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To be used in nitrous oxide-oxygen sedation systems for delivering to a patient a mixture of nitrous oxide and oxygen gases with a maximum nitrous oxide concentration of 70%.

The Accutron Digital Ultra Analgesia Gas Machine precisely meters oxygen and nitrous oxide medical gases for conscious sedation of patients in dental offices and hospitals. With the device, the attending physician or dentist is able to set both the percentage of nitrous oxide and the total flow to a desired level of sedation and flow. The Digital Ultra Analgesia Gas Machine has been designed with built in safety features that prevent the level of oxygen gas from falling below 30% of total flow, mechanical features that prevent the mix-up of gases, and fail-safe features to prevent the flow of nitrous oxide in the absence of oxygen gas.

The provided text describes a 510(k) premarket notification for the Accutron Digital Ultra Analgesia Gas Machine. This type of submission is for medical devices, not for AI/ML-based diagnostic or prognostic algorithms. Therefore, much of the requested information regarding acceptance criteria, study details, expert involvement, and AI performance metrics is not applicable to this document.

The document focuses on demonstrating substantial equivalence to legally marketed predicate devices based on safety and effectiveness, primarily through compliance with recognized voluntary standards and good manufacturing practices. It does not contain information about studies proving the device meets specific performance criteria in the way an AI algorithm would be evaluated.

Here's an attempt to answer the questions based only on the provided text, noting its limitations for AI-specific queries:

1-5, 7-9: Not Applicable (N/A) for this device and document type.

This submission is for a medical device (analgesia gas machine) that is not an AI/ML diagnostic or prognostic algorithm. Therefore, the concepts of acceptance criteria for AI performance, clinical study design for AI efficacy, expert ground truth adjudication for AI, MRMC studies, standalone algorithm performance, training set details, and ground truth establishment for AI are not applicable to this 510(k) submission.

The "acceptance criteria" for this device are implicitly tied to compliance with the listed voluntary standards and the demonstration of substantial equivalence to predicate devices in terms of safety and intended use. The "study" proving it meets these "criteria" is the documentation provided to the FDA demonstrating this compliance and equivalence, which is reviewed by the FDA.

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

This question is Not Applicable as the device is a physical gas machine, not an algorithm.

Summary of Device and Regulatory Submission:

The Accutron Digital Ultra Analgesia Gas Machine meters oxygen and nitrous oxide for conscious sedation. Key safety features include preventing oxygen from falling below 30% of total flow, mechanical prevention of gas mix-ups, and fail-safe features to stop nitrous oxide flow without oxygen.

The 510(k) submission (K052335) seeks to demonstrate substantial equivalence to predicate devices (K970163: Technical Medical Products Model 20 Alpha MX / Model 30 Ultra PC by Accutron Inc.; K945722: Matrx MDM, RA by Matrx Medical, Inc.). This equivalence is supported by adherence to various voluntary standards:

• EN 60601-1 / IEC 60601-1: "Medical Electrical Equipment - Part 1 : General Requirements for Safety"
• EN 60601-1-2 / IEC 60601-1-2: "Medical electrical equipment -- Part 1-2: General requirements for safety – Collateral standard: Electromagnetic compatibility – Requirements and tests"
• ISO 5356-1: "Anesthetic and respiratory equipment -- conical connectors"
• ASTM F1054-01: "Standard specification for conical fittings"
• CGA C-9:1988: "Standard color marking of compressed gas containers for medical use"
• CGA V-5:2000: "Diameter-Index Safety System (non-interchangeable low-pressure connections for medical gas applications)"
• ADA Recommendations: Designed to meet recommendations of the American Dental Association.

The "study" referenced in the prompt (though not a clinical study in the AI sense) is the comprehensive product design and risk management program, which aimed to eliminate or mitigate known health hazards, ensuring the device is "safe and effective when used as instructed by knowledgeable and trained personnel, and is substantially equivalent to the legally marketed predicate devices."

The FDA's review of the 510(k) documentation determined substantial equivalence, allowing the device to be marketed.

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To be used in nitrous oxide/oxygen sedation systems for the delivery to a patient a mixture of nitrous oxide and oxygen gases, for the removal from the treatment location of gases expired by the patient, and removal from the treatment location any excess gases delivered to a patient.

The RFS Vacuum Gauge Scavenging Circuit is designed for the removal of patient expired and excess gases during nitrous oxide / oxygen sedation procedures. For proper operation, the scavenging circuit must be connected to a vacuum pump capable of providing at least 45 LPM of scavenging on an operating vacuum pressure of 8 in Hg. The vacuum pump should vent exhaust gases to the outside of the building, away from fresh air intakes, windows, or walkways.

The scavenging circuit consists of the scavenging hub, which connects to a nasal hood, exhaust tubing and connectors; and the RFS Vacuum Gauge. The nasal hood fits over the patient's nasal cavity, which serves to deliver medical gases to the patient and which allows removal of expired, excess, and unused medical gases from the nasal hood and surrounding areas.

This device measures pressure, not flow. Setting the float ball at the yellow indicator level This device measures pressure. This will result in the NIOSH recommended 45 LPM provided the mask is correctly attached, and there are no kinks or obstructions in the flow path.

The provided text describes a 510(k) summary for the "RFS Vacuum Gauge Scavenging Circuit." This document is a premarket notification for a medical device and typically focuses on demonstrating substantial equivalence to a predicate device, rather than presenting a detailed study with acceptance criteria and performance metrics in the way a clinical trial report would for an AI/ML device.

Therefore, much of the requested information (acceptance criteria table, sample size, expert qualifications, adjudication, MRMC study, standalone performance, training set details) is not applicable or not provided in this type of regulatory submission for a physical scavenging circuit.

However, based on the provided text, we can infer some aspects related to the device's intended performance and its substantiation.

Here's an analysis based on the provided text:

Acceptance Criteria and Study Details

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

2. Sample size used for the test set and the data provenance

• Not Applicable / Not Provided. This document does not describe a test set or data provenance in the context of validating a diagnostic or AI device's performance through data analysis. It's a submission for a physical device, and performance is likely demonstrated through engineering verification and validation testing, not a clinical data-driven "test set."

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

• Not Applicable / Not Provided. Ground truth establishment by experts is not described for this type of device. Performance is assessed against engineering specifications and regulatory standards.

4. Adjudication method for the test set

• Not Applicable / Not Provided. Adjudication methods are not described for this type of device.

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. This is a physical medical device (scavenging circuit), not an AI-assisted diagnostic tool. Therefore, an MRMC study related to human reader performance with AI assistance is not relevant or mentioned.

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

• Not Applicable. This is a physical device, not an algorithm.

7. The type of ground truth used

• Engineering Specifications and Industry Standards: The "ground truth" for this device would be its ability to meet established engineering performance specifications (e.g., vacuum pressure, flow rates, sealing capabilities) and adherence to industry standards, such as the NIOSH recommended 45 LPM scavenging. This is determined through physical testing and measurement, not typically against expert consensus, pathology, or outcomes data in the way an AI diagnostic device would.

8. The sample size for the training set

• Not Applicable / Not Provided. This is a physical device, not a machine learning model, so there is no "training set."

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

• Not Applicable / Not Provided. As there is no training set, this question is not relevant.

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Accessory device for use with nitrous oxide / oxygen sedation systems to monitor the medical gas system for a low oxygen pressure condition (less than 40 ps), and activate an audio alarm when this condition exists.

The Guardian™ Low Pressure Monitor Model 34076 will consist of the following major sub-components:

• . Circuit board with battery holder and battery
• . Pressure switch
• Manifold and medical gas fittings (DISS)
• . Protective cover
  The intended major functional characteristics of the device are:
• . Audibly indicate proper initialization upon insertion of the battery (i.e. power up)
• Audibly indicate low tank pressure
• . Audibly indicate low remaining battery life, as determined by battery voltage

This document describes a medical device, the Guardian™ Low Pressure Monitor Model 34076, designed to monitor low oxygen pressure in nitrous oxide/oxygen sedation systems. The provided text outlines the device's characteristics, intended use, and a regulatory submission (510(k)) to the FDA. However, it does not contain a detailed study demonstrating specific acceptance criteria with reported device performance metrics in the format requested.

The document primarily focuses on regulatory approval (510(k) clearance based on substantial equivalence to a predicate device) rather than a specific performance study with quantitative results against acceptance criteria.

Therefore, many of the requested sections (sample size, expert qualifications, adjudication, MRMC study, standalone performance, ground truth details, training set size, etc.) cannot be addressed from the provided text.

Here's an attempt to extract what is available and highlight what's missing:

1. Table of Acceptance Criteria and Reported Device Performance

Missing Information: The provided text states the intended functional characteristics, which serve as de-facto acceptance criteria. However, it does not provide quantitative results or specific performance metrics demonstrating that the device actually meets these criteria. It only states that the system is "designed and tested to meet the requirements."

2. Sample size used for the test set and the data provenance

• Sample Size: Not specified in the provided text.
• Data Provenance: Not specified in the provided text. The text refers to "Non-Clinical Testing" and "rigorous application of a risk management program," but no details on the origin of data (e.g., country, retrospective/prospective) are given.

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

• Not specified in the provided text. There is no mention of an expert panel or ground truth establishment relevant to a test set's performance.

4. Adjudication method for the test set

• Not specified in the provided text.

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 device is a low-pressure monitor, not an AI-assisted diagnostic tool for which MRMC studies comparing human readers with and without AI assistance would be conducted.

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

• Not explicitly described as a formal validation study. The device itself is a standalone alarm system. Its performance evaluation would naturally be "standalone" in the sense that the alarm mechanism operates independently of human interpretation of a reading. However, the text does not detail a study specifically validating the "algorithm only" performance.

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

• Not specified. For a device like this, the "ground truth" would likely be derived from calibrated pressure gauges and simulated low-pressure conditions. However, the document does not detail how this ground truth was established or used in testing.

8. The sample size for the training set

• Not applicable/Not specified. This device does not appear to involve machine learning or AI that would require a "training set" in the conventional sense. Its function is based on a physical pressure switch and circuit logic.

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

• Not applicable/Not specified. As there's no mention of a training set, the establishment of ground truth for such a set is not detailed.

Summary of what can be inferred from the provided text:

• The device, a low-pressure monitor, has defined functional requirements (audible alarms for power-up, low pressure

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