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

The monitored physiological parameters include: ECG, respiration (RESP), temperature (TEMP), oxygen saturation of arterial blood (SpO2), pulse rate (PR), non-invasive blood pressure (NIBP), and carbon dioxide (CO2).

The arrhythmia detection and ST Segment analysis are intended for adult patients.

The SpO2 (Nellcor™) module is intended to be used for spot-check or continuous non-invasive monitoring of functional oxygen saturation of arterial hemoglobin (SpO2) and pulse rate (PR), in motion and no motion conditions, and in patients who are well or poorly perfused.

The Microstream™ capnography module is intended for continuous non-invasive monitoring of carbon dioxide concentration of the expired and inspired breath (etCO2) and respiration rate (RR). The monitor also provides the clinician with integrated pulmonary index (IPI), apnea per hour (A/hr) and oxygen desaturation index (ODI) values. IPI is not intended for patients up to the age of one year. A/hr and ODI are intended for ages 22 and up.

The monitors are not intended for MRI environments.

The RespArray patient monitor (hereinafter called RespArray) 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 abnormity so that doctors and nurses can deal with them in time.

The provided text is a 510(k) summary for the Edan Instruments Patient Monitor (RespArray). It focuses on establishing substantial equivalence to a predicate device, primarily through non-clinical performance and software verification/validation.

Crucially, the document explicitly states: "Clinical data: Not applicable." This means there was no clinical study conducted to prove the device meets specific acceptance criteria in a human-use setting, particularly relating to diagnostic accuracy where AI assistance or expert consensus would be relevant.

Therefore, many of the requested items, such as multi-reader multi-case studies, ground truth establishment for a test set, and sample sizes for clinical test sets, are not applicable to this submission as per the document's contents.

Here's a breakdown based on the provided text:

Acceptance Criteria and Reported Device Performance

The acceptance criteria discussed are primarily related to electrical safety, electromagnetic compatibility (EMC), and general performance of the physiological parameter measurements (ECG, RESP, NIBP, TEMP, SpO2, CO2). The document states that the device was found to comply with relevant standards and that bench testing shows it meets its accuracy specification and relevant consensus standards.

Table of Acceptance Criteria and Reported Device Performance (as inferred from the text):

Study Proving Device Meets Acceptance Criteria:

The study proving the device meets the acceptance criteria is described as "Non-clinical data" including:

• Electrical safety and electromagnetic compatibility (EMC) assessments.
• Performance testing-Bench
• Software Verification and Validation Testing.

Detailed Information on the Study:

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

   • Sample Size: Not explicitly quantified in terms of number of patients or physiological measurements. The testing was "bench" testing, meaning laboratory-based tests on the device's functionality and accuracy against reference standards, rather than patient-derived data.
   • Data Provenance: Not applicable in the context of clinical data. For testing against standards, it implies standardized test setups and simulated physiological signals/conditions.

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

   • Not applicable. No experts were used for ground truth establishment as it was non-clinical bench testing against established engineering and medical device performance standards. There's no "ground truth" of a diagnostic nature being established by human experts in this context.

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

   • Not applicable. Adjudication is relevant for human-interpreted diagnostic data. This study relies on objective measurements against engineering specifications and international standards.

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, not applicable. The device is a patient monitor, not an AI-assisted diagnostic tool that interprets medical images/signals for improved human reader performance. The submission explicitly states "Clinical data: Not applicable."

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

   • Yes, in essence. The "Performance testing-Bench" and "Software Verification and Validation Testing" evaluate the device's inherent algorithms and functionality in a standalone manner (without a human in the loop for diagnostic interpretation, but rather for operational functionality and accuracy of physiological measurements). However, this is not in the context of a diagnostic AI algorithm.

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

   • Reference standards and engineering specifications. The "ground truth" for the non-clinical tests is derived from established international and national standards for medical electrical equipment, specific performance criteria for various physiological measurement modules, and the device's own accuracy specifications validated through bench testing.

7. The sample size for the training set:

   • Not applicable for a clinical training set. This is not an AI/ML device in the sense of learning from a large dataset of patient cases. The device's algorithms are designed and verified, not "trained" on patient data in a machine learning sense.

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

   • Not applicable. As above, no clinical training set or associated ground truth establishment process is described or relevant for this type of device submission.

In summary, the provided 510(k) focuses on demonstrating substantial equivalence through adherence to recognized performance and safety standards via non-clinical bench testing and software validation, rather than clinical studies involving human patients or complex AI diagnostic algorithms requiring expert review and adjudication.

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