(177 days)
The CARDIOVIT F - 1 is a 12-channel ECG unit used for the recording, viewing, storage and transmission of ECG waveforms.
The CARDIOVIT FT-1 is designed for indoor use and can be used for all patient populations.
The CARDIOVIT FT-1 is used to diagnose cardiac abnormalities, and detect acute myocardial ischemia and infarctions in chest pain patients.
The CARDIOVIT FT-1 is intended for use in hospitals, cardiology units, out-patient clinical units and general physician's offices.
The CARDIOVIT FT-1 is a 12-lead ECG (Electrocardiograph) device used in the recording, analysis, viewing, storage and transmission of ECG waveforms.
The CARDIOVIT FT-1 does not provide a patient monitoring capability with alarm annunciation.
The CARDIOVIT FT-1 has a color display. It accepts user input via a touch panel or barcode scanner. It can generate a variety of reports that can be viewed on the display or printed on a strip chart recorder that is built into the device.
The CARDIOVIT FT-1 is mains- or battery- powered and uses sensors that come in contact with the patient.
The CARDIOVIT FT-1 is intended to function in the patient vicinity alongside other medical devices. It can operate as a stand-alone device or can be connected to the SCHILLER SEMA3 Data Management System via Ethernet (land-line or WiFi) in order to store reports and retrieve work orders for a given patient.
The provided FDA 510(k) summary for the CARDIOVIT FT-1 states that the device is substantially equivalent to predicate devices, but it does not include a study or specific acceptance criteria for the device's diagnostic performance (e.g., accuracy in diagnosing cardiac abnormalities or detecting myocardial ischemia/infarctions).
Instead, the performance data provided focuses on:
- Electrical safety, essential performance, and electromagnetic compatibility (EMC) testing: This confirms compliance with various IEC standards (IEC 60601-1, IEC 60601-1-2, IEC 60601-1-6, IEC 60601-2-25, IEC 62366).
- Software Verification and Validation Testing: This confirms the software's compliance with FDA guidance for "moderate" level of concern software.
Therefore, many of the requested details about acceptance criteria for diagnostic accuracy, sample sizes for test sets, ground truth establishment, expert qualifications, and MRMC studies are not present in this document.
Here's a breakdown of the information that can be extracted and what is missing:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria Category | Specific Criteria | Reported Device Performance/Compliance |
|---|---|---|
| Diagnostic Performance (e.g., Sensitivity/Specificity for Cardiac Abnormalities) | MISSING - NOT SPECIFIED IN THIS DOCUMENT | MISSING - NO DIAGNOSTIC PERFORMANCE STUDY RESULTS INCLUDED |
| Electrical Safety | Compliance with IEC 60601-1:2005 + CORR. 1:2006 + CORR. 2:2007 + AM1:2012 | Successfully tested to IEC 60601-1:2005 + CORR. 1:2006 + CORR. 2:2007 + AM1:2012 (or IEC 60601-1: 2012 reprint) |
| Electromagnetic Compatibility (EMC) | Compliance with IEC 60601-1-2:2014 | Successfully tested to IEC 60601-1-2:2014 |
| Usability | Compliance with IEC 60601-1-6:2010 (Third Edition) + A1:2013 | Successfully tested to IEC 60601-1-6:2010 (Third Edition) + A1:2013 |
| Electrocardiographs | Compliance with IEC 60601-2-25:2011 | Successfully tested to IEC 60601-2-25:2011 |
| Application of Usability Engineering | Compliance with IEC 62366:2007 (First Edition) + A1:2014 | Successfully tested to IEC 62366:2007 (First Edition) + A1:2014 |
| Medical Device Software Life Cycle Processes | Compliance with IEC 62304:2006 | Successfully tested to IEC 62304:2006 |
| Software Verification & Validation (Level of Concern) | Compliance with FDA Guidance for "moderate" level of concern software | Software verification and validation testing was conducted as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The software is considered a "moderate" level of concern. |
2. Sample size used for the test set and the data provenance
Not applicable for diagnostic performance. The document only references compliance with general engineering and software standards, not a specific clinical test set for diagnostic accuracy.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable for diagnostic performance. This information is not provided as there is no diagnostic performance study detailed.
4. Adjudication method for the test set
Not applicable for diagnostic performance. This information is not provided as there is no diagnostic performance study detailed.
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. The document describes a traditional ECG device, not an AI-assisted diagnostic tool. Therefore, an MRMC study related to AI assistance would not be applicable and is not mentioned.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
No. The device is an electrocardiograph, which directly records and analyzes ECG waveforms. The "analysis" component is likely based on established algorithms for ECG interpretation, but there is no mention of a separate standalone algorithmic performance study in the context of AI or machine learning for diagnostic accuracy. The performance is tied to its measurement and interpretation capabilities as part of a medical device, which is primarily assessed through its adherence to standards for electrocardiographs (e.g., IEC 60601-2-25) and its equivalence to predicate devices.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not applicable for diagnostic performance. This information is not provided as there is no diagnostic performance study detailed. The device records ECG waveforms, and its "diagnosis" capabilities would generally rely on internal algorithms that interpret these waveforms based on established cardiology principles. The substantial equivalence argument relies on comparing this interpretation to that of predicate devices, which implicitly assumes the predicate devices have an acceptable "ground truth" performance.
8. The sample size for the training set
Not applicable. This device is an electrocardiograph, and while it performs "analysis," the document does not indicate that this analysis is based on machine learning or AI that would require a "training set" in the modern sense (e.g., for deep learning). Its algorithms are based on established signal processing and diagnostic rules for ECG interpretation.
9. How the ground truth for the training set was established
Not applicable. As above, there is no mention of a "training set" in the context of machine learning.
§ 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).