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The Bio Compression Systems' Sequential, pneumatic compression devices intended for either primary or adjunctive treatment of lymphedema, peripheral edema, venous insufficiency, and venous stasis ulcers. Sequential Circulators are also intended for the prophylaxis of deep vein thrombosis (DVT). Intended for use in a home or healthcare setting.

Bio Compression Systems' Sequential Circulators are sequential pneumatic compression device which consists of a segmented pneumatic compression sleeve ("qarment") connected to a pneumatic compression pump ("pump"). The pump cyclically inflates the garment's segments ("chambers") in sequence from the distal end toward the trunk of the body. The inflation of the garment compresses the limb on which it is worn, stimulating the movement of interstitial fluid and blood flow.

The core components of the pump are the motor, air compressor, disc valves, and micro switch. The air compressor generates air flow into a stationary disc valve. The motor moves a rotating disc valve. The geometry of the disc valve directs air flow and cyclically triggers the micro switch.

The pump is controlled by softkeys and an LED display (models SC-1004-DL, SC-1008-DL, SC-2004-DL, SC-2008-DL) or by a touch screen LCD (models SC-4004-DL, SC-4008-DL).

The device uses the Predicate Device's garments.

The provided document is a 510(k) premarket notification for a medical device (Sequential Circulators) and does not describe a study that establishes acceptance criteria for a device based on expert performance or to prove a device meets acceptance criteria through a comparison with human readers or a standalone algorithm study.

Instead, this document focuses on demonstrating substantial equivalence to a previously cleared predicate device, as required for 510(k) submissions to the FDA. The "acceptance criteria" discussed are primarily related to general performance, electrical safety, electromagnetic compatibility, and software verification/validation, rather than clinical performance based on ground truth established by experts.

Therefore, many of the requested points related to acceptance criteria and performance studies in the context of AI/ML or expert adjudication in a clinical setting cannot be directly extracted from this document.

However, I can provide the information that is present:

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

The document doesn't present "acceptance criteria" in a typical table format with quantitative performance metrics for a diagnostic or AI device (e.g., sensitivity, specificity). Instead, it lists various tests and validations performed to ensure the device performs as intended and is safe. The "reported device performance" is implied by the satisfactory completion of these tests, leading to the substantial equivalence determination.

Here's an interpretation based on the document's content:

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

The document does not specify sample sizes in terms of a "test set" for clinical evaluation in the way an AI/ML device submission would. The testing described (electrical safety, functional verification, comparative pressure) refers to engineering and bench testing of the devices themselves, not a dataset of patient cases.

• Sample Size: Not applicable in the context of clinical "test sets" for diagnostic performance. The sample size would refer to the number of device units tested.
• Data Provenance: Not applicable. The testing is described as being conducted on the subject device and reference devices, not on patient data.

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

This information is not applicable to this 510(k) submission. The device is a pneumatic compression device, not a diagnostic imaging device or an AI/ML algorithm requiring expert ground truth for clinical performance evaluation. The "ground truth" for this device's performance is its direct physical output (e.g., pressure, cycle time) verified through engineering tests against specifications.

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

Not applicable. There is no expert adjudication mentioned as this is not a diagnostic or AI/ML device relying on interpretation of clinical data.

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-assisted device, and no MRMC study was conducted.

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

Not applicable. This is not an AI algorithm.

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

For this device, the "ground truth" used for testing would be the engineering specifications and established standards for medical devices (e.g., pressure accuracy, cycle time, electrical safety parameters). It is not clinical ground truth derived from expert consensus, pathology, or outcomes data.

8. The sample size for the training set

Not applicable. This device does not involve training of an AI/ML model.

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

Not applicable. No training set for an AI/ML model.

Summary of the Study Proving Device Meets Acceptance Criteria (as described in the 510(k)):

The study to prove the device meets its "acceptance criteria" (understood here as performance specifications and safety standards) consisted of a series of bench testing, engineering verification, and validation activities. These included:

• Electrical Safety and Electromagnetic Compatibility (EMC) Testing: Adherence to recognized national and international standards (e.g., ANSI/AAMI ES60601-1, IEC 60601-1-2, etc.).
• Predicate Device Routine Acceptance Tests Conducted on Subject Device: This implies the new device underwent the same internal quality control and performance tests as the predicate, including continuous and timed operation observation, pressure testing, and HiPot testing.
• Functional Verification and Validation Testing: Specific tests were conducted to verify and validate cycle time, treatment time, pressure setting endpoints, and overall functionality of all modes and settings.
• Comparative Pressure Testing: Specific tests were done to compare the pressure performance of the Subject Device and Predicate Device, particularly regarding individual chamber adjustment and pressure gradients. This was crucial for demonstrating substantial equivalence for critical performance parameters.
• Software Verification and Validation Testing: Conducted in accordance with FDA guidance for medical device software, confirming its functionality and safety (classified as "minor" level of concern).

The conclusion states: "The data included in this submission demonstrates that the Subject Device is substantially equivalent to the legally marketed Predicate Device and performs comparably to the Predicate Device that is currently marketed for the same intended use." This statement serves as the justification that the device meets the implied "acceptance criteria" for demonstrating substantial equivalence.

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The Massage Compression Boots are intended for the temporary relief of minor muscle aches and for the temporary increase in circulation to the treated areas. The device can simulate kneading and stroking of tissues by using an inflatable garment.

The subject device is a portable and rechargeable device. It is intended to be an over-the-counter portable inflatable tube massage system which simulates kneading and stroking of tissue by use of an inflatable garment. It can be used to temporarily increase blood circulation and temporarily relieve minor muscle aches and pains. The device, supplied clean and non-sterile, utilizes the pneumatically controlled chambers actuated by an electronically controlled air pump unit. All pump, battery and control components are protectively housed in a plastic case of the control unit that is permanently attached to an inflatable sleeve of multiple air chambers. An ON/OFF button, a SET button, and a display of LEDs provide for user interface. There is also a port for connecting the battery charger/AC adapter plug. The sleeve component consists of multiple air chambers/bladders encased inside a soft medical fabric or equivalent medical for increased patient comfort and biocompatibility compliance.

The provided text is a 510(k) summary for the JKH USA, LLC Massage Compression Boots. This document focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed acceptance criteria and the results of a standalone study proving the device meets specific performance criteria.

Therefore, many of the requested details about acceptance criteria, test set characteristics (sample size, provenance, expert consensus, adjudication), multi-reader multi-case studies, and ground truth for training/testing are not present in this type of regulatory submission. The FDA 510(k) pathway for Class II devices often relies on demonstrating that a new device is as safe and effective as a legally marketed predicate device, rather than requiring extensive clinical trials with pre-defined performance metrics like sensitivity, specificity, and reader studies that are common for AI/ML-driven diagnostic devices.

However, I can extract the information that is available from the document:

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

The document doesn't present a table of acceptance criteria and reported device performance in the typical sense of quantitative metrics for a new, often AI/ML-driven, medical device's diagnostic or predictive capabilities. Instead, it compares the subject device to predicate devices on various technical specifications to demonstrate substantial equivalence.

Here's a summary of the comparisons, which serve as the "performance" relevant to a 510(k) for this type of device:

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This document does not describe a clinical "test set" in the context of an AI/ML algorithm's performance evaluation. The substantial equivalence argument is based on non-clinical tests (bench tests for physical, electrical, and performance requirements, and software verification) and comparison to predicate devices, not patient data from a test set. Therefore, sample size and data provenance are not applicable in this context.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. As noted above, there was no clinical "test set" requiring expert-established ground truth for performance evaluation of a diagnostic or predictive algorithm.

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

Not applicable. No test set requiring adjudication was used.

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

No. This type of study is not mentioned or required for this 510(k) submission for a non-diagnostic pneumatic compression device.

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

This is not an AI/ML diagnostic algorithm. The device itself is a "Powered Inflatable Tube Massager." Its "performance" is demonstrated by meeting general safety standards and comparison of its physical and functional characteristics to legally marketed predicate devices, not by a standalone performance study in the way an AI algorithm would be evaluated. The software verification was performed according to FDA guidance, but this refers to the robustness and functionality of the device's control software, not a standalone diagnostic performance.

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

Not applicable in the context of an AI/ML diagnostic or predictive model. The "ground truth" for this device's performance relies on its adherence to safety standards (e.g., electrical, mechanical) and the functional equivalence (e.g., pressure range, cycle time, intended use) to predicate devices already deemed safe and effective.

8. The sample size for the training set

Not applicable. There is no mention of an AI/ML training set as this is not an AI/ML device in the context of what would typically require such a set (e.g., for image analysis or disease prediction).

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

Not applicable.

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