(495 days)
DeepRhythmAI is a cloud-based software for the assessment of cardiac arrhythmias using two lead ECG data in adult patients.
It is intended for use by a healthcare solution integrator to build web, mobile or another types of applications to let qualified healthcare professionals review and confirm the analytic result. The product supports downloading and analyzing data recorded in the compatible formats from dedicated ambulatory ECG devices such as Holter, event recorder, Mobile Cardiac Telemetry or other similar devices when tof the rhythm is necessary. The product can be electronically interfaced and perform analysis with data transferred from other computer-based ECG systems, such as an ECG management system. DeepRhythmAI can be integrated into medical devices. In this case, the medical device manufacturer will identify the indication for use depending on the application of their device.
DeepRhythmAI is not for use in life-supporting or sustaining systems or ECG Alarm devices. Interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms and other diagnostic information.
The DeepRhythmAl is a cloud-based software for automated analysis of ECG data. It uses a scalable Application Programming Interface (API) to enable easy integration with other medical products. The main component of DeepRhythmAl is an automated proprietary deep-learning algorithm, which measures and analyzes ECG data to provide qualified healthcare professional with supportive information for review.
DeepRhythmAl can be integrated into medical devices. The product supports downloading and analyzing data recorded in the compatible formats from dedicated ambulatory ECG devices such as Holter, event recorder, Mobile Cardiac Telemetry or other similar devices when the assessment of the rhythm is necessary. DeepRhythmAI can also be electronically interfaced and perform analysis with data transferred from other computer-based ECG systems, such as an ECG management system. DeepRhythmAl doesn't have a User Interface therefore it should be integrated with the external visualization software used by the ECG technicians for the ECG visualization and analysis reporting.
It is intended for use by a healthcare solution integrator to build web, mobile or another types of applications to let qualified healthcare professionals review and confirm the analytic result. .
DeepRhythmAI algorithm detects cardiac beats/arrythmias and intervals including:
- . QRS
- Heart rate determination
- RR Interval measurements
- Non-paced arrhythmias
- Non-paced ventricular arrhythmia calls
- Ventricular ectopic beats
- Supraventricular ectopic beats
DeepRhythmAl returns the interpretation result to be reviewed by a qualified healthcare professional. DeepRhythmAl when integrated with the other computer-based ECG systems, creates a semi-autonomous system for analysis of ECG recordings. All algorithm annotations must be analyzed and confirmed by a qualified healthcare professional. The subject device can only be integrated with the display product used by the monitoring center that allows for verification of the algorithm output, its correction and confirmation.
DeepRhythmAl is not for use in life-supporting or sustaining systems or ECG Alarm devices. Interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms and other diagnostic information.
Here's an analysis of the provided text, focusing on the acceptance criteria and study information for the DeepRhythmAI device:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly present a table of acceptance criteria with corresponding device performance for specific metrics (e.g., sensitivity, specificity for arrhythmia detection) as one might find in a detailed clinical performance study report. Instead, it states that the device was subjected to performance testing according to recognized consensus standards.
Acceptance Criteria (Implicit from Standards and General Statements):
| Performance Aspect | Standard / Requirement | Acceptance Indication |
|---|---|---|
| Arrhythmia detection and classification | ANSI/AAMI/IEC 60601-2-47:2012/(R)2016 | Device meets intended use; substantially equivalent to predicates. |
| Software development and validation | ANSI/AAMI/IEC 62304 & FDA "General Principles of Software Validation; Final Guidance for Industry and FDA Staff" (January, 2002) | Confirmed through performance testing. |
| Electrical safety and EMC (implied) | ANSI/AAMI/IEC 60601-2-47:2012/(R)2016 | Implied by adherence to standard. |
| Functional performance | Test results confirm DeepRhythmAI meets its intended use. | Device performs as intended for cardiac arrhythmia assessment. |
Reported Device Performance:
The document states that "All necessary testing was conducted on the DeepRhythmAl to support a determination of substantial equivalence to the predicate and reference devices. Test results confirm that DeepRhythmAl meets its intended use." However, specific quantitative performance metrics (e.g., sensitivity, specificity, accuracy, positive predictive value, negative predictive value for different arrhythmias) are not provided in this summary.
2. Sample Size Used for the Test Set and Data Provenance
The document does not provide details on the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective nature of the data). It only mentions that performance testing was conducted according to specific standards.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The document does not specify the number of experts used or their qualifications for establishing ground truth for any test set.
4. Adjudication Method for the Test Set
The document does not mention any adjudication method (e.g., 2+1, 3+1, none) used for the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was performed or any effect size of human readers improving with AI vs. without AI assistance. The device is explicitly stated to not be for standalone diagnosis and requires review by a qualified healthcare professional.
6. Standalone (Algorithm Only) Performance Study
The document states: "All algorithm annotations must be analyzed and confirmed by a qualified healthcare professional." and "Interpretation results are not intended to be the sole means of diagnosis. It is offered to physicians and clinicians on an advisory basis only..." These statements strongly suggest that the device is not intended or validated for standalone performance. Its integration with human review is a fundamental aspect of its intended use.
7. Type of Ground Truth Used
The document does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data, etc.) for its performance testing. Given its function, it is highly probable that expert-annotated ECG data would be used, but this is not confirmed in the provided text.
8. Sample Size for the Training Set
The document does not provide details on the sample size used for the training set for the DeepRhythmAI's deep-learning algorithm.
9. How the Ground Truth for the Training Set Was Established
The document does not provide details on how the ground truth for the training set was established.
§ 870.1425 Programmable diagnostic computer.
(a)
Identification. A programmable diagnostic computer is a device that can be programmed to compute various physiologic or blood flow parameters based on the output from one or more electrodes, transducers, or measuring devices; this device includes any associated commercially supplied programs.(b)
Classification. Class II (performance standards).