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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 (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), and cardiac output (C.O.).

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

The NIBP monitoring supports iCUFS algorithm and iFAST algorithm. The iCUFS algorithm is intended for adult, pediatric and neonatal patients. The iFAST algorithm is intended for adult and pediatric 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 CX&UX series Patient Monitor including CX10/CX12/CX15/UX10/UX12/UX15 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.

Minor differences from the predicate device are limited to some modifications of monitoring parameter specifications. These updates do not change the fundamental scientific technology of the cleared predicate device and thus do not raise any questions about the safety and effectiveness of the subject device.

The provided FDA 510(k) clearance letter details the device's technical specifications and comparisons to predicate devices, along with the non-clinical performance data and adherence to various IEC and ISO standards. However, it explicitly states: "Clinical data: The subject device did not require new clinical studies to support substantial equivalence."

This means that the submission for this Patient Monitor device (CX10, CX12, CX15, UX10, UX12, UX15) relies on demonstrating substantial equivalence to a legally marketed predicate device (Edan Instruments, Inc., Patient Monitor Model iX10, iX12, iX15, K232962) through non-clinical performance testing and software verification/validation, rather than new clinical trials or studies involving human patients.

Therefore, the requested information regarding acceptance criteria and studies that prove the device meets acceptance criteria through clinical performance (e.g., sample size for test set, expert involvement, MRMC studies, ground truth establishment for test/training sets, effect size of human reader improvement with AI) cannot be extracted from this document, as such clinical studies were explicitly not required for this 510(k) submission.

The document focuses on demonstrating that the new device's technical specifications and performance are similar to the predicate device, and that it complies with relevant safety and performance standards through bench testing.

Here's what can be extracted from the provided text regarding acceptance criteria and the type of study performed, specifically focusing on the non-clinical aspects:

Device: Patient Monitor (CX10, CX12, CX15, UX10, UX12, UX15)

The acceptance criteria for this device are implicitly tied to its performance meeting the standards and accuracy specifications of the predicate device and relevant international standards. Since no new clinical studies were conducted, the "proof" comes from non-clinical bench testing and software validation.

1. Table of Acceptance Criteria and Reported Device Performance (Non-Clinical/Bench Testing)

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

• Sample Size: Not applicable in terms of human subjects or patient data test sets, as "new clinical studies" were not required. The "test set" refers to bench testing and functional system-level validation. The specific number of test cycles or a detailed breakdown of test cases for bench testing is not provided in this summary.
• Data Provenance: The data primarily originates from Edan Instruments Inc. (Shenzhen, Guangdong, China) through internal engineering and quality assurance processes for non-clinical bench testing and software validation. It is not patient data, so concepts like "retrospective or prospective" do not apply.

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

• Not applicable for clinical ground truth: Since no clinical studies were performed requiring human interpretation or diagnosis for a test set, no medical experts (e.g., radiologists) were used to establish ground truth in this context.
• Internal experts: Bench testing and software validation would have involved engineers and quality assurance professionals, whose qualifications are implicit in the quality system (21 CFR Part 820) but not specified in detail here.

4. Adjudication Method for the Test Set:

• Not applicable: Adjudication methods (e.g., 2+1, 3+1) are relevant for clinical studies involving multiple readers. This was not a clinical study. Bench testing relies on established technical specifications and standard compliance.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:

• No: No MRMC study was performed as no new clinical studies were required or conducted. Therefore, there's no effect size of human readers improving with AI assistance. The device is a patient monitor, not an AI-assisted diagnostic tool.

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

• Yes (for the technical components): The "performance testing-Bench" effectively represents a standalone evaluation of the device's functional components (ECG, NIBP, SpO2, etc.) and software against defined technical specifications and standards. The "software verification and validation testing" also represents a standalone evaluation of the algorithm and software functions. The specific algorithms (e.g., iCUFS, iFAST for NIBP, arrhythmia analysis logic) are tested independently for their accuracy against known inputs or reference standards as part of bench testing.

7. The Type of Ground Truth Used:

• Technical/Reference Standards: For the bench testing, the "ground truth" would be derived from:
  • Reference standards/simulators: Calibrated medical equipment, physiological simulators, and test signals (e.g., known ECG waveforms, simulated blood pressure readings, temperature standards) are used to provide the "true" values against which the device's measurements are compared.
  • Defined specifications: The device's internal design specifications and the requirements of the referenced IEC/ISO standards serve as the "ground truth" for compliance testing.
• Not clinical ground truth: No expert consensus, pathology, or outcomes data from real patients were used for establishing ground truth for this submission.

8. The Sample Size for the Training Set:

• Not applicable: The device is a patient monitor, not a machine learning/AI algorithm that typically undergoes a distinct "training" phase with a large dataset. Its functionality is based on established physiological measurement principles and programmed algorithms. Any internal calibration or algorithm refinement would be part of the product development process, not a dedicated "training set" in the AI/ML sense.

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

• Not applicable: As there was no "training set" in the context of an AI/ML model, the concept of establishing ground truth for it does not apply to this 510(k) submission.

In summary, this 510(k) clearance relies on demonstrating that the new Patient Monitor is substantially equivalent to a previously cleared predicate device, primarily through robust non-clinical bench testing and software validation, proving compliance with established medical device standards and functional specifications. No new clinical studies with patient data were required or conducted for this specific submission.

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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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The RespArray™ patient monitor is intended to be used for monitoring, storing, 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 (NellcorTM) 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 conditions, and in patients who are well or poorly perfused.
The MicrostreamTM 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. Allr 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) Premarket Notification summary for the "Patient Monitor: RespArray" device. This type of submission focuses on demonstrating substantial equivalence to legally marketed predicate devices, rather than conducting new clinical trials for de novo clearance or PMA approval. Therefore, the details requested about acceptance criteria, specific study design (like MRMC studies, sample sizes, expert ground truth establishment for AI/algorithm performance), and training set information are not typically found in these types of submissions, as the FDA review here centers on comparing the new device's specifications and performance to an existing, already cleared device.

The document primarily highlights the device's technical specifications and how they compare to a predicate device (Edan Instruments, Inc, Patient Monitor Model X8, X10, X12 - K192514), along with compliance with relevant electrical safety, EMC, and performance standards. It explicitly states "Clinical data: Not applicable."

Given this, I will extract the information that is present in the document and indicate where the requested information is not applicable or not provided within the scope of a 510(k) submission focused on substantial equivalence.

Analysis of the Provided Document for Device Acceptance Criteria and Study Proof

The provided document is a 510(k) premarket notification. For devices cleared via a 510(k), the primary "acceptance criterion" is often substantial equivalence to a legally marketed predicate device, demonstrated through comparative testing and adherence to recognized standards. Direct, explicit "acceptance criteria" presented as quantitative performance targets with a detailed study to prove they are met (as might be seen in AI/ML clearances for algorithms with novel functionalities) are typically not included in this type of submission for a patient monitor.

The "study" that proves the device meets the acceptance criteria is primarily non-clinical performance testing (bench testing) and software verification/validation to show that the device performs as intended and is as safe and effective as its predicate.

Here's a breakdown of the requested information based on the provided document:

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

The document does not provide a table of explicit acceptance criteria/performance targets with quantitative results in the way one might expect for a new AI/ML algorithm. Instead, it demonstrates performance by stating compliance with recognized consensus standards and by comparing the subject device's specifications to those of its predicate device, showing "similar design features and performance specifications."

The closest representation of "performance" and "acceptance" is the "Predicate Device Comparison" table (pages 5-6). This table implicitly acts as the performance comparison against the predicate device that serves as the "acceptance" benchmark for substantial equivalence.

The document concludes that "the subject and predicate devices have similar design features and performance specifications. The technological differences between the subject and predicate devices do not raise different questions of safety or effectiveness."

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

The document states "Clinical data: Not applicable." Therefore, there isn't a "test set" in the sense of patient data used for clinical validation of, for example, an AI algorithm's performance. The "testing" primarily refers to non-clinical bench testing.

• Sample size: Not applicable for patient data test set. For bench testing, samples would be physical devices, components, or simulated signals, but a "sample size" in terms of patient numbers is not provided.
• Data provenance (e.g., country of origin of the data, retrospective or prospective): Not applicable, as no clinical data test set was used/provided.

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

Not applicable. Since no clinical data test set was described and "Clinical data: Not applicable" is stated, there was no need for expert ground truth establishment for a test set. This type of information would be relevant for AI/ML device clearances where human expert annotation is part of the ground truth creation.

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

Not applicable, as no clinical test set requiring adjudication of ground truth was described.

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. The document explicitly states "Clinical data: Not applicable." MRMC studies are typically for evaluating the impact of AI algorithms on human reader performance, which is not the scope of this 510(k) submission for a patient monitor.

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

The device is a patient monitor with various physiological parameter measurements and alarms. Its "performance" is inherent in its ability to accurately measure these parameters and detect events like arrhythmias. The non-clinical bench testing demonstrated its standalone performance by showing compliance with relevant standards (e.g., IEC 60601-2-27 for ECG, IEC 80601-2-30 for NIBP, ISO 80601-2-61 for pulse oximeter).

While not explicitly called "standalone algorithm performance" in the AI/ML sense, the "Performance testing-Bench" section (page 8) confirms that "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." This demonstrates the device's functional performance in isolation.

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

For the non-clinical bench testing, the "ground truth" would be established by:

• Reference instruments or calibrated signals (e.g., precise electrical signals for ECG, known pressure values for NIBP, calibrated gas mixtures for CO2).
• Standardized measurement protocols defined by the cited IEC/ISO standards.
• Accuracy specifications found within those standards or the device's own specifications.

There's no mention of expert consensus, pathology, or outcomes data as "ground truth" because this is a measurement and alarm device, not a diagnostic imaging AI algorithm, and no clinical data was used for validation in this submission.

8. The sample size for the training set

Not applicable. This device is a patient monitor, not an AI/ML algorithm that undergoes a distinct training phase on a dataset. The underlying algorithms for parameter measurement (e.g., NIBP oscillometric algorithm, arrhythmia detection) are established engineering designs, not typically "trained" in the machine learning sense with large datasets.

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

Not applicable, as there is no specific "training set" for an AI/ML algorithm described. The "ground truth" for the development and calibration of the monitor's measurement algorithms would have been established through engineering principles, laboratory testing with calibrated instruments, and referencing physiological models and data, but this is part of the device's fundamental design and not a separate "training set" as understood in current AI/ML contexts.

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The monitors are intended to be used for monitoring, storing, 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 LM-8 monitor monitors parameters such as ECG (3-lead, 12-lead selectable), Respiration (RESP), Functional arterial oxygen saturation (SpO2), Invasive or noninvasive blood pressure (dual-IBP, NIBP), Temperature (dual-TEMP), Expired CO2 and Quick Temperature (Quick TEMP).

The LM-10 monitor monitors parameters such as ECG (3-lead, 12-lead selectable), Respiration (RESP), Functional arterial oxygen saturation (SpO2), Invasive or noninvasive blood pressure (2/4 channels IBP, NIBP), Cardiac Output (C.O.), Temperature (dual-TEMP) and Expired CO2.

The LM-12 monitor monitors parameters such as ECG (3-lead, 12-lead selectable), Respiration (RESP), Functional arterial oxygen saturation (SpO2), Invasive or noninvasive blood pressure (2/4 chamels IBP, NIBP), Cardiac Output (C.O.), Temperature (dual-TEMP), Expired CO2 and Anesthetic gas (AG).

The LM-15 monitors parameters such as ECG (3-lead, 5-lead, 12-lead selectable), Respiration (RESP), Functional arterial oxygen saturation (SpO2), Invasive or noninvasive blood pressure (2/4 channels IBP, NIBP), Cardiac Output (C.O.), Temperature (dual-TEMP), Expired CO2 and Anesthetic gas (AG).

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

The monitors are not intended for MRI environments.

LM-8, LM-10, LM-12 and LM-15, patient monitor integrates parameter measuring modules, display and recorder in one device, featuring in compactness, lightweight and portability. Replaceable built-in battery facilitates patient transport. Large high-resolution display provides clear view of 10 waveforms and full monitoring parameters. Patient Monitor can monitor vital signal such as ECG, respiration (RESP), non-invasive blood pressure (NIBP), oxygen saturation of the blood (SpO2), temperature (TEMP), invasive blood pressure (IBP), cardiac output (C.O.), CO2 and anesthetic gas (AG). Those signals are digitized, processed and examined for alarm conditions, after that presents all those information on the color TFT display. The monitor also provides advantageous operating control for the user.

The provided text is an FDA 510(k) summary for a Patient Monitor (models LM-8, LM-10, LM-12, LM-15). It primarily focuses on demonstrating substantial equivalence to predicate devices through technical comparisons and compliance with general performance standards.

However, the document does not contain the detailed information necessary to answer all aspects of your request regarding acceptance criteria and a study proving the device meets those criteria in the context of an AI/algorithm-driven medical device performance study.

Specifically, the document lacks:

• A explicit table of acceptance criteria for algorithm performance (e.g., sensitivity, specificity, F1-score for arrhythmia detection).
• Detailed results of a study demonstrating the device meets specific performance criteria for arrhythmia detection or ST-segment analysis (beyond basic functional checks).
• Information on sample size for test sets directly related to algorithm performance (as opposed to overall device safety/functionality).
• Data provenance, number of experts for ground truth, adjudication methods, or MRMC studies, which are typical for AI/ML device evaluations.
• Training set details for any AI/ML components.

The "Performance data" section refers to "Clinical data" for validation, but these appear to be general functional validation tests on physiological parameters (ECG, RESP, SpO2, NIBP, etc.) to ensure the monitors function as intended, rather than a specific study to validate the performance of the arrhythmia detection and ST Segment analysis algorithm against clinical ground truth. The statement "The clinical data demonstrate that the subject devices perform comparably to the predicate device that is currently marketed for the same intended use" is a high-level conclusion without supporting details beyond the comparative features table.

Based on the provided text, here's what can be extracted and what is missing:

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

The document doesn't provide a specific table of quantitative acceptance criteria for the arrhythmia detection and ST segment analysis algorithm (e.g., sensitivity, specificity thresholds) and corresponding reported performance metrics. It lists general parameters and their measurement ranges, which are functional specifications, not performance criteria for an arrhythmia detection algorithm.

General device functional specifications (from comparison table, not acceptance criteria for algorithm):

The document notes that "The arrhythmia detection and ST Segment analysis are intended for adult and pediatric patients" and that "Clinical tests were performed on the LM-8, LM-12 and LM-15 monitors to validate their performance in terms of ECG...". However, it does not specify what constituted "validation" for these particular algorithmic features or what the performance metrics were. The "Conclusion" states: "The clinical data demonstrate that the subject devices perform comparably to the predicate device that is currently marketed for the same intended use." This is the reported device performance for these features: "comparable to predicate."

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

• Sample Size for Test Set: Not specified for the "Clinical data" related to ECG/arrhythmia/ST validation. The statement is general: "Clinical tests were performed on the LM-8, LM-12 and LM-15 monitors..."
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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 specified. The document does not describe how ground truth for arrhythmia or ST segment analysis was established for clinical testing.

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

• Not specified.

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 is not mentioned. This device is a monitor, not an AI-assisted diagnostic tool for interpretation by a human reader in the typical sense of an MRMC study for imaging. It provides "arrhythmia detection and ST Segment analysis" algorithms directly to the user.

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

• The "Clinical data" statement indicates the monitors' performance was validated. Given the nature of a patient monitor, the arrhythmia and ST segment analysis would inherently be "standalone" algorithmic functions integrated into the device, providing automated analysis. However, specific performance metrics (like sensitivity/specificity of the algorithm itself) from this standalone evaluation are not presented.

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

• Not specified. It is generally implied that such devices are validated against accepted physiological measurement standards and potentially manually confirmed ECG interpretations, but the document does not detail this for the arrhythmia/ST segment analysis.

8. The sample size for the training set:

• Not applicable/Not specified. The document does not indicate that the arrhythmia detection or ST segment analysis algorithms utilize machine learning or require a "training set" in the sense of AI/ML development. It's likely these are based on established rule-based or signal processing algorithms, not learned from data.

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

• Not applicable. (See #8)

In summary, this 510(k) submission successfully demonstrates substantial equivalence through technical specifications, comparisons to predicate devices, and compliance with general safety and performance standards (e.g., electrical safety, EMC, biocompatibility, software verification/validation). However, it does not detail a specific performance study for its arrhythmia detection and ST segment analysis algorithms in a way that typically applies to AI/ML clearance, which would include explicit acceptance criteria, detailed test set characteristics, and ground truth methodologies. The "clinical data" section is very high-level and only states comparability to predicate devices.

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The monitors are intended to be used for monitoring, storing, 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 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.), anesthetic gas (AG), bispectral index (BIS), respiration mechanice cardiography (ICG).

BIS is intended for use on adult and pediatric patients.

ICG monitoring is intended for use on adults only.

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

The monitors are additionally intended for use during patient transport inside hospitals.

The monitors are not intended for MRI environments.

LMPLUS series Patient Monitor including LMPLUS-12, LMPLUS-15 and LMPLUS-17 which 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 LMPLUS series Patient Monitor realize the monitoring of physiological parameters by configuration with different parameter modules which include SpO2 (pulse oxygen saturation, pulse rate and SpO2 plethysmogram) with EDAN SpO2 module or Nellcor SPO2 module, NIBP (systolic pressure, diastolic pressure and pulse rate), TEMP, ECG, RESP (respiration), CO2, IBP, C.O. and AG (anesthetic gas), RM (respiratory mechanics), BIS (bispectral index) and ICG (impedance cardiography).

The above is the maximum configuration for LMPLUS series Patient Monitor, the user may select different monitoring parameters in according with their requirements.

LMPLUS-12 configures with 12.1-inch color TFT touch screen, LMPLUS-15 and LMPLUS-17 with same screen except different sizes 15-inch and 17-inch separately. Three models are all build-in Lithium-ion battery, support software upgrade online and networking.

The provided document focuses on the 510(k) summary for the CAF Medical Solutions Inc. Patient Monitor (models LMPLUS-12, LMPLUS-15, and LMPLUS-17), demonstrating its substantial equivalence to a predicate device (Edan Instruments, Inc. Patient Monitor, models elite V5, elite V6, and elite V8). The document primarily presents a feature-by-feature comparison and non-clinical performance data, with a brief mention of clinical tests.

Therefore, the information regarding acceptance criteria and the study proving the device meets them will be limited to what is explicitly stated in the document or can be inferred from the provided test types and standards. A full, detailed study proving acceptance criteria for specific performance metrics (like sensitivity, specificity, or inter-reader variability for an AI model) is not present in this type of regulatory submission document, which focuses on substantial equivalence to a predicate rather than a novel AI algorithm.

Based on the provided document, here's what can be extracted and inferred regarding performance and validation:

The document indicates that the device's performance was evaluated against various recognized standards for patient monitors, which inherently define acceptable performance ranges for each physiological parameter. The study primarily aims to show that the new device meets these established standards and performs comparably to the predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a "table of acceptance criteria" in the format of a pre-defined threshold that the device must meet for a specific study's outcome (e.g., "sensitivity > X%"). Instead, it shows a feature-by-feature comparison to a predicate device, including various performance specifications (e.g., accuracy, measurement range) that are in line with industry standards for patient monitors. The "Acceptance Criteria" are implicitly defined by the parameters and accuracy/range specifications of the predicate device and the relevant IEC/ISO standards the device claims compliance with. The "Reported Device Performance" for the subject device (LMPLUS models) is stated to be "Same" as the predicate device across all listed specifications.

Here's an illustrative table based on the provided comparison, highlighting key physiological parameters:

Note: The table above is a summary of just a few representative parameters from the much larger comparison table (Table 1) in the document. The general "Acceptance Criteria" for all listed parameters are the identical specifications of the predicate device.

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

The document states: "Clinical tests were performed on the LMPLUS 12, LMPLUS 15 and LMPLUS 17 monitors to validate their performance in terms of noninvasive blood pressure (NIBP) and SpO2 accuracy."

However, the specific sample sizes for these clinical tests (number of patients, number of measurements) and the data provenance (e.g., country of origin, retrospective or prospective nature) are not detailed in this 510(k) summary. This level of detail would typically be found in the full test report, which is referenced but not included.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document mentions "clinical tests" for NIBP and SpO2 accuracy. For these types of physiological measurements, the ground truth is typically established by:

• Reference Devices: Using highly accurate, calibrated reference measurement devices.
• Clinical Protocols: Adhering to established clinical protocols for data collection (e.g., for NIBP, a protocol like ISO 81060-2 which often involves comparisons to invasive arterial measurements or calibrated sphygmomanometers by trained healthcare professionals).

There is no mention of human experts being used to establish "ground truth" in the context of interpretation (e.g., radiologists for imaging, unlike an AI algorithm for image analysis). The device measures physiological parameters, and accuracy is validated against established, objective measurement techniques, not expert consensus on qualitative data. Therefore, the concept of "experts establishing ground truth" as it applies to subjective judgments or interpretations (which is common for AI/ML in imaging) is not directly applicable here.

4. Adjudication Method for the Test Set

Given that the clinical tests mentioned are for quantitative physiological parameter accuracy (NIBP and SpO2), adjudication methods like 2+1 or 3+1 (common in studies involving multiple readers for subjective assessments) are not applicable. Accuracy is determined by comparing device readings to a reference standard, not by expert consensus on interpretations.

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

No MRMC study was mentioned or implied.
This device is a patient monitor, not an AI-assisted diagnostic tool that would involve human readers interpreting cases. Therefore, a study to measure how much human readers improve with AI assistance is not relevant to this type of device and was not performed.

6. Standalone Performance (Algorithm Only without Human-in-the-Loop)

The document does not describe the device as having a distinct "algorithm" component for analysis that would be evaluated in isolation. It's a physiological monitoring device. Its accuracy in measuring parameters like NIBP and SpO2 is its "standalone performance." The clinical tests mentioned (for NIBP and SpO2 accuracy) would indeed be an assessment of the device's ability to accurately measure these parameters independently, which is effectively its standalone performance. The results are implied by the statement "the subject devices perform comparably to the predicate device."

7. Type of Ground Truth Used

For the clinical tests (NIBP and SpO2 accuracy), the ground truth would be established through:

• Reference Standard Measurements: Using a highly accurate and validated reference device (e.g., an invasive arterial line for NIBP, or a co-oximeter for SpO2) or an established standardized method as per relevant ISO standards (e.g., ISO 81060-2 for NIBP).
• Physiological Data: Direct physiological measurements, not pathology, outcomes data, or expert consensus on subjective interpretations.

8. Sample Size for the Training Set

This document describes a conventional patient monitor, not a medical device that utilizes AI/ML requiring a distinct "training set" of data to learn from. Therefore, there is no mention of a training set or its sample size. The device's algorithms for processing physiological signals are based on established engineering principles and signal processing, not machine learning from a large training dataset.

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

As there is no training set for this type of device, this question is not applicable.

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The Patient Monitoring Cables are intended to be used with ECG, EKG, Spo2 and BP monitoring devices. The Patient Monitoring Cables are used to connect electrodes, catheters, and/or sensors placed at appropriate sites on the patient to a monitoring device for general monitoring and/or diagnostic evaluation by heath care professional.

Patient Monitoring Cables are the replacements for similar cables manufactured by Original Equipment Manufacturers (OEM) for their respective monitors. It is a non-patient-contact, insulated, shielded, electrical cord with a connector (plug) at both ends designed to transmit electrical power and/or signal (data) between medical devices (e.g., to connect ECG electrodes, SpO2 sensor, IBP transducer to a patient monitor). It is not intended to connect to the mains (i.e., not a mains power cable), does not generate any type of power and/or signal, and has no additional non-electrical conducting or processing functionality.

This document does not contain an AI/ML device study. Instead, it is a 510(k) premarket notification for Patient Monitoring Cables. The filing asserts substantial equivalence to predicate devices (K082959 and K142489) based on the fact that the subject device's cables are "exactly the same" and "identical" to the predicate devices.

Therefore, the requested information about acceptance criteria, device performance, sample sizes, expert ground truth, adjudication methods, MRMC studies, standalone performance, and training set details for an AI/ML device study is not applicable to this submission.

The document primarily discusses:

• Intended Use: Patient Monitoring Cables are used with ECG, EKG, SpO2, and BP monitoring devices to connect electrodes, catheters, and/or sensors to a monitoring device for general monitoring and/or diagnostic evaluation by healthcare professionals.
• Comparison to Predicate Devices: The submission states that the subject device is "exactly the same" as the predicate devices in terms of intended use, design/appearance, cable length, material, usage (reusable and disposable), sterility, and conformance to standards (IEC 60601-1, EC53, ISO 10993-5, -10).
• Non-clinical Test Data: The device meets recognized standards for safety (IEC 60601-1), performance (ANSI AAMI EC53), and biocompatibility (ISO 10993-1, -5, -10). Biocompatibility tests for cytotoxicity, sensitization, and skin irritation were conducted on the proposed device, showing no adverse effects.
• Clinical Test Data: No clinical testing was deemed necessary because the subject and predicate devices are stated to be "exactly the same."

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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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Patient Monitoring Cables are intended to be used to connect sensors, placed at appropriate sites on the patient to a monitoring device for general monitoring by health care professional.

Patient Monitoring Cables (SpO2 Extension Cable, CR002-5301) is comprised of Plug, Cable/Leadwires and Connector. It's intended to plug into monitoring device and connect with SpO2 sensor, for transmitting signals which generated by SpO2 sensor to monitoring device.

The provided text describes the 510(k) premarket notification for a medical device: "Patient Monitoring Cable (SpO2 Extension Cable)" (K192404).

Based on the content, this document does not describe a study involving an AI/ML algorithm or human readers for diagnostic purposes. Instead, it details the substantial equivalence review for a physical medical cable. Therefore, many of the requested points regarding AI/ML device acceptance criteria and study designs are not applicable.

However, I can extract the relevant information regarding the acceptance criteria and the "study" (referring to the testing performed for this physical device) that proves it meets those criteria:

Device: Patient Monitoring Cable (SpO2 Extension Cable)
Purpose: Connects SpO2 sensors to a monitoring device to transmit signals.

Here's the breakdown based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance (as applicable for a physical cable):

2. Sample Size Used for the Test Set and Data Provenance:
This information is not explicitly stated in the document. The testing performed would involve internal lab bench testing on manufactured samples of the cable, not a "test set" in the context of clinical data or AI validation. Data provenance (country of origin, retrospective/prospective) is not applicable here as it's a physical device and not based on clinical data collection for performance evaluation.

3. Number of Experts Used to Establish Ground Truth for Test Set and Their Qualifications:
Not applicable. Ground truth for a physical cable's performance (e.g., electrical safety, material biocompatibility) is established by adhering to recognized international and national standards and performing lab tests. It does not involve expert consensus in the way an AI diagnostic algorithm would.

4. Adjudication Method for the Test Set:
Not applicable. There is no "adjudication method" in the context of a physical cable's testing for electrical and material performance.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:
No. This is not an AI/ML diagnostic device, so MRMC studies are not relevant.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:
Not applicable. This is not an AI/ML algorithm.

7. The Type of Ground Truth Used:
For this device, "ground truth" refers to compliance with recognized engineering and medical device standards (e.g., IEC 60601-1, AAMI EC53, ISO 80601-2-61 for electrical performance and signal integrity; ISO 10993 for biocompatibility) and physical testing results demonstrating adherence to these standards.

8. The Sample Size for the Training Set:
Not applicable. This device does not involve machine learning algorithms, so there is no training set.

9. How the Ground Truth for the Training Set Was Established:
Not applicable. There is no training set.

In summary, the provided document details the regulatory clearance process for a physical medical cable, demonstrating its substantial equivalence to predicate devices through adherence to established safety, performance, and biocompatibility standards via lab bench testing. It does not involve any AI/ML components or associated study methodologies like MRMC studies or ground truth establishment by clinical experts for diagnostic tasks.

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The BeneVision N12N15/N17/N19/N22 patient monitors are intended for monitoring, displaying, storing, storing, alarming, and transferring of multiple physiological parameters including ECG (3-lead, 5-lead or 12-lead selectable, Arrhythmia Detection, ST Segment Analysis, QT Analysis, and Heart Rate (HR)), Respiration Rate (Resp), Temperature (Temp), Pulse Oxygen Saturation (SpO2), Pulse Rate (PR), Non-invasive Blood Pressure (NIBP), Invasive Blood Pressure(IBP), Pulmonary Artery Wedge Pressure (PAWP), Cardiac Output (C.O.), Continuous Cardiac Output (CCO), Mixed/Central Venous Oxygen Saturation (SvO2/ScvO2), Carbon Dioxide (CO2), Oxygen (O2), Anesthetic Gas (AG), Impedance Cardiograph (ICG), Bispectral Index (BIS), Respiration Mechanics (RM), Neuromuscular Transmission Monitoring (NMT), Electroencephalograph (EEG), and Regional Oxygen Saturation (rSO2). The system also provides an interpretation of resting 12-lead ECG.

All the parameters can be monitored on single adult, pediatric, and neonatal patients except for the following: · The arrhythmia detection, BIS, RM, CCO, SvO2/ScvO2, PAWP, and NMT monitoring are intended for adult and pediatric patients only:

· C.O. monitoring is intended for adult patients only;

• ICG monitoring is intended for only adult patients who meet the following requirements: height: 122 to 229cm, weight: 30 to 155kg.

· rSO2 monitoring is intended for use in individuals greater than 2.5kg.

The monitors are to be used in healthcare facilities by clinical professionals or under their guidance. They should only be used by persons who have received adequate training in their use. The BeneVision N12/N15/N17/N19/N22 monitors are not intended for helicopter transport. hospital ambulance, or home use.

BeneVision N1 Patient Monitor:

The BeneVision N1 Patient Monitor is intended for monitoring, reviewing, storing , alarming, and transferring of multiple physiological parameters including ECG (3-lead, 5-lead or 12-lead selectable, Arrhythmia Detection, ST Segment Analysis, and Heart Rate (HR)), Respiration (Resp), Temperature (Temp), Pulse Oxygen Saturation (SpO2), Pulse Rate (PR), Non-invasive Blood Pressure (NIBP), Invasive Blood Pressure (IBP) , Pulmonary Artery Wedge Pressure (PAWP), Carbon Dioxide (CO2) and Oxygen (O2). The system also provides an interpretation of resting 12-lead ECG.

All the parameters can be monitored on single adult, pediatric, and neonatal patients except for the following:

• The arrhythmia detection and PAWP is intended for adult and pediatric patients only

The BeneVision N1 monitor is to be used in healthcare facilities. It can also be used during patient transport inside and outside of the hospital environment. It should be used by clinical professionals or under their guidance. It should only be used by persons who have received adequate training in its use. It is not intended for home use.

The subject BeneVision N Series Patient Monitors includes six monitors: BeneVision N12 Patient Monitor, BeneVision N15 Patient Monitor, BeneVision N17 Patient Monitor, BeneVision N19 Patient Monitor, BeneVision N22 Patient Monitor, BeneVision N1 Patient Monitor. Mindray's BeneVision N Series Patient Monitors provide a flexible software and hardware platform to meet the clinical needs of patient monitoring.

The provided text is a 510(k) Summary for the Mindray BeneVision N Series Patient Monitors. It mainly focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study with acceptance criteria and specific performance metrics in the format requested.

The document discusses functional and system level testing and bench testing to validate performance and ensure specifications are met, but it does not provide a table of acceptance criteria alongside reported device performance data, nor does it detail the specifics of such a study.

Here's a breakdown of the requested information based on the provided text, highlighting what is not available:

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

   • Not Available. The document states that "functional and system level testing showed that the devices continue to meet specifications and the performance of the device is equivalent to the predicate" and "the subject device meets its accuracy specification." However, it does not provide the specific numerical acceptance criteria or the reported performance data in a table. It only lists measurement ranges and accuracies for various parameters (ECG, Respiration Rate, Temperature, SpO2, Pulse Rate, NIBP, IBP, C.O., CCO, SvO2/ScvO2, CO2, O2, AG, ICG, RM, NMT, EEG, rSO2) under the "Technological Comparison" tables (Table 2 and Table 3). These are presented as specifications rather than acceptance criteria for a study.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

   • Not Available. The document mentions "functional and system level testing" and "bench testing" but provides no details on the sample size of data used for these tests, nor its provenance (country, retrospective/prospective nature).

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 / Not Available. This device is a patient monitor, and its performance is assessed against established physical measurement standards and algorithms, not typically against expert interpretation of data for ground truth in the way an AI diagnostic algorithm might be (e.g., radiologists for medical images). The document does not mention any expert review process for determining ground truth.

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

   • Not Applicable / Not Available. As mentioned above, the assessment appears to be against physical specifications and consensus standards, not an expert-adjudicated 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

   • No, not done. The document does not mention an MRMC study or any assessment of human reader improvement with AI assistance. This device is a patient monitor providing physiological parameters, not an AI-assisted diagnostic tool for human interpretation.

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

   • N/A (Implicitly standalone for most parameters against physical standards). The performance evaluation described largely implies standalone testing against specifications and standards. For example, for ECG, SpO2, NIBP, etc., the device's internal algorithms process signals and provide readings, which are then assessed for accuracy. The "reporting" is of the device's ability to measure parameters correctly, not its ability to assist a human.

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

   • Physical Standards and Reference Devices/Methods. The ground truth for the measurements would be established by validated physical standards, calibrated equipment, and comparison to reference methods (e.g., for invasive blood pressure, cardiac output). The document implies validation against these types of established standards and specifications.

8. The sample size for the training set

   • Not Applicable / Not Available. The document does not describe the development or training of AI algorithms that would require a "training set" in the context of machine learning. The patient monitor's algorithms are based on established physiological signal processing, not deep learning models.

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

   • Not Applicable. See point 8.

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