(242 days)
Monitoring of the following parameters and their relative of relative of relative changes in fluid volume in adult patients:
- Bioelectrical Impedance
- ECG Amplitude
- PPG Amplitude
- Skin Temperature
The Zynex Monitoring System, Model CM-1600, simultaneously monitors various parameters of a patient's body. These parameters include Bioelectrical Impedance, Electrocardiogram (ECG) Amplitude, Photoplethysmography (PPG) Amplitude, and Skin Temperature. A Relative Index value is calculated as a combination of the changes in these parameters and is represented by a single number. The CM-1600 System is comprised of two (2) subsystems, the Wearable and the Monitor. The CM-1600 Wearable is designed and developed by Zynex Monitoring Solutions, and it collects physiological parameters and transmits those parameters to the CM-1600 Monitor via wireless communication. The CM-1600 Monitor is a Zynex-branded, third-party all-in-one medical grade tablet.
The provided text is a 510(k) summary for the Zynex Monitoring System, Model CM-1600. It describes the device, its intended use, a comparison to a predicate device, and performance data provided in support of substantial equivalence.
However, the provided document does not contain information regarding an AI/ML device, expert ground truth establishment, or specific details on studies proving the device meets acceptance criteria related to AI/ML performance metrics (like sensitivity, specificity, or AUC). The device described, the Zynex Monitoring System, Model CM-1600, monitors physiological parameters such as Bioelectrical Impedance, ECG Amplitude, PPG Amplitude, and Skin Temperature and calculates a "Relative Index" as a combination of changes in these parameters. This appears to be a traditional medical monitoring device, not a device incorporating artificial intelligence or machine learning for diagnostic or interpretive purposes.
Therefore, many of the requested points, such as AI/ML acceptance criteria, sample size for AI/ML test sets, expert adjudication methods, MRMC studies, or ground truth establishment for AI/ML models, cannot be answered from the provided text because the text does not describe an AI/ML device or studies typically associated with its validation.
The available information related to device performance and validation is summarized below:
Device: Zynex Monitoring System, Model CM-1600
Intended Use: Monitoring of Bioelectrical Impedance, ECG Amplitude, PPG Amplitude, and Skin Temperature, and their relative changes in fluid volume in adult patients. It provides numerical values to aid diagnosis by a physician; it is the physician's responsibility to make proper judgments based on these measurements.
1. A table of acceptance criteria and the reported device performance
The document does not specify "acceptance criteria" in terms of numerical performance thresholds (like accuracy percentages or error rates) for the physiological measurements themselves. Instead, it relies on demonstrating compliance with recognized standards and substantial equivalence to a predicate device through various performance tests.
| Test Name | Testing Criteria | Reported Device Performance (Pass/Fail) |
|---|---|---|
| Biocompatibility | ISO 10993 | Pass |
| Device Safety, Electrical Safety, and Electromagnetic Compatibility (EMC) | IEC 60601-1 and 60601-1-2 | Pass |
| Wireless Coexistence Testing | AAAMI-TIR63 | Pass |
| Performance Testing | V&V/Predicate Testing | Pass |
Note: The document states "Animal Testing: No Testing Required" and "Clinical Testing: No Testing Required", indicating these were not part of the performance data submitted for this 510(k) clearance.
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
The document does not detail specific sample sizes for "test sets" in the way one would for an AI/ML algorithm. The performance testing (V&V/Predicate Testing) demonstrates that the device meets applicable requirements and is substantially equivalent to the predicate device. This typically involves engineering and functional validation, not data-driven performance assessments on patient cohorts for diagnostic accuracy. No information is provided regarding data provenance (country of origin, retrospective/prospective). Since clinical testing was not required/conducted, there isn't patient-specific data being referred to in this context.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. The document does not describe the establishment of a "ground truth" by experts for diagnostic purposes or for validating an AI/ML model. The device provides raw physiological parameters and a "Relative Index," which are direct measurements or calculations, not interpretations requiring expert consensus for ground truth.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. There is no mention of a test set requiring adjudication in the context of diagnostic or interpretive performance.
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 is not an AI-assisted diagnostic device, and no MRMC study was mentioned or conducted.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. The device provides physiological measurements and a calculated index. There isn't an "algorithm only" component that generates a diagnosis or interpretation independent of human interpretation for which standalone performance would be relevant in the context of AI/ML.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
Not applicable. For this type of physiological monitoring device, "ground truth" would relate to the accuracy of its direct measurements (e.g., how accurately it measures bioelectrical impedance or temperature) against a calibrated standard, not against expert consensus on a diagnosis, pathology, or outcomes data, as these are beyond the scope of a monitoring device providing raw or relative change data.
8. The sample size for the training set
Not applicable. The document does not describe the use of a "training set" for an AI/ML model for this device.
9. How the ground truth for the training set was established
Not applicable. As no training set for an AI/ML model is mentioned, the method for establishing its ground truth is not discussed.
§ 870.2770 Impedance plethysmograph.
(a)
Identification. An impedance plethysmograph is a device used to estimate peripheral blood flow by measuring electrical impedance changes in a region of the body such as the arms and legs.(b)
Classification. Class II (special controls). The device, when it is a body composition analyzer which is not intended to diagnose or treat any medical condition, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 870.9.