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The device is air pressure massager intended to temporarily relieve minor muscle aches and/or pains, and to temporarily increase circulation to the treated areas.

The system consists of an air pump, leg sleeves and hoses working together as one unit. The air pump is connected to the dedicated sleeves via a series of hoses; one sleeve has 4 compression chambers. The compression massage direction is from limb end to body center by inflating the air chambers sequentially and then deflating as one cycle, the pressure can be adjusted to avoid any discomfort to the patient. The sleeve works under the action of sensor and microprocessor. This software is to control the timing and the pressure reflected by the sensor, it cycles the airflow to reach the function of cycling compression of body parts.

The provided text describes a 510(k) premarket notification for the "Pressure Therapy System PT1003". This document focuses on demonstrating substantial equivalence to predicate devices rather than proving a predetermined set of acceptance criteria for a novel AI/imaging device. As such, many of the requested categories for AI/imaging device studies (like MRMC studies, effect size of AI assistance, expert qualifications, sample provenance, training sets, etc.) are not applicable or not provided in this regulatory submission.

However, I can extract information related to the performance characteristics, safety testing, and how the device's adherence to standards and safety was demonstrated.

Here's a breakdown of the available information:

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

The document doesn't explicitly state "acceptance criteria" in the format typically used for AI/imaging performance metrics (e.g., sensitivity, specificity, AUC). Instead, it demonstrates compliance with recognized medical device standards and functional tests to ensure safety and performance comparable to legally marketed predicate devices.

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

This document does not describe a test set in the context of clinical data for AI/imaging performance. The testing performed consists of bench testing and electrical/biocompatibility safety evaluations on the device itself. Therefore, concepts like "sample size" for a data set or "data provenance" (country of origin, retrospective/prospective) are not applicable. The tests were performed on the "PT1003" device following defined procedures.

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

Not applicable. "Ground truth" for AI/imaging interpretation is not relevant for this type of device (a powered inflatable tube massager). The evaluation focuses on engineering, safety, and functional performance.

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

Not applicable. This is not a study requiring adjudication of expert interpretations.

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

Not applicable. This is not an AI/imaging device.

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

Not applicable. This is not an AI/imaging device. The "standalone" performance here refers to the device's functional and safety performance in isolation, which was indeed assessed through the listed standards and function tests.

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

The "ground truth" for this device's evaluation is primarily established by regulatory standards (e.g., IEC, ISO, AAMI ANSI ES) and the functional and safety performance of legally marketed predicate devices. The device is deemed safe and effective if it meets these standards and its performance characteristics (like pressure range, cycle time) are comparable to or safely within the established parameters of already cleared devices.

8. The sample size for the training set

Not applicable. This is not an AI/imaging device, and therefore does not involve a "training set" in the machine learning sense.

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

Not applicable. As above, no training set for AI was used.

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The device is indicated for use by medical professionals and patient at home, who are under medical supervision, in treating many conditions, such as: Primary lymph edema following trauma and sport injuries, Post immobilization edema, Venous insufficiencies, Lymph edema.

The system consists of an air pump, leg sleeves and hoses working together as one unit. The air pump is connected to the dedicated sleeves via a series of hoses; one sleeve has 4 compression chambers. The compression massage direction is from limb end to body center by inflating the air chambers sequentially and then deflating as one cycle, the pressure can be adjusted to avoid any discomfort to the patient. The sleeve works under the action of sensor and microprocessor. This software is to control the timing and the pressure reflected by the sensor, it cycles the airflow to reach the function of cycling compression of body parts.

The provided text is a 510(k) Pre-market Notification for the "Pressure Therapy System PT1003", which seems to be a medical device. This document focuses on demonstrating substantial equivalence to a predicate device (PT1002), rather than providing detailed acceptance criteria and a study design for a new, AI-powered diagnostic device.

Therefore, the information required to answer your specific questions about AI/ML device acceptance criteria and a study proving performance (e.g., sample size for test set, data provenance, number of experts for ground truth, MRMC study, standalone performance, training set details) is not present in the provided document.

The document discusses:

• Device Description: A pneumatic compression system (PT1003) for treating conditions like lymphedema and venous insufficiencies.
• Predicate Device: PT1002, an earlier version by the same manufacturer.
• Substantial Equivalence: The primary goal of the 510(k) submission is to show that PT1003 is substantially equivalent to PT1002 in terms of intended use, indications, and technological characteristics.
• Minor Differences: Changes in appearance and PCB components (microprocessors, touch keys) are noted, with the manufacturer asserting these changes do not affect safety or performance.
• Performance Characteristics: Mentions adherence to various safety standards (AAMI ANSI ES 60601-1, IEC 60601-1-2, ISO10993-5, ISO 10993-10) and general "function tests" (min/max air pressure, electric current, airflow, noise level, modes A and B). It states that test results are "almost same" as the predicate device.

Without the specific details about AI/ML components and their performance evaluation, I cannot generate the table or answer the questions related to AI device acceptance criteria and study proving its performance.

The document's "Performance characteristic" section is very general and does not provide:

• Specific Acceptance Criteria (quantitative thresholds for performance metrics).
• Detailed Study Design: e.g., how the "function tests" were conducted, the sample size of devices tested, the number of measurements taken, or the statistical methods used to compare results between PT1003 and PT1002.
• Ground Truth Establishment: For a simple physical therapy device evaluating pressure and airflow, "ground truth" would likely be derived from calibrated measurement instruments, not expert consensus or pathology, and these details are not provided.

In summary, the provided text describes a traditional medical device 510(k) submission, not an AI/ML powered device, and therefore lacks the information needed to answer your questions about AI device validation.

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