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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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The iX series Patient Monitors including iX10, iX12, iX15 are intended to be used for monitoring, storing, and reviewing of, and to generate alarms for, multiple physiological parameters of adults and pediatics (including neonates). The monitors are intended for use by trained healthcare professionals in hospital environments.

The monitored physiological parameters include: ECG, respiration (RESP), temperature (TEMP), functional oxygen saturation of arterial hemoglobin (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 NIBP monitoring supports iCUFS algorithm and iFAST algorithm is intended for adult, pediatric and neonatal patients. The iFAST algorithm is intended for adult and pediatic patients (≥3 years of age). Both measurement algorithms are also intended for use with pregnant women, including pre-eclamptic patients. NIBP MAP is not applicable to pregnant women.

The Spot Temp with T2A module can only measure temperature of adult and pediatric (> 1 year of age) patients. The monitors are not intended for MRI environments.

The cardiac output (C.O.) is only intended for adult patients.

The iX series Patient Monitors including iX10, iX12, iX15 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.

This document describes the premarket notification (510(k)) for the Edan Instruments, Inc. Patient Monitor (iX10, iX12, iX15) and its equivalence to a predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the new device were generally established by compliance with various international standards for medical electrical equipment and specific performance characteristics. The reported device performance indicates that the device meets these standards and its specified accuracy.

For the Quick Temp Module (T2A), the provided text details specific clinical study results. Other parameters were tested for compliance with relevant standards through non-clinical testing.

2. Sample Size for Test Set and Data Provenance

For the Quick Temp Module (T2A) Predict Mode clinical accuracy study:

• Sample Size: 142 valid cases for sublingual and axillary temperature measurements.
• Data Provenance: Clinical investigation (prospective study). No country of origin is explicitly stated, but the company is based in China.

For other parameters, specific sample sizes for non-clinical (bench) testing are not provided, but the document states "Edan has conducted functional and system level testing to validate the performance of the results of the bench testing show that the subject device meets its accuracy specification and meet relevant consensus standards."

3. Number of Experts and their Qualifications for Ground Truth

The document does not specify the number or qualifications of experts used to establish ground truth for the clinical study of the Quick Temp Module (T2A). It only mentions that the study compared the new module against the "direct mode of F3000 Temp Module of M3A Vital signs monitor," implying the predicate device served as a reference for accuracy.

For other non-clinical tests, the ground truth is implicitly defined by the specifications and performance requirements outlined in the referenced international standards.

4. Adjudication Method for the Test Set

The document does not describe a specific adjudication method (e.g., 2+1, 3+1) for the clinical study or any other test sets.

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

No multi-reader multi-case (MRMC) comparative effectiveness study is mentioned in the provided text. The device is a patient monitor, and its performance evaluation typically focuses on the accuracy and reliability of its physiological parameter measurements, not on improving human reader performance with AI assistance.

6. Standalone (Algorithm Only) Performance Study

Yes, standalone performance was evaluated for the components of the device. The non-clinical data section describes extensive functional and system-level testing to validate the performance against relevant consensus standards. This includes specific tests for algorithms like cardiac rhythm and ST segment measurement. The clinical study for the Quick Temp Module (T2A) also assesses the standalone performance of that specific module.

7. Type of Ground Truth Used

• For the Quick Temp Module (T2A) clinical study: The ground truth was established by comparison to a reference device's direct temperature measurement mode ("direct mode of F3000 Temp Module of M3A Vital signs monitor"). This is a form of reference standard comparison (using a predicate device as the reference).
• For other parameters (e.g., ECG, NIBP, SpO2, CO2, AG): The ground truth for non-clinical testing is implicitly based on the specifications and performance requirements outlined in the referenced international consensus standards (e.g., IEC 60601 series, ISO 80601 series, AAMI standards).

8. Sample Size for the Training Set

The document does not provide information about a training set since this is a patient monitoring device and not a machine learning algorithm as typically understood in the context of large-scale image-based diagnostics. The "algorithms" mentioned (e.g., arrhythmia detection, NIBP algorithms) are likely engineered signal processing algorithms rather than deep learning models requiring large training datasets.

9. How Ground Truth for Training Set was Established

Not applicable, as no training set for a machine learning model is described in the provided text. The algorithms for the patient monitor's functions are developed and validated against established physiological principles and engineering standards.

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