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DCM is a small worn activity monitor designed for documenting physical movement associated with applications in physiological monitoring.

The device is intended to monitor the activity associated with movement during sleep.

DCM can be used to analyze circadian rhythms and assess activity in any instance where quantifiable analysis of physical motion is desirable.

DCM is indicated for monitoring of adult patients only.

DCM is a wrist-worn wearable device intended to continuously record high resolution digital acceleration data associated with a patient's physical movement.
In practice, a healthcare professional or researcher can prescribe the device to collect physiological data from patients during sleep and in applications where quantifiable analysis of physical motion is desirable.
The device is set up to collect data by the healthcare professional then placed on the subject's wrist. The device is designed to be worn during normal activities and/or during sleep over a period of days to weeks. The patient does not need to interact with the device to control data collection.
The data stored on the device can be transmitted to the cloud for storage, and made accessible to healthcare professionals or researchers for further analysis. Downloaded data can be post-processed based on the timestamp and magnitude of acceleration along each axis.
The DCM system comprises a system of components:

• wearable biosensor (PW010)
• off the shelf mobile device (PW030) running the DCM mobile app (PW400)
• cloud-based data storage and data processing (PW100) (back-end)
• investigator dashboard (PW500) accessed through a web browser (front-end)

The provided FDA 510(k) clearance letter for the DCM (PW-DCM) device does not describe a study involving a test set, ground truth experts, or human readers for assessing device performance related to diagnostic accuracy or interpretation.

Instead, the document focuses on the technical performance of the device as a physical activity monitor, comparing it to a predicate device (Actigraph LEAP) primarily on its physical and operational characteristics. The acceptance criteria and "study" described are more akin to verification and validation (V&V) testing of hardware and software components, rather than a clinical performance study measuring accuracy against a diagnostic gold standard involving human interpretation.

Therefore, many of the requested categories (e.g., number of experts, adjudication method, MRMC study, effect size on human readers, type of ground truth for diagnostic accuracy) are not applicable or cannot be extracted from this document, as the device's function is data collection and not direct diagnostic interpretation.

However, I can extract the information that is present and explain why other information is not available from this document.

Acceptance Criteria and Reported Device Performance

The table below summarizes the technical acceptance criteria for the DCM device and the reported outcomes, as found in the "Summary of Testing" section.

Study Details (based on available information)

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

   • Test set sample size: Not explicitly stated for each test. The tests described are bench tests ("Bench testing with the biosensor in a range of orientations," "Bench testing with mobile app paired to biosensor," "manual interruption and restoration of connectivity"). This implies testing of device units, not a patient cohort.
   • Data provenance: Not explicitly stated. Given the nature of the tests (bench testing, design validation), the "data" being generated is measurement data from the device itself rather than clinical patient data. The document does not refer to geographical origin or patient type for these validation tests.
   • Retrospective or Prospective: Not applicable in the context of device design verification and validation testing. These are controlled engineering tests.

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

   • Not applicable for these types of tests. The "ground truth" for these engineering and software tests would be established by calibrated measurement equipment (e.g., accelerometers for accuracy, timing devices for accuracy) and adherence to international standards (e.g., IEC 62366-1 for usability, IEC 60601 series for safety, IEC 62304 for software). There is no mention of human experts interpreting data to establish a ground truth for diagnostic purposes because the device's function is data collection, not interpretation.

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

   • None. This concept is for clinical performance studies where multiple human readers interpret medical images or data. The described tests are technical performance evaluations.

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 comparative effectiveness study was not done. The document explicitly states: "DCM did not require clinical studies to support substantial equivalence to the predicate device." The device is a "small worn activity monitor designed for documenting physical movement," not a device that provides AI-assisted interpretations for human clinicians.

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

   • Yes, a form of standalone testing was done for the technical performance. The "Summary of Testing" section describes tests where the device's inherent capabilities (e.g., acceleration measurement, timing accuracy, data storage) were evaluated against predetermined engineering criteria. This is performance of the algorithm/device itself, without human interpretation in the loop beyond setting up the test and interpreting the test results (e.g., "PASS").

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

   • Technical/Engineering Standards and Calibrated Equipment: For accuracy measurements, the ground truth would be from highly accurate, calibrated reference instruments. For safety, EMC, and software, the ground truth is adherence to established international standards (e.g., IEC 60601-1, IEC 62304) and internal design specifications. There is no biological or clinical "ground truth" (e.g., pathology, outcomes data, expert consensus on patient diagnosis) applied here.

7. The sample size for the training set:

   • Not applicable / Not disclosed. The document does not describe a machine learning algorithm that requires a "training set" in the context of clinical AI. The device collects raw acceleration data. While there might be internal algorithms for processing this data (e.g., activity counts, sleep/wake detection, circadian rhythm analysis from raw data), the document describes validation of the data collection capability, not the performance of an AI model trained on a specific dataset for diagnostic tasks.

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

   • Not applicable. As no training set for a clinical AI algorithm is described, there's no ground truth establishment for such a set.

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Master Caution Device (MCD) is intended to condition an electrocardiographic signal, so that it can be transmitted digitally via Bluetooth technology and cell-phone or communication device to a remote location. The Master Caution Device (MCD) is designed to be used by a patient to transmit a 12 lead ECG, posture and motion , respiration and skin temperature (IR) signals, in near real-time to enable review at a physician's office, hospital or other remote medical receiving center. Master Caution Device (MCD) target population is adults above the age of 21.

The HealthWatch Master Caution Device (MCD) is a personal, hand-held battery powered, ECG device that can be connected to any approved and market cleared, ten standard ECG electrodes, configuring a 12-lead ECG device.

The MCD is a miniature ECG device with an embedded processor containing data acquisition, data storage, data processing accelerometers, respiration, skin temperature (IR) and BT (Bluetooth) capabilities. The HealthWatch Master Caution Device (MCD) acquires ECG data via the connected electrodes. The HealthWatch Master Caution Device (MCD) transmits the data in near real time to a suitable Bluetooth communication device for forwarding to a remote location for professional review.

A communication device is defined as any device that is capable of receiving the ECG data via Bluetooth and forwarding it via WIFI or cellular network (3G/4G). Communication devices can be cellphones, computers or other dedicated communication modems.

The provided document is a 510(k) premarket notification for the Healthwatch Master Caution Device (MCD). It asserts substantial equivalence to predicate devices rather than providing a detailed study proving device performance against specific acceptance criteria. Therefore, several of the requested sections cannot be fully populated as the information is not present in the document.

Here's an analysis based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state specific quantitative acceptance criteria or corresponding reported device performance metrics in a readily extractable table. Instead, it relies on demonstrating substantial equivalence to predicate devices and adherence to relevant performance standards.

The document mentions that "Bench testing demonstrated that the HealthWatch Master Caution Device (MCD™) is as safe and effective as the cleared predicate device." This is a general statement rather than specific performance data.

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

The document does not detail a specific test set or data provenance for performance evaluation of the MCD itself. It states that for the MCD, performance bench tests were conducted to demonstrate equivalence to the predicate device. However, no sample sizes or data provenance are provided for these bench tests.

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 as the document does not describe a clinical study with a ground truth established by experts for the MCD directly.

4. Adjudication method for the test set

This information is not provided as the document does not describe a clinical study with an adjudication method for the MCD directly.

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 such MRMC comparative effectiveness study is mentioned for the Master Caution Device (MCD). The MCD is described as a device that conditions and transmits ECG signals, not an AI-assisted diagnostic tool.

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

This is not applicable as the MCD is a hardware device for signal acquisition and transmission, not an algorithm, and its intended use is for review by a physician.

7. The type of ground truth used

For the MCD, the document does not indicate the use of specific ground truth types (e.g., pathology, outcomes data) in a clinical study. The device's performance is gauged against its ability to acquire and transmit physiological signals accurately, and its equivalence to predicate devices through bench testing and compliance with standards.

8. The sample size for the training set

This information is not applicable. The MCD is a hardware device for signal acquisition and transmission and does not involve a "training set" in the context of machine learning or AI.

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

This information is not applicable for the same reasons as above.

Summary based on the document:

The HealthWatch Master Caution Device (MCD) received 510(k) clearance based on demonstrating substantial equivalence to legally marketed predicate devices. The primary arguments for equivalence are:

• Same Intended Use: The MCD's intended use is to condition and transmit electrocardiographic signals and other physiological data (posture, motion, respiration, skin temperature) for remote review.
• Similar Technology: It utilizes the same transmission method (Bluetooth) as its predicate devices.
• Performance Bench Tests: Bench testing was conducted to verify that the MCD is "as safe and effective" as the cleared predicate device, without raising new safety or effectiveness concerns. These tests presumably assessed the device's ability to acquire and transmit the specified signals correctly and reliably. However, specific performance metrics or detailed results from these bench tests are not provided in this summary.
• Compliance with Standards: The device complies with several international and US standards related to medical electrical equipment safety, electromagnetic compatibility, software life cycle processes, risk management, and labeling (IEC 60601-1, IEC 60601-1-2, IEC 60601-2-25, ISO 62304, ISO 14971, ISO 15223-1, ASTM D4169).
• Reliance on Predicate Device Studies: The document explicitly states, "Due to the comprehensive clinical studies already performed by the predicate and other devices, published in scientific literature, and since the performance testing shows its substantial equivalence, HealthWatch believes that animal and clinical studies are not necessary to determine the substantial equivalence of the HealthWatch Master Caution Device (MCDTM)." This indicates that no new clinical studies or detailed performance studies with human subjects were conducted for the MCD itself to prove its effectiveness beyond substantial equivalence.

Therefore, the document emphasizes regulatory compliance and comparison to existing devices rather than a detailed performance study with specific acceptance criteria and outcome measurements for the MCD as a standalone product.

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