(165 days)
Sonio Detect is intended to analyze fetal ultrasound images and clips using machine learning techniques to automatically detect views, detect anatomical structures within the views and verify quality criteria of the views.
The device is intended for use as a concurrent reading aid during the acquisition and interpretation of fetal ultrasound images.
Sonio Detect is a Software as a Service SaaS solution that aims at helping sonographers, OB/GYNs, MFMs and Fetal surgeons (all three designated as healthcare professionals i.e. HCP) to perform their routine fetal ultrasound examinations in real-time. Sonio Detect can be used by Healthcare Professionals HCPs during fetal ultrasound exams for Trimester 1, Trimester 2 and Trimester 3 of the fetus (Gestational Age: from 11 weeks to 37 weeks). The software is intended to assist HCPs in assuring during and after their examination that the examination is complete and all images were collected according to their protocol.
Sonio Detect receives fetal ultrasound images and clips from the ultrasound machine, that are submitted through the edge software by the performing healthcare professional, in real-time and performs the following:
- . Automatically detect views;
- Automatically detect anatomical structures within the supported views; .
- Automatically verify quality criteria of the supported views by checking whether they . conform to standardized quality criteria.
Quality criteria are related to: - the presence or absence of an anatomical structure; ●
- the zoom level for some views.
Sonio Detect then automatically associates the image to its detected view. It also highlights in yellow the view and/or the corresponding quality criteria if there are unverified items : quality criteria not verified or view not detected.
The end user can interact with the software to override the Sonio Detect's outputs (reassign the image to another view or unassign it or assign it if it was not assigned, change the status of a quality criteria from verified to unverified or from unverified to verified). The user has the ability to review and edit/override the matching at any time during or at the end of the exam.
Sonio Detect Acceptance Criteria and Study Details
1. Acceptance Criteria and Reported Device Performance
The acceptance criteria for Sonio Detect are implicitly defined by the reported performance metrics, which the FDA has deemed sufficient for substantial equivalence. The reported performance is presented as sensitivities, specificities, and proportions of correctly read annotations.
| Performance Metric | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|
| 3D Fetal Ultrasound Image Detection Sensitivity | High sensitivity | 0.980 (95% Wilson's CI: 0.930, 0.994) |
| Doppler Fetal Ultrasound Image Detection Sensitivity | High sensitivity | 0.963 (95% Wilson's CI: 0.908, 0.985) |
| Fetal Ultrasound Views Detection Proportion Correct | High proportion | 0.923 (95% Wilson's CI: 0.905, 0.938) |
| T1 Fetal Ultrasound Views Detection Sensitivity | High sensitivity | 0.942 (Point estimate) |
| T2/T3 Fetal Ultrasound Views Detection Sensitivity | High sensitivity | 0.919 (Point estimate) |
| T2/T3 Fetal Brain Anatomical Structure Detection Sensitivity | High sensitivity | 0.857 (Point estimate) |
| T2/T3 Fetal Brain Anatomical Structure Detection Specificity | High specificity | 0.963 (Point estimate) |
| T2/T3 Fetal Heart Anatomical Structure Detection Sensitivity | High sensitivity | 0.900 (Point estimate) |
| T2/T3 Fetal Heart Anatomical Structure Detection Specificity | High specificity | 0.982 (Point estimate) |
| Zoom Level Verification Sensitivity (Brain Views) | High sensitivity | 0.952 (95% Wilson's CI: 0.909-0.976) |
| Zoom Level Verification Specificity (Brain Views) | High specificity | 0.906 (95% Wilson's CI: 0.758-0.968) |
2. Sample Size and Data Provenance for Test Set
- Sample Size: 17,885 fetal ultrasound images.
- Data Provenance: The data was collected from 7 clinical sites in the United States, France, and Germany. This indicates a multi-national dataset. The data was retrospective as it was "independent of the data used during model development (training/fine tuning/internal validation) and establishment of device operating points."
3. Number of Experts and Qualifications for Ground Truth (Test Set)
The document does not explicitly state the number of experts used or their specific qualifications (e.g., years of experience) for establishing the ground truth of the test set. However, it indicates that the device automatically detects fetal ultrasound views "through reading of annotations on images." This implies that human experts (presumably sonographers, OB/GYNs, MFMs, or Fetal surgeons, as these are the intended users) provided the initial annotations that served as the ground truth.
4. Adjudication Method (Test Set)
The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for establishing the ground truth of the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was performed. The document explicitly states: "Clinical Study: Not applicable. Clinical studies are not necessary to establish the substantial equivalence of this device." Therefore, there is no reported effect size of human readers improving with AI vs. without AI assistance.
6. Standalone Performance Study
Yes, a standalone performance study was done. The document states: "Sonio conducted a standalone performance testing on a dataset of 17885 fetal ultrasound images..." This indicates the algorithm's performance was evaluated without human intervention in the loop during the assessment of the test set.
7. Type of Ground Truth Used (Test Set)
The ground truth for the test set was established through "reading of annotations on images." This suggests the ground truth was based on expert annotations or labeling of the ultrasound images, likely by the qualified healthcare professionals who generated the initial data.
8. Sample Size for Training Set
The document does not explicitly state the sample size for the training set. It refers to "data used during model development (training/fine tuning/internal validation)" but does not provide a specific number of images or cases for this phase.
9. How Ground Truth for Training Set was Established
The method for establishing the ground truth for the training set is not explicitly detailed. However, given that the test set's ground truth was based on annotations, it is highly probable that the training set's ground truth was established through a similar process of expert annotation or labeling of the fetal ultrasound images and clips.
§ 892.1550 Ultrasonic pulsed doppler imaging system.
(a)
Identification. An ultrasonic pulsed doppler imaging system is a device that combines the features of continuous wave doppler-effect technology with pulsed-echo effect technology and is intended to determine stationary body tissue characteristics, such as depth or location of tissue interfaces or dynamic tissue characteristics such as velocity of blood or tissue motion. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.