(328 days)
The Maternal Fetal Monitoring – Central Nurse System (MFM-CNS) is a clinical data managing software application and is indicated for antepartum monitoring of pregnant women in a healthcare setting.
The MFM-CNS is intended to manage perinatal monitoring data acquired from bedside monitors or manual input for viewing at the central nursing station. The system also produces an electronic medical record.
The MFM-CNS has display fields for the following obstetric data:
- patient demographics
- provider notes
- FHR
- uterine activity (via tocodynamometry or IUP)
- maternal heart rate
- SpO2
- NIBP
- respiratory rate
- temperature
- pulse rate
MFM-CNS is a software production who runs on PC station with Microsoft Windows XP operating system. MFM-CNS by connecting one central station with some bedside fetal / maternal monitors, carries out centralized monitoring management for many beds. It can monitor a pregnant woman during the whole parturition process, and all the monitoring information can be recorded, saved and printed, and alarm when the parameter exceed the user defined limit and poor signal quality. At the same time, the old records can be searched conveniently and quickly.
Device features:
- Connect maximum 32 bedside fetal / maternal monitors with Ethernet.
- Display FHR, UA, Maternal HR, PR, SpO2, NIBP, RR and TEMP numerics on the screen.
- The screen displays all the monitors simultaneously, or displays one monitor in full screen.
- 24-hour CTG, 1440-group Maternal Vital Sign data, 200-group NIBP data review.
- Print CTG report, Maternal Vital Sign list, NIBP list on the paper
- Audible & visible alarm when FHR or maternal vital sign exceeds the user defined limit or poor signal quality.
- Patient information, CTG, Maternal Vital Sign list and NIBP list can be saved, and burned into CDs for backup.
- Support user accessing control.
This 510(k) submission for the Edan Instruments, Inc. Central Monitoring System (MFM-CNS) does not contain detailed information about specific acceptance criteria and a study proving the device meets those criteria in the way a typical diagnostic or AI/ML device submission would. This is because the MFM-CNS is a "perinatal monitoring system and accessories," primarily a data management and display software for existing medical devices (bedside fetal/maternal monitors), not a device performing independent diagnostic analysis or making clinical decisions. Its "performance" is more related to its functionality, reliability, and accuracy in data handling and display, rather than diagnostic accuracy metrics like sensitivity or specificity.
Therefore, many of the requested categories (e.g., sample size for test set, number of experts for ground truth, MRMC study, standalone performance, ground truth type for training set) are not applicable or not provided in the document for this type of device.
Here's an analysis of the information provided, addressing the questions where possible:
Acceptance Criteria and Device Performance Study for Edan Instruments, Inc. Central Monitoring System (MFM-CNS)
1. Table of Acceptance Criteria and Reported Device Performance
The provided 510(k) summary does not outline specific numerical acceptance criteria (e.g., "accuracy must be >X%") or report quantitative device performance metrics (e.g., sensitivity, specificity, AUC). Instead, the "acceptance criteria" for this type of device are implicitly tied to its stated functionality, safety, and substantial equivalence to a predicate device.
The "reported device performance" is essentially a confirmation that the device functions as intended and meets safety and performance requirements through various testing methods.
| Acceptance Criteria (Inferred from testing types) | Reported Device Performance (Summary of Conclusion) |
|---|---|
| Functional Requirements: |
- Connects to max 32 bedside monitors.
- Displays specified FHR, UA, Maternal HR/PR/SpO2/NIBP/RR/TEMP numerics.
- Displays all monitors simultaneously or one in full screen.
- Provides 24-hour CTG, 1440-group Maternal Vital Sign data, 200-group NIBP data review.
- Prints CTG, Maternal Vital Sign, NIBP reports.
- Provides audible/visible alarms for out-of-limit parameters or poor signal.
- Saves patient info, CTG, Maternal Vital Sign, NIBP records, and can burn to CDs.
- Supports user access control. | Compliance Confirmed:
Verification and validation testing was done. The device demonstrated substantial equivalence to the predicate device, implying its functional, safety, and performance characteristics are comparable and acceptable for its intended use. The software performs its intended functions for data display, management, and archiving. |
| Software Quality: - Software testing conducted. | Compliance Confirmed:
Software testing was applied to the development of the MFM-CNS. |
| Risk Management: - Risk analysis conducted. | Compliance Confirmed:
Risk analysis was applied to the development of the MFM-CNS. |
| Safety: - Safety testing conducted. | Compliance Confirmed:
Safety testing was applied to the development of the MFM-CNS. |
| Performance (General): - Performance testing conducted. | Compliance Confirmed:
Performance testing was applied to the development of the MFM-CNS. |
2. Sample Size Used for the Test Set and Data Provenance
The document states "Verification and validation testing was done on MFM-CNS." However, it does not specify the sample size used for the test set (e.g., number of patient records, number of monitoring sessions).
Given the nature of the device as a data management system, the "test set" would likely constitute a series of simulated or real-world data streams from connected monitors, and various user interactions. The document does not provide details on the data provenance (e.g., country of origin, retrospective or prospective).
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
This information is not provided in the document. As a data management system, the "ground truth" for its performance would primarily involve confirming the accurate display and storage of data as received from the connected monitors, not an independent clinical diagnosis requiring expert consensus. Human experts may have been involved in verifying the UI/UX and data integrity, but their number and qualifications are not listed.
4. Adjudication Method for the Test Set
This information is not provided and is generally not applicable in the context of testing a data management and display system.
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 MRMC study was conducted or reported. This device is not an AI-assisted diagnostic tool; it is a display and management system for perinatal monitoring data. Therefore, the concept of "human readers improving with AI vs without AI assistance" does not apply.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
This information is not explicitly stated in the provided text. However, as it is a "Central Monitoring System" that displays data acquired from other devices and allows for manual input and user-defined limits, its primary function inherently involves a "human-in-the-loop." It is a software application designed to provide information to clinicians, not to make autonomous diagnostic decisions. Its validation would focus on the accuracy of data acquisition, display, archiving, and alarming functions, which are analogous to "standalone" performance of the software functions themselves.
7. The Type of Ground Truth Used
For a data management and display system like the MFM-CNS, the "ground truth" would be:
- Accurate input data: Ensuring the data received from bedside monitors is correctly interpreted and displayed.
- Functional correctness: Verification that display fields show the correct information, alarms trigger appropriately based on predefined limits, data is stored and retrieved accurately, and printing functions work as expected.
- User Interface/Experience (UI/UX) adherence: Conformance to design specifications for how information is presented.
It is not expert consensus, pathology, or outcomes data in a diagnostic sense, but rather a verification of the system's faithful handling and presentation of information.
8. The Sample Size for the Training Set
This information is not provided and is typically not relevant for a deterministic software application like this, which does not employ machine learning or AI models that require specific training data sets.
9. How the Ground Truth for the Training Set Was Established
This information is not provided and is generally not applicable as there's no indication of a "training set" in the context of an AI/ML algorithm. If the term "training set" refers to internal development and testing data, the "ground truth" would have been established through a combination of simulated data, test cases with known outputs, and direct comparison to the behavior of the predicate device or established specifications.
§ 884.2740 Perinatal monitoring system and accessories.
(a)
Identification. A perinatal monitoring system is a device used to show graphically the relationship between maternal labor and the fetal heart rate by means of combining and coordinating uterine contraction and fetal heart monitors with appropriate displays of the well-being of the fetus during pregnancy, labor, and delivery. This generic type of device may include any of the devices subject to §§ 884.2600, 884.2640, 884.2660, 884.2675, 884.2700, and 884.2720. This generic type of device may include the following accessories: Central monitoring system and remote repeaters, signal analysis and display equipment, patient and equipment supports, and component parts.(b)
Classification. Class II (performance standards).