(218 days)
DeepRhythmAl 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, Outpatient Cardiac Telemetry devices or other similar recorders when the assessment of 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. DeepRhythmAl 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.
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.
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 compatible formats from dedicated ambulatory ECG devices such as Holter, event recorder, Outpatient Cardiac Telemetry devices or other similar recorders used when assessment of the rhythm is necessary. The DRAI 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 User Interface therefore it should be integrated with the external visualization software used by the ECG technicians for the ECG visualization and analysis reporting.
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.
The provided document is a 510(k) Substantial Equivalence Determination letter from the FDA regarding the DeepRhythmAI device. It outlines the FDA's decision but does not contain detailed performance study data such as specific acceptance criteria and reported numeric device performance, sample sizes used for test and training sets, the number and qualifications of experts for ground truth, adjudication methods, MRMC study details, or the specific type of ground truth used.
The document states that "DeepRhythmAI has been subjected to performance testing according to the recognized consensus standards, ANSI/AAMI/IEC 60601-2-47:2012/(R)2016 and AAMI/ANSI/EC57:2012." It also mentions "Moreover, to enable robust device validation, the algorithm was tested against the proprietary database (MDG validation db) that includes a large number of recordings captured among the intended patient population." However, the specific results of these tests are not provided in this letter.
Therefore, many of the requested details cannot be extracted from the provided text.
Based on the information available:
1. Table of Acceptance Criteria and Reported Device Performance:
The document generaly states that the device was tested against mentioned standards and that "Test results confirm that DeepRhythmAl meets its intended use." However, specific numerical acceptance criteria and the corresponding reported performance values (e.g., sensitivity, specificity, accuracy for specific arrhythmias) are not provided in this document.
2. Sample size used for the test set and the data provenance:
- Test Set Sample Size: "the algorithm was tested against the proprietary database (MDG validation db) that includes a large number of recordings captured among the intended patient population." The exact number (sample size) is not specified.
- Data Provenance: The data is from a "proprietary database (MDG validation db)." The country of origin and whether it's retrospective or prospective data are not specified.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This information is not provided in the document.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
This information is not provided in the document.
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:
This type of study is not mentioned in the document. The device is described as "cloud-based software for the assessment of cardiac arrhythmias... 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." This suggests an AI-assisted workflow, but no MRMC study details are given.
6. If a standalone (i.e., algorithm only without human-in-the loop performance) was done:
The document states that "DeepRhythmAl is a cloud-based software for automated analysis of ECG data. 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." And the performance testing was done for "arrhythmia detection and automated analysis of ECG data," which implies standalone performance was evaluated against the mentioned standards. Specific standalone performance metrics are not provided.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
While it's implied that ground truth was established to validate the algorithm against standards, the specific type of ground truth (e.g., expert consensus of specific cardiologists, adjudicated clinical events) is not explicitly stated.
8. The sample size for the training set:
This information is not provided in the document. The document only mentions "proprietary deep-learning algorithm" implying a training process, but no details of the training set.
9. How the ground truth for the training set was established:
This information is not provided in the document.
§ 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).