SMART Bun-Yo-Matic X-Ray software is to be used by orthopaedic healthcare professionals for diagnosis and surgical planning in a hospital or clinic environment. The medical imaging type intended to be used as the input of the software is X-ray.

The SMART Bun-Yo-Matic X-Ray software provides:

· Visualization report of the three-dimensional mathematical models of the anatomical structures of the foot and ankle and three-dimensional models of orthopaedic fixation devices.

· Measurement templates containing radiographic measures of foot and ankle,

· Surgical planning application for visualization of foot and ankle anatomical three-dimensional structures, radiographic measures, and surgical instrument parameters.

The visualization report containing the measurements can be used for the diagnosis of orthopaedic healthcare conditions. The surgical planning application containing the visualizations of the measurements in the context of three-dimensional models, orthopaedic fixation device models and surgical instrument parameters can be used for the planning of treatments and operations to correct orthopaedic healthcare conditions of foot and ankle.

Device Description

The SMART Bun-Yo-Matic X-Ray device is a software tool that takes x-rays of the foot and produces 3D axes on contextual bone models to help a user plan for hallux valgus correction. The final output of the device is a case report that provides images of the patient's axes, as well as measurements prior to correction and following a surgical correction selected by the user.

AI/ML Overview

Device Acceptance Criteria and Performance Study: SMART Bun-Yo-Matic X-Ray

This response details the acceptance criteria and the study that proves the SMART Bun-Yo-Matic X-Ray device meets these criteria, based on the provided FDA 510(k) summary.

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria	Reported Device Performance
95% model conformance within 1.0mm distance to reference model (for image analytics)	The subject device meets the predicate's established acceptance criteria. Specific percentage met for this device is not explicitly stated, but "Results showed the subject device performed as intended."
2.0 degrees standard deviation for angular measurements (for image analytics)	The subject device meets the predicate's established acceptance criteria. Specific performance is not explicitly stated, but "Results showed the subject device performed as intended."
Surgical planning executes mathematical operations for estimated correction ± 1 degree for angular measurements	"Surgery planning executes mathematical operations for estimated correction ± 1 degree for angular measurements". The results indicated the device performed as intended.
Surgical planning executes mathematical operations for estimated correction ± 1.0 mm for distance measurements	"Surgery planning executes mathematical operations for estimated correction ± 1.0 mm for distance measurements". The results indicated the device performed as intended.

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

Test Set Sample Size: 97 x-ray and DRR (Digitally Reconstructed Radiographs) images.
Data Provenance: The x-ray and CBCT DRR were collected from various sites across USA, Germany, UK, Finland, and Korea. The data was collected from patients with different ages and racial groups, with a minimum of 5% male/female within each dataset, mean age approximately 35 years, and representatives from White (Non-)Hispanic, Hispanic, and Native American racial groups. Each dataset was balanced in terms of subjects with different foot alignment, demographics, imaging devices, and subjects from clinical subgroups ranging from control/normal feet to pre-/post-operative clinical conditions such as Hallux Valgus, and undefined indications. This implicitly suggests a retrospective collection for the purpose of algorithm development and testing.

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

Number of Experts: 2 clinicians.
Qualifications: "Over five (5) years of experience practicing medicine."

4. Adjudication Method for the Test Set

The adjudication method for establishing ground truth on the test set is not explicitly detailed beyond "Each clinician was given the same image data to review dorsoplantar and lateral x-ray images. Each clinician then marks on a spreadsheet the presence of the bone in the image." This suggests either independent marking or a simple consensus approach, but no specific adjudication rule (e.g., 2+1, 3+1) is mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study involving human readers assisting with or without AI and their improvement was not reported in this summary. The performance testing focused on the AI system's ability to meet preset technical/measurement accuracy criteria and its comparison to ground truth and manual measurements.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study Was Done

Yes, a standalone performance assessment study was done. The document states: "Performance testing was conducted. Testing included the following: AI/ML Testing. Comparison of the 2D-3D construction to manual measurements as well as ground truth. Comparison of the clinical acceptability of axes placement. Comparison of the planned surgical correction to the actual surgical correction." This indicates the algorithm's performance was evaluated against ground truth and manual measurements without direct human-in-the-loop interaction for the specific performance metrics. The training, tuning, and validation data were independent for this standalone assessment.

7. The Type of Ground Truth Used

The ground truth for the testing data was established by expert consensus (implied by 2 clinicians marking the presence of bone) and also involved manual measurements for comparison with the 2D-3D construction and the actual surgical correction for comparison with planned surgical correction.

8. The Sample Size for the Training Set

AI algorithm for bone identification: 1,5776 (likely a typo, assumed to be 1,576 or 15,776) x-ray and CBCT DRR images.
Metal identification: 15 x-ray and CBCT DRR images.

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

The document states that the "AI algorithm for bone identification was developed using 1,5776 x-ray and CBCT DRR and metal identification was developed using 15 x-ray and CBCT DRR." While it mentions the training and tuning data were independent, it does not explicitly describe how the ground truth for the training set was established. It can be inferred that a similar expert labeling process was likely used, but the details are not provided in this summary.

Summary

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Disior Ltd % Haylie Fast Manager of Regulatory Affairs Paragon 28, Inc. 14445 Grasslands Dr. Englewood. Colorado 80134

Re: K240736

July 2, 2024

Trade/Device Name: SMART Bun-Yo-Matic X-Ray Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: March 18, 2024 Received: June 3, 2024

Dear Haylie Fast:

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 (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 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 medical devices and radiation-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,
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

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Indications for Use

Submission Number (if known)

K240736

Device Name

SMART Bun-Yo-Matic X-Ray

Indications for Use (Describe)

The SMART Bun-Yo-Matic X-Ray software provides:

· Visualization report of the three-dimensional mathematical models of the anatomical structures of the foot and ankle and three-dimensional models of orthopaedic fixation devices.

· Measurement templates containing radiographic measures of foot and ankle,

· Surgical planning application for visualization of foot and ankle anatomical three-dimensional structures, radiographic measures, and surgical instrument parameters.

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

Prescription Use (Part 21 CFR 801 Subpart D)

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

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510(K) SUMMARY

510(k) Number:	K240736
Manufacturer:	Disior LtdHTC Helsinki, Building Pinta, Tammasaarenkatu 3Helsinki Uusimaa, FL, 00180, Finland
Contact:	Aarno JussilaDirector of Enabling TechnologyPhone: +358 40 6734939Email: AJussila@paragon28.com
Prepared By:	Haylie FastManager of Regulatory AffairsParagon 28, Inc.14445 Grasslands Dr.,Englewood, CO, 80112Phone: 720-994-5489
Date Prepared:	March 31, 2024
Device Trade Name:	SMART Bun-Yo-Matic X-RAY
Device Class and Common Name:	Class II, Automated Radiological Image Processing Software
Classification:	21 CFR 892.2050: Medical image management and processingsystem
Product Codes:	QIH
Indications for Use:	SMART Bun-Yo-Matic X-Ray software is to be used byorthopaedic healthcare professionals for diagnosis and surgicalplanning in a hospital or clinic environment. The medical imagingtype intended to be used as the input of the software is X-ray.The SMART Bun-Yo-Matic X-Ray software provides:Visualization report of the three-dimensional mathematical models of the anatomical structures of the foot and ankle and three-dimensional models of orthopaedic fixation devices,Measurement templates containing radiographic measures of foot and ankle,Surgical planning application for visualization of foot and ankle anatomical three-dimensional structures, radiographic measures, and surgical instrument parameters.The visualization report containing the measurements can be used for the diagnosis of orthopaedic healthcare conditions. The surgical planning application containing the visualizations of the measurements in the context of three-dimensional structural models, orthopaedic fixation device models and surgical instrument

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parameters can be used for the planning of treatments and operations to correct orthopaedic healthcare conditions of foot and ankle.

The SMART Bun-Yo-Matic X-Ray device is a software tool that Device Description: takes x-rays of the foot and produces 3D axes on contextual bone models to help a user plan for hallux valgus correction. The final output of the device is a case report that provides images of the patient's axes, as well as measurements prior to correction and following a surgical correction selected by the user.

The device uses machine learning derived outputs. Details on the validation are summarized below. The testing of the algorithm included 97 images that were a combination of plain-film native xray images and Cone Beam Computed Tomography (CBCT) Digitally Reconstructed Radiographs (DRR) from CT images series.

Study Subjects

The AI algorithm for bone identification was developed using 1,5776 x-ray and CBCT DRR and metal identification was developed using 15 x-ray and CBCT DRR. Testing was carried out using 97 x-ray and DRR. Out of 97 image studies, 42 were from individual patients with some studies from same patient with different foot alignment or laterality. The x-ray and CBCT DRR were collected from various sites across USA, Germany, UK, Finland, and Korea. The x-ray and CBCT DRR were collected from patients with different ages and racial groups, with minimum of 5% male/female within each dataset, with mean age approximately 35 years, and representatives from White (Non-)Hispanic, Hispanic, and Native American racial groups. Each dataset was balanced in terms of subjects with different foot alignment, demographics, imaging devices and with subjects from clinical subgroups ranging from control/normal feet to pre-/post-operative clinical conditions such as Hallux Valgus, and undefined indications.

Imaging Systems

The 97-image x-ray and DRR test set was collected from five (5) different systems from five (5) different manufacturers. This set of five (5) imaging systems contained three (3) x-rav and two CBCT DRR machines. From the total test data 31% of the images were collected from a Siemens - Aristos and Ysio X-Pree X-ray system, 13% were collected from an iRAy Gamma X-ray system 1% were collected from a Swissray Medical AG ddR Element System X-ray system, 53% were collected from a Carestream OnSight 3D Extremity Scanner CBCT DRR, and 2% were collected from a CurveBeam PedCAT CBCT DRR.

Ground Truth

The ground truth for the testing data was established by 2 (2) clinicians with over five (5) years of experience practicing medicine. Each clinician was given the same image data to review

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dorsoplantar and lateral x-ray images. Each clinician then marks on a spreadsheet the presence of the bone in the image.

Training, Tuning, and Validation Data Independence The SMART Bun-Yo-Matic X-ray software machine learning algorithm training and tuning data used during the algorithm development, as well as test data used in the standalone software performance assessment study, were all independent data sets. Each x-ray or CBCT DRR was allowed to be allocated to only data set.

Predicate: Bonelogic (K223757)

Substantial Equivalence:

Bonelogic (K223757)

The Indications for Use of the subject device and the predicate device are similar. Both devices are intended to take medical imaging as an input and provide measurements from 3D structures to assist in surgical planning. The predicate device does this through the 3D model, and the subject device shows axes of patient anatomy in the context of a generic bone model through a series of images.

The subject and predicate devices have similar technological characteristics. The primary differences are the images needed as the input, the output of the software, the user interface and the items provided in the presurgical planning capabilities. In support of the claim of substantial equivalence the comparison between the subject and predicate systems, performance testing has been done to demonstrate that the differences do not introduce new questions of safety and effectiveness.

	Subject Device	Primary Predicate Device
Manufacturer	Disior Ltd	Disior Ltd
Trade Name	SMART Bun-Yo-MaticX-Ray	Bonelogic
510(k)	Subject Device	K223757
Input	Weight-bearing plain filmX-Rays from sagittal andtransverse views	Computed tomographyDICOM Computedtomography
Output	Automated case report ofthe 3D axes of patientanatomy, surgicalinstrument parameters, andvisualization of implant	3D model of patient anatomy,automated case report
Measuringand Planning	Perform measurements forpresurgical planning	Perform measurements forpresurgical planning
UserInterface	Graphical user interface(GUI) to a web applicationused with a standard webbrowser.	Graphical user interface(GUI) that is standaloneapplication based.

Performance Testing:

All necessary testing has been performed on the SMART Bun-Yo-Matic X-ray device to assure substantial equivalence to its predicate and demonstrate the subject device performs as intended.

Software Verification and Validation

Software verification and validation were carried out based on the "Guidance for the Content of Premarket Submissions for Software

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	Contained in Medical Devices", at the unit, integration, and systemlevels to determine substantial equivalence to the predicate device.For image analytics, the subject device meets the predicatesestablished acceptance criteria of 95% model conformance within1.0mm distance to reference model and 2.0 degrees standarddeviation for angular measurements. Surgery planning executesmathematical operations for estimated correction $\pm$ 1 degree forangular measurements and $\pm$ 1.0 mm for distance measurements.
	Performance TestingPerformance testing was conducted. Testing included the following:AI/ML Testing.Comparison of the 2D-3D construction to manual measurements as well as ground truth. Comparison of the clinical acceptability of axes placement. Comparison of the planned surgical correction to the actual surgical correction.
	Results showed the subject device performed as intended.
	Clinical data are not needed to support the safety and effectivenessof the subject device.
Conclusions:	The SMART Bun-Yo-Matic X-Ray device subject to thissubmission possesses similar intended use and has similartechnological characteristics as the predicate device, where thedifferences are shown through performance testing to not raisequestions of safety and effectiveness. All performance testingconducted for the SMART Bun-Yo-Matic X-Ray device met thepredetermined acceptance criteria or were otherwise consideredacceptable. As such, the SMART Bun-Yo-Matic X-Ray device issubstantially equivalent to the predicate device for the intended use.

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).