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The monitors are intended to be used for monitoring, storing, recording, and reviewing of, and to generate alarms for, multiple physiological parameters of adults and pediatrics. The monitors are intended for use by trained healthcare professionals in hospital environments. The monitored physiological parameters include: ECG, respiration (RESP), temperature (TEMP), oxygen saturation of arterial blood (SpO2), pulse rate (PR), non-invasive blood pressure (NIBP), invasive blood pressure (IBP), carbon dioxide (CO2), cardiac output (C.O.), and Anaesthesia gas(AG). The arrhythmia detection and ST Segment analysis are intended for adult patients. The monitors are not intended for MRI environments.

The iM series Patient Monitor including iM50, iM60, iM70 and iM80 can perform long-time continuous monitoring of multiple physiological parameters. Also, it is capable of storing, displaying, analyzing and controlling measurements, and it will indicate alarms in case of abnormalities so that doctors and nurses can respond to the patient's situation as appropriate.

Based on the provided text, the device in question is a Patient Monitor (Model: iM50, iM60, iM70, iM80), which monitors various physiological parameters. The document focuses on demonstrating substantial equivalence to a predicate device, rather than providing detailed acceptance criteria and a standalone study for a novel AI device. Therefore, much of the requested information regarding AI-specific evaluation (e.g., sample size for AI test sets, expert adjudication, MRMC studies, AI effect size, ground truth establishment for training) is not applicable or not present in this 510(k) summary.

However, I can extract information related to the device's self-contained performance testing and regulatory acceptance criteria.

Acceptance Criteria and Device Performance for Patient Monitor (iM Series)

The document primarily relies on bench testing and software verification and validation to demonstrate that the iM series Patient Monitor meets its accuracy specifications and relevant consensus standards, thereby establishing substantial equivalence to a predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present explicit "acceptance criteria" in a quantitative table for this specific device in the manner typically seen for novel AI models. Instead, it compares the technical specifications of the subject device to a predicate device and states that "the results of the bench testing show that the subject device meets its accuracy specification and meet relevant consensus standards."

The comparison table (pages 5 & 6) implicitly indicates that "acceptance" for the subject device's performance corresponds to ranges/specifications that are identical or comparable to the cleared predicate device.

Notes on the 'AG Module': The document explicitly states for the AG (Anesthesia Gas) module that its "indication is not present in the primary predicate, but is present in Edan Patient Monitor V series K160981." This implies that while it differs from the immediate primary predicate, it is substantially equivalent to a different, already cleared, predicate device from the same manufacturer.

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

• Sample Size: The document does not specify a distinct "test set" sample size in terms of patient data or number of tests. The performance data section refers to "functional and system level testing" and "bench testing." This implies testing against specifications and standards rather than a clinical dataset of a specific size.
• Data Provenance: Not specified. Given it's a bench test, it would typically be conducted in a laboratory setting. There's no mention of country of origin for test data, nor whether it's retrospective or prospective patient data, as clinical data was deemed "Not applicable."

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

Not applicable. This is a physiological monitor, not an AI diagnostic device requiring expert consensus for ground truth on images or signals. The "ground truth" for the device's performance would be derived from calibrated measurement references and established engineering principles in bench testing.

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

Not applicable. As above, this is not an AI diagnostic device relying on human expert review for 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

Not applicable. This device is a physiological monitor, not an AI-assisted diagnostic tool that requires human readers for interpretation.

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

The device's performance in terms of its physiological measurements and alarm detection is inherently "standalone" in that it performs these functions without direct human intervention in the measurement process itself, generating outputs for healthcare professionals. The bench testing performed would be considered evaluating this standalone performance against technical specifications and standards.

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

For the non-clinical performance (bench testing), the "ground truth" is based on:

• Calibration standards: Using known, precise inputs (e.g., electrical signals simulating ECG, precise pressures for NIBP, known gas concentrations for CO2/AG) to verify the accuracy of the device's measurements.
• Consensus Standards: Adherence to recognized international standards for medical electrical equipment (e.g., IEC 60601 series, ISO 80601 series). These standards define acceptable performance limits and test methodologies.

8. The sample size for the training set

Not applicable. This document does not describe an AI/ML device that requires a "training set" in the conventional sense. The device's algorithms are likely based on established physiological signal processing, not deep learning models trained on large datasets.

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

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

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The SimpleSENSE System is intended for use at home, or at a healthcare facility, under the direction of a licensed medical professional, to record, display and store the following physiological data: a) 2 leads of Electrocardiogram; b) Respiration rate measured through thoracic impedance; c) Heart Sounds; and d) Activity including posture. The device is intended for use when the clinician decides to evaluate the physiologic signals as an aid to diagnosis and treatment. The SimpleSENSE System is intended to be used by patients at rest and not performing any activities or movements. ECG recordings are indicated for the manual assessment of cardiac rhythm disturbances. The device does not produce alarms and is not intended for active patient monitoring (real-time). The device is not intended for use as life supporting equipment on high-risk patients such as critical care patients. The device is not intended for use in the presence of a pacemaker.

The Nanowear SimpleSENSE device is the next generation diagnostic monitoring technology that captures electrocardiographic (ECG) signals, respiration rate though thoracic impedance, heart sounds, activity including posture with sensors embedded on a wearable textile garment. The signals are stored and wirelessly transmitted to a smartphone, and forwarded to a medical professional for review. The garment is designed to be unobtrusive to everyday activity and provide an easy and efficient means of capturing ECG, respiration rate, heart sounds and activity data from patients. The garment is designed to be unobtrusive to everyday activity and provide an easy and efficient means of capturing ECG data from patients. The device consists of three (3) components:

• The SimpleSENSE Garment: an integrated network of nanosensor electrodes for measuring ECG and respiratory rate from thoracic impedance, and incorporating a MEMS microphone for measuring heart sounds.
• The SimpleSENSE Signal Acquisition Unit (SAU): data acquisition, storage, and transmission to an iPhone 7 using iOS 13.4. Incorporates an accelerometer to measure activity.
• The SimpleSENSE Mobile Application: mobile application for to start/stop a recording and to forward the test report to the medical professional.

Here's a breakdown of the acceptance criteria and study information for the Nanowear SimpleSENSE device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The FDA 510(k) summary provided does not explicitly list acceptance criteria in a quantitative table format with corresponding reported performance values for clinical metrics. Instead, it describes various performance evaluations against design specifications and equivalence to predicate devices.

However, based on the Performance testing section, we can infer some key areas of evaluation:

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

The summary states that "Equivalency testing against predicate/reference devices" was performed as part of the performance testing. However, it does not provide any details regarding the sample size used for clinical testing or the data provenance (e.g., country of origin, retrospective or prospective) for this equivalency testing.

3. Number of Experts and Qualifications for Ground Truth

The document does not specify the number of experts or their qualifications used to establish ground truth for any clinical test sets. The indications for use mention evaluation by a "licensed medical professional" and "physician who is skilled in rhythm interpretation" for the ECG data, but this pertains to the intended clinical use of the device, not necessarily how the ground truth for regulatory testing was established.

4. Adjudication Method

The document does not mention any adjudication method (e.g., 2+1, 3+1) for establishing ground truth in clinical test sets.

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

The document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance. The SimpleSENSE system is a data acquisition and display device intended for manual assessment, not an AI-driven interpretive device.

6. Standalone (Algorithm Only) Performance

The SimpleSENSE system itself is described as a device that records, displays, and stores physiological data for manual assessment by a clinician. It does not appear to have an inherent AI algorithm that provides interpretations or diagnoses. Therefore, a standalone (algorithm only) performance study as typically understood for AI devices would not be applicable or described for this device. The phrase "manual assessment of cardiac rhythm disturbances" reinforces this.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for any clinical testing (e.g., expert consensus, pathology, outcomes data). Given the nature of the device as a data recorder for manual assessment, it is implied that the "ground truth" for equivalency would come from comparisons to the outputs of the predicate devices or conventionally accepted methods for measuring parameters like ECG, respiration rate, and heart sounds.

8. Sample Size for the Training Set

The document does not mention a training set or its sample size. As the device is for data acquisition and display rather than AI interpretation, a separate training set for an algorithm is not discussed.

9. How Ground Truth for the Training Set Was Established

Since no training set for an AI algorithm is mentioned, the method for establishing ground truth for such a set is also not applicable or described in this summary.

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The monitor is intended to be used for monitoring, storing, and reviewing of, and to generate alarms for, multiple physiological parameters of adults, pediatrics and neonates. The monitor is intended for use by trained healthcare professionals in hospital environments.

Monitored parameters include: NIBP, SpO2, PR (pulse rate), Quick TEMP/Infrared TEMP.

The monitor is not intended for MRI environments. TEMP module is not intended for neonates.

The iM3 has three work modes, Monitor, Spot and Round, to measure physiological parameters, including non-invasive blood pressure (NIBP), oxygen saturation of the blood (SpO2), pulse rate (PR), and Quick TEMP/Infrared TEMP (TEMP).
• For the Monitor mode, the user can do continuous measurement, monitoring, alarming and manager patient data.
• For the Spot mode, also call spot check mode, the user can measure parameters quickly without creating a patient within the instrument workflow.
• The Round mode is designed to support hospital staff when doing ward rounds. The Round mode is similar as Spot mode. The difference between Round mode and Spot mode is that for one ward round, the iM3 can additionally store one ward round record automatically (the latest measurement) or manually (user can choose the measure results).

iM3 capabilities include storing, displaying measuring data. When necessary, alarms will be produced so that doctors and nurses can manage patient care appropriately.

The iM3can connect with EDAN's MFM-CMS Central Monitoring System to display the iM3 data on a remote work station. The MFM-CMS system received FDA 510(k) clearance on June 21, 2013(K120727)

The provided text is a 510(k) summary for the Edan Instruments iM3 Vital Signs Monitor, seeking FDA clearance based on substantial equivalence to predicate devices. This type of submission focuses on demonstrating that a new device is as safe and effective as a legally marketed predicate device.

The document does not describe a study that proves the device meets specific acceptance criteria in the context of an AI/ML algorithm being developed or tested against a ground truth for diagnostic or prognostic performance. Instead, it discusses the performance as it relates to comparison with a predicate device and compliance with established medical device standards.

Therefore, many of the requested details related to AI/ML validation studies (like sample size for test sets, data provenance, expert adjudication, MRMC studies, standalone performance, ground truth establishment for training/test sets) are not applicable to this document as it describes a traditional vital signs monitor seeking 510(k) clearance, not an AI/ML diagnostic or prognostic device.

However, based on the provided text, I can infer and extract information relevant to the device's functional performance criteria and what was demonstrated:

Acceptance Criteria and Reported Device Performance (Inferred from comparison to predicate and standard compliance):

The "acceptance criteria" here are implicitly defined by the performance specifications of the predicate devices and relevant IEC/ISO standards for vital signs monitors. The reported device performance is presented as being "similar" to the predicate and compliant with these standards.

1. Table of Acceptance Criteria and Reported Device Performance

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

• This document does not specify a "test set" in the context of an AI/ML model, but rather refers to performance validation against established standards.
• The comparison data presented in the tables are physical performance specifications of the device modules, not patient data from a clinical trial in the AI/ML sense. Actual human testing for NIBP, SpO2, and TEMP accuracy would have been done to meet the standards (e.g., ISO 81060-2:2013 for NIBP, ISO 80601-2-61: 2011 for SpO2, ISO 80601-2-56 for TEMP), but the specifics (sample sizes, provenance) are not detailed in this summary. These physical performance tests are typically done in controlled lab settings or with human subjects as required by the specific standard.

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

• Not applicable as this is not an AI/ML diagnostic device requiring expert consensus on image interpretation or similar. The "ground truth" for vital signs monitoring validation is typically measured physiological parameters from reference devices.

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

• Not applicable.

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

• Not applicable.

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

• Not applicable. The device directly measures physiological parameters. Its performance is inherent to its sensors and processing, and the accuracy claims are against reference measurements, not an independent algorithm being evaluated.

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

• The ground truth for the performance claims would be highly accurate reference measurements from precisely calibrated medical devices or established physiological measurement techniques (e.g., intra-arterial blood pressure for NIBP, co-oximetry for SpO2, highly accurate thermometers for TEMP). The document states compliance with relevant ISO/IEC standards which dictate the methods for determining accuracy, including the reference methods.

8. The sample size for the training set:

• Not applicable. This is a vital signs monitor, not a machine learning algorithm that requires a "training set" for its core function.

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

• Not applicable.

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