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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 intended use of LE-12CH electrocardiograph is to acquire ECG signals from adult and pediatric patients (beginning at birth through 21 vears of age) through body surface FCG electrocardiograph is only intended to be used in hospitals or healthcare facilities by doctors and trained healtheare professionals. The cardiogram recorded by the electrocardiograph can help users to analyze and diagnose heart disease. However, the interpreted ECG with measurements and interpretive statements is offered to clinicians on an advisory basis only.

CAF Medical's LE-12CH electrocardiograph device is a twelve-channel electrocardiograph, which features a user-friendly design, a high-resolution 8.4-inch (800*600) TFT LCD touch screen, an alphanumeric keypad and an optimized workflow.

The LE-12CH electrocardiograph can acquire 12-channel waveforms simultaneously, and can also print the 12-channel electrocardiograph waveforms simultaneously, using a 216 mm wide thermal printer.

An advanced digital filtering technique has been used in the LE-12CH device, including a baseline anti-drift filter, AC noise filter (50/60 Hz), EMG filter and low-pass filter, which can help the user record the ECG more clearly.

The LE-12CH electrocardiograph works by sampling the ECG signal from the patient's skin surface using special electrodes. This signal is then amplified and sampled in A/D format by the ECG board. The ECG board transfers the data to the system's MCU via UART. The MCU filters the data, sends it to the LCD, prints the waveforms on paper and stores the data in memory. This log can be stored in flash memory or sent to the PC via Ethernet, WIFI or RS232. During the examination, no substances are given to or removed from the patient. The LE-12CH electrocardiograph supports cardiac stress testing.

Here's an analysis of the provided text regarding the acceptance criteria and study for the LE-12CH device:

Based on the provided FDA 510(k) summary for the LE-12CH Electrocardiograph, the submission primarily relies on demonstrating substantial equivalence to a predicate device (K171942. Electrocardiograph with models SE-12, SE-12 Express, SE-1200 and SE-1200 Express. Edan Instruments, Inc.). This means the acceptance criteria are largely aligned with proving that the LE-12CH performs as safely and effectively as the predicate device based on common industry standards and performance specifications.

The document does not describe a specific clinical study (like an MRMC study or standalone algorithm performance study with a test set and ground truth) in the way one might expect for a novel AI/ML-driven diagnostic device. Instead, it details non-clinical testing to ensure compliance with relevant performance and safety standards, and then performs a comparison to the predicate device's specifications.

Therefore, many of the requested fields regarding detailed study methodology (sample size, data provenance, expert consensus, adjudication, effect size) are not applicable in the context of this 510(k) submission as it focuses on demonstrating equivalence through non-clinical means and direct comparison of specifications.

Here's the breakdown of the information that can be extracted or inferred:

Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the safety and performance standards the device was tested against and by the specifications matching the predicate device. The "reported device performance" is essentially that it meets these standards and matches the predicate.

Study Details (as per request, with N/A for non-applicable fields)

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

   • Not Applicable. The submission does not describe a clinical "test set" in the context of an algorithm's performance evaluation with patient data. It relies on non-clinical testing against standards and direct comparison of technical specifications to a predicate device.

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

   • Not Applicable. As no clinical test set requiring expert ground truth was documented, this information is not provided. The device includes "interpreted ECG with measurements and interpretive statements... Offered to clinicians on an advisory basis only," indicating that the device's interpretation is assistive and not designed to replace expert diagnosis directly without human review.

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

   • Not Applicable. No clinical test set with human adjudication was described.

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. The document explicitly states: "Clinical testing was not required to demonstrate the substantial equivalence of the subject device to the predicate device." Therefore, no MRMC study was conducted or reported.

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

   • No, not in the sense of a clinical performance study. The device itself is an electrocardiograph that acquires and processes ECG signals, providing interpreted ECG with measurements and interpretive statements. This processing is inherent to the device's function, but its "standalone performance" isn't evaluated as a diagnostic algorithm through a dedicated clinical study with ground truth. Its performance is demonstrated by meeting IEC standards and matching the predicate's technical specifications. The interpretations are explicitly "advisory only."

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

   • Not Applicable. For the purpose of this 510(k), ground truth for specific disease diagnoses from patient data was not established or used to evaluate the device's interpretive statements in a clinical performance study. The "ground truth" here is compliance with established engineering and safety standards (e.g., accuracy of heart rate detection against known input signals, filter performance) and the technical specifications of a legally marketed predicate device.

7. The sample size for the training set:

   • Not Applicable. The document provides no information on a training set, as it does not describe a machine learning algorithm that requires training data. The device's "advanced digital filtering technique" is a more traditional signal processing methodology, not explicitly stated as an AI/ML algorithm requiring a training set for its interpretive functionality.

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

   • Not Applicable. As there is no described training set, this information is not provided.

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