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Each device has similar intended uses. The VENTREX line of oxygen sensors and predicate devices have similar clinical applications. Each is used to determine the amount (expressed as % O2) of oxygen present in an atmosphere.

The VENTREX line of oxygen sensors are medical devices which produce an electrical current which is directly proportional to the amount of oxygen contained in a gas mixture. Like any oxygen sensor, the VENTREX oxygen sensor electrical out-put must be interpreted by an electronic monitoring instrument. As is the case with all oxygen sensors, the VENTREX sensors will be used in the hospital, in physician offices, out-patient care centers, extended care facilities, homes, emergency medical services and patient transport.

The provided text describes an oxygen sensor device VENTREX OXYGEN SENSOR and a comparison to predicate devices, but it does not contain the detailed information requested regarding specific acceptance criteria, a study proving those criteria were met, sample sizes, expert involvement, or ground truth establishment in the way typically associated with AI/ML device evaluations. This document predates the widespread use of AI/ML in medical devices, and focuses on performance standards rather than algorithmic performance metrics.

Therefore, I cannot populate the requested table or sections directly from the provided text in the manner intended for AI/ML device descriptions. The document primarily focuses on establishing substantial equivalence to predicate devices and compliance with an ISO standard.

However, I can extract the closest analogous information and explain why other sections cannot be filled:

Acceptance Criteria and Study Details for VENTREX OXYGEN SENSOR

The provided document describes the VENTREX Oxygen Sensor, a non-AI/ML device, and establishes its substantial equivalence to predicate devices and compliance with an international standard.

Due to the nature of the device (an oxygen sensor, not an AI/ML algorithm) and the time period of the submission (K963415), the information requested about AI/ML-specific study designs, expert adjudication, training/test sets, and ground truth establishment in the context of diagnostic performance (e.g., sensitivity, specificity for disease detection) is not present in the document. The "performance" discussed refers to the physical and electrical characteristics of the sensor.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not specify quantitative "acceptance criteria" in a a sensitivity/specificity or similar outcome metric format commonly seen for diagnostic AI. Instead, it refers to compliance with an international standard and comparable performance to predicate devices.

Note: The document does not provide specific numerical values for the predicate device performance to allow for a direct quantitative comparison in this table. It relies on a qualitative statement of comparability.

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

• Sample Size for Test Set: Not explicitly stated. The document mentions "The VENTREX line of oxygen sensors has been tested," but does not provide the number of sensors tested, batches, or specific test conditions.
• Data Provenance: Not specified. Given the nature of a hardware sensor, testing would likely occur in a controlled laboratory setting, not tied to specific countries of origin for patient data. It is a prospective test of the sensor's physical properties.

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

• Number of Experts: Not applicable. For a physical oxygen sensor, "ground truth" performance is typically established by calibrated reference equipment and validated test methods, not human expert consensus.
• Qualifications of Experts: Not applicable.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. Ground truth for sensor performance is determined by calibrated instruments and physical measurements, not human adjudication.

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

• MRMC Study: No. This is not an AI/ML diagnostic algorithm, so an MRMC study comparing human readers with and without AI assistance is not relevant or described.

6. Standalone (Algorithm Only) Performance Study

• Standalone Study: Yes, in a broad sense, the "testing" performed on the VENTREX oxygen sensors to comply with ISO/DIS 7767 and compare with predicate device performance can be considered a standalone performance evaluation of the device itself (the sensor hardware), independent of human interpretation or other components beyond the necessary monitoring instrument. However, this is not a "standalone algorithm performance" study as understood in AI/ML.

7. Type of Ground Truth Used

• Type of Ground Truth: Reference measurements from calibrated instruments and adherence to the specifications outlined in ISO/DIS 7767 would constitute the "ground truth" for the sensor's accuracy and performance characteristics.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This is a hardware device; there is no "training set" in the machine learning sense. The design and manufacturing process would be informed by engineering principles and validation, not data-driven machine learning training.

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

• Ground Truth for Training Set Establishment: Not applicable, as there is no training set for this type of device.

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