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The HFT750U ventilator is intended to provide ventilation and oxygen concentration for patients who are breathing spontaneously but need partial ventilation support due to respiratory failure or chronic respiratory insufficiency.

The device provides heated and humidified gas to the patient.

The device is intended for pediatric patients weighing 12.5 kg or greater to adult patients in non-invasive respiratory therapy. Patients with their upper airways bypassed are included.

The High Flow mode has a flow range from 1 to 60 lpm.

The HFT750U is intended for use in hospitals, hospital-type facilities, and in hospital transportation by qualified and trained users under the directions of a physician.

It is not intended for life support.

This HFT750U is a Continuous Ventilator, intended to provide the patient with a high flow of gas that contains a constant amount of oxygen in a non-invasive method, thereby assisting or regulating the patient's respiration use in a hospital or medical institutions.

It can be used by the upper airways bypassed patients. Equipped with various alarm and safety functions, High flow (HF), the Continuous Positive Airflow Pressure (CPAP) and Bilevel Positive Airway Pressure (bilevel) modes regulate the patient's respiration, but for correct diagnosis it should be used together with clinical information.

The provided FDA 510(k) clearance letter for the HFT750U Continuous Ventilator does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and the study that proves the device meets them.

The document is primarily a clearance letter and a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device based on similar indications for use, technological characteristics, and safety and performance standards. It lists types of performance testing conducted (e.g., Software Verification, Performance of Ventilation Modes), but it does not provide:

• Specific quantifiable acceptance criteria for device performance.
• Reported device performance data against those criteria.
• Details about the test set (sample size, provenance).
• Information on expert involvement in ground truth establishment or adjudication for performance studies.
• Details of any MRMC studies, standalone AI performance, or ground truth types used in specific performance evaluations.
• Training set information for any AI/ML component (which isn't explicitly mentioned as a core feature of this ventilator model, but could be implied by "Software Verification").

Given the limitations of the provided text, I will answer what can be inferred and highlight what information is missing.

Inferred Acceptance Criteria and Reported Device Performance (Table)

Based on the general nature of a continuous ventilator and the standards it claims to comply with, the acceptance criteria would revolve around meeting the specifications and a lack of hazardous function or failure. However, specific numerical acceptance criteria and direct performance metrics are not explicitly stated in the provided text.

The document implies that the device met acceptance criteria because it was cleared by the FDA and states "The sponsor has demonstrated through performance testing, design and non-clinical testing that the proposed device and predicates have been found to be substantially equivalent."

Here's an attempt to structure a table based on the types of performance aspects mentioned, acknowledging that specific data and criteria are absent:

Missing Information from the Provided Document:

1. Sample size used for the test set and the data provenance: Not mentioned. The document only lists types of tests, not the datasets or sample sizes for those tests.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable/not mentioned. This device is a ventilator, not an AI diagnostic tool requiring expert ground truth for image or signal interpretation. Performance is typically assessed against engineering specifications and physiological models.
3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable/not mentioned. Adjudication methods are typically used for establishing ground truth in human-in-the-loop or standalone AI studies for diagnostic/interpretive tasks, which is not the primary function of this device's "performance testing" as described.
4. 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 is not an AI diagnostic or assistive device in the context of "human readers" interpreting medical images or signals. Its "performance testing" would involve engineering and software validation, not human reader studies.
5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The document mentions "Performance of Ventilation Modes and Control Settings" and "Software Verification" which are forms of standalone testing for the device's core functionality and software. However, no specific metrics or study design for "standalone performance" (as it relates to AI algorithm performance) are provided.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc): For a ventilator, "ground truth" would typically be established by:
   • Engineering specifications: The device's output (flow, pressure, oxygen concentration) is measured against the design specifications.
   • Standardized test lung models: Simulation of patient physiology (e.g., lung compliance, resistance) to assess how the ventilator responds in different scenarios.
   • Reference measurement devices: Highly accurate external sensors to verify the device's internal measurements and deliveries.
   • Compliance with recognized standards: Adherence to standards like ISO 80601-2-12 for basic safety and essential performance of ventilators.
     The document does not explicitly state the specific "ground truth" methods used, but these are the common practices for such devices.
7. The sample size for the training set: Not applicable/not mentioned. The document describes a traditional medical device (ventilator) and its conformity to engineering and safety standards. There is no indication of a machine learning model that would require a "training set" in the context of an AI/ML device submission. The software verification refers to traditional software development and testing.
8. How the ground truth for the training set was established: Not applicable, for the same reason as point 7.

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