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The ECCOVISION™ is intended to measure the upper respiratory airway by means of acoustic reflection.

The Eccovision™ device is used to obtain an objective measurement of the upper respiratory airway. The device uses acoustic signal processing technology to provide graphical representation of the airway patency as a function of distance from the airway opening. The system consists of a control unit (which connects to customer owned personal computer), and software application, wave tube (one each for the Pharyngometer and Rhinometer) and electronic platform, mouthpieces and nose tips and filter strips. The device performs a dynamic test that determines the dimension of the oral airway past the glottis while the patient is breathing thorough either the mouthpiece or nose tip. A customer provided computer with the loaded Eccovision™ application software then processes the incident and reflected sound signals provides an area-distance curve representing the lumen together with minimal crosssectional area and volume. A measurement is obtained by passing a signal along a probe positioned in the mouth or nose then recover a signal by use of two (2) microphones in the wave tube. The signal is processed by the software and displayed on a screen or relayed to a printer, detailing the cross-sectional area of the airway as a function of distance from the teeth.

Here's a breakdown of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The text explicitly mentions:

• Approach to show equivalency between new core & legacy core using a model (fixed ADP)
• Approach to show that the new system is equivalent to legacy system on real patients.

However, the sample size for "real patients" is not specified in this document. The data provenance (country of origin, retrospective or prospective) is also not specified.

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

The document mentions that testing was performed by an "Evaluator" familiar with the business case, but does not specify the number of experts, nor their qualifications (e.g., medical specialists, years of experience).

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study is not mentioned in the provided text. The study focuses on demonstrating equivalence to predicate devices, not on comparing human readers with and without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The device is an "ECCOVISION™ Pharyngometer, SGS Eccovision™ Rhinometer, SGS Eccovision™ Rhino/Pharyngometer" which is a measurement device, not an AI algorithm generating readings for humans to interpret. Therefore, the concept of "standalone performance" in the context of an AI algorithm doesn't directly apply here. The device itself performs measurements; the software processes the signals and displays results. The tests aimed to show that the new system's measurements are equivalent to the predicate devices.

7. The Type of Ground Truth Used

The ground truth for the "fixed ADP model" test would be based on the characteristics of that model. For the "real patients" test, the ground truth for "volume measurements" would likely be derived from the predicate devices, as the goal was to show equivalence to the measurements produced by those established devices. The document implies that the measurements from the predicate devices served as a reference for establishing the acceptability of the new device's measurements. There is no mention of pathology or outcomes data as ground truth.

8. The Sample Size for the Training Set

The document does not specify a training set sample size. This device is described as an acoustic reflection measurement device, and the testing focuses on its measurement accuracy compared to predicate devices, not on training a machine learning model.

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

As no training set is mentioned in the context of machine learning, this question is not applicable.

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