(165 days)
No reference devices were used in this submission.
Yes
The intended use statement explicitly mentions "using machine learning techniques" and the device description and mentions of AI/ML section confirm the use of "Artificial Intelligence" and "machine learning techniques" for image analysis.
No.
The device is described as a "concurrent reading aid" and assists healthcare professionals in ensuring the completeness and quality of fetal ultrasound examinations. It does not directly provide therapy or treatment.
No
The device is described as a "concurrent reading aid" during the acquisition and interpretation of fetal ultrasound images. Its purpose is to help verify that the examination is complete and that images meet quality criteria. It does not provide a diagnosis itself. The user can also override the software's outputs.
Yes
The device is described as a "Software as a Service SaaS solution" that receives images from an ultrasound machine and performs analysis. There is no mention of any hardware component being part of the device itself, only that it receives input from an external hardware device (the ultrasound machine).
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVD Definition: An IVD is a medical device used to perform tests on samples taken from the human body (like blood, urine, tissue) to provide information about a person's health. This information is used for diagnosis, monitoring, or screening.
- Sonio Detect's Function: Sonio Detect analyzes images of the fetus obtained through ultrasound. It does not analyze biological samples from the patient. Its purpose is to assist healthcare professionals in the acquisition and interpretation of these images by automatically detecting views, structures, and quality criteria.
- Intended Use: The intended use clearly states it's a "concurrent reading aid during the acquisition and interpretation of fetal ultrasound images." This aligns with image analysis and workflow assistance, not laboratory testing of biological samples.
Therefore, Sonio Detect falls under the category of medical image analysis software, not an In Vitro Diagnostic device.
No
The input text does not contain any explicit statement that the FDA has reviewed, approved, or cleared a PCCP for this specific device. The relevant section explicitly states "Not Found" for PCCP information.
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 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
IYN, IYO, QIH
Device Description
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 requires the following:
- Edge Software (described below) to install on a server on the same network as the ● Ultrasound Machine;
- SaaS accessibility from internet browser.
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 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.
The list of views, anatomical structures and quality criteria that can be automatically detected and verified by the software are detailed in the tables 1, 2 and 3 below.
Table 1: List of views that can be automatically detected by the software
View | Gestational Age of the fetus
Transthalamic or Cavum septum Pellucidum or Midline falx /Transventricular or Choroid plexus | T1 and T2/T3
Profile or Nuchal translucency | T1 and T2/T3
Crown rump length | T1
Sagittal spine | T2/T3
Abdominal circumference | T2/T3
Long bone | T2/T3
Transcerebellar view | T2/T3
Upper lip, nose and nostrils | T2/T3
Four chambers | T2/T3
Left ventricular outflow tract | T2/T3
Right ventricular outflow tract | T2/T3
Table 2:List of anatomical structures that can be automatically detected by the software
View | Structure
Brain views and structures at T2/T3 |
Transthalamic view | • Midline falx
Transventricular view | • Cavum septum pellucidum
Transcerebellar view | • Cerebellum
Heart views and structures at T2/T3 |
Four Chambers | • Aorta
Three vessels | • Apex heart
LVOT | • Ascending aorta
| • Descending aorta
| • Interatrial septum
| • Interventricular septum
| • Left atrium
| • Left ventricle
| • Pulmonary trunk
| • Right atrium
| • Right ventricle
| • Superior vena cava
Table 3: List of quality criteria that can be automatically verified by the software
Quality criteria of the brain views |
For the transthalamic view, Sonio Detect automatically evaluates the following criteria: | Presence of the cavum septum pellucidum Absence of the cerebellum Brain occupies more than half of the width of the ultrasound image
For the transcerebellar view, Sonio Detect automatically evaluates the following criteria: | Presence of the cerebellum Presence of the cavum septum pellucidum Brain occupies more than half of the width of the ultrasound image
For the transventricular view, Sonio Detect automatically evaluates the following criteria: | Presence of the cavum septum pellucidum Brain occupies more than half of the width of the ultrasound image
Quality criteria of the heart views |
For the 4 chambers view, Sonio Detect automatically evaluates the following criteria: | Presence of the right ventricle Presence of the left ventricle Presence of the left atrium Presence of the right atrium Presence of the interventricular septum Presence of the interauricular septum Presence of the apex of the heart Presence of the descending aorta
For the 3 vessels and 3 vessels and trachea views, Sonio Detect automatically evaluates the following criteria: | Presence of the pulmonary trunk Presence of the ascending aorta Presence of the superior vena cava
For the LVOT view, Sonio Detect automatically evaluates the following criteria: | Presence of the left ventricle Presence of the aorta Presence of the right ventricle Presence of the left atrium Presence of the apex of the heart
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Fetal ultrasound images and clips
Anatomical Site
Not Found (Fetal anatomy, specifically brain and heart structures)
Indicated Patient Age Range
Gestational Age: from 11 weeks to 37 weeks
Intended User / Care Setting
Qualified and trained healthcare professional personnel in a professional prenatal ultrasound (US) imaging environment (this includes sonographers, MFMs, OB/GYN, and Fetal surgeons)
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 17885 fetal ultrasound images from 7 clinical sites in the United States, France and Germany representative of the intended use population. This 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)
Bench Testing
Sonio conducted a standalone performance testing on a dataset of 17885 fetal ultrasound images from 7 clinical sites in the United States, France and Germany representative of the intended use population. This 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 demonstrate that Sonio Detect:
- Automatically detects 3D fetal ultrasound images with high sensitivity (0.980: 95% . Wilson's Confidence Interval: 0.930, 0.994) and Doppler fetal ultrasound images with high sensitivity (0,963; 95% Wilson's Confidence Interval: 0.908, 0.985);
- . Automatically detects fetal ultrasound views through reading of annotations on images with high proportions of annotations read correctly (0,923; 95% Wilson's Confidence Interval: 0.905, 0.938);
- Automatically detects some T1 fetal ultrasound views with high sensitivity (0,942; ● Point estimate).
- Automatically detects some T2/T3 fetal ultrasound views with high sensitivity (0,919; ● Point estimate).
- Automatically detects some fetal brain anatomical structures in some T2/T3 brain views with high sensitivity (0.857; Point estimate) and high specificity (0.963; Point estimate), and so automatically verifies the corresponding quality criteria;
- Automatically detects some fetal heart anatomical structures in the some T2/T3 heart views with high sensitivity (0,900; Point estimate) and high specificity (0,982; Point estimate) and so automatically verifies the corresponding quality criteria.
- . Automatically verifies the zoom level for some brain views with high sensitivity (0.952; 95% Wilson's Confidence Interval: 0.909-0.976) and high specificity (0.906; 95% Wilson's Confidence Interval: 0.758-0.968).
Additionally the performance was also validated for subgroups including: Ultrasound machine manufacturer, BMI, maternal age, gestational age and ethnicity when appropriate.
Sonio Detect was validated only with GE, Canon, 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.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
- Sensitivity (3D fetal ultrasound images): 0.980 (95% Wilson's Confidence Interval: 0.930, 0.994)
- Sensitivity (Doppler fetal ultrasound images): 0.963 (95% Wilson's Confidence Interval: 0.908, 0.985)
- Proportions of annotations read correctly (fetal ultrasound views): 0.923 (95% Wilson's Confidence Interval: 0.905, 0.938)
- Sensitivity (some T1 fetal ultrasound views): 0.942 (Point estimate)
- Sensitivity (some T2/T3 fetal ultrasound views): 0.919 (Point estimate)
- Sensitivity (some fetal brain anatomical structures in some T2/T3 brain views): 0.857 (Point estimate)
- Specificity (some fetal brain anatomical structures in some T2/T3 brain views): 0.963 (Point estimate)
- Sensitivity (some fetal heart anatomical structures in the some T2/T3 heart views): 0.900 (Point estimate)
- Specificity (some fetal heart anatomical structures in the some T2/T3 heart views): 0.982 (Point estimate)
- Sensitivity (zoom level for some brain views): 0.952 (95% Wilson's Confidence Interval: 0.909-0.976)
- Specificity (zoom level for some brain views): 0.906 (95% Wilson's Confidence Interval: 0.758-0.968)
Predicate Device(s)
Reference Device(s)
No reference devices were used in this submission.
Predetermined Change Control Plan (PCCP) - All Relevant Information
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.
0
July 25, 2023
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Sonio % Florian Akpakpa Head of Regulatory Affairs and Quality Assurance 24 Rue du Faubourg Saint Jacques Paris. FR-75014 FRANCE
Re: K230365
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: June 26, 2023 Received: June 27, 2023
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 (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 located 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.
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
1
801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 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 4. Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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
Enclosure
2
Indications for Use
| Submission Number (if known) | K230365 |
|---|---|
| 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 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) |
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3
Image /page/3/Picture/1 description: The image contains the logo for Sonio. The logo consists of a stylized, abstract symbol on the left, resembling a curved shape with a dot above it. To the right of the symbol is the word "sonio" in lowercase letters. The color of both the symbol and the text is a shade of blue.
K230365
510(k) Summary
In accordance with 21 CFR 807.92 the 510(k) summary for Sonio Detect is provided below.
I. Submitter
| Applicant: | Sonio
24 rue du Faubourg Saint Jacques,
75014, 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 |
| Secondary Contact Person: | Donna-Bea Tillman
Senior Consulting
Biologics Consulting
Phone: +1 (410) 531-6542 - Direct
Email: dtillman@biologicsconsulting.com |
| Date Prepared: | July 25, 2023 |
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 Codes: | IYN (Primary) |
| IYO, QIH (Secondary) |
4
Image /page/4/Picture/0 description: The image shows the logo for Sonio. The logo consists of a stylized blue icon resembling a sound wave or a stylized letter 'S', followed by the word 'sonio' in a sans-serif font, also in blue. The logo is simple and modern, with a clean design.
III. Predicate Device
SonoLyst feature embedded in the GE Medical device Voluson SWIFT, K201828.
This predicate has not been subject to a design-related recall.
No reference devices were used in this submission.
IV. Device Description
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 requires the following:
- Edge Software (described below) to install on a server on the same network as the ● Ultrasound Machine;
- SaaS accessibility from internet browser.
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 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
5
Sonio
510(k) Premarket Notification Submission
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.
The list of views, anatomical structures and quality criteria that can be automatically detected and verified by the software are detailed in the tables 1, 2 and 3 below.
| Table 1: List of views that can be automatically detected by the software | |
|---|---|
| View | Gestational Age of the fetus |
| Transthalamic or Cavum septum Pellucidum or Midline falx / | |
| Transventricular or Choroid plexus | T1 and T2/T3 |
| Profile or Nuchal translucency | T1 and T2/T3 |
| Crown rump length | T1 |
| Sagittal spine | T2/T3 |
| Abdominal circumference | T2/T3 |
| Long bone | T2/T3 |
| Transcerebellar view | T2/T3 |
| Upper lip, nose and nostrils | T2/T3 |
| Four chambers | T2/T3 |
| Left ventricular outflow tract | T2/T3 |
| Right ventricular outflow tract | T2/T3 |
| Table 2:List of anatomical structures that can be automatically detected by the software | |||||
|---|---|---|---|---|---|
| View | Structure |
|---|---|
| Brain views and structures at T2/T3 | |
| Transthalamic view | • Midline falx |
| Transventricular view | • Cavum septum pellucidum |
| Transcerebellar view | • Cerebellum |
| Heart views and structures at T2/T3 | |
| Four Chambers | • Aorta |
| Three vessels | • Apex heart |
| LVOT | • Ascending aorta |
| • Descending aorta | |
| • Interatrial septum | |
| • Interventricular septum | |
| • Left atrium | |
| • Left ventricle | |
| • Pulmonary trunk | |
| • Right atrium | |
| • Right ventricle | |
| • Superior vena cava |
6
| Quality criteria of the brain views | |
|---|---|
| For the transthalamic view, Sonio | |
| Detect automatically evaluates the | |
| following criteria: | Presence of the cavum septum pellucidum Absence of the cerebellum Brain occupies more than half of the width of the |
| ultrasound image | |
| For the transcerebellar view, Sonio | |
| Detect automatically evaluates the | |
| following criteria: | Presence of the cerebellum Presence of the cavum septum pellucidum Brain occupies more than half of the width of the |
| ultrasound image | |
| For the transventricular view, Sonio | |
| Detect automatically evaluates the | |
| following criteria: | Presence of the cavum septum pellucidum Brain occupies more than half of the width of the |
| ultrasound image | |
| Quality criteria of the heart views | |
| For the 4 chambers view, Sonio | |
| Detect automatically evaluates the | |
| following criteria: | Presence of the right ventricle Presence of the left ventricle Presence of the left atrium Presence of the right atrium Presence of the interventricular septum Presence of the interauricular septum Presence of the apex of the heart Presence of the descending aorta |
| For the 3 vessels and 3 vessels and | |
| trachea views, Sonio Detect | |
| automatically evaluates the following | |
| criteria: | Presence of the pulmonary trunk Presence of the ascending aorta Presence of the superior vena cava |
| For the LVOT view, Sonio Detect | |
| automatically evaluates the following | |
| criteria: | Presence of the left ventricle Presence of the aorta Presence of the right ventricle Presence of the left atrium Presence of the apex of the heart |
Table 3: List of quality criteria that can be automatically verified by the software
7
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 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 and the predicate device have the same intended use for automatic detection of views as well as automatic detection of anatomical structures within the views and verification of quality criteria of the views by comparing them to standardized quality criteria. The Indications for Use statement for Sonio Detect is not identical to the predicate device: however. the difference does not affect the safety and effectiveness of the device relative to the predicate, and so does not constitute a new intended use.
VI. Comparison of Technological Characteristics with the Predicate Device
Table 4 provides a comparison of the Technological Characteristics of Sonio Detect to the predicate SonoLyst.
| Items | Predicate device 1: SonoLyst feature in
Voluson SWIFT - K201828 | Proposed device: Sonio Detect |
|-------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Manufacturer name | GE Medical | Sonio |
| Device name | SonoLyst feature embedded in the device
Voluson SWIFT | Sonio Detect |
| Regulation Number | - Ultrasonic Pulsed Doppler Imaging System. 21 CFR 892.1550, 90-IYN;
-Ultrasonic Pulsed Echo Imaging System, 21 CFR 892.1560, 90-IYO;
-Diagnostic Ultrasound Transducer, 21 CFR 892.1570, 90-ITX | - Accessory to Ultrasonic Pulsed Doppler Imaging System, 21 CFR 892.1550
-
Accessory to Ultrasonic Pulsed Echo Imaging System, 21 CFR 892.1560
-
Medical image management and processing system, 21 CFR 892.2050 |
| Product codes | IYN (primary)
IYO
ITX (secondary) | IYN (Primary)
IYO, QIH (Secondary) |
| Clinical outcome | - Images labeled with correct view -
Quality criteria are identified as “found” when detected and “not found” when not detected | - Images labeled with correct view
-
Quality criteria are identified as “Verified” when detected and “Not verified” when not detected |
Table 4: Comparison of technological characteristics
8
Image /page/8/Picture/0 description: The image shows the logo for Sonio. The logo consists of a blue abstract symbol on the left and the word "sonio" in blue on the right. The symbol appears to be a stylized representation of a sound wave or a stylized letter S.
Sonio 510(k) Premarket Notification Submission
| Items | Predicate device 1: SonoLyst feature in
Voluson SWIFT - K201828 | Proposed device: Sonio Detect |
|--------------------------|--------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Intended
Users | General purpose radiology evaluation and
specialized for OB/GYN | Qualified and trained healthcare professional
personnel in a professional prenatal
ultrasound (US) imaging environment (this
includes sonographers, MFMs, OB/GYN,
and Fetal surgeons) |
| Clinical
applications | Fetal/Obstetrics | Fetal/Obstetrics |
| Algorithm
Methodology | Artificial Intelligence | Artificial Intelligence
Lecture of biometrics
Colorimetry for 3D and Doppler |
| Platform | Embedded in the ultrasound equipment | Secure cloud-based and stand-alone software
compatible with ultrasound system from GE
Medical, Samsung and Canon |
Sonio Detect's intended users, clinical outcome and clinical applications are similar to those of the predicate device, SonoLyst.
Sonio Detect differ to SonoLyst in the following:
- Algorithm methodology: Sonio Detect algorithm technology is based on Artificial ● Intelligence, Lecture of biometrics on the image and Colorimetry identification for 3D and Doppler while the Predicate SonoLyst's algorithm technology is only based on Artificial Intelligence:
- Platform: Sonio Detect is a stand-alone cloud based software that can be used with . different ultrasound systems while the Predicate SonoLyst is embedded in the GE ultrasound system.
However, the differences in algorithm methodology and platform do not raise different questions of safety and effectiveness of the device.
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, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices."
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 verification ●
- Simulated use testing (Validation) ●
- Performance testing (Verification)
9
Bench Testing
Sonio conducted a standalone performance testing on a dataset of 17885 fetal ultrasound images from 7 clinical sites in the United States, France and Germany representative of the intended use population. This 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 demonstrate that Sonio Detect:
- Automatically detects 3D fetal ultrasound images with high sensitivity (0.980: 95% . Wilson's Confidence Interval: 0.930, 0.994) and Doppler fetal ultrasound images with high sensitivity (0,963; 95% Wilson's Confidence Interval: 0.908, 0.985);
- . Automatically detects fetal ultrasound views through reading of annotations on images with high proportions of annotations read correctly (0,923; 95% Wilson's Confidence Interval: 0.905, 0.938);
- Automatically detects some T1 fetal ultrasound views with high sensitivity (0,942; ● Point estimate).
- Automatically detects some T2/T3 fetal ultrasound views with high sensitivity (0,919; ● Point estimate).
- Automatically detects some fetal brain anatomical structures in some T2/T3 brain views with high sensitivity (0.857; Point estimate) and high specificity (0.963; Point estimate), and so automatically verifies the corresponding quality criteria;
- Automatically detects some fetal heart anatomical structures in the some T2/T3 heart views with high sensitivity (0,900; Point estimate) and high specificity (0,982; Point estimate) and so automatically verifies the corresponding quality criteria.
- . Automatically verifies the zoom level for some brain views with high sensitivity (0.952; 95% Wilson's Confidence Interval: 0.909-0.976) and high specificity (0.906; 95% Wilson's Confidence Interval: 0.758-0.968).
Additionally the performance was also validated for subgroups including: Ultrasound machine manufacturer, BMI, maternal age, gestational age and ethnicity when appropriate.
Sonio Detect was validated only with GE, Canon, 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 SonoLyst.
The technological characteristics differences identified and discussed in Section VI do not raise 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 device SonoLyst (K201828).