Sonio Detect (v3)

K252433 · Sonio · IYN · Mar 16, 2026 · Radiology

Device Facts

• Record ID: K252433
• Device Name: Sonio Detect (v3)
• Applicant: Sonio
• Product Code: IYN · Radiology
• Decision Date: Mar 16, 2026
• Decision: SESE
• Submission Type: Traditional
• Regulation: 21 CFR 892.1550
• Device Class: Class 2
• Attributes: AI/ML, Software as a Medical Device, Pediatric

Intended 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.

Device Story

Sonio Detect is a SaaS-based concurrent reading aid for fetal ultrasound exams; used by sonographers, OB/GYN MFMs, and fetal surgeons. Input: real-time fetal ultrasound images/clips from GE, Samsung, Canon, or Philips machines. Operation: Edge software on local server receives DICOM instances; uploads to cloud for AI analysis. AI automatically detects views, anatomical structures, and quality criteria; outputs bounding boxes for views/structures. Highlights unverified items in yellow; allows user to override/edit assignments. Benefit: assists HCPs in ensuring exam completeness and protocol adherence; improves workflow efficiency during routine fetal ultrasound.

Clinical Evidence

Bench testing only. No clinical study performed. Performance validated on an independent dataset of 22,496 fetal ultrasound images. Results show high sensitivity (0.87-0.94) and specificity (0.80-0.99) for view/structure detection across trimesters. Localization performance measured by mIoU (0.679-0.777). Subgroup analysis performed for ultrasound manufacturer, BMI, maternal age, image quality, and gestational age.

Technological Characteristics

SaaS solution; cloud-based and edge software. Compatible with GE, Samsung, Canon, and Philips ultrasound systems. Uses AI/ML for image analysis. Outputs include view detection, anatomical structure detection, quality criteria verification, and bounding box localization. No specific hardware materials or sterilization required as it is a software-only accessory.

Indications for Use

Indicated for use by healthcare professionals (sonographers, OB/GYN MFMs, fetal surgeons) to assist in the acquisition and interpretation of fetal ultrasound images during routine examinations for trimesters 1, 2, and 3 (gestational age 11 to 41 weeks).

Regulatory Classification

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.

Predicate Devices

• Sonio Detect v2 (K240406)

Submission Summary (Full Text)

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FDA U.S. FOOD & DRUG ADMINISTRATION

March 16, 2026

Sonio
Florian Akpakpa
Director of Regulatory Affairs and Compliance Officer
147 rue d'Aboukir
Paris, 750002
France

Re: K252433
Trade/Device Name: Sonio Detect (v3)
Regulation Number: 21 CFR 892.1550
Regulation Name: Ultrasonic Pulsed Doppler Imaging System
Regulatory Class: Class II
Product Code: IYN, IYO, QIH
Dated: February 14, 2026
Received: February 17, 2026

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.

U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov

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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).

Your device is also subject to, among other requirements, the Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13485 clause 8.3 (Nonconforming product), ISO 13485 clause 8.5.2 (Corrective action), and ISO 13485 clause 8.5.3 (Preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and ISO 13485 clause 7.5) and document changes and approvals in the Medical Device File (ISO 13485 clause 4.2.3).

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 (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-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (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.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

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-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/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-devices/device-advice-comprehensive-regulatory-

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assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email ([e-mail]) or phone [phone] 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

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| Indications for Use | | |
| --- | --- | --- |
| Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions. | K252433 | ? |
| Please provide the device trade name(s). | | ? |
| Sonio Detect (v3) | | |
| Please provide your Indications for Use below. | | ? |
| 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. | | |
| Please select the types of uses (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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Sonio

510(k) Premarket Notification Submission

S

sonio

K252433

# 510(k) Summary

In accordance with 21 CFR 807.92 the 510(k) summary for Sonio Detect v3 is provided below.

# I. Submitter

| Applicant: | Sonio
147 rue d’Aboukir,
75002, Paris France |
| --- | --- |
| Primary Contact Person: | Florian Akpakpa
Director Regulatory Affairs and Compliance Officer
Sonio
Phone: [phone]
Email: [e-mail] |
| Date Prepared: | February 13, 2026 |

# 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 v2 cleared in K240406.

This predicate has not been subject to a design-related recall.

No reference devices were used in this submission.

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# 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 41 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
- Automatically output bounding box for views and structures

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

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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.

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. Choroid Plexus
2. Profile/Nuchal translucency
3. Abdominal circumference
4. Hand
5. Foot
6. Crown Rump Length
7. Doppler 3VT
8. Lower Arm
9. Upper Leg
10. Lower Leg
11. Doppler 4CH
12. Adnexa
13. Ovaries
14. Umbilical cord insertion |
| Second and Third trimester | 1. Transthalamic or Cavum septum pellucidum or Midline falx/Transventricular or Choroid Plexus
2. Transcerebellar view
3. Profile
4. Lips and Nose
5. Orbits
6. 4 Chambers
7. LVOT
8. Sagittal Spine
9. Abdominal circumference
10. Axial Bladder
11. Axial Kidneys
12. Cervix/Placenta
13. Hand
14. Foot
15. External genitalia (female or male)
16. 3 Vessels/3 Vessels and Trachea (3VV)
17. 3 Vessels and Trachea (3VT)
18. Doppler 3VT |

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sonio

# 510(k) Premarket Notification Submission

| Trimester | View |
| --- | --- |
| | 19. Humerus
20. Femur
21. Radius/Cubitus
22. Tibia/Fibula
23. Upper arm
24. Lower arm
25. Upper leg
26. Adnexa
27. Ovaries
28. Placenta Cord Insertion
29. Coronal Face
30. Doppler 4CH
31. Umbilical Cord Insertion
32. Coronal Kidneys
33. Diaphragm
34. Maxilla
35. Mandible
36. Aortich Arch
37. Ductal Arch
38. Bicaval View
39. Palate
40. Corpus Callosum |

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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 Trimesters T2/T3 |
| --- | --- | --- | --- |
| Brain views & structures | | | |
| Transthalamic
Transventricular
Transcerebellar
Choroid plexus | Thalami on the transthalamic view | X | X |
| | Cavum septum pellucidum | - | X |
| | Sylvian fissure | - | X |
| | Choroid Plexus | - | X |
| | Cisterna Magna | - | X |
| | Cerebellum | - | X |
| | Vermis | - | X |
| Thorax and Heart views & structures | | | |
| 4 chambers
3VV
3VT
RVOT
LVOT
Abdominal circumference
Axial view of the kidneys
Diaphragm
Crown Rump length
Abdominal cord insertion | Adrenal gland | - | - |
| | Apex of the heart | - | X |
| | Descending aorta | - | X |
| | Interatrial septum | - | X |
| | Interventricular septum | X | X |
| | Kidneys | - | - |
| | Left atrium | X | X |
| | Left ventricle | X | X |
| | Right atrium | - | X |
| | Right ventricle | - | X |

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# 510(k) Premarket Notification Submission

| View name | Structures to be detected | First Trimester T1 | Second/Third Trimesters T2/T3 |
| --- | --- | --- | --- |
| | Spine | - | - |
| | Stomach (axial / sagittal) | X | X |
| | Superior vena cava | - | X |
| | Tricuspid valve | - | X |
| | Umbilical vein | - | X |
| | Aorta on LVOT View | - | X |
| | Aorta on RVOT or 3 vessels view | - | X |
| | Pulmonary artery trunk on 3 vessels View | - | X |
| | Pulmonary artery with visible bifurcation | - | X |
| | Portal sinus | - | X |
| | Bowels | X | X |
| | Spleen | - | X |
| | Gallbladder | - | X |
| | Bladder | X | X |
| | Trachea | - | - |
| | Liver | - | X |
| | Lungs | - | X |
| | Ribs | X | X |
| | Sagittal heart | X | - |
| | CRL/NT/Profile views & structures | | |
| CRL Profile/NT Corpus Callosum | Nasal bone | - | - |
| | Diencephalon | X | - |

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| View name | Structures to be detected | First Trimester T1 | Second/Third Trimesters T2/T3 |
| --- | --- | --- | --- |
| | Fourth ventricle on NT view | X | - |
| | Nuchal translucency | - | - |
| | Palate | X | X |
| | Corpus Callosum | - | - |
| | Liquid space under the chin | X | - |
| | Midbrain tectum | - | - |
| | Choroid plexus on sagittal plane | - | - |
| | Cisterna magna on NT view | X | - |
| | Brainstem on NT view | X | - |
| | Tip of the mandible | - | - |
| Placenta views | | | |
| Placenta / Cervix | Cervix | - | X |
| | Maternal bladder | - | X |
| | Placenta | - | X |
| Spine views | | | |
| Sagittal spine | Cervical Spine | - | X |
| | Lumbar Spine | - | X |
| | Sacral Spine | - | X |
| Facial views | | | |
| Lips and nose
Orbits
Coronal face | Lenses | - | - |
| | Orbits | - | X |
| | Nose tip | - | X |

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# 510(k) Premarket Notification Submission

| View name | Structures to be detected | First Trimester T1 | Second/Third Trimesters T2/T3 |
| --- | --- | --- | --- |
| | Nare | - | X |
| | Upper lip | - | X |
| | Lower lip | - | X |

# List of localization that can be automatically identified (bounding box)

Bounding boxes can be automatically detected and displayed for the views and anatomical structures.

| View name | Structure name |
| --- | --- |
| Brain views & structures | |
| Choroid plexus | Thalami/cerebral peduncles on axial view |
| Thorax and Heart views & structures | |
| - | Left Ventricle |
| | Left Atrium |
| | Ribs |

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Quality criteria are related to:
- The presence of an anatomical structure;
- The absence of an anatomical structure;

For instance the transthalamic view, at the second and third trimester, Sonio Detect can evaluate the following criteria.

Table 3: List of quality criteria that can be automatically verified by Sonio Detect

| Quality criteria | 1st trimester | 2nd/3rd Trimester |
| --- | --- | --- |
| 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 | X |

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 |

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# 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.

Sonio Detect v3 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 v3 and the predicate differ in the clinical outcomes. Sonio Detect v3 outputs localization for views and anatomical structures whereas the predicate Sonio Detect v2 does not output localization.

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 v3 to the predicate Sonio Detect v2 cleared in K240406.

Table 5: Comparison of technological characteristics

| Items | Predicate device: Sonio Detect v2 | Proposed device : Sonio Detect v3 |
| --- | --- | --- |
| Manufacturer 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) |

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# 510(k) Premarket Notification Submission

| Items | Predicate device: Sonio Detect v2 | Proposed device : Sonio Detect v3 |
| --- | --- | --- |
| Features | - 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. | - Sonio Detect automatically detect views
- Sonio Detect automatically detect anatomical structures within the supported views
- Sonio Detect automatically verifies the quality criteria and characteristics of the supported views.
- Sonio Detect automatically localizes views and anatomical structures |
| Algorithm Methodology | Artificial Intelligence
Lecture of biometrics
Colorimetry for 3D and Doppler | Artificial Intelligence |
| Platform | Secure cloud-based and stand-alone software compatible with ultrasound system from GE Medical, Samsung, Canon and Philips | Secure cloud-based and stand-alone software compatible with ultrasound system from GE Medical, Samsung, Canon and Philips |

Sonio Detect v3 and its predicate differs in the following:

- Features : Sonio Detect v3 automatically localizes views and anatomical structures.
- Algorithm Methodology : Sonio Detect v3 uses Artificial Intelligence and does not include lectures of biometrics and colorimetry for 3D and Doppler.

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

Sonic conducted a standalone performance testing on a dataset of 22496 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 v3 performs the following, as summarized in table below:

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Table 6: results of the standalone performance testing

| Items (fetal ultrasound views, anatomical structures and characteristics automatically detected) | Sensitivity PE (95%CI) | Specificity PE (95%CI) |
| --- | --- | --- |
| Automatic detection of 14 T1 fetal ultrasound images | 0.886 (0.876-0.898) | 0.981 (0.979-0.982) |
| Automatic detection of 40 T2/T3 fetal ultrasound images | 0.901 (0.896-0.905) | 0.993 (0.993-0.994) |
| Automatic localization of 1 view (T1) | mIoU: 0.777 (0.743-0.811) | |
| Automatic detection of 1 fetal brain anatomical structure on the view “Transthalamic” at T1 | 0.815 (0.751-0.871) | 0.938 (0.901-0.973) |
| Automatic detection of 7 fetal brain anatomical structures on the views “Transthalamic”, “Transventricular”, “Transcerebellar” at T2/T3 | 0.941 (0.934-0.947) | 0.951 (0.943-0.958) |
| Automatic detection of 8 fetal thorax and heart anatomical structures on the views “4 chambers”, “LVOT”, “RVOT”, “Three vessels”, “Three vessels and trachea”, “Abdominal Circumference”, “Axial view of the kidneys”, “Diaphragm”, “Abdominal cord insertion” at T1 | 0.872 (0.848-0.892) | 0.921 (0.913-0.928) |
| Automatic detection of 24 fetal thorax and heart anatomical structures on the views “Four chambers”, “LVOT”, “RVOT”, “Three vessels”, “Three vessels and trachea”, “Abdominal Circumference”, “Axial view of the kidneys”, “Diaphragm”, “Abdominal cord insertion” at T2/T3 | 0.914 (0.906-0.920) | 0.963 (0.961-0.965) |
| Automatic detection of 3 fetal placenta anatomical structures on the view “Placenta / Cervix” at T2/T3 | 0.887 (0.874-0.899) | 0.962 (0.950-0.972) |

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# 510(k) Premarket Notification Submission

| Items (fetal ultrasound views, anatomical structures and characteristics automatically detected) | Sensitivity PE (95%CI) | Specificity PE (95%CI) |
| --- | --- | --- |
| Automatic detection of 6 fetal Sagittal Fetus anatomical structures on the views “Crown Rump Length”, “Profile” at T1 | 0.869 (0.849-0.896) | 0.848 (0.822-0.875) |
| Automatic detection of 1 fetal Sagittal Fetus anatomical structures on the views “Profile” at T2/T3 | 0.883 (0.852-0.913) | 0.800 (0.754-0.842) |
| Automatic detection of 5 fetal Coronal Face anatomical structures on the views “Lips and nose”, “Orbits”, “Coronal face” at T2/T3 | 0.922 (0.897-0.947) | 0.901 (0.883-0.923) |
| Automatic detection of 3 fetal Spine anatomical structures on the view “Sagittal Spine” at T2/T3 | 0.839 (0.818-0.862) | 0.852 (0.829-0.873) |
| Automatic localization of 1 brain anatomical structures (on T1) | mIoU: 0.683 (0.632-0.734) | |
| Automatic localization of 3 thorax and heart anatomical structures (on T1) | mIoU: 0.679 (0.653-0.705) | |
| Automatic detection of the Anterior placenta location for the view “Placenta / Cervix” at T2/T3 | 0.925 (0.894-0.949) | 0.918 (0.885-0.948) |
| Automatic detection of the Posterior placenta location for the views “Placenta / Cervix”at T2/T3 | 0.918 (0.885-0.948) | 0.925 (0.894-0.949) |
| Automatic detection of the “Female sex” for fetal sex for the view “External Genitalia” at T2/T3 | 1.000 (1.000-1.000) | 0.985 (0.969-1.000) |
| Automatic detection of the “Male sex” for fetal sex for the view “External Genitalia” at T2/T3 | 0.985 (0.969-1.000) | 1.000 (1.000-1.000) |

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Additionally, the performance for the detection of views and structures was also validated for subgroups including: Ultrasound machine manufacturer, BMI, maternal age, confounding cases, Image quality, geography, gestational age and race/ethnicity when appropriate.

Sonic 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 intended users, intended use and clinical applications are similar to those of the predicate device Sonio Detect v2, the cleared version in K240406.

The clinical outcome differences identified and discussed in Section II do not raise different questions of safety and effectiveness of the device.

The features and algorithm methodology differences identified and discussed in Section III 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 Sonio Detect v2 (K240406).

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