(77 days)
No reference devices were used in this submission.
Yes
The intended use and device description explicitly state that the device uses "machine learning techniques" and the algorithm methodology is listed as "Artificial Intelligence".
No
The device is described as a concurrent reading aid and analysis tool for fetal ultrasound images, not for directly treating any condition.
No
The device is described as a "concurrent reading aid" that assists healthcare professionals in ensuring the completeness and quality of their fetal ultrasound examinations. It automatically detects views, anatomical structures, and verifies quality criteria, but it does not provide a medical diagnosis or treatment decision. The user retains the ability to override the software's outputs, indicating that the final diagnostic determination rests with the healthcare professional.
Yes
The device is described as a "Software as a Service SaaS solution" and its components are software (Edge Software and SaaS accessibility). While it interacts with an ultrasound machine and a server, the device itself, as described, is the software that performs the analysis and provides the concurrent reading aid. The hardware (ultrasound machine, server) is a platform for the software, not part of the regulated device itself.
Based on the provided information, this device is NOT an IVD (In Vitro Diagnostic).
Here's why:
- IVDs analyze samples taken from the human body. This device analyzes images of the human body (specifically, fetal ultrasound images and clips).
- The intended use is a "concurrent reading aid" for analyzing images. This is a function related to medical imaging analysis, not the analysis of biological samples.
- The device description focuses on image processing and analysis. It describes detecting views, anatomical structures, and quality criteria within the ultrasound images.
- There is no mention of analyzing blood, urine, tissue, or any other biological sample.
The device falls under the category of medical imaging software that uses AI/ML for analysis and interpretation assistance. While it is a medical device, it does not meet the definition of an In Vitro Diagnostic device.
No
The letter does not state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The section "Control Plan Authorized (PCCP) and relevant text" explicitly states "Not Found".
Intended Use / Indications for Use
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 and characteristics of the views.
The device is intended for use as a concurrent reading aid during the acquisition and interpretation of fetal ultrasound images
Product codes (comma separated list FDA assigned to the subject device)
IYN, IYO, QIH
Device Description
Sonio Detect is a Software as a Service SaaS solution that aims at helping sonographers, OB/GYN MFMs and Fetal surgeons (all three designated as healthcare professionals i.e. HCP in the following) 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 (GA: 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 requires the following:
- Edge Software (described below) to install on a server on the same network as the Ultrasound Machine;
- SaaS accessibility from any internet browser (recommended browser: Google Chrome).
Sonio's Edge Software is a light-weight application that runs on a server (computer) connected to the same network as the Ultrasound Machine. Sonio Edge Software is installed on the HCP server (computer) and network and the main purpose is to receive DICOM instances from the Ultrasound Machine and upload them to Sonio's Cloud to be used by Sonio Detect.
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 and characteristics of the supported views by checking whether they conform to standardized quality criteria
Quality criteria are related to:
- The presence of an anatomical structure;
- The absence of an anatomical structure:
Characteristics are related to other items than quality criteria:
- Location of the placenta
- Fetus sex
Sonio Detect then automatically associates the image to its detected view. It also highlights in yellow the view and/or the corresponding quality criteria or characteristics if there are unverified items: quality criteria or characteristics 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, changes the status of a quality criteria from verified to unverified or from unverified to verified) and manually set the characteristics of the views. The user has the ability to review and edit/override the matching at any time during or at the end of the exam.
Mentions image processing
Yes
Mentions AI, DNN, or ML
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 and characteristics of the views.
Algorithm Methodology: Artificial Intelligence
Input Imaging Modality
Fetal ultrasound images and clips, 3D fetal ultrasound images, Doppler fetal ultrasound images
Anatomical Site
Fetal brain, fetal thorax and heart, placenta, CRL/NT/Profile, External genitalia.
Indicated Patient Age Range
From 11 weeks to 37 weeks gestational age.
Intended User / Care Setting
Sonographers, OB/GYN MFMs and Fetal surgeons (all three designated as healthcare professionals i.e. HCP).
Care Setting: Not Found.
Description of the training set, sample size, data source, and annotation protocol
Not Found.
Description of the test set, sample size, data source, and annotation protocol
Sonio conducted a standalone performance testing on a dataset of 36 769 fetal ultrasound images. This global validation dataset was independent of the data used during model development (training/fine tuning/internal validation) and establishment of device operating points.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Study Type: Standalone performance testing, Software Verification and Validation Testing.
Sample Size: 36 769 fetal ultrasound images for standalone performance testing.
AUC: Not Found.
MRMC: Not Found.
Standalone Performance:
The results of the standalone performance testing demonstrated that Sonio Detect performs the following:
- Automatic detection of 3D fetal ultrasound images: Sensitivity 0.892 (0.836-0.931)
- Automatic detection of Doppler fetal ultrasound images: Sensitivity 0.973 (0.937-0.988)
- Automatic detection of fetal ultrasound views through reading of annotations on images: Sensitivity 0.913 (0.852-0.951)
- Automatic detection of 7 T1 fetal ultrasound images: Sensitivity 0.914 (0.906-0.921)
- Automatic detection of 18 T2/T3 fetal ultrasound images: Sensitivity 0.937 (0.933-0.940)
- Automatic detection of 8 fetal brain anatomical structures on the views "Transthalamic", "Transventricular", "Transcerebellar" at T2/T3: Sensitivity 0.934 (0.925-0.943), Specificity 0.949 (0.942-0.955)
- Automatic detection of 6 fetal thorax and heart anatomical structures on the views "Four chambers", "LVOT", “RVOT", "Three vessels or Three vessels and trachea", "Abdominal Circumference", "Axial view of the kidneys" at T1: Sensitivity 0.861 (0.841-0.878), Specificity 0.938 (0.926-0.948)
- Automatic detection of 21 fetal thorax and heart anatomical structures on the views "Four chambers", "LVOT", “RVOT”, "Three vessels or Three vessels and trachea", "Abdominal Circumference”, “Axial view of the kidneys" at T2/T3: Sensitivity 0.919 (0.913-0.924), Specificity 0.976 (0.974-0.978)
- Automatic detection of 4 fetal placenta anatomical structures on the views "Placenta insertion", "Placenta location" at T2/T3: Sensitivity 0.967 (0.955-0.975), Specificity 0.856 (0.838-0.871)
- Automatic detection of 8 fetal CRL/NT/Profile anatomical structures on the views "Crown Rump Length", “Nuchal Translucency”, “Profile” at T1: Sensitivity 0.898 (0.885-0.910), Specificity 0.862 (0.845-0.878)
- Automatic detection of 6 fetal CRL/NT/Profile anatomical structures on the views "Crown Rump Length", “Nuchal Translucency”, “Profile” at T2/T3: Sensitivity 0.893 (0.879-0.906), Specificity 0.956 (0.949-0.962)
- Automatic detection of the Anterior placenta location for the views "Placenta insertion", "Placenta location" at T2/T3: Sensitivity 0.959 (0.918-0.980), Specificity 0.966 (0.924-0.986)
- Automatic detection of the Posterior placenta location for the views "Placenta insertion", "Placenta location" at T2/T3: Sensitivity 0.966 (0.924-0.986), Specificity 0.959 (0.918-0.980)
- Automatic detection of the "Female sex" for fetal sex for the view "External Genitalia": Sensitivity 0.977 (0.942-0.991), Specificity 0.987 (0.963-0.996)
- Automatic detection of the "Male sex" for fetal sex for the view "External Genitalia": Sensitivity 0.987 (0.963-0.996), Specificity 0.977 (0.942-0.991)
Key Results: The results of verification and performance testing demonstrate the safe and effective use of Sonio Detect. Sonio Detect was validated only with GE, Canon, Philips and Samsung Ultrasound devices and is intended only to be used with these Ultrasound vendors.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Sensitivity, Specificity
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
No reference devices were used in this submission.
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found.
§ 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.
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April 26, 2024
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health and Human Services logo. To the right of that is the FDA logo, which consists of the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
Sonio % Florian Akpakpa Head of Quality Assurance and Regulatory Affairs 17 Rue du Faubourg Montmartre Paris. 75009 FRANCE
Re: K240406
Trade/Device Name: Sonio Detect Regulation Number: 21 CFR 892.1550 Regulation Name: Ultrasonic Pulsed Doppler Imaging System Regulatory Class: Class II Product Code: IYN, IYO, QIH Dated: February 9, 2024 Received: February 9, 2024
Dear Florian Akpakpa:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
1
Your device is also subject to, among other requirements, the Quality System (OS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Yanna S. Kang -S
Yanna Kang, Ph.D. Assistant Director Mammography and Ultrasound Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
2
Indications for Use
Form Approved: OMB No. 0910-0120 Expiration Date: 07/31/2026 See PRA Statement below.
Submission Number (if known)
Device Name
Sonio Detect
Indications for Use (Describe)
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 and characteristics of the views.
The device is intended for use as a concurrent reading aid during the acquisition and interpretation of fetal ultrasound images
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
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DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
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3
Image /page/3/Picture/1 description: The image shows the logo for Sonio. The logo consists of a stylized blue wave-like shape on the left, followed by the word "sonio" in blue lowercase letters. There is a small blue circle above the "i" in "sonio."
510(k) Summary
In accordance with 21 CFR 807.92 the 510(k) summary for Sonio Detect is provided below.
I. Submitter
| Applicant: | Sonio
17 Rue du Faubourg Montmartre,
75009, Paris France |
|-------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|
| Primary Contact Person: | Florian Akpakpa
Head of Regulatory Affairs and Quality Assurance
Sonio
Phone: +33 6 19 38 71 45
Email: florian.akpakpa@sonio.ai |
| Date Prepared: | February 9th, 2024 |
II. Device
| Device Trade Name: | Sonio Detect |
|---|---|
| Classification Name: | 21 CFR 892.1550 - accessory to Ultrasonic Pulsed Doppler Imaging System |
| 21 CFR 892.1560 - accessory to Ultrasonic Pulsed Echo Imaging System | |
| 21 CFR 892.2050 - Medical Image Management and Processing System | |
| Regulatory Class: | Class II |
| Product Code: | IYN (primary) |
| IYO, QIH (Secondary) |
III. Predicate Device
Sonio Detect cleared in K230365.
This predicate has not been subject to a design-related recall.
No reference devices were used in this submission.
4
IV. Device Description
Sonio Detect is a Software as a Service SaaS solution that aims at helping sonographers, OB/GYN MFMs and Fetal surgeons (all three designated as healthcare professionals i.e. HCP in the following) 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 (GA: 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 requires the following:
- Edge Software (described below) to install on a server on the same network as the ● Ultrasound Machine;
- . SaaS accessibility from any internet browser (recommended browser: Google Chrome).
Sonio's Edge Software is a light-weight application that runs on a server (computer) connected to the same network as the Ultrasound Machine. Sonio Edge Software is installed on the HCP server (computer) and network and the main purpose is to receive DICOM instances from the Ultrasound Machine and upload them to Sonio's Cloud to be used by Sonio Detect.
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 and characteristics of the supported views by checking whether they conform to standardized quality criteria
Quality criteria are related to:
- The presence of an anatomical structure; ●
- . The absence of an anatomical structure:
Characteristics are related to other items than quality criteria:
- . Location of the placenta
- . Fetus sex
Sonio Detect then automatically associates the image to its detected view. It also highlights in yellow the view and/or the corresponding quality criteria or characteristics if there are unverified items: quality criteria or characteristics 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, changes the status of a quality criteria from verified to unverified or from unverified to verified) and manually set the characteristics of the views. The user has the ability to review and edit/override the matching at any time during or at the end of the exam.
5
The list of views, anatomical structures, quality criteria and characteristics that can be automatically detected and verified by the software are detailed in tables 1, 2, 3 and 4 below.
Table 1: List of views per trimester that can be automatically detected by Sonio Detect
| Trimester | View |
|---|---|
| First Trimester | 1. Transthalamic or Cavum septum pellucidum or Midline falx/Transventricular or Choroid Plexus |
- Profile/Nuchal translucency
- 4 Chambers
- Abdominal circumference
- Hand
- Foot
- Crown Rump Length |
| Second and Third trimester | 1. Transthalamic or Cavum septum pellucidum or Midline falx/Transventricular or Choroid Plexus - Transcerebellar view
- Profile
- Lips and Nose
- Orbits
- 4 Chambers
- LVOT
- RVOT
- 3 vessels/3 vessels and trachea
- Sagittal Spine
- Abdominal circumference
- Axial Bladder
- Axial Kidneys
- Long bone
- Hand
- Foot
- External genitalia (female and male)
- Placenta insertion |
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Image /page/6/Picture/0 description: The image contains the logo for Sonio. The logo consists of a stylized blue icon to the left of the word "sonio" in a sans-serif font, also in blue. The icon appears to be a stylized sound wave or a heart shape, with a circular dot above it.
Table 2: List of anatomical structures that can be automatically detected by Sonio Detect
| View name | Structures to be detected | First
Trimester T1 | Second/Third
Trimester T2/T3 |
|-------------------------------------|----------------------------------------------|-----------------------|---------------------------------|
| Brain views & structures | | | |
| Transthalamic
view | Thalami on the transthalamic view | - | X |
| | Cavum septum pellucidum | - | X |
| | Pillars of the fornix | - | X |
| Transventricular
view | Sylvian fissure | - | X |
| view | Ventricle | - | X |
| Transcerebellar
view | Choroid Plexus | - | X |
| | Cisterna Magna | - | X |
| | Cerebellum | - | X |
| Thorax and Heart views & structures | | | |
| | Adrenal gland | - | X |
| | Apex of the heart | - | X |
| | Descending aorta | - | X |
| | Interatrial septum | - | X |
| | Interventricular septum | X | X |
| | Kidneys | - | X |
| | Left atrium | X | X |
| 4 chambers
3 vessels | Left ventricle | X | X |
| 3 vessels and
trachea | Mitral valve | - | X |
| | Pulmonary vein | - | X |
| RVOT
LVOT | Right atrium | X | X |
| | Right ventricle | X | X |
| Abdominal
circumference | Stomach | X | X |
| Axial view of
the kidneys | Superior vena cava | - | X |
| | Tricuspid valve | - | X |
| | Umbilical vein | - | X |
| | Ascending aorta on LVOT View | - | X |
| | Ascending aorta on RVOT or 3 vessels
view | - | X |
| | Pulmonary artery trunk on 3 vessels
View | - | X |
| | Pulmonary artery with visible
bifurcation | - | X |
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Image /page/7/Picture/0 description: The image contains the logo for Sonio. The logo consists of a stylized blue icon resembling a curved shape with a dot above it, followed by the word "sonio" in blue, lowercase letters. The logo is simple and modern, with a clean design.
| Sonio |
|---|
| 510(k) Premarket Notification Submission |
| View name | Structures to be detected | First
Trimester T1 | Second/Third
Trimester T2/T3 |
|----------------------------------------------------------|----------------------------------------------------------------------------|--------------------------|------------------------------------|
| CRL/NT/Profile/Corpus callosum views & structures | | | |
| CRL
NT
Profile
Corpus
Callosum | Nasal bone | X | X |
| | Diencephalon | X | - |
| | Fourth ventricle on NT view | X | - |
| | Nuchal translucency | X | - |
| | Palate | X | X |
| | Corpus Callosum | - | X |
| | Liquid space under the chin | X | - |
| | Midbrain tectum | - | X |
| | Vermis | - | X |
| | Choroid plexus on sagittal plane | - | X |
| Cisterna magna on NT view | X | - | |
| Brainstem on NT view | X | - | |
| Placenta view & structures | | | |
| Placenta
insertion | Cervix | - | X |
| | Maternal bladder | - | X |
| | Internal cervical os | - | X |
| | Placenta | - | X |
| View name | Quality criteria | First
trimester
T1 | Second/Third
Trimester
T2/T3 |
| Quality criteria of the brain views | | | |
| Transthalamic
view | Presence of the cavum septum pellucidum
or of the pillars of the fornix | - | X |
| | Presence of the cavum septum pellucidum | - | X |
| | Presence of the Sylvian fissure | - | X |
| | Absence of the cerebellum | - | X |
| | Presence of the thalami | - | X |
| Transventricular
view | Presence of the ventricle | - | X |
| | Absence of the thalami | - | X |
| | Presence of the cavum septum pellucidum | - | X |
| Transcerebellar
view | Presence of the cerebellum | - | X |
| | Presence of the cisterna Magna | - | X |
| | Presence of the cavum septum pellucidum | - | X |
| Quality criteria of the thorax and heart views | | | |
| 4 chambers
view | Presence of the Left ventricle | X | X |
| | Presence of the Right ventricle | X | X |
| | Presence of the Left atrium | X | X |
| | Presence of the Right atrium | X | X |
| | Presence of the interventricular septum | X | X |
| | Presence or the inter atrial septum | - | X |
| | Presence of the apex of the heart | - | X |
| | Presence of the mitral valve | - | X |
| | Presence of the tricuspid valve | - | X |
| | Presence of the descending aorta | - | X |
| 3 vessels and 3
vessels and
trachea views | Presence of at least one pulmonary vein | - | X |
| | Presence of the Pulmonary artery | - | X |
| | Presence of the ascending aorta | - | X |
| View name | Quality criteria | First
trimester
T1 | Second/Third
Trimester
T2/T3 |
| LVOT view | Presence of the Left ventricle | - | X |
| | Presence of the Left atrium | - | X |
| | Presence of the ascending aorta | - | X |
| | Presence of the apex of the heart | - | X |
| | Presence of the right ventricle | - | X |
| | Presence of the interventricular septum | - | X |
| RVOT view | Presence of the pulmonary artery with
visible bifurcation | - | X |
| | Presence of the right ventricle | - | X |
| | Presence of the ascending aorta | - | X |
| | Presence of the stomach | X | X |
| Abdominal
circumference
view | Presence of at least one adrenal gland | - | X |
| | Presence of the descending aorta | - | X |
| | Presence of the umbilical vein | - | X |
| | Absence of the kidneys | - | X |
| Axial view of
the two kidneys | Presence of two kidneys | - | X |
| | Absence of the stomach | - | X |
| Quality criteria of CRL/NT/Profile/Corpus callosum views | | | |
| Nuchal
Translucency
view | Presence of the nasal bone | X | - |
| | Presence of the nuchal translucency | X | - |
| | Presence of the cisterna magna | X | - |
| | Presence of the fourth ventricle | X | - |
| | Presence of the Diencephalon | X | - |
| | Presence of the brainstem | X | - |
| | Presence of the palate | X | - |
| | Presence of liquid space under the chin | X | - |
| CRL view | Presence of the nasal bone | X | - |
| | Presence of liquid space under the chin | X | - |
| | Presence of the palate | X | - |
| View name | Quality criteria | First
trimester
T1 | Second/Third
Trimester
T2/T3 |
| Profile view | Presence of the palate | - | X |
| Profile view | Presence of the nasal bone | - | X |
| Corpus
Callosum view | Presence of the corpus callosum | - | X |
| | Presence of the plexus choroid
(third
ventricle) | - | X |
| | Presence of the midbrain tectum | - | X |
| | Presence of the vermis | - | X |
| Quality criteria of Placenta view | | | |
| Placenta
insertion | Presence of the internal cervical os | - | X |
| | Presence of the cervix | - | X |
| | Presence of the maternal bladder | - | X |
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Image /page/8/Picture/0 description: The image shows the logo for "sonio". The logo consists of a blue abstract shape resembling a stylized sound wave or a curved checkmark on the left, followed by the word "sonio" in a sans-serif font, also in blue. The logo is simple and modern, with a clean design.
Table 3: List of quality criteria that can be automatically verified by Sonio Detect
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Image /page/9/Picture/0 description: The image contains the logo for Sonio. The logo consists of a stylized blue icon resembling a curved shape with a dot above it, followed by the word "sonio" in lowercase, also in blue. The logo is simple and modern in design.
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Image /page/10/Picture/0 description: The image shows the logo for Sonio. The logo consists of a blue abstract shape resembling a sound wave or a stylized letter 'S', followed by the word 'sonio' in a sans-serif font, also in blue. A small blue circle is positioned above the 'i' in 'sonio'.
Table 4 : List of characteristics that can be automatically verified by Sonio Detect
| View name | Characteristics | First trimester
T1 | Second/Third
Trimester T2/T3 |
|-------------------|-----------------|-----------------------|---------------------------------|
| Genitalia view | Male | - | X |
| | Female | - | X |
| Placenta location | Anterior | - | X |
| | Posterior | - | X |
V. Indications for Use
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 and characteristics of the views.
The device is intended for use as a concurrent reading aid during the acquisition and interpretation of fetal ultrasound images.
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Sonio
510(k) Premarket Notification Submission
Sonio Detect and the predicate have similar intended use. Both devices are used as a concurrent aid to automatically detect fetal ultrasound views, automatically detect fetal anatomical structures within the views.
The indications for use of Sonio Detect v2 and the predicate differ in the characteristics verification. Sonio Detect v2 automatically verifies both the characteristics and quality criteria of the views whereas the predicate Sonio Detect only automatically verifies the quality criteria. However, these differences do not raise new questions regarding safety and effectiveness of the device when used as labeled.
VI. Comparison of Technological Characteristics with the Predicate Device
Table 5 provides a comparison of the Technological Characteristics of Sonio Detect to the predicate Sonio Detect cleared in K230365.
| Items | Predicate device: Sonio Detect -
K230365 | Proposed device: Sonio Detect v2 |
|--------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Manufacture
r name | Sonio | Sonio |
| Device name | Sonio Detect | Sonio Detect |
| Regulation
Number | 21 CFR 892.1550 - accessory to
Ultrasonic Pulsed Doppler Imaging
System
21 CFR 892.1560 - accessory to
Ultrasonic Pulsed Echo Imaging System
21 CFR 892.2050 - Medical Image
Management and Processing System | 21 CFR 892.1550 - accessory to
Ultrasonic Pulsed Doppler Imaging
System
21 CFR 892.1560 - accessory to
Ultrasonic Pulsed Echo Imaging System
21 CFR 892.2050 - Medical Image
Management and Processing System |
| Product code | IYN (primary)
IYO, QIH (Secondary) | IYN (primary)
IYO, QIH (Secondary) |
| Features | - Sonio Detect automatically detects
views
- Sonio Detect automatically detects
anatomical structures within the
supported views - Sonio Detect automatically verifies the
quality criteria of the supported views by
checking whether they conform to
standardized quality criteria. | - Sonio Detect automatically detects
views - Sonio Detect automatically detects
anatomical structures within the
supported views - Sonio Detect automatically verifies the
quality criteria and characteristics of the
supported views. |
| Algorithm
Methodology | Artificial Intelligence
Lecture of biometrics
Colorimetry for 3D and Doppler | Artificial Intelligence
Lecture of biometrics
Colorimetry for 3D and Doppler |
| Platform | Secure cloud-based and stand-alone
software compatible with ultrasound
system from GE Medical, Samsung and
Canon | Secure cloud-based and stand-alone
software compatible with ultrasound
system from GE Medical, Samsung,
Canon and Philips |
Table 5: Comparison of technological characteristics
Sonio Detect v2 and its predicate device, Sonio Detect, use the same algorithm methodology.
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Sonio Detect v2 and its predicate differs in the following:
- the platform: Sonio Detect v2 and its predicate differ in their compatibility with ultrasound machine manufacturers. Both devices support ultrasound systems from GE Medical, Samsung and Canon. However, only Sonio Detect v2 is compatible with the ultrasound system from the manufacturer Philips.
However, these differences do not raise new questions regarding safety and effectiveness of the device when used as labeled.
VII. Performance Data
The following performance data were provided in support of the substantial equivalence determination.
Software Verification and Validation Testing
Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Content of Premarket Submissions for Device Software Functions."
The following quality assurance measures were applied to the development of the system:
- Risk Analysis
- Design Reviews ●
- Software Development Lifecycle ●
- Algorithm Verification (Algorithm internal validation) ●
- Software units verification ●
- Software verification
- . Simulated use testing (Validation)
- Performance testing
- Cybersecurity testing .
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Bench Testing
Sonio conducted a standalone performance testing on a dataset of 36 769 fetal ultrasound images. This global validation dataset was independent of the data used during model development (training/fine tuning/internal validation) and establishment of device operating points.
The results of the standalone performance testing demonstrated that Sonio Detect performs the following, as summarized in table below:
| Items (fetal ultrasound views,
anatomical structures and
characteristics automatically
| detected) | Sensitivity | Specificity | ||
|---|---|---|---|---|
| Point | ||||
| Estimate | Wilson CI | |||
| (95%) | Point | |||
| Estimate | Wilson CI | |||
| (95%) | ||||
| Automatic detection of 3D fetal | ||||
| ultrasound images | 0.892 | (0.836-0.931) | - | - |
| Automatic detection of Doppler | ||||
| fetal ultrasound images | 0.973 | (0.937-0.988) | - | - |
| Automatic detection of fetal | ||||
| ultrasound views through reading of | ||||
| annotations on images | 0.913 | (0.852-0.951) | - | - |
| Automatic detection of 7 T1 fetal | ||||
| ultrasound images | 0.914 | (0.906-0.921) | - | - |
| Automatic detection of 18 T2/T3 | ||||
| fetal ultrasound images | 0.937 | (0.933-0.940) | - | - |
| Automatic detection of 8 fetal brain | ||||
| anatomical structures on the views | ||||
| "Transthalamic", | ||||
| "Transventricular", | ||||
| "Transcerebellar" at T2/T3 | 0.934 | (0.925-0.943) | 0.949 | (0.942-0.955) |
| Automatic detection of 6 fetal | ||||
| thorax and heart anatomical | ||||
| structures on the views "Four | ||||
| chambers", "LVOT", “RVOT", | ||||
| "Three vessels or Three vessels and | ||||
| trachea", "Abdominal | ||||
| Circumference", "Axial view of the | ||||
| kidneys" at T1 | 0.861 | (0.841-0.878) | 0.938 | (0.926-0.948) |
| Automatic detection of 21 fetal | ||||
| thorax and heart anatomical | ||||
| structures on the views "Four | ||||
| chambers", "LVOT", “RVOT”, | ||||
| "Three vessels or Three vessels and | ||||
| trachea", "Abdominal | ||||
| Circumference”, “Axial view of the | ||||
| kidneys" at T2/T3 | 0.919 | (0.913-0.924) | 0.976 | (0.974-0.978) |
| Automatic detection of 4 fetal | ||||
| placenta anatomical structures on | ||||
| the views "Placenta insertion", | ||||
| "Placenta location" at T2/T3 | 0.967 | (0.955-0.975) | 0.856 | (0.838-0.871) |
| Items (fetal ultrasound views, | ||||
| anatomical structures and | ||||
| characteristics automatically | ||||
| detected) | Sensitivity | Specificity | ||
| Point | ||||
| Estimate | Wilson CI | |||
| (95%) | Point | |||
| Estimate | Wilson CI | |||
| (95%) | ||||
| Automatic detection of 8 fetal | ||||
| CRL/NT/Profile anatomical | ||||
| structures on the views "Crown | ||||
| Rump Length", “Nuchal | ||||
| Translucency”, “Profile” at T1 | 0.898 | (0.885-0.910) | 0.862 | (0.845-0.878) |
| Automatic detection of 6 fetal | ||||
| CRL/NT/Profile anatomical | ||||
| structures on the views "Crown | ||||
| Rump Length", “Nuchal | ||||
| Translucency”, “Profile” at T2/T3 | 0.893 | (0.879-0.906) | 0.956 | (0.949-0.962) |
| Automatic detection of the Anterior | ||||
| placenta location for the views | ||||
| "Placenta insertion", "Placenta | ||||
| location" at T2/T3 | 0.959 | (0.918-0.980) | 0.966 | (0.924-0.986) |
| Automatic detection of the Posterior | ||||
| placenta location for the views | ||||
| "Placenta insertion", "Placenta | ||||
| location" at T2/T3 | 0.966 | (0.924-0.986) | 0.959 | (0.918-0.980) |
| Automatic detection of the "Female | ||||
| sex" for fetal sex for the view | ||||
| "External Genitalia" | 0.977 | (0.942-0.991) | 0.987 | (0.963-0.996) |
| Automatic detection of the "Male | ||||
| sex" for fetal sex for the view | ||||
| "External Genitalia" | 0.987 | (0.963-0.996) | 0.977 | (0.942-0.991) |
Table 6: results of the standalone performance testing
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Sonio
510(k) Premarket Notification Submission
Additionally, the performance for the detection of views and structures was also validated for subgroups including: Ultrasound machine manufacturer, BMI, maternal age, gestational age and race/ethnicity when appropriate.
Sonio Detect was validated only with GE, Canon, Philips and Samsung Ultrasound devices and is intended only to be used with these Ultrasound vendors.
The results of verification and performance testing demonstrate the safe and effective use of Sonio Detect.
Clinical Study
Not applicable. Clinical studies are not necessary to establish the substantial equivalence of this device.
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VIII. Conclusions
Sonio Detect's intended users, clinical outcome and clinical applications are similar to those of the predicate device Sonio Detect, the cleared version in K230365.
The technological characteristics differences identified and discussed in Section VI do not raise any different questions of safety and effectiveness of the device.
Furthermore, results of successful verification and validation activities and additional bench performance testing do not raise any new issue regarding the safety and effectiveness of the device.
Thus, Sonio Detect is substantially equivalent to its predicate Sonio Detect (K230365).