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o two e700, e600 and e500 are a time-cycled, volume-constant and pressure-controlled (only e700) emergency and transport ventilator designed for use in the pre-hospital, inter-hospital and transport settings. It is intended for use with adult, child, infant patients with a tidal volume from 50 ml (100 ml for e500) upwards who are in respiratory and/or cardiac arrest or respiratory distress and who require ventilatory support.

The proposed o_two e700, o_two e600 and o_two e500 are time-cycled, volume-constant and pressure controlled (o_two e700 only) emergency and transport ventilators. They are electronically controlled, pneumatically powered ventilators which can be run using AC/DC power adapter or an internal rechargeable Lithium Ion battery pack. The devices use a 4.3" TFT screen to display live ventilation parameters (Tidal and minute volumes, proximal airway pressure and breathing rate) as well as ventilation pressure & volume wave forms, ventilation modes, settings, alarm limits and battery status. The wide range of both visual and audible alarms provides the healthcare professional with warnings of any changes in patient or device parameters.

The provided text describes the O-Two Medical Technologies' e700, e600, and e500 Electronic Transport Ventilators and their substantial equivalence to predicate devices, but it does not contain the specific information requested in the prompt regarding acceptance criteria, performance tables, sample sizes, expert qualifications, or ground truth establishment.

The document is a 510(k) premarket notification for device clearance, which focuses on demonstrating substantial equivalence to existing legally marketed devices (predicates) rather than providing detailed acceptance criteria and performance study outcomes in the format you've requested.

Here's what can be extracted based on the document, and what is explicitly not available:

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

• Not explicitly provided in the requested format with specific numerical acceptance criteria and performance metrics for an AI/algorithm-based device.

• The document primarily presents a comparative table showing the characteristics and specifications of the proposed devices against the predicate devices (Oxylog 3000 and CAREvent PAR) to demonstrate "Substantial Equivalence." Instead of acceptance criteria, it lists features and parameters and asserts "Yes" for substantial equivalence or notes specific differences.

  • Example from the document (partial):

• The "Summary of Performance Testing" section lists qualitative findings from a comparative bench test:
  • "Both ventilators delivered equivalent volume-time, pressure-time and flow-time wave forms under the same ventilation mode;"
  • "Both ventilators were responsive to spontaneous breathing trigger;"
  • "Ventilation Vt, frequency and I:E ratio or Ti of both units were close to target or preset parameters;"
  • "Both units delivered similar pressure supports."

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

• Not available. The document mentions "comparative side-by-side bench testing" but does not specify sample sizes (e.g., number of test cases, simulated patient scenarios, or real patient data) or data provenance. It's bench testing, implying simulated rather than patient data.

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 / Not available. This is a ventilator device, and the testing described is bench testing against established performance metrics and comparison to predicate devices, not an AI/imaging diagnostic device requiring human expert ground truth for interpretation.

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

• Not applicable / Not available. Adjudication methods are typically used for establishing ground truth in diagnostic studies, which is not the nature of this submission.

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 was not done. This is not relevant for an electronic transport ventilator. The study focused on proving substantial equivalence through bench testing and comparison of specifications to predicate devices, not on human reader performance with or without AI assistance.

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

• Not applicable. The device is a ventilator, a physical system with embedded software, not a standalone algorithm for diagnostic or interpretative tasks. The "Summary of Performance Testing" refers to bench testing of the device's operational performance.

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

• For the bench testing, the "ground truth" would be the expected physical performance parameters and waveforms based on established engineering and medical device standards (e.g., IEC 60601 series, EN 794-3, ISO 10651-3, ISO 80601-2-12) and the known performance of the predicate devices. The device's output (volume-time, pressure-time, flow-time waveforms, Vt, frequency, I:E ratio, etc.) was compared against these established targets.

8. The sample size for the training set

• Not applicable / Not available. This is not an AI/machine learning device that uses a "training set" in the conventional sense. The device's software logic is developed based on engineering principles and validated through testing.

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

• Not applicable / Not available. As this is not an AI/machine learning device requiring a training set, the concept of establishing ground truth for a training set does not apply.

In summary: The provided FDA document is a 510(k) clearance letter and summary for a medical device (electronic transport ventilator). It demonstrates "substantial equivalence" to predicate devices through comparative bench testing and specifications. It does not contain the specific information about AI/algorithm performance, detailed acceptance criteria tables, sample sizes for AI model development/testing, or expert-based ground truth evaluations as typically found in submissions for AI-enabled diagnostic or interpretive devices.

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