 2+1, 3+1) is mentioned or relevant.

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

• No. This is not an AI/diagnostic device. An MRMC study is not relevant to demonstrating the safety and effectiveness of a surgical instrument of this type. The comparison is primarily against a predicate device based on physical, electrical, and biological performance characteristics, rather than diagnostic accuracy or human reader performance.

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

• Not Applicable. This is a physical surgical instrument that always requires a human in the loop for its operation. It does not contain an "algorithm" in the sense of an AI/machine learning component that could operate standalone.

7. The Type of Ground Truth Used

The "ground truth" for this medical device's performance validation is derived from:

• Mechanical and Electrical Engineering Standards: Compliance with relevant IEC standards (e.g., IEC 60601 series).
• Biocompatibility Standards: Compliance with ISO 10993-1.
• Predefined Acceptance Criteria: Established internally by the manufacturer for bench tests (Dimensional attributes, Seal Test, Tip Pull to Failure).
• Comparative Performance: Demonstrating substantial equivalence in thermal behavior to a predicate and reference device in animal models.
• Usability Standards: Compliance with IEC 62366-1 for human factors/usability.

8. The Sample Size for the Training Set

• Not Applicable. This device does not use an AI/machine learning component, so there is no concept of a "training set" for an algorithm.

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

• Not Applicable. As per point 8, there is no training set for an algorithm.

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