Search Results

The Flowrest® delivers warmed humidified high-flow breathing gases. The Flowrest® is intended for treating spontaneously breathing patients who require warmed and humidified high-flow respiratory gases within the homecare, subacute and hospital settings.

The Flowrest® is an integrated non-invasive high flow device incorporating a blower, an air flow heater, a heated humidifier, and a dual lumen breathing circuit and cannula. Filtered room air is drawn in through a blower, where it passes a heater. The heated air is split between the inner lumen and outer lumen of the dual lumen breathing circuit. The heated air in the inner lumen passes through a heated humidifier and is delivered to the patient through a cannula. The heated air in the outer lumen passes through the entire length of the breathing circuit and serves to insulate the humidified patient air, thus minimizing condensation and rainout.

The Flowrest® is comprised of two functional, yet integrated components. One is a motorized fan assembly that provides the physician prescribed high flows of heated breathing and insulating gases between 15 and 35 liters per minute. The fan speed is directly related to flow rate and is controlled by software. The blower assembly output connects directly to a humidification chamber at the front of the device.

The second component of the Flowrest® is a heated humidifier. The water is contained in the humidification chamber positioned on a heater plate at the front of the unit. The chamber connects directly to the blower assembly. Air flow from the device passes through the heated humidification chamber, is warmed and humidified and after passing through the breathing circuit, is delivered to the patient through a cannula. The second flow of warm air through the outside lumen of the delivery tube bypasses the humidification chamber through the top of the humidifier chamber. Ambient temperature is monitored in order to reduce humidified patient air condensation. Temperature controls are controlled by software.

The provided document is a 510(k) summary for the Vapotherm Flowrest®, a respiratory gas humidifier. It details the device's intended use, technological characteristics, and a comparison to a predicate device to establish substantial equivalence.

However, the document does not contain the kind of information typically found in studies proving an AI/Machine Learning device's performance against acceptance criteria. Specifically, it lacks:

• A table of acceptance criteria for AI performance metrics (e.g., sensitivity, specificity, AUC).
• Details about a test set (sample size, provenance).
• Information on expert ground truth establishment (number of experts, qualifications, adjudication).
• MRMC studies, standalone AI performance, or training set details.
• The type of ground truth used (pathology, outcomes data, etc.)

The document focuses on the engineering and safety performance of a medical device (a humidifier) against established medical device standards (e.g., ISO, IEC), and its substantial equivalence to a predicate device.

Therefore, I cannot fulfill your request for AI-specific performance criteria and study details based on the provided text. The "Performance Data" section in the document refers to compliance with safety and design standards for a gas humidifier, not the performance of an AI algorithm.

If this was an AI/ML device, the information would typically look like this (hypothetically, what you are asking for):

1. A table of acceptance criteria and the reported device performance (Hypothetical for an AI device):

2. Sample size used for the test set and the data provenance (Hypothetical):

• Test Set Sample Size: 5,000 cases (e.g., chest X-rays, retinal images).
• Data Provenance: Retrospective data collected from multiple hospitals across the United States, United Kingdom, and Germany.

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

• Number of Experts: 3 board-certified radiologists/pathologists/ophthalmologists.
• Qualifications: All experts had at least 10 years of post-residency experience in their respective specialties, with sub-specialty expertise relevant to the target condition.

4. Adjudication method (Hypothetical):

• Adjudication Method: 2+1 (two experts independently read each case; in case of disagreement, a third, senior expert provided a decisive read). Or, alternatively, consensus reading among all three experts.

5. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done (Hypothetical):

• MRMC Study Done: Yes.
• Effect Size of Human Reader Improvement: The MRMC study showed that human readers aided by the AI significantly improved their diagnostic accuracy (e.g., AUC increased from 0.82 to 0.91, a statistically significant gain of 0.09) and reduced their reading time by 20% compared to reading without AI assistance for detecting Condition X.

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

• Standalone Performance: Yes, standalone performance was evaluated on a held-out test set (as described in point 2) and met the pre-defined acceptance criteria (as in point 1).

7. The type of ground truth used (Hypothetical):

• Ground Truth Type: Expert consensus (as established by the process in point 3 and 4), validated against, wherever possible, pathology reports or long-term clinical outcomes data.

8. The sample size for the training set (Hypothetical):

• Training Set Sample Size: 50,000 cases.

9. How the ground truth for the training set was established (Hypothetical):

• Training Set Ground Truth: Established by a combination of clinical reports, a single expert read (for efficiency with high volume), and automated labeling where robustly feasible, with a subset verified by multi-expert consensus. The training data was regularly reviewed and curated to ensure label accuracy.

In summary, the provided document K103316 does not describe a study related to AI/ML device performance. It is for a traditional medical device (a humidifier) and focuses on compliance with established safety and performance standards for that type of device, demonstrating substantial equivalence to a predicate.

Ask a specific question about this device