(159 days)
The BrightHeart View Classifier device is intended to analyze fetal 2D ultrasound images and video clips using machine learning techniques to automatically detect standard views during fetal heart scanning.
The BrightHeart View Classifier device is intended to be used as an adjunct to the acquisition and interpretation of fetal anatomic ultrasound examinations at the second or third trimester of pregnancy performed with transabdominal probes.
BrightHeart View Classifier is a cloud-based software-only device which uses artificial intelligence (AI) to detect standard views during fetal heart scanning in fetal ultrasound images and video clips.
BrightHeart View Classifier is intended to be used by qualified, trained healthcare professional personnel in a professional prenatal ultrasound (US) imaging environment (this includes sonographers, MFMs, OB/GYN, and Fetal surgeons), to help fetal ultrasound examination acquisition and interpretation of 2D grayscale ultrasound by providing automatic classification of video clips and images into standard views, by automatically extracting example frames of standard views from video clips, and by automatically assessing whether the documentation of each standard view in video clips and images satisfies an acquisition protocol defined by the center. Annotated DICOM files generated by the device cannot be modified by the user.
Here's a breakdown of the acceptance criteria and the study details for the BrightHeart View Classifier, based on the provided FDA 510(k) Clearance Letter:
1. Table of Acceptance Criteria and Reported Device Performance
The FDA letter does not explicitly state pre-defined acceptance criteria values that the device needed to meet. Instead, it reports the device's performance metrics directly from the validation study. However, based on the performance report, we can infer the achieved performance and understand that these values were deemed sufficient for clearance.
| Performance Metric | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|
| Mean Standard View Recognition Sensitivity | High (e.g., >0.90) | 0.939 (95% CI, 0.917 ; 0.960) |
| Mean Standard View Recognition Specificity | High (e.g., >0.95) | 0.984 (95% CI, 0.973 ; 0.996) |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 2290 clinically acquired images and frames from video clips.
- Number of Fetal Ultrasound Examinations: 579
- Country of Origin of Data: U.S.A. and France.
- Retrospective or Prospective: The document implies retrospective data ("clinically acquired images and frames").
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
- Number of Experts: Two experts: "a sonographer and an MFM specialist".
- Qualifications: "with experience in fetal echocardiography". Specific years of experience are not mentioned.
4. Adjudication Method for the Test Set
- Adjudication Method: Independence was maintained in the ground truth establishment. "The reference standard was derived from the dataset through a truthing process in which a sonographer and an MFM specialist with experience in fetal echocardiography determined the presence or absence of standard views on fetal ultrasound images. The truthing process was conducted independently of the BrightHeart View Classifier device." This indicates a consensus or independent review process, but not a specific 2+1 or 3+1 adjudication as those usually imply a tie-breaker. It seems like both experts independently determined the ground truth.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly described. The study evaluated the standalone performance of the AI device.
6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance
- Yes, a standalone performance study was conducted. The reported sensitivity and specificity values are for the BrightHeart View Classifier identifying standard views on its own.
7. Type of Ground Truth Used
- Type of Ground Truth: Expert consensus (from a sonographer and an MFM specialist with experience in fetal echocardiography).
8. Sample Size for the Training Set
- The sample size for the training set is not explicitly stated in the provided document. The document only mentions that "The ultrasound examinations used for training and validation are entirely distinct from the examinations used in performance testing."
9. How the Ground Truth for the Training Set Was Established
- The document does not explicitly state how the ground truth for the training set was established. However, given the nature of the device and the ground truth method for the test set, it is highly probable that a similar expert review and annotation process was used 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).