(205 days)
AM1000 is intended for use as a tool which non-invasively measures and displays the magnetic signals produced by the electric currents of the heart.
AMCG's MCG-S will be used for purpose of measurement and analysis in adult cardiology. Magnetocardiography (MCG) is a non-invasive, non-contact, radiation-free, multichannel body surface mapping technique to record biomagnetic signals of X- and Y-components from the heart generated by the same ionic currents underling the electrocardiogram. This system consists of Dewar with 96-channel SQUIDs, gantry, electronics, power supply device, and software. 48 of these SQUID sensors are used for detecting the X-components, and the remaining 48 sensors are for the Y-components of the magnetic field. The device does not measure and/or display the magnetic field in the Z-component. The system increases the output signal of the SQUID by applying the DROS (Double Relaxation Oscillation SQUID) method to increase the measurement sensitivity of the cardiac magnetic field signal, and a wide range of channel area is applied to measure the magnetic field area throughout the heart within a short time. In addition, in order to minimize the mechanical vibration of the sensor, the sensor is installed in a vacuum area, and Dewar, which has excellent noise shielding and insulation performance, is applied. The system is controlled through noise-free optical fiber signals and can control, measure, process and analyze signals through software.
The provided text describes the 510(k) summary for the MCG-S (AM1000) device, demonstrating its substantial equivalence to a predicate device (CS-MAGII, K121825). However, it does not contain information about specific acceptance criteria and a study proving the device meets those criteria in the way typically expected for a clinical performance study involving human subjects or AI performance metrics.
Instead, the performance data section focuses on bench testing, electromagnetic compatibility, electrical safety, and software verification and validation. There is no mention of a study involving patients, clinicians, ground truth established by experts, or AI performance metrics like sensitivity, specificity, or AUC.
Here's a breakdown based on your request, highlighting the absence of certain information:
1. Table of Acceptance Criteria and Reported Device Performance:
The document lists performance tests conducted and states that they "met the internal criteria." However, the specific numerical acceptance criteria for each test are not provided, nor are the reported numerical device performance results beyond the qualitative statement of "met internal criteria."
| Acceptance Criteria Category | Specific Acceptance Criteria (Not provided in detail) | Reported Device Performance (Qualitative) |
|---|---|---|
| Bench Testing | (e.g., Specific sensitivity values, boil-off rates, accuracy thresholds) | Met internal criteria |
| - Sensitivity Test | Not specified | Met internal criteria |
| - L-He Boil-off Rate Test | Not specified | Met internal criteria |
| - Dimensional Test | Not specified | Met internal criteria |
| - Sensor Operation Test | Not specified | Met internal criteria |
| - Accuracy Test | Not specified | Met internal criteria |
| - Heart Positioning Test | Not specified | Met internal criteria |
| - Heart Rate Measurement Test | Not specified | Met internal criteria |
| - Sensor Calibration Test | Not specified | Met internal criteria |
| Electromagnetic Compatibility | Compliance with IEC 60601-1-2 | Met requirements |
| Electrical Safety | Compliance with IEC 60601-1 | Met requirements |
| Software Verification & Validation | Adherence to FDA guidance "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." | Documentation provided and testing conducted |
2. Sample Size Used for the Test Set and Data Provenance:
- Sample Size: Not applicable in the context of this submission, as there's no mention of a test set involving patient data for clinical performance evaluation. The performance tests appear to be technical/engineering in nature (e.g., sensitivity of the SQUID, helium boil-off rates).
- Data Provenance: Not applicable.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications:
- Number of Experts: Not applicable. There is no mention of experts involved in establishing ground truth for a clinical test set.
- Qualifications: Not applicable.
4. Adjudication Method for the Test Set:
- Adjudication Method: Not applicable. There is no mention of a test set requiring adjudication.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
- MRMC Study: No, an MRMC comparative effectiveness study was not done.
- Effect Size: Not applicable.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:
- Standalone Study: No, a standalone study assessing an algorithm's performance without human-in-the-loop was not done, as the device is a magnetocardiograph (hardware with embedded software for measurement and display), not an AI algorithm for diagnosis or interpretation that would typically require such a study for its "standalone" performance. The software verification and validation relate to the proper functioning of the device's control, measurement, processing, and analysis functions, not necessarily AI-driven diagnostic interpretation.
7. Type of Ground Truth Used:
- Type of Ground Truth: Not applicable in the context of clinical performance. The "ground truth" for the reported performance tests would be the established engineering specifications and measurement standards for each technical test (e.g., a calibrated reference for sensitivity, a controlled environment for boil-off rate).
8. Sample Size for the Training Set:
- Sample Size: Not applicable. The document does not describe a machine learning algorithm or AI component that would require a "training set" in the conventional sense for diagnostic model development.
9. How the Ground Truth for the Training Set Was Established:
- Ground Truth Establishment: Not applicable, as there is no mention of a training set for an AI algorithm.
In summary:
This 510(k) submission focuses on demonstrating substantial equivalence primarily through technical specifications, safety (electrical and electromagnetic compatibility), and basic functionality (sensor performance, heart rate measurement, etc.) against a predicate device. It does not present a clinical performance study with defined acceptance criteria for diagnostic accuracy, expert-established ground truth, or AI-specific performance metrics. The device is described as a tool to measure and display magnetic signals, implying that its performance evaluation revolves around the accuracy and reliability of these physical measurements rather than algorithmic interpretation for diagnosis.
§ 870.2340 Electrocardiograph.
(a)
Identification. An electrocardiograph is a device used to process the electrical signal transmitted through two or more electrocardiograph electrodes and to produce a visual display of the electrical signal produced by the heart.(b)
Classification. Class II (performance standards).