(313 days)
The Caption Interpretation Automated Ejection Fraction software is used to process previously acquired transthoracic cardiac ultrasound images, to store images, and to manipulate and make measurements on images using an ultrasound device, personal computer, or a compatible DICOM-compliant PACS system in order to provide automated estimation of left ventricular ejection fraction. This measurement can be used to assist the clinician in a cardiac evaluation.
The Caption Interpretation Automated Ejection Fraction Software is indicated for use in adult patients.
The Caption Interpretation Automated Ejection Fraction Software ("AutoEF") applies machine learning algorithms to process echocardiography images in order to calculate left ventricular ejection fraction. The cleared Caption Interpretation AutoEF performs left ventricular ejection fraction measurements using apical four chamber or apical two chamber cardiac ultrasound views, or the parasternal long-axis cardiac ultrasound view in combination with an apical four chamber view. The software selects the image clips to be used, performs the AutoEF calculation, and forwards the results to the desired destination for clinician viewing. The output of the Ejection Fraction estimate stated as a percentage, along with an indication of confidence regarding that estimate.
Here's a breakdown of the acceptance criteria and study details for the Caption Interpretation Automated Ejection Fraction Software, based on the provided FDA 510(k) summary:
1. Acceptance Criteria and Reported Device Performance
| Acceptance Criterion (Primary Endpoint) | Predicate Device Performance (K200621) | Subject Device Performance (K210747) | Meets Acceptance? |
|---|---|---|---|
| Root Mean Square Deviation (RMSD) of Ejection Fraction (EF) % below a set threshold compared to reference ground truth EF. | 7.94 RMSD EF % (95% CI) | 7.21 RMSD EF % (95% CI) | Yes (demonstrated improvement) |
| Outlier Rate (EF error >15%) | 1.61% | 1.09% | Yes (comparable performance, slight improvement) |
| Outlier Rate (EF error >20%) | 0% | 0.55% | Yes (comparable performance) |
Notes on Acceptance Criteria:
- The specific "set threshold" for RMSD is not explicitly stated in the provided text. However, the summary indicates that the "primary endpoint for the subject device was met."
- The summary highlights that the subject device demonstrated "slightly improved performance" in RMSD compared to the predicate, and "comparable performance" in outlier rates.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: Over 186 acquired studies.
- Data Provenance:
- Country of Origin: Not explicitly stated.
- Retrospective or Prospective: Retrospective, non-interventional validation study.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
- This information is not explicitly stated in the provided text. The text only mentions that the device's measurements were "compared to the biplane method ejection fraction" as the reference. It doesn't detail who performed these biplane measurements or how many experts were involved.
4. Adjudication Method for the Test Set
- The text describes the ground truth as the "biplane method ejection fraction." It does not describe an adjudication method for the test set, implying that the biplane measurements served as the direct reference without further expert consensus or adjudication.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size
- No, an MRMC comparative effectiveness study was not done. The study described is a standalone performance validation of the algorithm against a declared ground truth (biplane method ejection fraction). There is no mention of human readers assisting or being compared to the AI.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
- Yes, a standalone performance study was done. The described study directly compares the Caption Interpretation Automated Ejection Fraction Software's output to the "biplane method ejection fraction" without human intervention in the loop for the device's calculation. The device provides an "automated estimation of left ventricular ejection fraction."
7. The Type of Ground Truth Used
- Expert Consensus / Clinical Standard: The ground truth used was the biplane method ejection fraction. This is a widely accepted clinical method for calculating EF, typically performed by trained professionals (e.g., echocardiographers, cardiologists). While the text doesn't specify if it was a "consensus" of multiple experts, it refers to a established clinical method.
8. The Sample Size for the Training Set
- The training set included an "additional 30% of training data from three ultrasound devices and two clinical sites" for retraining of algorithms, compared to the predicate device. The absolute sample size of the training set is not explicitly stated.
9. How the Ground Truth for the Training Set Was Established
- The text states that "Images and cases used for verification and validation testing were carefully separated from training datasets." While it doesn't explicitly detail how the ground truth for the training set was established, it's generally understood that for machine learning algorithms in medical imaging, the training data would also require some form of expert labeling or ground truth establishment (e.g., manual segmentation and measurement by cardiologists, confirmed by clinical standards like the biplane method). The summary does not provide specific details on this process for the training data.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).