The Radius VSM and accessories are intended to be used as both a wearable multi-parameter patient monitor and an accessory to a multi-parameter patient monitor that is intended for multi-parameter physiological patient monitoring in hospital and healthcare facilities.

The Radius VSM and accessories are indicated for the monitoring of hemodynamic (including ECG, arrhythmia detection, non-invasive blood pressure, SpO2, Pulse Rate, PVi, heart rate, and temperature), and respiratory (e.g., impedance, acoustic, and pleth-based respiration rate) physiological parameters along with the orientation and activity of adults.

The Radius VSM and accessories are indicated for the non-invasive continuous monitoring of functional oxygen saturation of arterial hemoglobin (SpO2) and Pulse Rate (PR) of well or poorly perfused adults during both no motion and motion conditions.

The Radius VSM and accessories are indicated for continuous monitoring of skin temperature of adults.

The Radius VSM and accessories are indicated for monitoring of the orientation and activity of patients including those susceptible to pressure ulcers.

The Radius VSM and accessories are indicated for the continuous non-invasive monitoring of PVi as a measure of relative variability of the photoplethysmograph (pleth) of adults during no motion conditions. PVi may be used as a noninvasive dynamic indicator of fluid responsiveness in select populations of mechanically ventilated adult patients. Accuracy of PVi in predicting fluid responsiveness is variable and influenced by numerous patient, procedure and device related factors. PVi measures the variation in the plethysmography amplitude but does not provide measurements of stroke volume or cardiac output. Fluid management decisions should be based on a complete assessment of the patient's condition and should not be based solely on PVi.

Devices with Masimo technology are only indicated for use with Masimo accessories.

Radius VSM Accessories:

Radius VSM ECG Electrodes are disposable, single-patient use ECG electrodes intended to acquire ECG signals from the surface of the body. They are indicated for use on adults for up to 3 days of skin surface contact.

Radius VSM Blood Pressure Cuffs are accessories intended to be used with a noninvasive blood pressure measurement system to measure blood pressure. They are indicated for use on adults during no motion conditions.

The Radius VSM and accessories are an FDA cleared (K223498), wearable, battery-operated, multi-modular patient monitoring platform that allows for the ability to scale and tailor the use of different monitoring technologies based upon the hospital and clinician's assessment of what technologies are appropriate.

As part of this submission, a MAP feature is being added to the Radius VSM. The feature is a software feature that uses the previously cleared systolic and diastolic measurement capabilities to automate the calculation of MAP using the following formula: MAP = 1/3* Systolic + 2/3*Diastolic.

The MAP is calculated by the Radius VSM NIBP Module and displayed on the Radius VSM Wearable Monitor. There were no other features added as part of this submission.

The provided 510(k) clearance letter and summary discuss the addition of a Mean Arterial Pressure (MAP) feature to the previously cleared Radius VSM and Accessories device. The primary focus of the performance data section is on validating this new MAP feature.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided document:

Acceptance Criteria and Reported Device Performance

The document states that the acceptance criterion for Blood Pressure (including MAP) is:

"Meets ISO 81060-2 (Mean difference of ≤5 mmHg with a standard deviation of ≤8 mmHg)"

The document directly states that the results of the clinical testing supported the clinical performance of the MAP in accordance with ISO 81060-2. While specific numerical results (e.g., the exact mean difference and standard deviation achieved) are not explicitly provided in the summary table, the clearance implies that these metrics fell within the specified ISO 81060-2 limits for the MAP feature.

Table 1: Acceptance Criteria and Reported Device Performance for MAP Feature (as inferred from the document)

Study Details for MAP Feature Validation

1. Sample Size Used for the Test Set and Data Provenance:

   • Sample Size: The document does not explicitly state the numerical sample size (number of subjects/patients) used for the clinical test set. It only mentions "clinical study data."
   • Data Provenance: The document does not specify the country of origin. It indicates it was a "clinical study" and implies it was prospective ("clinical testing is provided to support its performance" for the added feature).

2. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of those Experts:

   • Not applicable as the ground truth was established by an objective reference device, not human experts.

3. Adjudication Method for the Test Set:

   • Not applicable, as the method for ground truth establishment was comparison to a reference device.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

   • No, an MRMC study was not done. The study was a comparison of the device's calculated MAP to invasively measured MAP from a reference device. This is a technical performance validation, not a study assessing human reader improvement with AI assistance.

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

   • Yes, this was a standalone performance study. The Radius VSM automatically calculates the MAP based on the NIBP measurements (Systolic and Diastolic Pressure). The clinical testing validated the accuracy of this calculation against a reference standard, without human intervention in the MAP calculation or interpretation for the test itself.

6. The Type of Ground Truth Used:

   • Reference Ground Truth: Invasively measured MAP values from a 510(k) cleared reference device (K171801). This reference device is identified as "IntelliVue Multi-Measurement Module X3." This constitutes a device-based reference standard or instrument-based ground truth.

7. The Sample Size for the Training Set:

   • The document does not provide information about a training set since the MAP feature appears to be a direct calculation using a standard formula (MAP = 1/3* Systolic + 2/3*Diastolic) rather than a machine learning model that requires a training phase. While the device as a whole (Radius VSM) likely had training and validation phases for its other parameters, the specific "addition of a Mean Arterial Pressure (MAP) feature" is described as a software feature that "automates the calculation" using a known formula. Therefore, a separate training set for this specific MAP feature is unlikely to have been required or used in the conventional machine learning sense.

8. How the Ground Truth for the Training Set was Established:

   • As inferred above, a specific training set and ground truth establishment for this isolated MAP calculation feature are not described, given its nature as a direct formulaic calculation.

Summary of Key Information:

The core of this submission revolves around adding a simple, formula-based calculation for MAP. The primary study presented is a clinical validation confirming that the device's computed MAP aligns with a known industry standard (ISO 81060-2) when compared against an invasive reference device. This is a technical performance validation rather than a complex AI-driven diagnostic study.

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The PRESSUREALERT® Pressure Monitoring System monitors the activity of patients who are susceptible to pressure ulcers by measuring pressure at various points of the body. It allows healthcare providers to medividualized turn management plan for each patient by continuously monitoring pressure on various points of the body of each patient. The PRESSUREALERT® Pressure Monitoring System provides alerts when patient activity deviates from the pressure prevention parameters set for by the healthcare providers. The device is in medical, nursing, and longterm care facilities including independent living, assisted living, and rehabilitation facilities.

The PRESSUREALERT® Pressure Monitoring System is composed of a wireless pressure sensing dressing assembly as part of a system supported by the PRESSUREALERT® Management Software for providing a warning to the healthcare provider that soft tissue pressure has exceeded a predetermined level that, over a period of time, would necessitate that the patient should be moved to prevent or at least reduce the risk of soft tissue damage.

The PRESSUREALERT® Pressure Monitoring System functions as a pressure monitoring system with the primary function to monitor a patient that is laying down on their back or otherwise in a position that may result in the patient's weight applying pressure to an area of the patient's body that is susceptible to pressure ulcers/injuries, such as soft tissue overlying a bony prominence.

The dressing assembly, with the enclosed pressure sensor, is applied on the patient's body that is susceptible to damage from soft tissue pressure. The identified areas are referred to as "at-risk" in the instructional documentation and intended use and include up to eleven (11) sites defined as the upper spine, head-skull, hip (right or left), Ischia (right or left), heels and elbows (right or left), and sacrum.

The material components of the PRESSUREALERT® Pressure Monitoring System, and described in detail in the Device Description section, includes:

• i. PRESSUREALERT® Segmented Oval Dressing
• ii. PRESSUREALERT® Round Dressing
• iii. PRESSUREALERT® Sacral Dressing
• iv. PRESSUREALERT® Oval Sensor
• v. PRESSUREALERT® Round Sensor

The Walgreen Health Solutions, LLC's PRESSUREALERT® Pressure Monitoring System did not perform any clinical studies. All acceptance criteria were met through non-clinical testing.

Here's a breakdown of the requested information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

Since only non-clinical bench tests were performed, there was no "test set" in the context of patient data. The tests were likely performed on a sample of the manufactured devices. The document does not specify the exact number of devices or components used for each bench test.

• Data Provenance: Not applicable as no human-subject data was used. All testing was non-clinical and conducted in a laboratory setting.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

Not applicable. Ground truth for non-clinical bench testing is established by engineering specifications, regulatory standards, and objective measurements, not by expert consensus on patient data.

4. Adjudication Method for the Test Set

Not applicable. As no clinical studies were performed, there was no need for adjudication of patient data.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No. No clinical studies, including MRMC studies, were conducted.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The device is a hardware and software system that provides alerts to healthcare providers. While the sensors and software operate autonomously to detect pressure and generate alerts, the overall system is designed to be human-in-the-loop as it aids healthcare providers in executing turn management plans. There wasn't a separate "algorithm only" performance evaluation presented in the context of clinical outcomes. The non-clinical tests evaluate the physical and electronic performance of the components.

7. The Type of Ground Truth Used

For the non-clinical tests, the "ground truth" was based on:

• Engineering specifications: For physical attributes like ingress protection and drop resistance.
• Performance standards: For battery life and pressure threshold (32 mmHg is a physiological threshold related to capillary pressure).
• Objective measurements: Performed during the bench testing.

8. The Sample Size for the Training Set

Not applicable. The document does not describe any machine learning or AI components that would require a "training set" in the typical sense for image analysis or diagnostic algorithms. The software appears to be rule-based for pressure detection and alerting.

9. How the Ground Truth for the Training Set Was Established

Not applicable, as no training set was explicitly mentioned or used in this submission.

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ANNE One is a wireless monitoring platform indicated for the measurement of electrocardiography (ECG) waveforms. heart rate, respiratory rate, function of arterial hemoglobin (SpO2), pulse rate, activity, body position, fall detection, skin temperature by qualified healthcare professionals in home and healthcare settings. ANNE One is compatible with third-party, FDA-cleared devices for noninvasive blood pressure, SpO2, pulse rate, and body temperature measurements. The device is indicated for monitoring ECG waveforms and heart rate on ambulatory patients. The device is not intended to monitor or measure respiratory rate. SpO2, pulse rate, or noninvasive blood pressure while the patient undergoes significant motion or is active.

ANNE One continuously monitors the orients to aid in the prevention of pressure ulcers for at-risk patients. The system provides visual notification when the pation has not changed from a preset threshold of time.

The device is intended for use on general care patients who are 12 years of age or older as a general patient monitor to provide continuous physiological information as an aid to diagnosis and treatment. The data from ANNE One are transmitted wirelessly for display, storage, and analysis. The device is not intended for use on critical care patients.

ANNE One is a wireless monitoring platform that streams and stores real-time biosignals including electrocardiography (ECG), photoplethysmography (PPG), 3-axis accelerometry, and temperature to measure vital signs such as heart rate, respiratory rate, SpO2, pulse rate, skin temperature, and body temperature. The ECG signal is not intended for automated arrhythmia detection or classification; rather it is intended for manual interpretation, and the automated computation of heart rate through QRS identification using the well-known Pan-Tompkins beat detection algorithm. The displayed waveform is only intended for display as a check for normal ECG rhythm. The waveform is not intended for manual discrimination of any arrhythmias or cardiac conditions. The system features two skin-mounted, bio-integrated sensors that pair with the ANNE View software application for the continuous display, storage, and analysis of vital sign measurements and physiological waveforms. The system is also compatible with optional FDA-cleared third-party devices for SpO2, non-invasive blood pressure, and body temperature measurements.

Here's an analysis of the acceptance criteria and the studies that prove the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document primarily focuses on demonstrating substantial equivalence to predicate and reference devices, with specific performance values provided for SpO2 and Respiratory Rate from clinical studies. The other parameters are stated to meet relevant standards or are equivalent to predicate/reference device performance.

2. Sample Size Used for the Test Set and Data Provenance

• SpO2 Accuracy Study:

  • Sample Size: n=12 healthy subjects.
  • Data Provenance: Not explicitly stated (e.g., country of origin), but implies a controlled clinical setting. The study is described as "Sibel validated the accuracy...".
  • Retrospective/Prospective: Implied prospective as it's a validation study conducted by Sibel.

• Respiratory Rate Accuracy Study:

  • Sample Size: n=40 healthy adult subjects.
  • Data Provenance: Not explicitly stated (e.g., country of origin), but implies a controlled clinical setting. The study is described as "Sibel validated the accuracy...".
  • Retrospective/Prospective: Implied prospective as it's a validation study conducted by Sibel.

• Other Parameters: For other parameters like Heart Rate, ECG waveform display, Activity, etc., "performance testing" and adherence to "consensus standards" are mentioned, but specific sample sizes for these test sets are not provided in this summary. The comparison table also mentions "ambulatory databases" for ECG during motion, but no sample size is given.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts

• SpO2 Accuracy Study: Ground truth was established by "blood gas analysis." This typically involves laboratory analysis, not expert consensus on visual assessment.
• Respiratory Rate Accuracy Study: Ground truth was established by "etCO2" (end-tidal CO2). This is a physiological measurement, not an expert consensus.
• Other Parameters: No information is provided regarding experts or their qualifications for establishing ground truth for other parameters. Ground truth for these values would likely derive from established measurement techniques compliant with the referenced standards.

4. Adjudication Method for the Test Set

• Given that the ground truth for SpO2 and Respiratory Rate relied on objective physiological measurements (blood gas analysis and etCO2, respectively), there was likely no "adjudication method" in the sense of reconciling disagreements between multiple graders or clinicians. The measurements themselves serve as the ground truth.

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 MRMC comparative effectiveness study involving human readers and AI assistance is mentioned in the provided text. The device is a "wireless monitoring platform" for physiological measurements. Its primary function is to collect and display vital sign data, not to interpret complex medical images or data that typically require a human reader for adjudication or enhancement by AI.

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

• Yes, the performance data provided for SpO2 and Respiratory Rate are standalone algorithm capabilities. The device measures these parameters and the reported accuracy is the algorithm's performance against a reference standard, without human intervention in the measurement or calculation process. The description of "automated computation of heart rate through QRS identification using the well-known Pan-Tompkins beat detection algorithm" also indicates a standalone algorithmic function.

7. The Type of Ground Truth Used

• SpO2: Blood gas analysis (objective physiological measurement).
• Respiratory Rate: EtCO2 (objective physiological measurement).
• Heart Rate: Implied to be derived from ECG signals, with validation against established standards (e.g., IEC 60601-2-27, IEC 60601-2-47), which would use a recognized reference for HR. The Pan-Tompkins algorithm is for beat detection, which is then used to compute HR.
• Other Parameters (Skin Temperature, Body Position, Activity, ECG Waveform Display, Pulse Rate): Ground truth is likely established through a combination of physical reference measurements and adherence to recognized consensus standards (e.g., ISO, IEC). Bench testing is mentioned for several parameters.

8. The Sample Size for the Training Set

• The document does not explicitly state the sample size for any training set for machine learning models. The device description mentions the use of "the well-known Pan-Tompkins beat detection algorithm" for heart rate, which is a classical signal processing algorithm and may not require a 'training set' in the modern machine learning sense. While algorithms are likely involved in respiratory rate and SpO2 calculations, the summary focuses on validation, not the development or training phase.

9. How the Ground Truth for the Training Set Was Established

• As training set information is not provided, the method for establishing its ground truth is also not detailed.

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Radius VSM:

The Radius VSM and accessories are intended to be used as both a wearable multi-parameter patient monitor and an accessory to a multi-parameter patient monitor that is intended for multi-parameter physiological patient monitoring in hospital and healthcare facilities.

The Radius VSM and accessories are indicated for the monitoring of hemodynamic (including ECG, arrhythmia detection, non-invasive blood pressure, SpO2, Pulse Rate, PVi, heart rate, and temperature), and respiratory (e.g., impedance, acoustic, and pleth-based respiration rate) physiological parameters along with the orientation and activity of adults.

The Radius VSM and accessories are indicated for the non-invasive continuous monitoring of functional oxygen saturation of arterial hemoglobin (SpO2) and Pulse Rate (PR) of well or poorly perfused adults during both no motion and motion conditions.

The Radius VSM and accessories are indicated for continuous monitoring of skin temperature of adults.

The Radius VSM and accessories are indicated for monitoring of the orientation and activity of patients including those susceptible to pressure ulcers.

The Radius VSM and accessories are indicated for the continuous non-invasive monitoring of PVI as a measure of relative variability of the photoplethysmograph (pleth) of adults during no motion conditions. PVi may be used as a noninvasive dynamic indicator of fluid responsiveness in select populations of mechanically ventilated adult patients. Accuracy of PVi in predicting fluid responsiveness is variable and influenced by numerous patient, procedure and device related factors. PV i measures the variation in the plethysmography amplitude but does not provide measurements of stroke volume or cardiac output. Fluid management decisions should be based on a complete assessment of the patient's condition and should not be based solely on PVi.

Devices with Masimo technology are only indicated for use with Masimo accessories.

Radius VSM Accessories:

Radius VSM ECG Electrodes are disposable, single-patient ECG electrodes intended to acquire ECG signals from the surface of the body. They are indicated for use on adults for up to 3 days of skin surface contact.

Radius VSM Blood Pressure Cuffs are accessories intended to be use with a noninvasive blood pressure measurement system to measure blood pressure. They are indicated for use on adults during no motion conditions.

The Radius VSM and Accessories is a wearable, multi-modular patient monitoring platform that allows for the ability to scale and tailor the use of monitoring technologies based upon the hospital's and clinician's assessment of what technologies are appropriate. The purpose of this submission is the premarket notification for the introduction of Masimo Radius VSM and Accessories, including its use with the previously cleared Root (K191882) and Masimo Patient SafetyNet (K071047).

The Radius VSM and Accessories system comprises of the Radius VSM Wearable Monitor, Radius VSM ECG Module and Electrodes, and the Radius VSM NiBP Module and Cuff.

The provided text describes the acceptance criteria and study results for the Masimo Radius VSM and Accessories device, focusing specifically on the Non-invasive Blood Pressure (NiBP) feature.

1. Acceptance Criteria and Reported Device Performance (NiBP Feature):

The clinical performance analysis for the NiBP feature supported by the Masimo Radius VSM device had the following acceptance criteria and reported values:

The device met all specified acceptance criteria for the NiBP feature.

2. Sample Size and Data Provenance for the Test Set:

• Sample Size:
  • NiBP Feature: 89 subjects.
  • ECG Waveform Comparison: 31 subjects.
  • Patient Posture, Position, and Activity: 20 subjects.
  • Aggregate Respiration Rate (First Study): 48 subjects.
  • Aggregate Respiration Rate (Second Study): The number of healthy volunteer subjects is not explicitly stated, but it's implied to be a separate group for validation of integration.
• Data Provenance: The document does not explicitly state the country of origin. The studies are described as "clinical studies," implying prospective data collection for the purpose of validating the device. The term "healthy volunteer subjects" used in the fifth study further suggests prospective, controlled data collection.

3. Number of Experts and Qualifications for Ground Truth:

The document does not specify the number or qualifications of experts used to establish ground truth for any of the studies mentioned.

4. Adjudication Method for the Test Set:

The document does not describe any specific adjudication method for the test set data.

5. MRMC Comparative Effectiveness Study:

No mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study or human readers improving with AI assistance is made in the provided text. The studies focus on device performance against reference measurements or previously cleared monitors/algorithms, not on human-AI collaboration.

6. Standalone Performance (Algorithm Only):

• For the NiBP feature, the study was conducted to validate the clinical performance of the Radius VSM's NiBP feature against reference blood pressure measurements, implying standalone performance of the algorithm integrated into the device.
• For the ECG waveform comparison, the device's ECG output was compared to an existing FDA-cleared ECG monitor, indicating standalone performance of the device's ECG functionality.
• For the patient posture, position, and activity feature, the testing supported the "correct integration of the algorithm that was previously cleared," suggesting a focus on the device's implementation of an existing standalone algorithm.
• For the Aggregate Respiration Rate, the algorithm's performance was evaluated against manually annotated capnography data, indicating standalone algorithm performance.

7. Type of Ground Truth Used:

• NiBP: Clinical performance was validated through comparison against "reference blood pressure measurements."
• ECG: Comparison against an "FDA cleared ECG monitor."
• Patient Posture, Position, and Activity: Based on the "correct integration of the algorithm that was previously cleared." The original ground truth for this algorithm (K191882) is not detailed here, but the study validates its implementation in the new device.
• Aggregate Respiration Rate: "Reference respiration rate derived from manual annotated capnography data."

8. Sample Size for the Training Set:

The document does not provide information on the sample size used for training sets for any of the algorithms or features. The studies described are validation (test set) studies.

9. How Ground Truth for the Training Set was Established:

As no information regarding training sets is provided, there is no detail on how their ground truth was established. The document focuses on the validation of integrated features, some of which (like PVi, RRa, and position monitoring) leverage previously cleared Masimo technologies, implying that their development and training (if applicable) occurred prior to this submission.

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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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QuietCare-Networked is intended for use in monitoring the environmental conditions and activity (motion) of an individual living in a senior housing community. QuietCare-Networked recognizes and monitors certain patterns of activity including but not limited to bathroom and bedroom activity, residence entry/exit, and interaction with food and medication storage.

Caregivers are provided with information and notification about the occurrence of, and changes in, these monitored activity patterns and environmental conditions. Noteworthy occurrences and changes are communicated to caregivers through direct notification (pager, voice alert, email) as well as a secure Internet website.

Data from QuietCare-Networked should not be relied on as medical advice or clinical diagnosis. Caregivers should always rely on licensed medical professionals in making all health decisions and should use the information provided by QuietCare as a resource in that process.

Caregivers should not rely solely on the use of QuietCare-Networked for care management of clients/residents. Caregivers should use standard care practices established within their care organization to ensure the safety and wellness of senior clients/residents.

Care Innovations QuietCare-Networked uses advanced motion sensors to monitor Activities of Daily Living for senior residents who require care assistance. It provides alerts and reporting information to care givers when conditions or trends are detected that indicate the senior resident may need care intervention.

The Intel-GE Care Innovations QuietCare-Networked device is a Class I Bed-Patient Monitor that uses motion sensors to monitor Activities of Daily Living for senior residents.

This submission explicitly states that clinical performance data was not used to demonstrate safety and efficacy. The device's equivalency was established by comparing its technological characteristics to predicate devices. Therefore, the information requested regarding acceptance criteria, study details, sample sizes, ground truth establishment, expert qualifications, and adjudication methods is not applicable to this particular 510(k) submission.

The device was deemed substantially equivalent based on similarities in software functionality, data collection methods, sensor types, communication methods, connectivity, communication protocol, and display method to existing commercially distributed predicate devices.

Here's a breakdown of why many of your excellent questions cannot be answered from the provided document:

• Acceptance Criteria & Reported Device Performance: Not provided as no clinical performance study was conducted.
• Sample Size (Test Set) & Data Provenance: Not applicable as there was no test set or clinical study.
• Number of Experts & Qualifications: Not applicable as there was no ground truth establishment by experts for a clinical study.
• Adjudication Method: Not applicable.
• Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: No, this type of study was not conducted or presented.
• Standalone Performance (Algorithm Only): Not performed as a separate clinical study.
• Type of Ground Truth Used: Not applicable, as no clinical ground truth was established for performance evaluation.
• Sample Size for Training Set: Not applicable, as no training set for a clinical algorithm was mentioned.
• How Ground Truth for Training Set was Established: Not applicable.

Summary from the provided 510(k) Notification:

Study Details:

The 510(k) submission for the Intel-GE Care Innovations QuietCare-Networked did not rely on an assessment of clinical performance data to demonstrate safety and efficacy. Instead, substantial equivalence was claimed based on a comparison of technological characteristics with predicate devices.

Therefore, the following details are not applicable in this context:

• Sample size used for the test set and data provenance: N/A (no test set/clinical study performed).
• Number of experts used to establish the ground truth for the test set and their qualifications: N/A (no ground truth established for a clinical study).
• Adjudication method for the test set: N/A.
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.
• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: No standalone performance study was described.
• The type of ground truth used: N/A (no clinical ground truth was established).
• The sample size for the training set: N/A (no training set for clinical performance was mentioned).
• How the ground truth for the training set was established: N/A.

The submission concluded that the device introduces no new questions concerning safety or efficacy because its technological characteristics (software functionality, data collection, sensor types, communication methods, connectivity, communication protocol, and display method) are substantially equivalent to the predicate devices.

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DynaSense monitors orientation and activity of patients susceptible to pressure ulcers. It allows healthcare providers to implement individualized turn management plans and continuously monitor each patient. DynaSense 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.

DynaSense is a patient monitoring system that has been designed for use in hospitals, nursing homes, or other patient care facilities to aid standard care procedures for patients who are susceptible to pressure ulcers. The system monitors and reports patient activity and orientation as well as alerts the user (i.e., healthcare provider) when activity levels deviate from parameters set by healthcare providers. DynaSense is comprised of Patient Sensors, Relay Antennas, a USB RF Transceiver, Mesh Network Server Software, and User Interface software.

Each Patient Sensor is associated with a single patient, such that the patient's orientation and activity can be monitored. Data collected by the Patient Sensor is automatically communicated wirelessly to a nearby Relay Antenna, which subsequently relays these data to be displayed on the User Interface and maintained in a database. The system's Relay Antennas that are plugged into electrical outlets on the walls of the facility and the USB RF Transceiver that is plugged into the computer, on which the Mesh Network Server Software is installed or accessed, form a wireless network that allows data to be transmitted for display. The Mesh Network Server Software manages this network of Relay Antennas and USB RF Transceiver and collects the data from the Patient Sensors to allow monitoring of multiple patients on a single screen within the User Interface.

The Centauri Medical, Inc. DynaSense System, a bed-patient monitor, was reviewed for substantial equivalence (K130752). The device is intended to monitor patient orientation and activity to aid in pressure ulcer prevention.

Acceptance Criteria and Device Performance:

The provided document does not explicitly state quantitative acceptance criteria or a detailed table of device performance against such criteria. Instead, it broadly states that "the collective results of the testing demonstrate that the chosen materials, the manufacturing processes, and design of DynaSense meet the established specifications necessary for consistent performance during its intended use." It also concludes that the device "does not raise new questions of safety or effectiveness for monitoring patient activity when compared to the predicate devices."

The study described primarily focuses on qualitative assessments and established engineering standards to demonstrate substantial equivalence to predicate devices (Wireless MedCARE VivaTRAK™ System (K101109) and AFrame Digital MobileCare Monitor™ (K090138)).

Key Information from the Study:

1. Acceptance Criteria and Reported Device Performance:

   Acceptance Criteria Type	Reported Device Performance
   Design Verification (e.g., software verification)	The collective results of testing demonstrate that the design meets established specifications necessary for consistent performance.
   Electrical Safety Testing	The collective results of testing demonstrate that the device performs as intended in its intended use environment and does not raise new questions of safety or effectiveness.
   Electromagnetic Compatibility (EMC) Testing	The collective results of testing demonstrate that the device performs as intended in its intended use environment and does not raise new questions of safety or effectiveness.
   Safety and Effectiveness compared to Predicate Devices	"The collective testing results demonstrated that DynaSense does not raise new questions of safety or effectiveness for monitoring patient activity when compared to the predicate devices." Implies performance comparable to predicate devices in terms of patient activity monitoring, orientation tracking, and alert functionality to aid in pressure ulcer prevention. The device has "the same intended use and similar technological characteristics" as the predicate devices, with no differences raising new safety or effectiveness concerns. The device “performs as intended in its intended use environment.”

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

   The document does not explicitly state the sample size of a test set, nor does it detail the specific data provenance (e.g., country of origin, retrospective/prospective). The performance testing mentioned is "design verification (e.g., software verification), Electrical Safety, and Electromagnetic Compatibility testing," which are typically conducted in a laboratory or controlled environment rather than with 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 provided in the document. The testing described focuses on engineering validation and regulatory compliance, not clinical performance assessed by experts against a ground truth in a clinical setting.

4. Adjudication method for the test set:

   This information is not provided. Given the nature of the testing (design, electrical, EMC), clinical adjudication methods would not typically apply.

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 MRMC comparative effectiveness study was done, as this is a patient monitoring device, not an imaging interpretation or diagnostic AI tool that would involve human "readers" or AI assistance for interpretation. The device itself provides alerts to healthcare providers based on set parameters.

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

   The device is a standalone monitoring system. It operates to detect patient orientation and activity and generate alerts based on predefined parameters. The "human-in-the-loop" component is where healthcare providers respond to these alerts and use the information to implement turn management plans. The core functionality of monitoring and alerting is performed by the algorithm/system autonomously.

7. The type of ground truth used:

   The document does not specify a "ground truth" in the clinical sense (e.g., pathology, outcomes data). The "ground truth" for the engineering tests would be the established specifications and accepted standards for electrical safety, EMC, and software functionality. For example, in electrical safety testing, the ground truth is adherence to voltage, current, and insulation limits.

8. The sample size for the training set:

   This information is not provided. The development of monitoring systems like DynaSense typically involves engineering design, calibration, and validation, rather than the "training set" concept common in machine learning or AI models developed from large datasets. While there is "Mesh Network Server Software" and "User Interface software," the description does not suggest a deep learning or similar AI model that would require a distinct "training set" for classification or prediction tasks.

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

   This information is not applicable, as no explicit "training set" in the context of machine learning/AI models is mentioned. Rather, the device's functionality is based on programmed logic and sensor data interpretation.

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The VivaTRAK™ system monitors in-bed activity and care delivery for patients susceptible to pressure ulcers and falls as well as those that require 24 hour monitoring of their general activity levels in medical, nursing and long-term care facilities including independent living, assisted living and rehabilitation facilities. The system providers information related to patient status and care delivered.

The VivaTRAK™ system is not intended to provide automated treatment decisions or used as a substitute for professional healthcare judgment. The VivaTRAK™ system is not a replacement or substitute for vital signs monitoring or alert equipment. All patient medical diagnosis and treatment are to be performed under direct supervision and oversight of an appropriate health care professional.

The VivaTRAK 110 system is used for monitoring in-bed patient activity and care delivery. The system monitors in-bed patient activity with the BedSense sensor, an under-the-mattress activity sensing pad, processing and wireless transmission of activity data with the ActivSense "M Bed Computer, and providing pager, email, phone and display notifications and care reports to the nursing staff, and then verifying using RFID readers that care was actually delivered with the VivaTRAKTM application. Care reports consisting of a notification, a RFID scan and bed activity are stored in a database and form the basis for reports used to improve quality of care and work flows at the facility.

The provided text is a 510(k) Pre-Market Notification Summary for the VivaTRAK™ system. This type of document is for regulatory clearance and focuses on demonstrating substantial equivalence to existing devices rather than a detailed performance study with acceptance criteria and statistical analysis as might be found in a clinical trial report for AI/ML devices.

Therefore, much of the requested information regarding acceptance criteria, sample sizes for test and training sets, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth establishment for a specific device performance evaluation is not present in this document. The summary focuses on regulatory comparisons and claims of meeting functional specifications rather than presenting detailed study results in the manner an AI/ML study would.

Here's a breakdown of what can be extracted and what information is missing:

Acceptance Criteria and Reported Device Performance

The document states: "Wireless MedCARE has verified and validated that the VivaTRAK™ system meets its functional, performance, safety, and efficacy specifications and requirements." However, it does not disclose what these specific functional, performance, safety, and efficacy specifications (i.e., acceptance criteria) are, nor does it provide a table of performance metrics to demonstrate meeting those criteria.

Table of Acceptance Criteria and Reported Device Performance (Information Not Provided):

Study Information (Based on Available Text)

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

   • Sample Size: Not specified.
   • Data Provenance: Not specified. The document states a "non-clinical performance summary" without detailing the type of data (e.g., patient data, simulated data) or its origin.

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

   • Not applicable/Not specified. The document does not describe a study involving expert-established ground truth for performance evaluation in the context of diagnostic accuracy. The device monitors in-bed activity and care delivery, which is likely validated through direct observation or automated logging, rather than expert interpretation of images or signals.

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

   • Not applicable/Not specified. This is typically relevant for studies where human disagreement needs to be resolved for ground truth or performance assessment, which is not described for this device.

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. An MRMC study was not described or implied. The VivaTRAK™ system is described as providing information to nursing staff and verifying care delivery, not as an AI/ML diagnostic aid that human readers would interpret or use to improve performance.

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

   • The document implies that the system operates autonomously in monitoring activities and generating reports and notifications. It states, "The VivaTRAK™ system monitors in-bed patient activity... processing and wireless transmission of activity data... and providing pager, email, phone and display notifications and care reports to the nursing staff, and then verifying using RFID readers that care was actually delivered..." This suggests a standalone functional operation. However, no specific "standalone performance study" with detailed results (e.g., accuracy of activity detection, notification timeliness) is provided. Instead, it broadly states "meets its functional, performance, safety, and efficacy specifications."

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

   • Not explicitly stated. Given the device's function (monitoring in-bed activity and care delivery), the "ground truth" would likely be derived from direct observation of patient activity, direct logging of care delivery interactions (via RFID scans), or other objective measurements, rather than expert consensus on diagnostic images or pathology.

7. The sample size for the training set:

   • Not applicable/Not specified. While the system "processes" data, the document does not suggest it uses machine learning models that require a distinct "training set" in the common sense of AI/ML development. It's a sensor-based monitoring system.

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

   • Not applicable/Not specified, as no training set for an AI/ML model is described.

Summary of Device and Performance Claims from Document:

The VivaTRAK™ system is a "Monitor, Bed Patient" that:

• Monitors in-bed patient activity with an under-the-mattress sensor (BedSense).
• Processes and wirelessly transmits activity data with an "ActivSense™ Bed Computer."
• Provides pager, email, phone, and display notifications and care reports to nursing staff.
• Verifies care delivery using RFID readers and the VivaTRAK™ application.
• Stores care reports for quality improvement and workflow analysis.

The claims regarding performance are general: "Wireless MedCARE has verified and validated that the VivaTRAK™ system meets its functional, performance, safety, and efficacy specifications and requirements." The 510(k) clearance is primarily based on demonstrating substantial equivalence to predicate devices (AFrame Digital, Inc.'s AFrame MobileCare Monitor™, Stanley Security Solution's TAS Stilite, and Emfit Ltd's SafeBed) rather than presenting novel performance metrics from a detailed clinical/technical study with specific acceptance criteria.

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The MobileCare™ Monitor 2100 system includes a MyPHD™ personal help device that is intended to monitor residents in home and long-term care facilities including independent living, assisted living and rehabilitation settings. The monitor can be placed on the wrist using the Velcro strap and used like a watch by the resident. The other form of MyPHD offered has no wrist straps so it can be clipped to the waist or used in a bandage for attachment at other locations on the person as may be appropriate or preferred by the user or healthcare provider. The system provides an alert to designated caregivers or professional staff automatically at pre-set thresholds to indicate an impact has occurred. The system also includes an emergency (panic) button that can be pressed by the monitored individual to alert caregivers as needed. The users of the system include staff and residents. The product is intended to be used on a 24-hour basis. The system is not intended to provide automated treatment decisions, nor is it to be used as a substitute for professional healthcare judgment. All patient medical diagnosis and treatment are to be performed under direct supervision and oversight of an appropriate healthcare professional.

The MobileCare™ Monitor 2100 system includes a MyPHD™ personal help device that is intended to monitor residents in home and long-term care facilities including independent living, assisted living and rehabilitation settings. The monitor can be placed on the wrist using the Velcro strap and used like a watch by the resident. The other form of MyPHD offered has no wrist straps so it can be clipped to the waist or used in a bandage for attachment at other locations on the person as may be appropriate or preferred by the user or healthcare provider. The system provides an alert to designated caregivers or professional staff automatically at pre-set thresholds to indicate an impact has occurred. The system also includes an emergency (panic) button that can be pressed by the monitored individual to alert caregivers as needed. The users of the system include staff and residents. The product is intended to be used on a 24-hour basis. The system is not intended to provide automated treatment decisions, nor is it to be used as a substitute for professional healthcare judgment. All patient medical diagnosis and treatment are to be performed under direct supervision and oversight of an appropriate healthcare professional.

The provided 510(k) summary for the KOGO 138 MobileCare Monitor™ does not contain information about specific acceptance criteria related to a clinical study or device performance metrics like sensitivity, specificity, or accuracy. It focuses on demonstrating substantial equivalence to predicate devices primarily through comparison of technical characteristics and intended use, and conformance to non-clinical safety (FCC regulations) and software validation standards.

Therefore, the following information cannot be extracted from the provided text:

• A table of acceptance criteria and the reported device performance: No specific performance metrics or acceptance criteria are stated. The document indicates software validation was performed, but no results are provided.
• Sample size used for the test set and the data provenance: Not applicable as no clinical test set is described.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
• Adjudication method for the test set: Not applicable.
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.
• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The device is a monitor with alerting capabilities; performance is not typically described in terms of "algorithm only" in this context. The document mentions an "impact sensor that may indicate a fall" and an "emergency (panic) button."
• The type of ground truth used: Not applicable as no clinical performance study is detailed with ground truth.
• The sample size for the training set: Not applicable as a machine learning training set is not mentioned for this device type.
• How the ground truth for the training set was established: Not applicable.

Here's a summary of the available information regarding acceptance criteria and studies:

1. Acceptance Criteria and Reported Device Performance:

The document primarily relies on demonstrating substantial equivalence to predicate devices and adherence to non-clinical standards rather than clinical performance metrics.

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

• Sample Size: Not specified, as no clinical test set for performance evaluation is described in the provided text. The evaluation focuses on non-clinical aspects and substantial equivalence.
• Data Provenance: Not applicable, as no clinical performance data are presented.

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

• Not applicable, as no clinical test set requiring expert-established ground truth is described.

4. Adjudication method for the test set:

• Not applicable, as no clinical test set requiring adjudication is described.

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, a multi-reader multi-case comparative effectiveness study was not done or reported. This type of study is more common for diagnostic imaging AI devices, whereas the MobileCare Monitor™ is a monitoring and alerting system.

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

• The document does not describe a "standalone" algorithmic performance study in the context typically used for AI/ML devices (e.g., measuring accuracy of an image analysis algorithm). The device itself functions as a standalone monitoring system that provides alerts.

7. The type of ground truth used:

• Not applicable, as no clinical performance study with defined ground truth is described. The "performance" aspect refers to software validation.

8. The sample size for the training set:

• Not applicable, as no machine learning algorithm requiring a training set is explicitly mentioned or detailed in the provided information. The device functions based on sensors and pre-set thresholds, not a learned model.

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

• Not applicable, as no machine learning algorithm or training set is described.

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