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510(k) Data Aggregation
(84 days)
The non-invasive CerOx 3215FO monitor is intended for use as an adjunct monitor of microcirculation blood flow in tissue. The CerOx 3215FO monitor is intended for monitoring of adults. The prospective clinical value of data from the CerOx 3215FO monitor has not been demonstrated in disease states. The CerOx 3215FO monitor should not be used as the sole basis for diagnosis or therapy.
The CerOx Model 3215FO uses the well-established principles of near infrared spectroscopy (NIRS) and flowmetry to monitor blood flow in tissue.
The provided text describes the CerOx Model 3215FO as being identical to its predicate device, CerOx Model 3210F, technically and operationally. Therefore, there are no specific acceptance criteria or a dedicated study for the CerOx Model 3215FO described in this document. Instead, the submission relies on the established safety and effectiveness of the predicate device.
The "study" described is a comparison study demonstrating equivalence to the predicate device, not an independent assessment against performance criteria.
Here's the information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (for Predicate Device) | Reported Device Performance (CerOx Model 3215FO) |
|---|---|
| Equivalence to CerOx Model 3210F in optical output power of lasers | Optical output power of lasers found to be equivalent to CerOx 3210F |
| Equivalence to CerOx Model 3210F in acoustic output power of ultrasound transducers | Acoustic output power of ultrasound transducers found to be equivalent to CerOx 3210F |
| Equivalence to CerOx Model 3210F in amplitude of light signals detected | Amplitude of light signals detected on a laboratory setup found to be equivalent to CerOx 3210F |
| Same intended use and indications for use as CerOx 3210F | Same intended use and indications for use as CerOx 3210F |
2. Sample size used for the test set and the data provenance
The document does not specify a "test set" in the traditional sense of patient data. The "tests performed" were laboratory-based comparisons of physical characteristics (optical output power, acoustic output power, amplitude of light signals) between the new device and the predicate device. Therefore, there's no information on a sample size of patient data or its provenance.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. The "ground truth" here is the performance of the predicate device, CerOx Model 3210F, as assessed through physical measurements, not clinical expert interpretation.
4. Adjudication method for the test set
Not applicable. The "tests performed" were technical comparisons, not clinical evaluations requiring adjudication.
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 blood flow monitor, not an AI-assisted diagnostic imaging device that involves human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This refers to a device's standalone performance. While the device does generate data independently, the study described is a comparison of physical characteristics, not a standalone clinical performance study. The device's function is to "monitor blood flow in tissue," which implies generating readings for a human operator.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The "ground truth" used for this submission is the established performance and technical specifications of the predicate device, CerOx Model 3210F. The tests validated that the CerOx Model 3215FO matched these established metrics.
8. The sample size for the training set
Not applicable. This describes a medical device, not an AI/ML algorithm that requires a training set.
9. How the ground truth for the training set was established
Not applicable. This describes a medical device, not an AI/ML algorithm that requires a training set.
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