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The AIROS 8 Sequential Compression Device utilizes gradient pneumatic compression, which is intended for treatment of patients with the following conditions:

• Lymphedema
• Venous stasis ulcers
• Venous insufficiency
• Peripheral edema

The device is safe for both home and hospital use.

The AIROS 8 Sequential Compression Device is a gradient pneumatic compression device. The device is used for treatment and management of venous or lymphatic disorders. The application of gradient sequential compression increases blood flow and encourages extracellular fluid clearance.

The AIROS 8 system consists of the device and 8-chambered garments. The device provides cycles of compressed air and sequentially inflates the garments from distal to proximal.

The digitally-controlled device consists of an electrically generated source of compressed air, tubing to convey the pressurized air to the sleeve, and like the predicate, pressure is applied cyclically for a specified period of time, according to the physician's prescription.

The provided text is a 510(k) Premarket Notification from AIROS Medical, Inc. for their AIROS 8 Sequential Compression Device. This document primarily focuses on establishing substantial equivalence to a predicate device (also the AIROS 8, K172779) and discusses the device's indications for use, technological characteristics, and conformity to certain standards.

The document does not describe:

• Acceptance criteria for a specific performance metric of an AI/ML algorithm.
• A study proving the device meets such acceptance criteria.
• Sample sizes for test sets or training sets in the context of AI/ML.
• Data provenance, expert involvement for ground truth, or adjudication methods.
• MRMC comparative effectiveness studies or standalone AI performance.
• Specific types of ground truth used (e.g., pathology, outcomes data).

Instead, the document details "Functional Performance Testing" which includes items like Alarm Testing, LED/LCD Testing, Cycle Time Testing, Pressure Accuracy Testing, etc. These are tests typical for a physical medical device (a sequential compression device), confirming its mechanical and electrical specifications, not the performance of an AI/ML algorithm.

Therefore, I cannot fulfill the request as the provided text does not contain information related to AI/ML acceptance criteria or the study that proves an AI/ML device meets those criteria. The AIROS 8 Sequential Compression Device is a hardware device, not an AI/ML powered one, based on the provided document.

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The AIROS 8 Sequential Compression Device utilizes gradient pneumatic compression, which is intended for treatment of patients with the following conditions:

• · Lymphedema
• Venous stasis ulcers
• · Venous insufficiency
• · Peripheral edema
  The device is safe for both home and hospital use,

The AIROS 8 Sequential Compression Device is a gradient pneumatic compression device. The device is used for treatment and management of venous or lymphatic disorders. The application of gradient sequential compression increases blood flow and encourages extracellular fluid clearance.
The AIROS 8 system consists of the device and 8-chambered garments. The device provides cycles of compressed air at certain adjustable pressures, and sequentially inflates the garments from distal to proximal. The pressure at each chamber can be individually adjusted to accommodate different therapy needs.

Here's an analysis of the provided text regarding the AIROS 8 Sequential Compression Device, focusing on acceptance criteria and study data:

Based on the provided 510(k) summary for the AIROS 8 Sequential Compression Device, the document does not contain the detailed information typically found in an AI/ML-based medical device submission regarding acceptance criteria, performance metrics, and study details (like sample size for test/training sets, expert qualifications, ground truth establishment, or clinical outcome studies).

The AIROS 8 device is a physical medical device (a sequential compression device) and its 510(k) submission focuses on demonstrating substantial equivalence to a predicate device (CircuFlow 5208) based on technological characteristics and functional performance testing, rather than AI/ML algorithm performance.

Therefore, I cannot populate the requested table and answer many of the specific questions as they are designed for AI/ML device assessment. The provided text describes a traditional medical device submission.

However, I can extract the acceptance criteria and performance testing described for this type of physical device:

Acceptance Criteria and Reported Device Performance (for a physical medical device)

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

The document does not present quantitative acceptance criteria or specific numerical performance results in a table format. Instead, it states that "Testing was performed and to ensure that the system meets its specifications" and lists the types of functional performance tests conducted. The conclusion is that the device is "substantially equivalent" to the predicate.

Interpretation for a physical device: The acceptance criteria for these tests would likely be "passes" or "meets specification" for each functional test, implying the device performed as intended and comparably to the predicate. Specific numerical targets for pressure accuracy, cycle time, noise, etc., would be in the full submission, but are not in this summary.

Study Details (as inferable for a physical medical device)

The following points are mostly not applicable (N/A) in the context of this 510(k) summary for a physical compression device, as they are typically relevant to AI/ML or diagnostic device studies. The "study" here is primarily functional performance and electrical safety testing.

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

• Sample Size: Not specified. For functional testing of a physical device, this would typically involve testing a representative sample of manufactured units (e.g., a few devices from a production batch).
• Data Provenance: Not specified, but generally refers to in-house laboratory testing of the manufactured device.
• Retrospective/Prospective: N/A. This is functional testing, not a clinical data collection.

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

• N/A. Ground truth as understood in AI/ML (e.g., disease presence/absence determined by experts) is not relevant for the functional testing of a pneumatic compression device. Testing relies on engineering specifications and measurement equipment.

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

• N/A. Adjudication is not applicable for functional engineering tests.

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

• N/A. This device is not an AI/ML diagnostic or assistive tool for human readers.

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

• N/A. This device does not have an AI algorithm. Its "performance" is its mechanical and electrical function.

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

• N/A. The "ground truth" for the listed functional tests would be engineering specifications, direct measurements, and established quality control protocols for pressure, time, electrical safety, etc.

8. The sample size for the training set

• N/A. This concept is for AI/ML models. No training set is mentioned as this is not an AI/ML device.

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

• N/A. Not applicable, as there is no training set for an AI/ML model for this device.

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