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The Intuitive Surgical Endoscopic Instrument Control Systems (da Vinci S and da Vinci Si Surgical Systems Models IS1200, IS2000, IS3000) are intended to assist in the accurate control of Intuitive Surgical Endoscopic EndoWrist Instruments and Accessories including rigid endoscopes, blunt and sharp endoscopic dissectors; scissors, scalpels, ultrasonic/harmonic shears; forceps/pick-ups, needle holders, endoscopic retractors, stabilizers, electrocautery and accessories for endoscopic manipulation of tissue, including grasping, cutting, blunt and sharp dissection, approximation, ligation, electrocautery, suturing, delivery and placement of microwave and cryogenic ablation probes and accessories, during urologic surgical procedures, general laparoscopic surgical procedures, gynecological laparoscopic surgical procedures, transoral otolaryngology surgical procedures restricted to benign and malignant tumors classified as T1 and T2, general thoracoscopic surgical procedures, and thoracoscopically-assisted cardiotomy procedures. The system can also be employed with adjunctive mediastinotomy to perform coronary anastomosis during cardiac revascularization. The system is indicated for adult and pediatric use (except for transoral otolaryngology surgical procedures). It is intended to be used by trained physicians in an operating room environment in accordance with the representative. specific procedures set forth in the Instructions for Use.

The Monopolar Curved Scissors Tip Cover Accessory is an electrically isolating sleeve that is placed over the distal tip of the Monopolar Curved Scissors. The Tip Cover Accessory acts to isolate the metal parts of the instrument so that only the intended electrode (the scissor blades) is exposed for surgical application.

This document is a 510(k) summary for a device modification, specifically for the Monopolar Curved Scissors Tip Cover Accessory. It states that the device is substantially equivalent to a predicate device and that performance tests were conducted to demonstrate this. However, it does not provide detailed acceptance criteria or present a formal study with statistical findings in the manner typically seen for complex AI/ML device submissions.

Here's a breakdown based on the information provided, highlighting what is missing concerning your specific questions for AI/ML device studies:

1. Table of Acceptance Criteria and Reported Device Performance:

The document broadly states: "Performance tests (bench tests) were conducted to demonstrate that the subject device is substantially equivalent to the predicate device, and that the design output meets the design input requirements. The results of the testing did not raise any new types of safety or effectiveness questions."

This is a general statement. The specific acceptance criteria (e.g., electrical isolation resistance, durability under specific conditions) and the numerical results of the reported device performance are not detailed in this summary. It only indicates that the criteria were met and no new safety/effectiveness questions arose.

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

This document describes a medical device accessory, not an AI/ML algorithm. Therefore, the concept of a "test set" for an algorithm's performance on data (like images or medical records) is not applicable here. The "performance tests" mentioned would have involved physical testing of the device.

• Sample Size for Test Set: Not specified in terms of units tested, but these would be physical units of the accessory.
• Data Provenance (country of origin, retrospective/prospective): Not applicable, as this is a physical device accessory and not an AI/ML diagnostic or predictive tool relying on medical data.

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

Not applicable. For a physical device accessory like a tip cover, "ground truth" is typically established through engineering specifications, material properties, and physical testing against predefined standards, rather than expert interpretation of medical data.

4. Adjudication Method:

Not applicable. Adjudication methods (like 2+1, 3+1) are used to resolve disagreements among multiple human readers on a test set's ground truth, which is not relevant for this type of device modification.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

No, an MRMC study was not done. This type of study is specifically designed to evaluate the impact of an AI system on human reader performance for diagnostic tasks, which is not relevant for a physical accessory like a tip cover.

6. Standalone Performance Study:

A standalone performance study for an algorithm was not done. The "performance data" refers to bench tests of the physical accessory itself, demonstrating its functional characteristics.

7. Type of Ground Truth Used:

The "ground truth" for this device's performance would be based on engineering specifications and physical measurement standards used in bench testing (e.g., electrical resistance limits, physical dimensions, material integrity). It is not based on expert consensus, pathology, or outcomes data in the context of clinical AI/ML studies.

8. Sample Size for the Training Set:

Not applicable. This device is a physical accessory, not an AI/ML algorithm that requires a training set.

9. How the Ground Truth for the Training Set Was Established:

Not applicable. (See #8)

In summary, this 510(k) pertains to a physical medical device accessory modification and does not align with the typical requirements or study designs for AI/ML medical devices. The information you're asking for, particularly regarding sample sizes for training/test sets, expert ground truth, and AI/ML specific study types (MRMC, standalone), is not provided because it's not relevant to this type of submission.

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