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QMAPP® is intended for use by professional healthcare providers for physiological/hemodynamic monitoring. The system may be used to display and analyze surface ECG (Electrocardiogram), respiration, invasive pressures, pulse oximetry (SpO2), End tidal CO2 (EtCO2), fractional flow reserve (FFR), non-invasive blood pressure (NiBP), surface body temperature, cardiac output and intra-cardiac ECG. QMAPP® provides also clinical data acquisition, medical image/data processing and analytical assessment. QMAPP® is intended for use in the areas of, but not limited to cardiology, cardiac catheterization, electrophysiology, radiology, invasive radiology. QMAPP® can be used standalone and in networked environments. The system is intended for patient/procedural data management, such as documentation, logging, reporting, trending, storing, reviewing, carrying out clinical calculations and exporting various representations of the acquired data. Data may also be acquired from and/or send to other devices, such as physiological monitoring system, information management systems, image acquisition/storage devices and other medical devices.

The QMAPP® system offers a complete physiological/hemodynamic monitoring and reporting system. The system is built from three units: an Amplifier, Live Monitoring CPU and Reporting CPU. The Amplifier Unit has various sensors connected with the patient, e.g. ECG, SpO2 and NiBP. The Amplifier Unit is connected to the Live Monitoring CPU via a dedicated Ethernet connection. The acquired patient information can be visualized on a Live Monitoring CPU. Typically located in the technical room. A software application executed on the Live Monitoring CPU can visualize the patient information. Also the Amplifier Unit can be controlled, i.e. most importantly, to set acquisition and filtering parameters for the different sensors, by the Live Monitoring CPU. Optionally the Monitoring unit can be connected via a dedicated Ethernet connection to a Reporting CPU, typically located in the technical room. On the Reporting CPU a database is installed which facilitates data storage and retrieval. A software application executed on the Reporting CPU serves as a patient data management system. It can e.g. be used for analysis, calculation and reporting in various representations of patient information.
The QMAPP® system, can operate standalone or it can be part of a typical hospital network infrastructure. The latter offers the possibility to send or receive information from and to other devices. The software has several communication modules, based on HL7 or DICOM protocols to interface with third party equipment/systems.
• The QMAPP® system works with 3rd party 510(k) cleared SpO2 module (Covidien Nellcor, K083325), NiBP module (CAS Medical Systems, MAXNIBP ND+, e.g. used in FDA cleared device CAS Medical Systems, 740 Select, K150620) and EtCO2 sensors e.g. used in FDA cleared device CLEO Patient Monitor, K142244.

The provided FDA 510(k) Clearance Letter for the QMAPP® System describes the device, its intended use, and a summary of non-clinical tests conducted to support its substantial equivalence. However, the document does not contain the specific details required to fully address your request regarding acceptance criteria and the comprehensive study that proves the device meets them.

Here's a breakdown of what can and cannot be extracted from the provided text, and where the requested information is missing:

Information Present in the Document:

• Overall Device Performance: The "NON-CLINICAL TESTS" section lists various characteristics on which "Bench testing" was carried out, implicitly suggesting these are areas where performance was evaluated. The "Referenced Standards and Performance Testing" section explicitly states that the QMAPP® system "meets the requirements of following performance Standards."
• Study Type: The studies mentioned are "Bench testing," "Usability Testing," and "Software verification and validation testing." The clearance is based on a "Traditional 510(k)" and relies on "non-clinical data."
• Ground Truth Type (for non-clinical testing): For the performance characteristics listed (ECG, Heart rate, SpO2, NiBP, IBP, Cardiac Output, Intra cardiac ECG, Skin Temperature, ECG impedance for Rate of respiratory effort, Measurement accuracy), the "ground truth" would be established by the physical standards and reference systems used during bench testing for each specific measurement. For example, a calibrated heart rate simulator would provide the ground truth for heart rate accuracy.
• Sample Size for Training Set: Not explicitly mentioned, but the document refers to a "software verification and validation testing," implying a dataset (likely synthetic or previously collected) was used.
• How Ground Truth for Training Set was Established: Not explicitly mentioned.

Missing Information (Crucial for your request):

The document focuses on demonstrating substantial equivalence to predicate devices through technical characteristics and adherence to recognized standards. It does not present a detailed study report with specific acceptance criteria, reported performance against those criteria, or the methodology of how "ground truth" was established for clinical or test datasets in the manner you've requested for an AI/ML context.

The QMAPP® system is a physiological/hemodynamic monitoring system, not specifically an AI/ML device that requires a comparison of algorithmic output against expert consensus on a test set, multi-reader multi-case studies, or standalone algorithm performance. The "clinical data acquisition, medical image/data processing and analytical assessment" mentioned are functions of the system, but the document does not elaborate on an AI/ML component with associated performance metrics.

Based on the provided text, here is what can be inferred and explicitly stated, with clear indications of missing information for your request:

1. Table of Acceptance Criteria and Reported Device Performance

The document states that the QMAPP® system was tested against and "meets the requirements of following performance Standards." These standards themselves contain detailed acceptance criteria for various parameters. The table below excerpts the performance characteristics mentioned in the "SUBSTANTIAL EQUIVALENCE SUMMARY TABLE" and "NON-CLINICAL TESTS" sections. Crucially, the document does not provide the specific numerical acceptance criteria (e.g., minimum accuracy percentages, maximum deviations) or the actual measured performance values against those criteria in a consolidated table. Instead, it states that the device "meets the requirements" of the listed standards and has "Accuracy" values which are the specifications for the device itself, not acceptance criteria of a study.

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

• Sample Size for Test Set: Not specified. The document only mentions "Bench testing," "Usability Testing," and "Software verification and validation testing." These are typically performed in a lab environment.
• Data Provenance (e.g., country of origin of the data, retrospective or prospective): Not specified. Given it's bench testing, actual patient data provenance is not directly relevant for the stated tests, but the data used for software verification and validation testing (if involving patient data) is not detailed.

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

• Number of Experts & Qualifications: Not applicable/not specified. For bench testing of physiological monitoring devices, the "ground truth" comes from calibrated testing equipment and reference signals, not expert human interpretation in the way, for example, a radiology AI would be evaluated. The "Software verification and validation testing" is also not described as relying on expert review of a patient dataset for ground truth.

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

• Adjudication Method: Not applicable/not specified. This methodology is typically used when comparing an algorithm's output to human expert interpretations, which is not the type of testing described for this physiological monitor.

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

• MRMC Study: Not applicable. The QMAPP® system is described as a physiological/hemodynamic monitoring, data acquisition, and analytical assessment system. It is not presented as an AI-assisted diagnostic tool designed to improve human reader performance in interpreting images or complex clinical scenarios.

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

• Standalone Performance: The described "Bench testing" and "Software verification and validation testing" can be considered "standalone" in the sense that they evaluate the device's inherent measurement and processing capabilities without a human in the loop for interpretation, but for a physiological monitor, the ultimate "human-in-the-loop" is the clinician using the displayed information. The document does not describe an AI algorithm that operates entirely independently to make a diagnosis or prediction in the same way an AI for image analysis might.

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

• Type of Ground Truth: For the "Bench testing" of physiological parameters, the ground truth would be established by calibrated reference standards and simulated physiological signals. For instance, a signal generator provides a known ECG waveform or blood pressure reading, and the device's measurement is compared to this known input.

8. The sample size for the training set

• Sample Size for Training Set: Not specified. The document mentions "Software verification and validation testing," which would involve a dataset, but its size is not detailed. There is no mention of a "training set" in the context of an AI/ML model, as the device is not presented as such.

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

• How Ground Truth for Training Set was Established: Not specified. If a "training set" was used for software validation (e.g., for signal processing algorithms), the ground truth would likely be established through
  • Synthetic data: Ground truth is known by design.
  • Previously validated physiological data: Data collected with highly accurate reference devices, where the "truth" for various physiological parameters is established by the reference device's measurements.

In summary: The FDA 510(k) clearance document for the QMAPP® System confirms that the device meets relevant performance standards through non-clinical bench testing and software validation. However, it does not provide the detailed acceptance criteria and study particulars, particularly those related to expert-adjudicated test sets, MRMC studies, or specific AI/ML training/testing methodologies, because the device is presented as a traditional physiological monitor, not an AI-powered diagnostic system.

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The Empatica Health Monitoring Platform is a wearable device and paired mobile and cloud-based software platform intended to be used by trained healthcare professionals or researchers for retrospective remote monitoring of physiologic parameters in ambulatory individuals 18 years of age and older in home-healthcare environments. As the platform does not provide real-time alerts related to variation of physiologic parameters, users should use professional judgment in assessing patient clinical stability and the appropriateness of using a monitoring platform designed for retrospective review.

The device is intended for continuous data collection supporting intermittent retrospective review of the following physiological parameters:

• Pulse Rate,
• Blood Oxygen Saturation under no-motion conditions,
• Respiratory Rate under no motion conditions,
• Peripheral Skin Temperature,
• Electrodermal Activity,
• Activity associated with movement during sleep

The Empatica Health Monitoring Platform can be used to analyze circadian rhythms and assess activity in any instance where quantifiable analysis of physical motion is desirable.

The Empatica Health Monitoring Platform is not intended for SpO2 monitoring in conditions of motion or low perfusion.

The Empatica Health Monitoring Platform is intended for peripheral skin temperature monitoring, where monitoring temperature at the wrist is clinically indicated.

The Empatica Health Monitoring Platform is not intended for Respiratory Rate monitoring in motion conditions. This device does not detect apnea and should not be used for detecting or monitoring cessation of breathing.

The Empatica Health Monitoring Platform is not intended for Pulse Rate monitoring in patients with chronic cardiac arrhythmias, including atrial fibrillation and atrial/ventricular bigeminy and trigeminy, and is not intended to diagnose or analyze cardiac arrhythmias. The Empatica Health Monitoring Platform is not a substitute for an ECG monitor, and should not be used as the sole basis for clinical decision-making.

The Empatica Health Monitoring Platform is a wearable device and software platform composed by:

• A wearable medical device called EmbracePlus,
• A mobile application running on smartphones called "Care App",
• A cloud-based software platform named "Care Portal".

The EmbracePlus is worn on the user's wrist and continuously collects raw data via specific sensors. These data are wirelessly transmitted via Bluetooth Low Energy to a paired mobile device where the Care App is up and running. The data received are analyzed by one of the Care App software modules, EmpaDSP, which computes the user physiological parameters. Based on the version of the Care App installed, the user can visualize a subset of these physiological parameters. The Care App is also responsible for transmitting, over cellular or WiFi connection sensors' raw data, device information, Care App-specific information, and computed physiological parameters to the Empatica Cloud. On the Empatica Cloud, these data are stored, further analyzed, and accessible by healthcare providers or researchers via a specific cloud-based software called Care Portal.

The Empatica Health Monitoring Platform is intended for retrospective remote monitoring of physiological parameters in ambulatory adults in home-healthcare environments. It is designed to continuously collect data to support intermittent monitoring of the following physiological parameters and digital biomarkers by trained healthcare professionals or researchers: Pulse Rate (PR), Respiratory Rate (RR), blood oxygen saturation (SpO2), peripheral skin temperature (TEMP), and electrodermal activity (EDA). Activity sensors are used to detect sleep periods and to monitor the activity associated with movement during sleep.

The provided FDA 510(k) clearance letter and its attachments describe the acceptance criteria and study that proves the Empatica Health Monitoring Platform (EHMP) meets those criteria, specifically concerning a new Predetermined Change Control Plan (PCCP) for the SpO2 quality indicator (QI) algorithm.

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are outlined for the proposed modification to the SpO2 Quality Indicator (QI) algorithm. The reported device performance is presented as a statement of equivalence to the predicate device, implying that the acceptance criteria are met, as the 510(k) was cleared.

Note: For the pre-existing functionalities (Pulse Rate, Respiratory Rate, Peripheral Skin Temperature, Electrodermal Activity, Activity and Sleep), the document states that "no changes to the computation... compared with the cleared version" have been introduced, implying their previous acceptance criteria were met and remain valid.

2. Sample Sizes and Data Provenance

• Test Set Sample Size: Not explicitly stated for the SpO2 algorithm modification. The document only mentions "enhancing the development dataset with new samples" for the ML-based algorithm and clinical testing was "conducted in accordance with ISO 80601-2-61... and ... FDA Guidelines for Pulse Oximeters." These standards typically require a certain number of subjects and data points, but the exact numbers are not provided in this public summary.
• Data Provenance: Not specified in the provided document. It does not mention the country of origin, nor whether the data was retrospective or prospective.

3. Number and Qualifications of Experts for Ground Truth

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The document states the platform is "intended to be used by trained healthcare professionals or researchers," and later discusses "professional users" and "clinical interpretation," implying that the ground truth for clinical studies would likely involve such experts, but their specific roles, numbers, and qualifications for establishing ground truth are not detailed.

4. Adjudication Method for the Test Set

The adjudication method for establishing ground truth for the test set is not explicitly mentioned in the provided document.

5. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study

There is no mention of a Multi Reader Multi Case (MRMC) comparative effectiveness study being conducted, nor any effect size regarding human readers improving with AI vs. without AI assistance. The device is for "retrospective remote monitoring" by healthcare professionals, implying an AI-driven data collection/analysis with human review, but not necessarily human-AI collaboration in real-time diagnostic interpretation that an MRMC study would evaluate.

6. Standalone (Algorithm Only) Performance

The acceptance criteria for the SpO2 QI algorithm include "Bench testing conducted using a functional tester to simulate a range of representative signal quality issues." This falls under standalone performance, as it tests the algorithm's ability to discriminate data quality without direct human input. Clinical testing also evaluates the algorithm's accuracy (Arms error) in comparison to an established standard, which is also a standalone performance measure.

7. Type of Ground Truth Used

• For the SpO2 QI ML algorithm: The ground truth for low-quality and high-quality data discrimination seems to be an internal standard/reference based on the "FDA-cleared SpO2 algorithm" and potentially expert labeling of data quality during the "enhancing the development dataset."
• For the SpO2 Accuracy (Arms Error): The ground truth for SpO2 values would be established in accordance with ISO 80601-2-61, which typically involves comparing the device's readings against a laboratory co-oximeter or a reference pulse oximeter for arterial oxygen saturation.

8. Sample Size for the Training Set

The document mentions "enhancing the development dataset with new samples" for the ML-based algorithm but does not specify the sample size for the training set.

9. How Ground Truth for Training Set was Established

The ground truth for training the ML-based SpO2 QI algorithm was established by "enhancing the development dataset with new samples." It also mentions performing "feature extraction and engineering on window lengths spanning a 10-30-second range." While it doesn't explicitly state the methodology, given the context of a "binary output" (high/low quality), it implies a labeling process, likely by human experts or based on predefined criteria derived from the previous FDA-cleared algorithm's performance on various data types. For the SpO2 accuracy, the ground truth would typically be established by a reference method consistent with the mentioned ISO standard and FDA guidance.

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WARD-CSS is a clinical decision support system that remotely integrates, analyzes and displays continuous vital sign data (via a mobile or web application) from medical devices for nonpediatric hospitalized patients within non-critical care units.

WARD-CSS uses a set of standardized rules based on scientific and clinical evidence to detect and alert on clinically relevant vital sign deviations when used by trained health care professionals in hospitals.

WARD-CSS is not intended to replace current monitoring practices or replace health care professionals' judgment. WARD-CSS is a tool intended to help health care professionals manage monitored patients and make clinical care decisions.

WARD-CSS is a stand-alone software intended for use in continuous monitoring of patients and near real-time analysis of vital signs for the purpose of notifying healthcare professionals in case of clinically relevant vital sign deviations.

WARD-CSS utilizes knowledge-based algorithms to evaluate clinically relevant vital signs deviations to help drive clinical management.

The system is intended to be used as an adjunct to current monitoring practice in the general med/surg floors of the hospitals

The system assists healthcare professionals when monitoring patients on their wards by:

• Providing a real-time monitoring overview of vital signs for all patients. ●
• Alerting the healthcare professionals when a patient deteriorates. ●

The following types of alerts are detected by WARD-CSS in the vital sign data:

• Desaturation ●
• Hypertension
• Hypotension ●
• Bradypnea ●
• Tachypnea ●
• Tachycardia
• Bradycardia
• Hypotension and Bradycardia
• Hypotension and Tachycardia ●
• Bradypnea and Desaturation
• Fever

The WARD-CSS consists of a Mobile App, Web App and Backend Server. The Mobile App is used by healthcare professionals (HCPs) to monitor patients. The HCP will receive notifications of the alerts to their mobile phones. Within this app, the HCP can also document vital signs into an electronic health record system. The Web App is used by administrative users to manage hospitals, wards, users, and monitors. The Backend Server is used to receive and process all incoming data and manage all data used in the apps.

The provided text describes the acceptance criteria and a study to prove the device, WARD-CSS, meets these criteria, primarily focusing on alert reduction.

Here's the breakdown of the information requested:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria here are implicitly related to the reduction of alert overload, as this is the primary focus of the clinical testing described for WARD-CSS. The performance is measured by the reduction in alert rates compared to a baseline (thresholds only) and an intermediate step (thresholds with time durations).

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

• Sample Size: 794 patients
• Data Provenance: Retrospective analysis of four cohorts from prospective clinical safety studies conducted from 2020-2024. The country of origin is not explicitly stated, but the submission is for the US FDA, implying an interest in data relevant to this regulatory body. The data consists of vital sign data.

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

This information is not provided in the document. The study focuses on quantifying alert rates based on set thresholds and algorithms rather than human expert-established ground truth for specific events that led to the alerts. The 'ground truth' here is the objective vital sign data and the predefined rules/thresholds that trigger alerts.

4. Adjudication Method for the Test Set

This information is not provided as the study's focus is on algorithmic alert reduction based on vital sign data and predefined rules, not on expert adjudication of alert significance.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size of Human Improvement with AI vs. Without AI Assistance

A MRMC study comparing human readers with and without AI assistance was not done. The study's objective was to quantify the reduction in system-generated alerts due to the WARD-CSS algorithms, not to measure human performance improvement.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, the study described is a standalone (algorithm only) performance assessment of the WARD-CSS system's alert reduction methodology. It retrospectively analyzes vital sign data against the different alert generation methodologies (thresholds only, thresholds + time durations, and WARD-CSS's full algorithm including alert filters) to demonstrate the reduction in the number of alerts produced by the algorithm itself.

7. The Type of Ground Truth Used

The ground truth used in this study is objective vital sign data combined with predefined, standardized rules and thresholds based on scientific and clinical evidence. The analysis quantifies how often these pre-defined rules would trigger an alert under different algorithmic conditions (basic thresholds, thresholds with time durations, and thresholds with time durations and alert filters). It is not based on expert consensus, pathology, or outcomes data in the traditional sense of clinical event validation.

8. The Sample Size for the Training Set

The document does not explicitly mention a separate training set size. The clinical testing section refers to a "literature review to support software algorithm development and determine the alert thresholds," suggesting that the rules and thresholds were established based on existing clinical knowledge and literature. The 794 patients were used for the retrospective analysis of alert rates, which effectively acts as a test/validation set for the alert reduction logic rather than a dataset for algorithm training in a machine learning sense.

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

Since an explicit training set (for machine learning) is not detailed, the "ground truth" for the algorithm's rules and thresholds was established through a "literature review to support software algorithm development and determine the alert thresholds." This implies that the rules are based on scientific and clinical evidence from medical literature.

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The iCare App is intended for use in the home and clinical settings as and their healthcare professionals to view test results which are measured by iHealth devices to better manage user's health and get feedback from their professional care team.

The iCare App can also connect to medical devices and or non-medical devices and get data from devices during measurement or from the data stored in memory of the device for enhanced data managements. Data can be transmitted, displayed, and stored in the App.

The iCare APP is a mobile application on both Android and iOS platforms.iCare allows users to better manage their own health by enabling them to measure their vital signs, access their results and relevant health information with just their smart device and internet connection, and receive feedback from their professional care team.

iCare includes a patient darshboard featuring the Home, Health, Plus, Education, and Profile tabs. Accessory devices can be connected to the system to allow for collection of blood sugar, blood pressure, blood oxygen, and/or weight measurements. The patient darshboard functionality includes the ability to start measuring, allows users to view and track measurements, and export testing schedules for blood sugar, blood pressure, blood oxygen, and weight measurements; send messages to their professional care team; view previous appointment history information; view medication instructions; add entries to the food diary and review feedback from their registered dietician; set timers; and access articles and videos about health knowledge.

The provided text is a 510(k) Summary for the iCare App, focusing on its substantial equivalence to a predicate device. It primarily details regulatory information, device description, and non-clinical test summaries. It does not contain information about a study that proves the device meets specific performance acceptance criteria for a medical diagnostic or screening function.

The iCare App is classified as a "Medical Device Data System" (MDDS) that transmits, displays, and stores data from connected medical devices. Its function is to aid users and healthcare professionals in viewing test results for health management. It explicitly states: "Both devices make no interpretation, evaluation, medical judgments, or recommendations for treatment." This means the app itself doesn't perform diagnostic functions that would require specific performance metrics like sensitivity, specificity, or AUC against a ground truth.

Therefore, many of the requested criteria, such as acceptance criteria for diagnostic performance, a test set, expert ground truth establishment, MRMC studies, or standalone algorithm performance, are not applicable or not provided in this document because the device is a data management system, not a diagnostic algorithm.

Here's a breakdown of the applicable information based on the provided text:

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

The document does not present a table of quantitative performance acceptance criteria for diagnostic accuracy, sensitivity, or specificity, because the iCare App is an MDDS for data management, not a diagnostic tool. Instead, acceptance criteria are implied through the successful completion of non-clinical tests that demonstrate the basic functionality, safety, and effectiveness for its intended use as a data display and storage system.

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

• Sample Size for Test Set: Not applicable for diagnostic performance as the device is not a diagnostic algorithm. The document mentions non-clinical testing (software, wireless, cybersecurity, usability) but does not specify "test set" sizes in the context of clinical data for diagnostic performance.
• Data Provenance: Not applicable in the context of clinical diagnostic data. The document focuses on the technical aspects of the software.

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

• Number of Experts: Not applicable, as the device does not perform diagnostic interpretations requiring expert-established ground truth for clinical cases.
• Qualifications of Experts: Not applicable.

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

• Adjudication Method: Not applicable, as there is no clinical test set requiring ground truth adjudication for diagnostic performance.

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

• MRMC Study: No, an MRMC study was not done. The iCare App is an MDDS and does not involve AI assistance for human readers in a diagnostic capacity. It makes "no interpretation, evaluation, medical judgments, or recommendations for treatment."
• Effect Size: Not applicable.

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

• Standalone Performance: No, a standalone performance study in the context of diagnostic accuracy was not done. The device's function is data transmission, display, and storage, not diagnostic algorithm performance.

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

• Type of Ground Truth: Not applicable for clinical diagnostic performance. For the software verification and validation, the "ground truth" would be the successful execution against specified requirements and accepted software engineering practices and FDA guidance.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. This document does not describe a machine learning model that was trained on a dataset. The iCare App is a software application for data management, not an AI/ML algorithm requiring a training set of clinical data for diagnostic purposes.

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

• Ground Truth Establishment for Training Set: Not applicable, as there is no mention of a training set for an AI/ML algorithm.

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The Connex Spot Monitors are intended to be used by clinicians and medically qualified personnel for monitoring of noninvasive blood pressure, pulse rate, noninvasive functional oxygen saturation of arteriolar hemoglobin (SpO2), and body temperature in normal and axillary modes of neonatal, pediatric, and adult patients. Monitoring respiration rate from photoplethysmogram (Masimo RRp®) is indicated for adult and pediatric patients greater than two years old. The most likely locations for patients to be monitored are general medical or surgical floors and general hospital and alternate care environments.

This product is available for sale only upon the order of a physician or licensed health care professional.

Welch Allyn Connex® Spot Monitor (CSM) is an integrated, configurable vital signs monitor. CSM is intended to be used by clinicians and medically qualified personnel for measuring or monitoring patient vital signs. The specific vital sign measurements available are determined by the sensor/processing technology integrated into the base unit including:

• · Noninvasive blood pressure (NIBP) provides measurements of noninvasive blood pressure and pulse rate
• · Spo2 provides pulse rate, respiration rate, and noninvasive functional oxygen saturation of arteriolar hemoglobin
• · Thermometer measures temperature in neonatal, pediatric, and adult patients
• · Respiration rate from photoplethysmogram (RRp)
• The custom scores option provides custom calculations based on patient vital sign values and modifiers determined by the user

The CSM 1.53, in the same manner as the CSM predicate device, can display and transmit patient data that is electronically or manually entered from external and accessory devices, including weight and height data, manually entered respiration rate, barcode scanned patient and clinician data, and other patient or facility information to a connected Host system. Data is transmitted electronically via USB, Wi-Fi, Bluetooth or ethernet communications to electronic record systems, and for remote display and alarming (e.g., at a central station).

This FDA 510(k) summary provides information for the Welch Allyn Connex® Spot Monitor; 901058 Vital Signs Monitor Core (CSM), which is receiving an update to include Masimo RRp® (respiration rate from photoplethysmogram). However, the document does not contain a specific table of acceptance criteria for this new feature nor detailed results of a study proving the device meets said criteria.

Instead, the document states:

• "Non-clinical testing was performed on the CSM to verify safety and efficacy of the device."
• "All updates were implemented through the design control process and verified to not impact safety and efficacy of the device."
• "The non-clinical tests performed confirm that the subject CSM device is substantially equivalent to the legally marketed predicate CSM (K142356)."

Therefore, I cannot provide the specific details requested in your prompt as they are not present in the provided text. The document focuses on regulatory compliance through substantial equivalence, listing general standards applied, rather than specific performance metrics and a detailed study report for the RRp feature.

To answer your request based only on the provided text, the following information is unavailable:

1. A table of acceptance criteria and the reported device performance: Not provided.
2. Sample size used for the test set and the data provenance: Not provided. The testing is referred to as "non-clinical testing," but no specifics on test data are given.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided.
4. Adjudication method for the test set: Not provided.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance: Not conducted, as this is a medical device for vital sign monitoring, not an AI-assisted diagnostic imaging device requiring human reader improvement studies.
6. If a standalone performance study was done: "Non-clinical testing" was performed, indicating testing of the algorithm/device itself, but specific details of a standalone study (like methodologies, results, and metrics) are not provided.
7. The type of ground truth used: Not provided.
8. The sample size for the training set: Not provided.
9. How the ground truth for the training set was established: Not provided.

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The ADI Sensinel CPM (Cardiopulmonary Management) System is a wireless remote monitoring system intended for use by healthcare professionals for spot checking of physiological data in home and healthcare settings. This can include:

• ECG

• · Heart Auscultation Sounds
• · Skin Temperature
• · Thoracic Impedance (including Changes in Thoracic Impedance)
• · Respiration Rate and relative changes in Tidal Volume
  · Heart Rate
• Diastolic Heart Sounds Strength
• · Body Posture (including Tilt Angle)

Data are transmitted wirelessly from the ADI CPM (Cardiopulmonary Management) Wearable and Base Station for storage and analysis. The device is intended for use on general care adult patients who are 18 years of age or older to provide physiological information. The data from the ADI CPM (Cardiopulmonary Management) System Platform are intended for use by healthcare professional as an aid to diagnosis and treatment.

The ADI CPM System is intended to be used by patients at rest and not performing any activities or movements. This system is for spot checking and does not have continuous monitoring capability. The device does not produce alarms and is not intended for active patient monitoring (real-time).

The ADI CPM System in contraindicated for those patients with life threatening arrhythmias requiring immediate medical intervention.

Not Found

The provided FDA 510(k) clearance letter and Indications for Use document for the Sensinel Cardiopulmonary Management (CPM) System (ADCP1100) do not contain the detailed information required to describe the acceptance criteria and the study that proves the device meets those criteria.

This document primarily states that the device has been found substantially equivalent to legally marketed predicate devices and outlines the regulatory requirements and indications for use. It does not include performance data, study designs, sample sizes, ground truth methodologies, or expert qualifications.

To answer your request, the following information would typically be found in the 510(k) submission's technical documentation (e.g., performance testing details, clinical study reports), which is not included in the provided text.

Therefore, I cannot extract the information required for the detailed answer as it is not present in the given text.

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CareConsole is intended to be used in conjunction with biometric health care measuring devices, mobile applications, and questionnaires to collect and store data, and for clinician-scheduled monitoring at home and/or in medical facilities. The CareConsole platform securely sends data from a patient monitoring device to a central electronic log of patient information, from which notifications, alerts, and reports can be generated and data can be securely viewed by healthcare professionals, authorized caregivers, and patients. Clinicians would then determine how and when to best treat their patients in response to the notifications, alerts, and biometric data. For use by individuals 12 years and older who do not have an emergency health care condition.

· Data can be sent via intranet networks, the internet, landline telephones, and other mobile devices.

· The software supports communication between patients and clinicians, caregivers, or researchers, such as through bidirectional audio and video e-visits, telephone calls, and mobile text messages.

CareConsole is intended for use in capturing remote patient monitoring data from non-invasive remote monitoring devices and to provide remote hospital-at-home level of care where determined necessary by a health care professional. CareConsole is not intended for use in emergency situations or by a patient in an acute care medical facility.

The CareConsole ("CareConsole") is a software only device. CareConsole enables clinicians and patients to conduct bidirectional audio-video conversations and collects patient-reported outcomes and self-care activities via assessment questionnaires. Biometric measurements, using third party devices, can be taken by the patient while being observed over videoconference by a clinician who is located remotely, or measurements can be made by the patient at any time, without being observed by a clinicians can also use CareConsole to exchange messages with patients by text or telephone.

The system is indicated when health professionals wish to directly interact with patients via video, voice and/or text, and/or view reports of medical parameters collected from patients with non-acute conditions using remote biometric measuring devices and questionnaires. The AMC system is not intended for use in emergency situations.

The provided text describes the regulatory clearance of the AMC Health CareConsole, a software-only device for remote patient monitoring. However, it does not contain specific details about acceptance criteria, the methodology of a study proving the device meets those criteria, or performance metrics from such a study.

The document mainly focuses on:

• Regulatory information: FDA clearance, regulation numbers, product codes, and general controls.
• Device description and intended use: How CareConsole functions (collects, stores, and transmits biometric data, facilitates communication) and its target users (individuals 12+, not for emergency situations).
• Predicate devices comparison: It asserts the substantial equivalence of the CareConsole to its predicate devices in terms of intended use and technological specifications, mainly through a comparison table.
• Compatibility with third-party devices: A list of integrated biometric devices.
• General statement on performance: Mentions that "validation and verification testing were performed under the company's Design Control Process" and "The testing has confirmed the device's conformity with specifications."

Therefore, based only on the provided text, I cannot complete the requested information about acceptance criteria and detailed study results. The document states that performance validation was done to meet regulatory requirements but does not provide the specifics of that validation.

Here's what I can extract or infer, and what is explicitly missing:

1. Table of Acceptance Criteria and Reported Device Performance

Missing: Specific quantitative acceptance criteria or performance metrics (e.g., accuracy percentages, latency times, success rates for data transmission, etc.). The document states that testing confirmed conformity but doesn't provide the results of that conformity testing.

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

• Missing: No information on specific sample sizes for any test sets.
• Data Provenance: Not specified (e.g., country of origin). The document implies the data is gathered remotely from patients using the system. It states "The CareConsole platform securely sends data from a patient monitoring device to a central electronic log of patient information." and lists various third-party devices, suggesting data would come from these sources. It's unclear if a specific "test set" with a defined provenance was used for a formal clinical performance study, or if "testing" refers to internal software validation.
• Retrospective/Prospective: Not specified.

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

• Missing: The document does not describe a study involving expert readers/evaluators for establishing ground truth, as it is a data aggregation and communication platform, not an AI diagnostic imaging device. The "ground truth" here would relate to the successful and accurate transfer, storage, and display of biometric data. The closest mention of experts is "Clinicians," "healthcare professionals," and "authorized caregivers" who view the data.

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

• Missing: Not applicable or not described, as the device is not an imaging AI requiring multiple reader adjudication.

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:

• Missing: Not applicable. The CareConsole is a data management and communication system, not an AI-assisted diagnostic tool for human readers. No MRMC study is mentioned or implied.

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

• Partially Addressed/Inferred: The device is "software only." Its primary function is automated data collection, transmission, storage, and presentation. Thus, its core performance is "standalone" in terms of these automated processes. The "human-in-the-loop" aspect comes in with clinicians viewing the data and interacting with patients, but the device's fundamental data handling is algorithmic. However, there are no specific standalone performance metrics provided (e.g., "algorithm achieved X% accuracy in data transmission"). The document states the "testing has confirmed the device's conformity with specifications."

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

• Inferred: For a device like CareConsole, the "ground truth" would likely be the accuracy and integrity of the data captured from the third-party devices, its faithful transmission, secure storage, and correct display. This would typically be verified against the direct output of the connected biometric device or a known, verified standard. There's no mention of expert consensus, pathology, or outcomes data being used to establish ground truth for the device's technical performance.

8. The sample size for the training set:

• Missing: No information provided about a training set, as this is a traditional software system, not described as a machine learning/AI model that typically requires a distinct training set. The "testing" mentioned refers to software verification and validation.

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

• Missing: Not applicable, as no training set for an AI model is described.

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The Happy Ring Health Monitoring System is a wearable device system to remotely monitor physiologic parameters of patients in professional healthcare facilities, such as hospitals or skilled nursing facilities, or their own home. The device is intended for use on individuals who are 22 years of age or older.

The device supports continuous data collection for monitoring of the following physiological parameters:

• Acceleration / Movement
• Electrodermal Activity (EDA)
• Blood Oxygen Saturation
• Pulse Rate
• Peripheral Skin Temperature

The Happy Ring Health Monitoring System is intended for peripheral skin temperature monitoring, where monitoring temperature at the finger is clinically indicated.

The Happy Ring Health Monitoring System is not intended for SpO2, pulse rate, respiration rate monitoring in conditions of motion or low perfusion.

The Happy Ring Health Monitoring System is a wearable device and software platform comprising:

• A wearable medical device smart ring,
• A mobile app-based bluetooth-to-internet gateway,
• A cloud-based API,
• A set of data processing algorithms, and
• A Physician data viewer.The Ring is worn on the user's finger and continuously collects raw data via specific sensors. These raw data are transmitted via Bluetooth Low Energy to a paired mobile device. The data received are transmitted by the mobile app gateway, via the cloud-based AP, to the data processing algorithms where various physiological parameters are computed. The raw and processed data are stored, further analyzed, and accessible by healthcare providers or researchers via the Physician data viewer.

The Happy Ring Health Monitoring System is intended for retrospective remote monitoring of physiological parameters in ambulatory adults in home-healthcare environments. It is designed to continuously collect data to support intermittent monitoring of the following physiological parameters by trained healthcare professions or researchers: Acceleration / movement, electrodermal activity (EDA), blood oxygen saturation, pulse rate, and peripheral skin temperature.

The provided text describes information about the Happy Ring Health Monitoring System, including its intended use, technological comparison to a predicate device, and a summary of non-clinical and clinical tests performed. However, it does not contain specific acceptance criteria for the device performance or detailed results of a study proving the device meets those criteria, beyond a general statement about SpO2 accuracy.

Therefore, I cannot fully complete the requested table and answer all questions with the provided text. I will extract all available information and explicitly state what is not present.

Here's a breakdown of the available information and what's missing:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size (SpO2 Clinical Study): 12 subjects
• Data Provenance: Not explicitly stated regarding country of origin or whether it was retrospective or prospective. The study was described as "clinical tests" and involved "subjects," which implies a prospective clinical trial.

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

• Not specified. The text mentions "oxyhemoglobin saturation using a radial arterial line" as the comparison method for SpO2, which is an objective measurement and doesn't typically involve expert consensus for ground truth establishment in the same way image interpretation might. For other physiological parameters, the ground truth establishment method is not detailed beyond "bench testing to verify performance."

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

• Not applicable/Not specified. Given the nature of objective physiological measurements (like radial arterial line for SpO2), a multi-reader adjudication method as seen in image interpretation studies is not typically used.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No. An MRMC study was not described. The study focused on the device's accuracy in measuring physiological parameters, not on how it assists human readers or clinicians in interpreting data or making decisions. The "Physician data viewer" is mentioned, suggesting human review, but no comparative effectiveness study with human readers is detailed.

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

• Yes, implicitly. The SpO2 and other bench tests (EDA, accelerometer, temperature) assess the device's ability to measure physiological parameters independently of human interpretation. The "data processing algorithms" compute the parameters, and their accuracy is evaluated.

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

• Reference Devices/Objective Measurements:
  • For SpO2: Oxyhemoglobin saturation using a radial arterial line (a gold standard, objective measurement).
  • For other parameters: Implied reference instruments for "bench testing to verify the performance." This typically means comparing the device's output against a highly accurate, calibrated reference measurement.

8. The sample size for the training set:

• Not specified. The document discusses the test set but provides no information about the training set size for the device's algorithms.

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

• Not specified. Since information about the training set size or its existence is not provided, the method for establishing its ground truth is also not elaborated upon in this document.

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The Wireless Vital Signs Monitor Professional (WVSM Pro) series monitors are intended to be used as continuous or spot check monitors and indicated as single or multi-parameter vital signs monitors. There are two monitor configurations WVSM Pro and TVSM (Tactical Vital Signs Monitor):

WVSM Pro is indicated to monitor electrocardiogram (ECG) 5- or 12-lead waveforms and heart rate (HR); temperature; noninvasive blood pressure (NIBP); Pulse Oximetry including functional oxygen saturation of arterial hemoglobin (SpO2), pulse rate (PR), pleth variability index (PVI), and perfusion index (PI); and capnography including end-tidal CO2 (ETCO2), fractional inspired CO2 (FiCO2) and respiration rate (RR).

TVSM is indicated to monitor noninvasive blood pressure (NIBP) and Pulse Oximetry including functional oxygen saturation of arterial hemoglobin (SpO2), pulse rate (PR), pleth variability index (PVI), and perfusion index (PI), The monitors use wireless communications to transmit vital signs data to a PC, laptop, or mobile device. Patient population: neonate/infant, pediatric and adult patients.

The monitors may be used in the following locations: Hospitals, healthcare facilities, emergency medical applications, during transport, and other healthcare applications.

The monitor is intended to be used by trained healthcare providers.

Wireless Vital Signs Monitor Professional (WVSM Pro) and Tactical Vital Sign Monitor (TVSM) are part of the WVSM Pro Series monitors. The WVSM® Pro series monitors are small, rugged, and highly mobile medical devices intended to be used as an adult, pediatric, and neonate patient vital signs monitor for spot-checking or continuous applications. The monitors are small enough to stay with the patient from point of injury through the triage and treatment process. It is designed as a single or multi-parameter vital signs monitor. The WVSM Pro is capable of acquiring the following physiological parameters: electrocardiogram (ECG) 5- or 12-lead waveforms and heart rate (HR); temperature; noninvasive blood pressure (NIBP); Pulse Oximetry including functional oxygen saturation of arterial hemoglobin (SpO2), pulse rate (PR), pleth variability index (PVI), and perfusion index (PI); and capnography including end-tidal CO2 (ETCO2), fractional inspired CO2 (FiCO2) and respiration rate (RR). The TVSM is capable of acquiring the following physiological parameters: noninvasive blood pressure (NIBP) and Pulse Oximetry including functional oxygen saturation of arterial hemoglobin (SpO2), pulse rate (PR), pleth variability index (PVI), and perfusion index (PI). Both models can be used as a standalone device or can transmit data via wireless communications to a PC, laptop, or mobile device (tablet or smartphone).

WVSM Pro series monitors are intended for use in pre-hospital, emergency room, inpatient care facilities, healthcare facilities, emergency medical applications, during transport, outpatient care, and other related healthcare scenarios. The WVSM Pro is intended to be used by trained healthcare providers by prescription only.

The basic principles of operation of the WVSM® Pro Series monitors include:

ECG: 5- or 12-lead waveforms generated via skin electrodes with right-leg drive. Note: The device does NOT include the following functions: Automated Waveform Measurements, Arrhythmia Detection, or Alarms for these functions.

Capnography: Infrared (IR) Spectroscopy is used to detect CO2 concentrations in expired air via mainstream or sidestream methods

Pulse oximetry: The plethysmography waveform from Red and IR LEDs are used to calculate functional oxygen saturation of arterial hemoglobin (SpO2), pulse rate (PR), pleth variability index (PVI), and perfusion index (PI)

Temperature: YSI 400 compatible thermistor sensors

Non-invasive blood pressure (NIBP): Oscillometric method

The WVSM® Pro Series patient monitor enclosures are primarily plastic and is not intended to contact the patient. The applied parts are OEM accessories that are FDA cleared and meet the biocompatibility requirements for intact skin contact.

This document, K233354, is a 510(k) premarket notification review from the FDA for the Athena GTX, Inc. WVSM Pro (Series) vital signs monitors. It establishes substantial equivalence to a predicate device (ZOLL Power M, K202275) without the need for new clinical testing.

Here's an analysis of the provided information regarding acceptance criteria and supporting studies:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly provide a table of acceptance criteria with specific performance metrics for the WVSM Pro (Series) monitor. Instead, it states that "performance data were provided in support of substantial equivalence determination" and lists various non-clinical tests and compliance with recognized standards.

The primary "acceptance criterion" for this 510(k) submission appears to be substantial equivalence to the predicate device (ZOLL Power M, K202275) across indications for use, technology, and intended use, without raising new questions of safety and effectiveness.

The document implicitly refers to performance by stating:

• "The device was evaluated and found to be in compliance with the standards identified below" (a list of IEC, ISO, and AAMI standards). These standards contain their own performance requirements and acceptance criteria for vital signs monitoring devices.
• "The device uses OEM modules for parameters that would normally require clinical testing, NIBP and Pulse Oximetry. These modules have had clinical testing performed and have been used in previously cleared devices." This implies that the performance of these modules, for which the predicate also relies on similar technology, is accepted based on prior clearances and clinical data.

Therefore, a table with specific acceptance values and corresponding WVSM Pro performance from this document cannot be constructed directly. The "device performance" is primarily demonstrated through compliance with recognized standards for safety and performance (e.g., IEC 60601 series, ISO 80601 series, AAMI ANSI EC53) and the use of previously validated OEM modules for certain functions.

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

Since no new clinical studies were conducted for the WVSM Pro (Series) device, there is no new test set sample size or data provenance explicitly described for this 510(k) application.

The clinical data that supports the performance of the NIBP and Pulse Oximetry modules, which were incorporated from OEM parts, would have originated from the studies conducted for those original modules. The document does not specify the sample sizes or provenance of these prior studies.

The non-clinical testing (software, safety, usability, cybersecurity) does not typically involve patient "test sets" in the same way clinical trials do.

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

As no new clinical testing was performed on the WVSM Pro (Series) that required establishing a ground truth with human experts, this information is not applicable and not provided in the document. The performance of the OEM modules is presumed to be based on the ground truth established in their original clinical validations.

4. Adjudication Method for the Test Set

Since no new clinical test set requiring human expert adjudication was created for this device, the adjudication method is not applicable and not described.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study was done. The document explicitly states, "Clinical testing was not necessary to show substantial equivalence." The device is intended to be used by trained healthcare providers, but its performance is not being compared to human readers or an AI-assisted human workflow in this submission.

6. Standalone Performance Study

No new standalone (algorithm only without human-in-the-loop performance) study was done for the WVSM Pro (Series). The device itself is a vital signs monitor, not typically an AI algorithm in the context of standalone performance a typical AI/CADe device would require.

The document does mention software verification and validation, ensuring the product works as designed, but this is not an "algorithm-only" performance study in the context of diagnostic AI.

7. Type of Ground Truth Used

For the WVSM Pro (Series) submission, the "ground truth" for demonstrating substantial equivalence relies on:

• Compliance with recognized performance standards: These standards (e.g., IEC, ISO, AAMI) establish the gold standard for performance expectations of vital signs monitors.
• Prior clinical validation and clearance of OEM modules: For NIBP and Pulse Oximetry, the ground truth was established during the original clinical testing of the commercially available OEM modules. This would typically involve comparison to reference clinical measurements from calibrated devices. The document does not provide details on the specific type of ground truth used in those prior studies (e.g., invasive arterial line for NIBP, co-oximetry for SpO2).

8. Sample Size for the Training Set

The WVSM Pro (Series) is a vital signs monitor and not described as an AI/Machine Learning device that requires a "training set" in the conventional sense for a standalone algorithm. Therefore, no training set sample size is provided or applicable in this context. The "software verification and validation" refers to traditional software engineering testing.

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

As no training set is described for an AI/ML algorithm within the WVSM Pro (Series), this information is not applicable.

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NeoBeat and NeoBeat Mini are indicated to continuously measure and display the heart rate of neonates using dry electrodes on the torso during transition, stabilization and/or resuscitation. The devices are intended to be used in healthcare facilities. NeoBeat is intended for use on newborns approximately 1.5-5 kg. NeoBeat Mini is intended for newborns approximately 0.5-2 kg.

The NeoBeat Newborn Heart Rate Meter is a battery-powered device placed on the torso of a newborn, indicated to measure the heart rate. NeoBeat does not store, display or output an ECG signal. The device is placed around the torso of the neonate such that the ECG dry electrodes contact the neonate's skin. It can be oriented caudally or cranially. In normal operation, the LED display presents the heart rate in large numerals. The display can also present other information, such as signal quality and error codes. The device comes with a charging stand.

Here's a breakdown of the acceptance criteria and study information for the NeoBeat/NeoBeat Mini device based on the provided text:

Acceptance Criteria and Device Performance

Study Information

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

   • Test Set Sample Size: 19 clinical cases representing over 4 hours of ECG data.
   • Data Provenance: The 19 cases were randomly selected from a large database containing newborn ECGs from four countries outside the United States.

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

   • Number of Experts: 2
   • Qualifications: An ICU physician and a scientific expert in ECG signal processing and analysis.

3. Adjudication method for the test set:

   • Not explicitly stated, but it implies a consensus given "Heart rate based on expert annotation was considered 'ground truth'." This suggests the two experts either agreed directly or their combined annotation formed the ground truth.

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 comparative effectiveness study was done. This study focused on the algorithm's standalone performance compared to expert-annotated ground truth, not on human reader improvement with or without AI assistance.

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

   • Yes, a standalone performance study was done. The study compared "Heart rate based on NeoBeat's algorithm" to the "ground truth" heart rate established by experts.

6. The type of ground truth used:

   • Expert consensus (specifically, expert annotation of QRS complexes).

7. The sample size for the training set:

   • Not explicitly stated. The document mentions a "large database containing newborn ECGs from four countries outside the United States" with "over 1000 cases of ECGs" collected from researchers. While this database was used to select the test cases, it is strongly implied that this database would have been used for training/development, but the exact size of the training set is not specified separately from the total database.

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

   • Not explicitly stated for the training set. For the test set, it was established by expert annotation of QRS complexes by an ICU physician and a scientific expert. It is reasonable to infer a similar method for the training set, given the nature of the data and the use of expert annotation for the test set.

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