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The Arthrex NanoScope System is intended to be used as an endoscopic video camera to provide visible light imaging in a variety of endoscopic diagnostic and surgical procedures, including laparoscopy, orthopedic, plastic surgery, sinuscopy, spine, and urology. The device is also intended to be used as an accessory for microscopic surgery.

For pediatric patients, the Arthrex NanoScope System is indicated for use in laparoscopy and orthopedic procedures.

The Arthrex NanoScope System provides real-time visible light illumination and imaging. The system includes a non-sterile reusable camera control unit (CCU) console and sterile disposable camera handpieces. The system integrates high-definition camera technology, LED lighting, and an imaging management system into a single console with touchscreen interface.

The provided document describes the Arthrex NanoScope System, a medical device used for endoscopic video imaging. However, this document does not contain information about a study proving the device meets specific acceptance criteria related to a specific performance metric (e.g., accuracy, sensitivity, specificity) for an AI/ML algorithm that identifies or classifies something.

The performance data section (pages 5 and 6) outlines various non-clinical bench testing, including:

• Biocompatibility testing: Performed according to ISO 10993 standards.
• Electrical, Mechanical, and Thermal (EMT) safety testing: Performed according to ANSI/AAMI ES60601-1 and IEC 60601-2-18 standards.
• Electromagnetic Compatibility (EMC) testing: Performed according to IEC 60601-1-2 standards.
• Software testing: Performed according to FDA guidance and IEC 62304.
• Design verification testing: Included inspection, engineering analysis, and functional testing.

These tests confirm the device's conformance to safety, performance, and software quality standards relevant to an endoscopic imaging system, rather than demonstrating the performance of an AI model against specific clinical metrics like sensitivity or specificity. The submission aims to expand indications and report software and device modifications, relying on equivalence to predicate devices rather than a de novo clinical study proving AI performance.

Therefore, I cannot fulfill the request to describe the acceptance criteria and the study that proves the device meets those criteria in the context of an AI/ML algorithm's performance, as the provided text does not describe such a study. The document explicitly states: "The Arthrex NanoScope System did not require animal testing or human clinical studies to support the determination of substantial equivalence." This further indicates that no clinical performance study, particularly one involving AI/ML and human-in-the-loop or standalone performance, was conducted as part of this submission.

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The Arthrex NanoScope system is intended to be used as an endoscopic video camera in a variety of endoscopic diagnostic and surgical procedures, including but not limited to: orthopedic, spine, laparoscopic, urologic, sinuscopic, and plastic surgical procedures. The device is also intended to be used as an accessory for microscopic surgery.

The Arthrex NanoScope System provides image processing and digital documentation for endoscopic procedures. The system utilizes a handpiece which provides distal LED illumination to the surgical site using a fiber optic bundle surrounding a high-resolution camera sensor.

The provided text is a 510(k) premarket notification letter and summary for the Arthrex NanoScope System. It describes the device, its intended use, and arguments for substantial equivalence to predicate devices. However, this document does not contain information about acceptance criteria or a study that proves the device meets those criteria in the context of AI/ML performance.

This document specifically states that:

• "Nonclinical (bench) testing was performed to demonstrate substantial equivalence to the predicate regarding thermal safety, electrical safety, optical performance, environmental conditions, power requirements, image capture, and video output and resolution."
• "An engineering analysis was performed to address the substantial equivalence of the field of view and the working diameter compared to the predicate device."

This indicates that the substantial equivalence was established through bench testing and engineering analysis for hardware performance, not through clinical studies or AI/ML performance evaluations against specific acceptance criteria. The device is an endoscopic video camera system, and the submission is to expand its indications for use (adding spine procedures) and extend the shelf life of a sterile component, not for a new AI/ML-driven diagnostic or assistive feature.

Therefore, I cannot extract the requested information regarding AI/ML acceptance criteria and study details from the provided text. The questions posed are highly relevant to AI/ML medical devices, but this document describes a traditional medical device submission (an endoscopic camera) where such metrics and studies are not typically required for regulatory clearance unless the device incorporates an AI/ML component with a diagnostic or assistive function.

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The Arthrex NanoScope system is intended to be used as an endoscopic video camera in a variety of endoscopic surgical procedures, including but not limited to: orthopedic, urologic, sinuscopic, and plastic surgical procedures. The device is also intended to be used as an accessory for microscopic surgery.

The Arthrex NanoScope System provides image processing and digital documentation for endoscopic procedures. The system comprises a camera control unit (CCU) and a handpiece that provides distal LED illumination to the surgical site using a fiber optic bundle surrounding a high-resolution camera sensor.

This a 510(k) summary for the Arthrex NanoScope System, which is an endoscopic video camera system. The submission seeks to prove substantial equivalence to a predicate device, the Arthrex Synergy UHD4 System (K153218). The provided document does not contain specific acceptance criteria or a detailed study report with performance metrics for the Arthrex NanoScope System.

However, it does indicate that bench testing was performed to demonstrate equivalence to the predicate regarding environmental conditions, power requirements, image capture, and video output and resolution. This implies that the acceptance criteria would relate to these specific performance aspects, with the predicate device's performance serving as the benchmark.

Since detailed study results and acceptance criteria are not explicitly present in the provided text, I will answer based on the information implied by the document about the type of study and the areas of comparison.

Here's an attempt to answer the questions based on the available information and logical inference for a 510(k) submission of this nature:

1. Table of Acceptance Criteria and Reported Device Performance

As specific numerical acceptance criteria and reported performance metrics are not provided in the document, this table is constructed based on the types of tests mentioned as being performed for equivalence. The acceptance criterion is implied to be "Matches or performs comparably to the predicate device."

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

The document mentions "bench testing." Typically, bench testing for a device like an endoscopic camera would involve a physical sample of the device rather than a "test set" of patient data.

• Sample Size: Not explicitly stated, but for bench testing, it would generally involve one or more manufactured units of the Arthrex NanoScope System.
• Data Provenance: Not applicable in the context of patient data. The "data" here would be the measurements and observations from the physical bench tests performed in a lab setting. It is not retrospective or prospective in the clinical sense.

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

This question is not directly applicable. Bench testing does not typically involve human experts establishing "ground truth" in the way a clinical study would (e.g., radiologists interpreting images). The "ground truth" for bench testing would be established by validated test equipment and objective measurements against predefined specifications or the predicate's performance.

4. Adjudication Method for the Test Set

Not applicable. Adjudication methods like 2+1 or 3+1 are used in clinical studies for reconciling conflicting expert opinions on data. Bench testing relies on objective measurements and established test protocols.

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 MRMC study was done. This device is an endoscopic video camera system, not an AI-assisted diagnostic tool. The submission focuses on the performance of the camera itself, primarily through bench testing comparing it to a predicate device.

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

This question is not applicable. The Arthrex NanoScope System is a hardware device (video camera system), not a standalone algorithm. Its performance is measured directly through its physical output (image quality, resolution, etc.), not as an algorithmic output requiring a human-in-the-loop for interpretation of an algorithm's classification.

7. The Type of Ground Truth Used

The "ground truth" for the bench testing would be the objective measurements obtained from testing the Arthrex NanoScope System against established engineering specifications or by directly comparing its performance characteristics (e.g., resolution, light output, power consumption) to that of the predicate device (Arthrex Synergy UHD4 System K153218) using calibrated equipment. It's not expert consensus, pathology, or outcomes data.

8. The Sample Size for the Training Set

This question is not applicable. The Arthrex NanoScope System is a hardware device; it does not involve machine learning or AI models that require a "training set" of data.

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

This question is not applicable, as there is no training set for this device.

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