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510(k) Data Aggregation
(34 days)
OLYMPIC CFM 6000
The Olympic CFM 6000 is intended to be used by a variety of clinicians to acquire and utilize EEG signals, when used in conjunction with other clinical data, in intensive care areas, Operating Room, Emergency Room, and clinical research lab:
- to monitor the state of the brain -
- for determination of, and long-term monitoring of, the neurological status of a patients that may have suffered an hypoxic-ischemic event.
- for monitoring of neurological status to assist in the clinical management and treatment of the patient by observing how the treatment affects the neurological status as shown by the CFM.
- to assist in the prediction of neurological outcome -
- to monitor and record frequency and intensity of seizures to assist in management of anti-convulsive therapy.
- to assist in the prediction of severity of Hypoxic-Ischemic Encephalopathy ー and long-term outcome in infants who have suffered an hypoxic-ischemic event.
The Olympic CFM 6000 consists of two main components: A Data Acquisition Module and a Main System module. The Data Acquisition Module is used to connect the patient electrode leads, amplify the signal, and perform the analog-to-digital conversion. The Main system accepts data from the amplifier. processes and stores the signal. and displays the CFM, impedance, and EEG traces and provides the user interface for control of the device.
The Olympic CFM 6000 aims to demonstrate clinical equivalence to its predicate device, the Olympic Medical Lectromed Cerebral Function Monitor (K020335), by reproducing the analog signal processing and display digitally.
1. Acceptance Criteria and Reported Device Performance
The provided text does not explicitly state numerical acceptance criteria for the device's performance. However, the overarching acceptance criterion is that the output of the CFM 6000 is "clinically identical" to that of the predicate device.
Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criterion | Reported Device Performance |
|---|---|
| Clinical identity of output to predicate device | Comparison tests using both bench and clinical input data demonstrated that the output of the CFM 6000 is "clinically identical" to that of the predicate device. |
2. Sample Size Used for the Test Set and Data Provenance
The document mentions "clinical input data" but does not specify the sample size or the provenance of this data (e.g., country of origin, retrospective or prospective nature).
3. Number of Experts and Their Qualifications for Ground Truth Establishment
The document does not provide information on the number of experts used to establish ground truth for the test set or their qualifications.
4. Adjudication Method for the Test Set
The document does not describe any adjudication method used for the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No information is provided about a multi-reader, multi-case (MRMC) comparative effectiveness study, nor any effect size regarding human reader improvement with or without AI assistance. This device is an EEG monitor, not an AI-assisted diagnostic tool.
6. Standalone Performance Study
The study described is focused on the comparison of the Olympic CFM 6000 against its predicate device to demonstrate clinical identity, implying it is assessing the standalone performance of the new device relative to a known standard. However, the term "standalone (i.e. algorithm only without human-in-the loop performance)" as it relates to AI is not applicable here, as this is a medical device, not an AI algorithm in the contemporary sense. The device is designed to acquire and process EEG signals for clinicians to interpret.
7. Type of Ground Truth Used
The ground truth for the comparison tests was the output of the predicate device, the Olympic Medical Lectromed Cerebral Function Monitor. The study aimed to show that the new device's output was "clinically identical" to this established predicate.
8. Sample Size for the Training Set
The document does not mention the use of a "training set" in the context of machine learning or AI. The development process described involves reproducing analog signal processing digitally, implying engineering and validation against the known behavior of the predicate device rather than training an algorithm on a distinct dataset.
9. How the Ground Truth for the Training Set Was Established
As no training set (in the AI context) is mentioned, there is no information on how its ground truth would have been established. The development likely involved direct engineering to mimic the existing analog CFM technology.
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