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 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.

AI/ML Overview

Here's a detailed breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for Sonio Detect:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for "Sonio Detect" are primarily reflected in the performance metrics presented in Table 6, specifically Sensitivity and Specificity for various detection tasks. The document does not explicitly state pre-defined thresholds for these metrics as "acceptance criteria" but rather reports the achieved performance. However, for the purpose of this response, we infer the reported performance values as the demonstrated capability that met FDA's requirements for substantial equivalence.

Acceptance Criterion (Implicitly, the reported performance)	Reported Device Performance (Point Estimate)	95% Wilson CI (Lower Bound)	95% Wilson CI (Upper Bound)
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
Automatic detection of 8 fetal brain anatomical structures on the views "Transthalamic", "Transventricular", "Transcerebellar" at T2/T3 (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
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 (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
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 (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
Automatic detection of 4 fetal placenta anatomical structures on the views "Placenta insertion", "Placenta location" at T2/T3 (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
Automatic detection of 8 fetal CRL/NT/Profile anatomical structures on the views "Crown Rump Length", “Nuchal Translucency”, “Profile” at T1 (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
Automatic detection of 6 fetal CRL/NT/Profile anatomical structures on the views "Crown Rump Length", “Nuchal Translucency”, “Profile” at T2/T3 (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
Automatic detection of the Anterior placenta location for the views "Placenta insertion", "Placenta location" at T2/T3 (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
Automatic detection of the Posterior placenta location for the views "Placenta insertion", "Placenta location" at T2/T3 (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
Automatic detection of the "Female sex" for fetal sex for the view "External Genitalia" (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
Automatic detection of the "Male sex" for fetal sex for the view "External Genitalia" (Specificity)	0.977	0.942	0.991

2. Sample Size Used for the Test Set and Data Provenance

Test Set Sample Size: 36,769 fetal ultrasound images.
Data Provenance: The document states this was a "global validation dataset." While specific countries are not mentioned, the use of "global" implies a diverse set of origins. It is also noted that the data was independent of that used for model development (training/fine-tuning/internal validation). The document does not explicitly state if the data was retrospective or prospective. Given it's a "validation dataset" of "images," it's typically retrospective, collected prior to the full validation study.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts

The document does not explicitly state the number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience") used to establish the ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not describe the adjudication method (e.g., 2+1, 3+1, none) used for establishing the ground truth for the test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Was an MRMC study done? No, the document explicitly states: "Clinical Study: Not applicable. Clinical studies are not necessary to establish the substantial equivalence of this device." This indicates that no MRMC comparative effectiveness study was conducted to assess the improvement of human readers with AI assistance. The performance reported is a standalone (algorithm only) performance.
Effect size of human readers improving with AI vs. without AI assistance: Not applicable, as no MRMC study was performed.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Was a standalone study done? Yes. The document clearly states: "Sonio conducted a standalone performance testing on a dataset of 36 769 fetal ultrasound images."

7. Type of Ground Truth Used

The ground truth for the test set was established through "reading of annotations on images" (as mentioned in Table 6). While the specific method of establishing these annotations (e.g., single expert, expert consensus, pathology, outcomes data) is not detailed, it would inherently involve expert review to create the "annotations." Given the nature of ultrasound image interpretation, it is highly likely based on expert consensus or expert-reviewed annotations, but this is not explicitly stated. It is inferred to be expert-derived given the context of medical image analysis.

8. Sample Size for the Training Set

The document states that the global validation dataset (36,769 images) was "independent of the data used during model development (training/fine tuning/internal validation)." However, it does not provide the specific sample size of the training set.

9. How the Ground Truth for the Training Set Was Established

The document mentions "model development (training/fine tuning/internal validation)," which implies that ground truth was established for these datasets to train and validate the AI models. However, it does not explicitly describe the method for establishing this ground truth (e.g., number of experts, qualifications, adjudication method). It can be inferred that a similar process involving expert annotations or review would have been used as for the test set, but this is not detailed.

Summary

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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).

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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

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Indications for Use

Form Approved: OMB No. 0910-0120 Expiration Date: 07/31/2026 See PRA Statement below.

Submission Number (if known)

K240406

Device Name

Sonio Detect

Indications for Use (Describe)

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.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."

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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

K240406

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

I. Submitter

Applicant:	Sonio17 Rue du Faubourg Montmartre,75009, Paris France
Primary Contact Person:	Florian AkpakpaHead of Regulatory Affairs and Quality AssuranceSonioPhone: +33 6 19 38 71 45Email: 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 System21 CFR 892.1560 - accessory to Ultrasonic Pulsed Echo Imaging System21 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.

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IV. Device Description

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).

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

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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 Plexus2. Profile/Nuchal translucency3. 4 Chambers4. Abdominal circumference5. Hand6. Foot7. Crown Rump Length
Second and Third trimester	1. Transthalamic or Cavum septum pellucidum or Midline falx/Transventricular or Choroid Plexus2. Transcerebellar view3. Profile4. Lips and Nose5. Orbits6. 4 Chambers7. LVOT8. RVOT9. 3 vessels/3 vessels and trachea10. Sagittal Spine11. Abdominal circumference12. Axial Bladder13. Axial Kidneys14. Long bone15. Hand16. Foot17. External genitalia (female and male)18. 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	FirstTrimester T1	Second/ThirdTrimester T2/T3
Brain views & structures
Transthalamicview	Thalami on the transthalamic view	-	X
	Cavum septum pellucidum	-	X
	Pillars of the fornix	-	X
Transventricularview	Sylvian fissure	-	X
view	Ventricle	-	X
Transcerebellarview	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 chambers3 vessels	Left ventricle	X	X
3 vessels andtrachea	Mitral valve	-	X
	Pulmonary vein	-	X
RVOTLVOT	Right atrium	X	X
	Right ventricle	X	X
Abdominalcircumference	Stomach	X	X
Axial view ofthe kidneys	Superior vena cava	-	X
	Tricuspid valve	-	X
	Umbilical vein	-	X
	Ascending aorta on LVOT View	-	X
	Ascending aorta on RVOT or 3 vesselsview	-	X
	Pulmonary artery trunk on 3 vesselsView	-	X
	Pulmonary artery with visiblebifurcation	-	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	FirstTrimester T1	Second/ThirdTrimester T2/T3
CRL/NT/Profile/Corpus callosum views & structures
CRLNTProfileCorpusCallosum	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
Placentainsertion	Cervix	-	X
	Maternal bladder	-	X
	Internal cervical os	-	X
	Placenta	-	X
View name	Quality criteria	FirsttrimesterT1	Second/ThirdTrimesterT2/T3
Quality criteria of the brain views
Transthalamicview	Presence of the cavum septum pellucidumor 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
Transventricularview	Presence of the ventricle	-	X
	Absence of the thalami	-	X
	Presence of the cavum septum pellucidum	-	X
Transcerebellarview	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 chambersview	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 3vessels andtrachea 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	FirsttrimesterT1	Second/ThirdTrimesterT2/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 withvisible bifurcation	-	X
	Presence of the right ventricle	-	X
	Presence of the ascending aorta	-	X
	Presence of the stomach	X	X
Abdominalcircumferenceview	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 ofthe two kidneys	Presence of two kidneys	-	X
	Absence of the stomach	-	X
Quality criteria of CRL/NT/Profile/Corpus callosum views
NuchalTranslucencyview	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	FirsttrimesterT1	Second/ThirdTrimesterT2/T3
Profile view	Presence of the palate	-	X
Profile view	Presence of the nasal bone	-	X
CorpusCallosum view	Presence of the corpus callosum	-	X
	Presence of the plexus choroid(thirdventricle)	-	X
	Presence of the midbrain tectum	-	X
	Presence of the vermis	-	X
Quality criteria of Placenta view
Placentainsertion	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 trimesterT1	Second/ThirdTrimester T2/T3
Genitalia view	Male	-	X
	Female	-	X
Placenta location	Anterior	-	X
	Posterior	-	X

V. Indications for Use

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
Manufacturer name	Sonio	Sonio
Device name	Sonio Detect	Sonio Detect
RegulationNumber	21 CFR 892.1550 - accessory toUltrasonic Pulsed Doppler ImagingSystem21 CFR 892.1560 - accessory toUltrasonic Pulsed Echo Imaging System21 CFR 892.2050 - Medical ImageManagement and Processing System	21 CFR 892.1550 - accessory toUltrasonic Pulsed Doppler ImagingSystem21 CFR 892.1560 - accessory toUltrasonic Pulsed Echo Imaging System21 CFR 892.2050 - Medical ImageManagement and Processing System
Product code	IYN (primary)IYO, QIH (Secondary)	IYN (primary)IYO, QIH (Secondary)
Features	- Sonio Detect automatically detectsviews- Sonio Detect automatically detectsanatomical structures within thesupported views- Sonio Detect automatically verifies thequality criteria of the supported views bychecking whether they conform tostandardized quality criteria.	- Sonio Detect automatically detectsviews- Sonio Detect automatically detectsanatomical structures within thesupported views- Sonio Detect automatically verifies thequality criteria and characteristics of thesupported views.
AlgorithmMethodology	Artificial IntelligenceLecture of biometricsColorimetry for 3D and Doppler	Artificial IntelligenceLecture of biometricsColorimetry for 3D and Doppler
Platform	Secure cloud-based and stand-alonesoftware compatible with ultrasoundsystem from GE Medical, Samsung andCanon	Secure cloud-based and stand-alonesoftware compatible with ultrasoundsystem 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 andcharacteristics automaticallydetected)	Sensitivity		Specificity
	PointEstimate	Wilson CI(95%)	PointEstimate	Wilson CI(95%)
Automatic detection of 3D fetalultrasound images	0.892	(0.836-0.931)	-	-
Automatic detection of Dopplerfetal ultrasound images	0.973	(0.937-0.988)	-	-
Automatic detection of fetalultrasound views through reading ofannotations on images	0.913	(0.852-0.951)	-	-
Automatic detection of 7 T1 fetalultrasound images	0.914	(0.906-0.921)	-	-
Automatic detection of 18 T2/T3fetal ultrasound images	0.937	(0.933-0.940)	-	-
Automatic detection of 8 fetal brainanatomical 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 fetalthorax and heart anatomicalstructures on the views "Fourchambers", "LVOT", “RVOT","Three vessels or Three vessels andtrachea", "AbdominalCircumference", "Axial view of thekidneys" at T1	0.861	(0.841-0.878)	0.938	(0.926-0.948)
Automatic detection of 21 fetalthorax and heart anatomicalstructures on the views "Fourchambers", "LVOT", “RVOT”,"Three vessels or Three vessels andtrachea", "AbdominalCircumference”, “Axial view of thekidneys" at T2/T3	0.919	(0.913-0.924)	0.976	(0.974-0.978)
Automatic detection of 4 fetalplacenta anatomical structures onthe 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 andcharacteristics automaticallydetected)	Sensitivity		Specificity
	PointEstimate	Wilson CI(95%)	PointEstimate	Wilson CI(95%)
Automatic detection of 8 fetalCRL/NT/Profile anatomicalstructures on the views "CrownRump Length", “NuchalTranslucency”, “Profile” at T1	0.898	(0.885-0.910)	0.862	(0.845-0.878)
Automatic detection of 6 fetalCRL/NT/Profile anatomicalstructures on the views "CrownRump Length", “NuchalTranslucency”, “Profile” at T2/T3	0.893	(0.879-0.906)	0.956	(0.949-0.962)
Automatic detection of the Anteriorplacenta location for the views"Placenta insertion", "Placentalocation" at T2/T3	0.959	(0.918-0.980)	0.966	(0.924-0.986)
Automatic detection of the Posteriorplacenta location for the views"Placenta insertion", "Placentalocation" at T2/T3	0.966	(0.924-0.986)	0.959	(0.918-0.980)
Automatic detection of the "Femalesex" for fetal sex for the view"External Genitalia"	0.977	(0.942-0.991)	0.987	(0.963-0.996)
Automatic detection of the "Malesex" 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).

Regulation Number and Section

§ 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.