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NaturaLase CO2 laser operating in tractionated mode is indicated for incision, excision, ablation, vaporization, and coagulation of body soft tissues including intraoral tissues, in medical specialties including aesthetic (dermatology and plastic surgery), otolaryngology (ENT), gynecology, neurosurgery, dental and oral surgery and genitourinary surgery.

NaturaLase CO2 laser operating in fractional mode is indicated for ablative skin resurfacing.

The NaturaLase CO2 is an RF excited CO2 laser. The main console of the NaturaLase CO2 laser system contains the following components:

• CO2 laser tube
• Power Supply
• Optical bench assembly
• Articulated Arm
• Hand-pieces: scanning and surgical
• Touch screen controller/user interface

The laser source is a sealed CO2 laser tube that is mounted vertically within the main cabinet and the laser beam is passed from the laser tube into the articulated arm. The NaturaLase CO2 laser unit is equipped with a switching-mode power supply, which converts the input line voltage to the high voltage required for laser operation. The optical bench assembly consists of the articulated arm mount, the aiming beam diode laser (3mW @ 650nm) and optics to align the laser beam and the aiming beam to the articulated arm. The laser energy is delivered through an articulated arm to a hand-piece (scanner or surgical) that distributes the laser energy on the target in a controlled and uniformed way. The user-interface microprocessor-based touch screen allows operator to control all operating functions.

The provided document is a 510(k) Premarket Notification from the FDA for a medical device called the "NaturaLase CO2 Laser System." This document aims to demonstrate the substantial equivalence of the new device to previously cleared predicate devices, rather than proving its performance against specific acceptance criteria for a new clinical indication or a new type of device requiring complex performance studies (like AI/ML-driven diagnostic devices).

Therefore, many of the typical acceptance criteria and study elements listed in your request (e.g., sample size for test sets, number of experts for ground truth, MRMC studies, standalone performance, ground truth types for training data) are not applicable to this type of regulatory submission. This document focuses on showing that the NaturaLase CO2 Laser System operates similarly to predicate devices and does not introduce new questions of safety or effectiveness.

Here's how to address your request based on the provided document:

Acceptance Criteria and Device Performance

For a device like the NaturaLase CO2 Laser System seeking 510(k) clearance, the primary "acceptance criterion" is demonstrating substantial equivalence to existing predicate devices. This is achieved by showing that the new device has the same intended use and technological characteristics, or if there are differences, that those differences do not raise new questions of safety or effectiveness.

The document demonstrates this through:

• Comparison of Technical Specifications: The core method of proving substantial equivalence is through direct comparison of the new device's technical specifications and operating principles with those of the predicate devices.
• Performance Testing (Bench Testing): The document refers to engineering verification and validation tests to ensure the device performs according to its own specifications and functions as intended, and conforms to relevant safety standards. This is not clinical performance testing against a disease state, but rather engineering and safety performance.

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

The "acceptance criteria" here are implicitly the parameters of the predicate devices a new device must match or fall within to demonstrate substantial equivalence, alongside compliance with relevant safety standards. The "reported device performance" are the specifications of the NaturaLase CO2 Laser System.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size: Not applicable for clinical test sets as this is a 510(k) based on substantial equivalence to existing devices, not a new clinical performance claim requiring patient data. The "testing performance" section refers to engineering and safety performance testing (bench testing), not testing on a patient sample.
• Data Provenance: Not applicable for clinical data. The testing was likely conducted at the manufacturer's facility or an independent laboratory (as stated for electrical safety and EMC testing).

3. 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)

• Not applicable. Ground truth for clinical outcomes is not established in this type of submission. The "ground truth" here is adherence to engineering specifications and safety standards.

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

• Not applicable. This type of adjudication pertains to expert review of clinical data, which was not part of this submission for substantial equivalence.

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

• Not applicable. This is not an AI/ML diagnostic device. It's a laser surgical instrument.

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

• Not applicable. This is not an AI/ML diagnostic device with a "standalone algorithm."

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

• The "ground truth" for this submission are the established technical specifications and safety standards (e.g., IEC standards, ISO standards) that medical devices of this type are expected to meet, and the performance parameters of legally marketed predicate devices.

8. The sample size for the training set

• Not applicable. There is no "training set" in the context of machine learning for this device. The development process relied on established engineering principles and prior knowledge from predicate devices.

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

• Not applicable. No machine learning training set or associated ground truth. The "ground truth" for device design and verification comes from engineering principles, industry standards, and the validated performance of predicate devices.

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