brAIn™ Shoulder Positioning is intended to be used as an information tool to assist in the preoperative surgical planning and visualization of a primary total shoulder replacement.

Device Description

The brAIn™ Shoulder Positioning software is a cloud-based application intended for shoulder surgeons. It is used to plan primary anatomic and reverse total shoulder replacement surgeries using FX Shoulder Solutions implants. The software is a webbased interface, where the user is prompted to upload their patient's shoulder CT-scan (DICOM series) accompanied with their information in a dedicated interface. The software automatically segments (using machine learning) and performs measurements on the scapula and humerus anatomy contained in the DICOM series. These segmentations are used for planning, which includes an interactive 3D viewer that allows for soft tissue visualization. Implants for the glenoid and humerus are positioned using this same 3D interface through a dedicated manipulation panel. The changes in shoulder anatomy resultant from the implants are relayed in a post-position interface that displays information related to distalization. The software outputs a planning multimodal summary that includes textual information (patient information, pre- and post-op measurements) and visual information (screen captures of the shoulder pre- and postimplantation).

AI/ML Overview

Here's the information about the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Acceptance Criteria	Reported Device Performance
Segmentation Performance: Mean Dice Similarity Coefficient (DSC) on the testing set greater than or equal to 0.95 for automatic segmentation when validated against manual segmentation.	All tests confirmed that the segmentation performance meets the acceptance criteria (DSC ≥ 0.95). The validation criterion was a Dice Similarity Coefficient of 0.95 or higher.
Shoulder Side Detection Performance: Correct detection of shoulder side (right or left) in DICOM images when compared to manual assessment.	All performance tests for Shoulder Side Detection validation were successfully completed with no deviations, confirming compliance with the required performance standards.
Measurement Accuracy Performance: Accuracy of software measurements when editing landmark positions similar to the reported accuracy of the predicate device.	All performance tests for Measurement Accuracy Validation were successfully completed with no deviations, confirming compliance with the required performance standards. The text does not provide a specific numerical acceptance criterion for this, but states it met "required performance standards" by being similar to the predicate.
Landmark Performance: Mean distance of 3 mm for landmark positions when compared to final positions adjusted by experts.	All performance tests for landmark validation were successfully completed with no deviations, confirming compliance with the required performance standards, with a 3 mm mean distance as the acceptance criterion.

Study Details

2. Sample size used for the test set and the data provenance:

Test Set Sample Size: 173 samples (pairs of 3D images with segmentation labels).
Data Provenance: The data corresponds to patients who underwent arthroplasty with an FX Shoulder implant, without specific selection. It represents diversity in shoulder types, imaging equipment, institutions, study year, and geographical provenance.
- Geographical Origin (Test Set):
  - Left shoulder (79 samples): 58.2% Europe (46), 41.8% USA (33)
  - Right shoulder (94 samples): 56.4% Europe (53), 43.6% USA (41)
Retrospective/Prospective: Not explicitly stated, but the description "data corresponds to patients that under arthroplasty... without any further specific selection" suggests it is likely retrospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

Number of Experts: Not explicitly stated.
Qualifications of Experts: For segmentation, the labels were created "manually by medical professionals." For shoulder side detection, ground truth was a "manual assessment performed by a Clinical Solutions Specialist." For landmark performance, ground truth involved "final positions adjusted by experts." Specific qualifications (e.g., years of experience, specialty) are not provided beyond "medical professionals" and "Clinical Solutions Specialist."

4. Adjudication method for the test set:

Not explicitly stated. The text mentions "manual segmentation performed" for the segmentation ground truth, "manual assessment" for shoulder side detection, and "final positions adjusted by experts" for landmark performance. It does not detail if multiple experts performed these tasks and how discrepancies were resolved (e.g., 2+1, 3+1).

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

No, a multi-reader multi-case (MRMC) comparative effectiveness study evaluating human reader improvement with AI assistance was not described in the provided text. The study focused on the standalone performance of the AI algorithm.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

Yes, standalone performance testing was done. The "Segmentation Performance Testing," "Shoulder Side Detection performance testing," and "Landmark Performance Testing" sections describe the algorithm's performance against established ground truth.

7. The type of ground truth used:

Expert Consensus/Manual Annotation:
- For segmentation: Manual segmentation performed by "medical professionals."
- For shoulder side detection: Manual assessment performed by a "Clinical Solutions Specialist."
- For landmark performance: "Final positions adjusted by experts."

8. The sample size for the training set:

Training Set Sample Size: 335 samples (pairs of 3D images with segmentation labels).

9. How the ground truth for the training set was established:

The text states, "The labels [for segmentation] were created manually by medical professionals." This implies the same method of ground truth establishment (manual annotation by medical professionals) was used for the training set as for the test set.

Summary

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March 30, 2025

Avatar Medical Adeline Francois VP QARA and Clinical Affairs 11 rue de Lourmel Paris. 75015 France

Re: K243292

Trade/Device Name: brAIn™ Shoulder Positioning Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: LLZ, QIH Dated: February 14, 2025 Received: February 14, 2025

Dear Adeline Francois:

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.

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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 System (QS) 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 OS 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.

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 Re"). 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-advicecomprehensive-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-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

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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,

Jessica Lamb

Jessica Lamb, PhD Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products 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

510(k) Number (if known) K243292

Device Name brAIn™ Shoulder Positioning

Indications for Use (Describe)

brAIn™ Shoulder Positioning is intended to be used as an information tool to assist in the preoperative surgical planning and visualization of a primary total shoulder replacement.

Type of Use (Select one or both, as applicable)

|X Prescription Use (Part 21 CFR 801 Subpart D)

| | Over-The-Counter Use (21 CFR 801 Subpart C)

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510(k) Summary

Device Name: brAIn™ Shoulder Positioning
2.1 Submission correspondent
2.2 Subject Device
2.3 Predicate Device
2.4 Device Description
2.5 Indications for Use Statement
2.6 Substantial Equivalence Discussion
2.7 Predicate Device Comparison
2.8 Performance Data
2.9 Statement of Substantial Equivalence

Device Name: brAIn™ Shoulder Positioning &

2.1 Submission correspondent ີ

Table 2A – Submission correspondent

510(k) Sponsor	Avatar Medical
Address	11 rue de Lourmel75015 Paris France
Correspondence Person	FRANCOIS AdelineVP QARA and Clinical Affairs
Contact Information	Email: adeline@avatarmedical.ai
Date of Submission	October 2024

2.2 Subject Device ₴

Table 2B – Subject Device

Trade Name	brAln™ Shoulder Positioning
Common Name	Planning Software for Total Shoulder Arthroplasty
Classification Name	System, Image Processing, Radiological
Regulation Number	21 CFR 892.2050
Regulation Name	Medical Image Management and Processing System

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Product Code:	QIH, automated radiological image processing software
Subsequent ProductCode:	LLZ, system, image processing, radiological
Regulatory Class	II
Classification Panel	Radiology

2.3 Predicate Device ¿

Table 2C – Predicate Device

Trade name	FX SPS
Common Name	Planning software for Total Shoulder Arthroplasty
Premarket Notification	K213922
Classification Name	System, Image Processing, Radiological
Regulation Number	21 CFR 892.2050
Regulation Name	Medical Image Management and Processing System
Product Code	LLZ
Regulatory Class	II
Classification Panel	Radiology

2.4 Device Description @

2.5 Indications for Use Statement @

brAIn™ Shoulder Positioning is intended to be used as an information tool to assist in the preoperative surgical planning and visualization of a primary total shoulder replacement.

2.6 Substantial Equivalence Discussion &

The following table compares brAIn™ Shoulder Positioning to the predicate device with respect to indications for use, principles of operation, technological characteristics, and performance testing. The comparison of the devices provides more detailed

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information regarding the basis for the determination of substantial equivalence. The subject device does not raise any new issues of safety or effectiveness based on the similarities to the predicate device.

Table 2D: Comparison of Characteristics

Feature/Function	Predicate Device:FX SPS(K213922)	Subject Device:brAIn™ Shoulder Positioning
Intended Users	Healthcare Professionals	Healthcare Professionals
Intended Environment	Healthcare facilities such as hospitals and clinics	Healthcare facilities such as hospitalsand clinics
Device Class	Class II	Class II
Type of software	Web-based software	Web-based software
Use time	Pre-operatively	Pre-operatively
Imaging used	CT-scan images	CT-scan images
Type of implants planned	Anatomical and reverse shoulder implants	Anatomical and reverse shoulderimplants
Manual/Automaticsegmentation	Manual segmentation performed.	Automatic segmentation performed.
User profiles	User profiles define the authorized actions for theuser.	User profiles define the authorizedactions for the user.
Case management	List of cases	List of cases
Tools	Milling tool	Milling tool (called reaming)
Patient Information Display	• Patient last name• Patient first name• Patient sex• Patient birthdate• Shoulder side• Surgery date	• Patient last name• Patient first name• Patient sex• Patient birthdate• Patient age• Patient height• Patient weight• Shoulder side• Surgery date
Bone representation	3D and 2D representation of the humerus and thescapula	3D and 2D representation of the humerusand the scapula.
2D DICOM Viewer	2D DICOM viewAxial views are displayed in 2D DICOM Viewerinterfaces.	The 2D DICOM Viewer Interface displays2D DICOM imagesAxial and coronal views are displayed intwo separate 2D DICOM Viewerinterfaces.
3D Viewer	3D reconstruction in an interactive viewing interface	3D reconstruction in an interactiveviewing interface with soft tissues (Pre-Position Interface only)
Planning step	• Case details,• Segmentation,• Glenoid	• Create plan,• Validate Segmentations,• Pre-position with the validation ofShoulder Landmarks,• Implant,• Glenoid/humerus,• Post position,• Surgical planning Report
Landmarks / Measurements	Manual measurements performed:Shoulder Measurements, scapula neck length,posterior subluxation, glenoid reaming depth.	Pre-positioning or Semi-automaticmeasurements performed:Shoulder Measurements, baseplateSeating, posterior subluxation, glenoidreaming depth.
Implant Initial Placement	Manual initial placement performed.	Pre-positioning or Semi-automatic initialplacement performed.
Planning report	Planning report comprising preoperative and plannedparameters.	Planning report comprising patientinformation, implant and measurementpre and Post position, with 8 images toillustrate the chosen implantconfiguration.
Recommendations	Does not include any predictions andrecommendations.	Does not include any predictions andrecommendations.

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The performance of brAln™ Shoulder Positioning was validated by nonclinical testing met the acceptance criteria (passed), demonstrating that the software fulfills its requirement specifications.

2.7 Predicate Device Comparison 

The subject device and the primary device have similar indications for use in that they are both intended to use CT-scan imaging and the intended type of implants and duration of use are also similar.

The brAIn™ Shoulder Positioning has minor differences from the predicate device.

Internal verification and validation testing confications are met which are equivalent in design and technological characteristics as the predicate device. The testing results support that the Software validation of the acceptance of the device. brAIn™ Shoulder Positioning passed all testing and supports the claims of substantial equivalence to the predicate device.

Overall, these differences do not raise new questions of safety or effectiveness and therefore is substantially equivalent to the predicate device.

2.8 Performance Data ✆

Software Verification and Validation Tests:

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Safety and performance of the brAIn™ Shoulder Positioning has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification testing. The model was evaluated taking into account applicable requirements of the FD&C Act and 2081 implementing regulations.

Additionally, the software validation activities were performed in accordance with ANSI AAMI IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, in addition to the FDA Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and "Content of Premarket Submission for Cybersecurity in Medical Devices."

Segmentation Performance Testing:

The brAIn™ system's automatic segmentation was validated against manual segmentation, meeting a mean Dice Similarity Coefficient (DSC) on the testing set greater than or equal to 0.95. All tests confirmed that the segmentation performance meets the acceptance criteria, ensuring compliance.

The automatic segmentation provided by brAIn™ Shoulder Positioning was validated against manual segmentation performed. The validation criterion was a Dice Similarity Coefficient of 0.95 or higher, demonstrating that the segmentation produced by the model after post-processing closely matches the ground truth.

The dataset is comprised of 508 pairs of 3D images (DICOM series) together with their segmentation labels. The 3D images have on average 467 (axial) slices of resolution 512x512 pixels.

The dataset is divided into two partitions:

Training Set (65.9%, 335 samples)
- · Shoulder Side: 43.3% left (145), 56.7% right (190)
- o Gender: 57.9% female (84), 41.4% male (60), 0.7% unspecified (1) for left; 57.4% female (109), 42.1% male (80), 0.5% unspecified (1) for right
- Geographical Origin: 46.9% Europe (68), 56.4% USA (77) for left; 52.6% Europe (100), 47.4% USA (90) for right
· Testing Set (34.1%. 173 samples)
- · Shoulder Side: 45.7% left (79), 54.4% right (94)
- · Gender: 55.7% female (44), 44.3% male (35) for left; 63.8% female (60), 36.2% male (34) for right
- · Geographical Origin: 58.2% Europe (46), 41.8% USA (33) for left; 56.4% Europe (53), 43.6% USA (41) for right

The data corresponds to patients that under arthroplasty with an FX Shoulder implant without any further specific selection, representing a diversity in shoulder, imaging equipment, institutions, study year and geographical provenance. The protocol used for image acquisition is standard for patient undergoing a total shoulder arthroplasty.

The labels were created manually by medical professionals.

To prevent any form of data leakage, we took the following precautions: first, the training and testing datasets were split at the patient level to ensure that no patient data was included in both sets. Second, to ensure both of the dataset, we performed a stratified split based on patient gender, shoulder side and geographical region of origin. With this approach, we ensure that both sets accurately reflect the intended patient population. Besided to boost the representation of study collected in 2024 in the testing dataset compared to the training dataset, enabling thus a better generalization on future images and increased independence between datasets.

Shoulder Side Detection performance testing:

Validation compared the correct shoulder side (right or left) in DICOM images to a manual assessment performed by a Clinical Solutions Specialist. All performance tests for Shoudler Side Detection validation were successfully completed with no deviations, confirming compliance with the required performance standards.

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Measurement Accuracy_performance testing:

Validation compared the accuracy of the brAln™ Shoulder Positioning software measurement when editing the position of landmarks within the software to to the reported accuracy of the predicate. All performance tests for Measurement Accuracy Validation were successfully completed with no deviations, confirming compliance with the required performance standards.

Landmark Performance Testing:

Validation compared pre-positioning with final positions adjusted by experts, achieving accuracy similar to manual positioning with a 3 mm mean distance as the acceptance criterion. All performance tests for landmark validation were successfully completed with no deviations, confirming compliance with the required performance standards.

2.9 Statement of Substantial Equivalence &

The brAln™ Shoulder Positioning is as safe and effective as the predicate device. The brAln™ Shoulder Positioning has the same intended uses and similar indications, technological characteristics, and principles of operation as its predicate device.

The minor technological differences between the brAn™ Shoulder Positioning and its predicate device raise no new issues of safety or effectiveness. The performance data demonstrates that the brAIn™ Shoulder Positioning is as safe and effective as the predicate device.

By definition, a device is substantially equivalent to a predicate device has the same intended use and the same technological characteristics as the previously cleared predicate device has the same intended use and different technological characteristics that can be demonstrated that the device is substantially equivalent to the new device does not raise additional questions regarding its safety and effectiveness as compared to the predicate device.

brAIn™ Shoulder Positioning, as designed and manufactured, is determined to be substantially equivalent to the referenced predicate device.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).