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The RENU Medical Reprocessed Tri Pulse Compression Garment are to be used as a non-invasive therapeutic method to help prevent Deep Vein Thrombosis (DVT) and resulting pulmonary embolism.

The Reprocessed Tri Pulse Compression Garment is a compression garment that is attached to a patient's limb. It is designed to work with the Arjo Flowtron ACS800 Pump and Arjo Flowtron ACS900 Pump only. Each garment is compressed alternately, applying pressure to the patient's limb, to help prevent deep vein thrombosis.

Tri Pulse garments are constructed with a three-chamber bladder enclosed in a polyester garment, which is wrapped around the limb and secured with hook and eye tabs. When connected to the pump, the garment inflates through a single connecting tube to generate a sequential compression effect on the limb.

Reprocessing the Tri-Pulse Compression Garment is conducted by achieving high-level disinfection via thermal disinfection methods. This process is conducted by holding the devices at or above a predetermined specified temperature for a predetermined specified duration.

Although the document describes the regulatory clearance for a reprocessed medical device (Tri Pulse Compression Garment), it does not contain the information requested regarding acceptance criteria and a study proving a device meets those criteria for an AI/ML medical device.

The provided text focuses on:

• Device Type: Reprocessed medical device (compression garment)
• Regulatory Pathway: 510(k) premarket notification
• Substantial Equivalence: Demonstrated through bench testing (cycle verification testing) to ensure reprocessing does not adversely affect the device's performance compared to the OEM predicate device.
• Testing Method: Cycle Verification Testing, which involves reprocessing garments multiple times (10 cycles) and performing functional pressure and inflation time readings after each cycle.
• Ground Truth: For this type of device, the "ground truth" is adherence to OEM specifications (pressure, inflation time) after reprocessing, not clinical outcomes or expert consensus on image interpretation.
• Study Design: Bench testing, not a clinical study involving human subjects or AI algorithms.

Therefore, I cannot extract the following information from the provided text as it pertains to AI/ML device studies:

1. A table of acceptance criteria and the reported device performance: The document mentions "specifications outlined by the OEM predicate device" and "functional requirements" but doesn't present these in a table format with specific acceptance criteria and reported values for an AI/ML model.
2. Sample size used for the test set and the data provenance: The document mentions a "sample of devices" being reprocessed "10 times each" for the bench test, but this isn't an AI test set.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable as the ground truth for this device is based on physical performance metrics (pressure, inflation time) measured by equipment, not human experts interpreting data.
4. Adjudication method for the test set: Not applicable.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not applicable.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): The ground truth is the physical performance specifications of the OEM device (pressure and inflation time) after reprocessing.
8. The sample size for the training set: Not applicable, as this is not an AI/ML device.
9. How the ground truth for the training set was established: Not applicable, as this is not an AI/ML device.

In summary, the provided document does not contain the information needed to answer the prompt regarding AI/ML device acceptance criteria and study details.

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