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The EMS Talking Trach tracheostomy tube, cuffed, is intended to provide tracheal access for airway management of tracheostomized patient. The device allows tracheostomy patients with a functional larynx and an unobstructed upper airway to vocalize / phonate. The Inner Speech cannula allows vocalization with the tracheostomy tube's cuff to remain inflated for ventilator dependent patients. For adult patients > 30 kg.

The proposed EMS Talking Trach is a tracheostomy tube which allows patients who are ventilator dependent or spontaneously breathing to speak or phonate. It can be used with patients on ventilators while the cuff is "up" or inflated. It is offered in sizes from 4.0 to 10.0 mm with disposable standard and speaking inner cannula.

The provided text describes the EMS Talking Trach, a tracheostomy tube designed to allow patients to speak or phonate, even while on a ventilator with the cuff inflated. The submission focuses on demonstrating substantial equivalence to predicate devices rather than presenting a study with detailed acceptance criteria and performance data for a novel device.

Therefore, the requested information about acceptance criteria and a study proving the device meets them cannot be fully extracted in the typical sense for a diagnostic or AI device. However, based on the provided document, here's an interpretation of the performance claims and the study design where applicable:

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

The submission claims "substantial equivalence" as its primary acceptance criterion. The performance attributes mentioned are expected to be "substantially equivalent to predicate" for the EMS Talking Trach.

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

The document does not describe a clinical study with a "test set" in the context of device performance evaluation based on patient data for diagnostic accuracy. The performance claims are based on engineering tests for ventilator compatibility rather than human subject data. Therefore, details like data provenance or test set sample sizes for human subjects are not provided. The testing appears to be primarily bench testing (in vitro simulation) to assess ventilator performance.

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

Not applicable. The submission focuses on physical and functional equivalence to predicate devices, not on a diagnostic task requiring expert-established ground truth. The "ground truth" for ventilator performance would be established by the physical measurements from testing equipment.

4. Adjudication method for the test set

Not applicable, as no human-reviewed test set data for diagnostic performance is described.

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 medical instrument (tracheostomy tube), not an AI-powered diagnostic tool, and therefore, an MRMC study comparing human readers with and without AI assistance is not relevant or described.

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

Not applicable. This is a physical medical device, not an algorithm.

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

For the ventilator performance testing, the "ground truth" would be the objectively measured values of peak pressure and expiratory flow from the testing equipment, compared against the values obtained from the predicate devices under similar conditions. This is a form of engineering/bench test data ground truth.

8. The sample size for the training set

Not applicable. There is no training set in the context of machine learning or AI algorithms for this device submission.

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

Not applicable, as there is no training set.

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