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The Movement and Compressions System is intended to be a portable system, prescribed by healtheare professionals, to treat the following conditions by stimulating blood flow in the legs:

• · Aid in the prevention of DVT (deep vein thrombosis) by enhancing blood circulation; and,
• · As a prophylaxis for DVT by persons expecting to be stationary for long periods of time.

During use, the system also monitors patient orientation and movement. It allows healthcare providers and users to implement individualized patient management plans for DVT prophylaxis and patient mobility protocols by utilizing data accumulated by the patient on the previous day as a benchmark. The data displayed on the device allows providers to monitor the patient's orientation and activity, which can be used to identify risk factors for hospital-acquired events linked to immobility such as: deep vein thrombosis, pressure ulcers, pneumonia, atrophic muscles, and delirium.

The device can be used in the home or clinical setting. The device is intended for use in an adult patient population.

The Movement and Compressions System (The MACTM System) is a prescriptive, portable, rechargeable-battery powered, intermittent compression device designed to stimulate blood flow in the lower limb. The MAC System consists of the MAC Strap, MAC Charging Hub, and MAC Controller. The MAC Strap is a disposable single patient use strap that is wrapped around the patient's calf muscle. The MAC Controller houses a rechargeable battery, DC motor, gyroscope sensor, and microprocessor that is attached to the strap during use. The battery is removed from the controller for charging in the supplied MAC Charging Hub when not in use.

Compression is applied to the calf, immediately below the knee, by intermittent application of mechanical force by the device strap. When the strap is contracted, compression is applied to the patient's calf muscle. When the strap is retracted, compression force is released from the patient's calf muscle. Since mechanical force is used to provide intermittent compression, the system does not require a powered air supply, so the risk of aerosolization of potential contaminants or germs is mitigated as there is no blowing air. There are no air connections or pneumatic pumps to clean between patients.

The MAC system also monitors and displays patient orientation and movement information. This data is stored in a RFID tag in the MAC Strap. When the MAC Controller is connected to the MAC Strap, and functioning, all DVT prophylaxis compliance data, orientation and movement data is synced between the controller and the strap using Radio Frequency Identification (RFID) communication and stored between them.

The provided document, a 510(k) Premarket Notification for the "Movement and Compressions System (the MAC System)," primarily focuses on demonstrating substantial equivalence to predicate devices for regulatory clearance. While it outlines various performance tests, it does not contain the level of detail typically required to fully describe acceptance criteria for specific device performance metrics (beyond safety and electrical compliance) and a rigorous study proving the device meets these criteria in the context of clinical efficacy or deep learning model validation.

Specifically, for the "data accumulated by the patient on the previous day as a benchmark" and "monitor the patient's orientation and activity" functionalities, the document mentions "Performance testing also evaluated accuracy of mobility data and strap slippage." However, it does not provide quantitative acceptance criteria or detailed results for this "accuracy of mobility data" evaluation. It also does not elaborate on a study that would demonstrate how this data is "utilized" or its "effect size" regarding "how much human readers improve with AI vs without AI assistance" as it is related to a patient monitoring feature, not an AI-assisted diagnostic or interpretive system for human readers.

Therefore, many of the requested items related to deep learning model validation (such as sample size for test/training sets, data provenance, expert adjudication, MRMC studies, standalone performance, and ground truth establishment) are not present in this regulatory submission document as it pertains to a mechanical medical device with a monitoring feature, not an AI/ML-driven diagnostic or prognostic device requiring such extensive validation.

Below is an attempt to address the request based only on the information available in the provided text. Many fields will be marked as "Not provided/Not applicable" due to the nature of the device and the document.

Device Description and Functionality Summary:

The Movement and Compressions System (The MAC System) is a portable, rechargeable, intermittent compression device aimed at stimulating blood flow in the lower limb to aid in DVT prevention and prophylaxis. A key feature is its ability to monitor patient orientation and movement using a 6-axis gyroscope sensor and microprocessor, with data stored in an RFID tag in the MAC Strap. This mobility data is intended to help healthcare providers implement individualized patient management plans and identify risk factors for immobility-linked hospital-acquired events.

Acceptance Criteria and Reported Device Performance

The document primarily focuses on demonstrating substantial equivalence through safety, electrical, biocompatibility, and software testing, and general functionality. Specific quantitative acceptance criteria and performance data for the accuracy of mobility data are not detailed in a table format in the provided text. The document states that "Performance testing also evaluated accuracy of mobility data and strap slippage" and that the "device met all performance requirements," suggesting these evaluations were conducted and passed internal criteria, but the specific metrics and results are omitted from this public summary.

Study Details for Performance Evaluation (Specifically for Mobility Data/Other Performance)

1. Sample size used for the test set and the data provenance:

   • Test set sample size: Not provided. The document states "Performance testing" was done, but does not specify the number of subjects or data points used for evaluating mobility data accuracy or other performance aspects.
   • Data provenance: Not provided (e.g., country of origin, retrospective or prospective nature).

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

   • Not applicable as this is a sensor-based measurement device, not an AI/ML system requiring expert interpretation for ground truth. It's likely that a physical standard or reference measurement was used for "accuracy of mobility data."

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

   • Not applicable. Ground truth for sensor data accuracy would be established by comparison to a calibrated reference system or direct physical measurement, not human adjudication.

4. 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 MRMC study was done, and it is not applicable for this device. The device's mobility monitoring feature is for providing data to healthcare providers, not for assisting human readers in interpreting complex medical images or data from an AI-driven component where performance enhancement would be measured.

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

   • A standalone performance evaluation of the "accuracy of mobility data" was indicated as part of "Performance Testing." However, specific quantitative results or methodologies are not provided beyond the statement that it was "evaluated" and the device "met all performance requirements." This would have involved comparing the device's sensor output to a known, true value of orientation and movement (e.g., from a more precise measurement system). The "algorithm" here refers to the device's internal processing of sensor data.

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

   • For "accuracy of mobility data," the ground truth would likely be based on a physical reference standard or a highly accurate, calibrated measurement system for orientation and movement tracking, not expert consensus or pathology.

7. The sample size for the training set:

   • Not applicable. This document describes a traditional medical device with embedded sensor capabilities, not a deep learning model that requires a "training set" in the machine learning sense.

8. How the ground truth for the training set was established:

   • Not applicable, as there is no "training set" for a deep learning model. The device's internal algorithms for processing sensor data would typically be developed and validated against engineering specifications and reference measurements.

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The Leaf Patient Monitoring System monitors the orientation and activity of patients susceptible to pressure ulcers. It allows healthcare providers to implement individualized turn management plans and continuously monitor each patient. The Leaf Patient Monitoring System provides alerts when patient orientation or activity deviates from parameters set by healthcare providers. The device is intended for use in medical, nursing and long-term care facilities, including independent living, assisted-living and rehabilitation facilities.

The Leaf Patient Monitoring System is a medical device designed for use in hospitals, nursing homes, or other patient care facilities to monitor and report body orientation and activity, as well as to provide visual alerts for orientations and activity levels that fall outside of thresholds set by healthcare providers. The use of the Leaf Patient Monitoring System provides for continuous monitoring of patient position and allows caregivers to easily identify patients that are in need of caregiver-assisted turns according to the institution's guidelines or protocols. The use of the Leaf Patient Monitoring System can increase compliance with the care facility's prescribed patient tuning schedule and thereby may aid in the prevention of pressure ulcers.

The Leaf Patient Monitoring System is comprised of Patient Sensors, Leaf Antennas, and USB RF Transceivers, Turn Management Software, and a User Interface that can be viewed on a monitoring station. Each Leaf Patient Sensor is associated with a single patient, such that the patient's orientation, movements, and other care parameters can be monitored.

The provided text is a 510(k) premarket notification for the Leaf Patient Monitoring System. It focuses on demonstrating substantial equivalence to a predicate device (Centauri Medical, Inc. DynaSense System) rather than providing detailed acceptance criteria and a study proving the device meets those criteria, particularly in the context of an AI/algorithm-driven device with performance metrics like sensitivity, specificity, etc.

Based on the provided document, here's an attempt to answer the questions, highlighting where information is not present as per the request:

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

The document does not explicitly state quantitative acceptance criteria (e.g., minimum accuracy, sensitivity, specificity) for the Leaf Patient Monitoring System's performance in terms of monitoring patient orientation and activity, nor does it present specific reported performance metrics against such criteria. The focus is on demonstrating that the device "meets the established specifications necessary for consistent performance during its intended use" and "performs as intended."

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

The document mentions "System performance testing" as part of nonclinical bench testing but does not specify a sample size, test set, or data provenance (country of origin, retrospective/prospective). It does not describe a clinical study with patients to validate performance.

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)

This information is not provided as the document does not describe a clinical performance study involving expert-adjudicated ground truth.

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

This information is not provided.

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

An MRMC comparative effectiveness study was not mentioned or described. This device appears to be a monitoring system for patient orientation and activity, not an AI-assisted diagnostic imaging device that would typically involve human "readers." The system is designed to provide alerts and help caregivers with turn management, aiming to increase compliance with care facility protocols for pressure ulcer prevention.

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

The document describes "System performance testing" as part of nonclinical bench testing, implying standalone testing. However, the details of what this entailed (e.g., specific metrics for the algorithm's performance in detecting orientation changes) are not detailed. The device itself is described as a system that continuously monitors and communicates data wirelessly to a monitoring station that displays information via a user interface and provides alerts. Its purpose is to aid human caregivers rather than replace their decision-making entirely.

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

The document does not specify the type of ground truth used for its "System performance testing." Given it's a patient monitoring system for orientation and activity, ground truth would likely involve direct observation or independent measurement of patient position/movement, but this is not stated.

8. The sample size for the training set

The document does not mention a training set or its sample size. This suggests that while the device contains software, a deep learning or similar AI model requiring a large training set may not be the core technology being described for performance evaluation in this submission. The "Software verification" mentioned is more likely related to traditional software engineering validation.

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

As no training set is mentioned, this information is not provided.

Summary of what the document focuses on:

The document primarily focuses on demonstrating substantial equivalence to a predicate device (Centauri Medical, Inc. DynaSense System) based on:

• Identical intended use and indications for use.
• Similar technological characteristics, with minor modifications (updated aesthetics, minor display changes, related software updates, and a non-adhesive frame around the sensor adhesive).
• Labeling changes, specifically the removal of a contraindication for pacemaker/ICD patients with the addition of an appropriate warning statement, which was analyzed not to raise new issues of safety or effectiveness.
• Nonclinical Testing Summary: This included "System performance testing," "Software verification," and "Electrical Safety and EMC." The collective results are stated to "demonstrate that the materials chosen, the manufacturing processes, and design... meet the established specifications necessary for consistent performance" and "do not raise new questions of safety or effectiveness."

Essentially, the submission leverages the predicate device's prior clearance to establish safety and effectiveness, affirming that the new device is functionally the same or improved without introducing new risks that would necessitate extensive new clinical performance studies with detailed acceptance criteria and ground truth validation for novel AI algorithms.

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