Semi-automatic segmentation with manual or assisted input of bony structure identification from CT imaging input,
Three-dimensional mathematical models of the anatomical structures of foot and ankle,
Measurement templates containing radiographic measures of foot and ankle, and tools for manually obtaining linear and angular measurements,
Surgical planning application for foot and ankle using three-dimensional models of the anatomical structures and radiographic measures.

The three-dimensional models of the anatomical structures combined with the measurements can be used for the diagnosis of orthopaedic healthcare conditions. The surgical planning application containing the three-dimensional structural models combined with the measurements can be used for the planning of treatments and operations to correct orthopaedic healthcare conditions of foot and ankle.

Device Description

The Bonelogic is a software tool that segments bone anatomy using dedicated semiautomatic tools and fully automatic algorithms. More specifically, Bonelogic is intended to segment foot and ankle bones from computed tomography (CT) images. The segmented structures may then be used to create 3D models of their respective bones and replicate the anatomy of a patient. The semi-automatic tools of the software require a healthcare professional to mark the different bones in an initial 3D rendered model prior to when the segmentation process is initialized. This method is called the semi-automatic workflow with manual input. The software also comprises an optional semiautomatic workflow with assisted input that replaces the required user input with an estimate based on a locked artificial neural network (ANN) model. The fully automatic algorithm processes the final result in the same way based on input generated by the semi-automatic workflow with user input and with ANN model. The user still needs to mark the laterality and as a new step acknowledge the bones discovered by the ANN model.

AI/ML Overview

Here's a summary of the acceptance criteria and the study proving the device meets those criteria, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criterion (Primary for Segmentation)	Reported Device Performance (Segmentation)
95% model conformance within 1.0mm distance to reference model	Met (inferred, as predicate device meets this and subject device shown to be substantially equivalent)
2.0 degrees standard deviation for angular measurements	Met (inferred, as predicate device meets this and subject device shown to be substantially equivalent)

Acceptance Criterion (AI Algorithm - Bone Identification)	Reported Device Performance (AI Algorithm for Bone Identification)
Correctly identified bones of foot and ankle for 100% of images	100% (for 82 CT image series)

Acceptance Criterion (AI Algorithm - Metal Identification)	Reported Device Performance (AI Algorithm for Metal Identification)
Correctly identified metal for images (Specificity/Sensitivity implicitly desired high)	98.8% correct identification (Specificity 98%, Sensitivity 100%)

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

Test Set Sample Size: 82 CT image studies.
Data Provenance:
- Country of Origin: Collected from various sites across the USA and Europe, with a minimum of 50% of the images originating from the USA.
- Retrospective/Prospective: Not explicitly stated, but the description of collected studies from individual patients with varying conditions suggests a retrospective collection.

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

Number of Experts: Three (3).
Qualifications of Experts: U.S. Orthopedic surgeons (no specific years of experience mentioned).

4. Adjudication Method for the Test Set

Method: Majority vote of the three experts. Two matching responses out of three were required to establish the ground truth for bone and metal presence in each DICOM series.

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

No, an MRMC comparative effectiveness study was not conducted. The performance assessment was for the standalone AI algorithms.

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

Yes, a standalone performance assessment study was done for the AI algorithms.
- For bone identification, the algorithm correctly identified bones of the foot and ankle in 100% of the 82 CT image series.
- For metal identification, the algorithm correctly identified metal in 98.8% of the images, with a specificity of 98% and sensitivity of 100%.

7. Type of Ground Truth Used

Expert Consensus: The ground truth for bone and metal identification was established by the independent review and majority vote of three U.S. Orthopedic surgeons using a 3rd party software.

8. Sample Size for the Training Set

Bone Identification AI Algorithm: 145 CT image studies.
Metal Identification AI Algorithm: 130 CT image studies.

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

The document implies that the ground truth for the development of the AI algorithms (training and tuning) was similarly established, as it states: "The AI algorithm for bone identification was developed using 145 CT image studies and metal identification was developed using 130 CT image studies." It then goes on to describe the ground truth establishment for the test data. While not explicitly detailed for the training set itself, it can be reasonably inferred that a similar expert-driven process was used, especially given the emphasis on "independent data sets" for training, tuning, and testing.

Summary

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Image /page/0/Picture/0 description: The image contains two logos. On the left is the Department of Health & Human Services logo. On the right is the FDA logo, which is a blue square with the letters "FDA" in white, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue.

Disior Ltd % Alex Cadotte Associate Director, Software and Digital Health Regulatory Affairs Mcra. LLC 1050 K St NW Suite 1000 Washington, DC 20001

Re: K223757

December 8, 2023

Trade/Device Name: Bonelogic Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: November 7, 2023 Received: November 13, 2023

Dear Alex Cadotte:

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.

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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).

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 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-regulatory

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assistance/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 Assistant Director Iamging Sofware Team DHT8B: Division of Radiologic 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) K223757

Device Name Bonelogic 2.2

Indications for Use (Describe)

Bonelogic software is to be used by orthopaedic healthcare professionals for diagnosis and surgical planning in a hospital or clinic environment.

Bonelogic software provides:

· Semi-automatic segmentation with manual or assisted input of bony structure identification from CT imaging input,
· Three-dimensional mathematical models of the anatomical structures of foot and ankle,

• Measurement templates containing radiographic measures of foot and tools for manually obtaining linear and angular measurements,

• Surgical planning application for foot and ankle using three-dimensional models of the anatomical structures and radiographic measures.

The three-dimensional models of the anatomical structures combined with the measurements can be used for the diagnosis of orthopaedic healthcare conditions. The surgication containing the three-dimensional structural models combined with the measurements can be used for the planning of treatments and operations to correct orthopaedic healthcare conditions of foot and ankle.

Type of Use (Select one or both, as applicable)
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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 Trade Name:	Bonelogic 2.2
Manufacturer:	Disior LtdHTC Helsinki, Building PINTA, 4th floorTammasaarenkatu 3Helsinki, Finland 00180
Contact:	Aarno JussilaDirector of Enabling TechnologiesPhone: +358 40 6734939Email: aarno@disior.com
Prepared by:	Alex CadotteMCRA, LLC803 7th St NWWashington, DC 20001Office: 202.552.5800
Date Prepared:	December 7, 2023
Classifications:	21 CFR 892.2050 Medical image management and processingsystem.
Class:	II
Product Code:	QIH
Primary Predicate:	K203290

Indications For Use:

Bonelogic software is to be used by orthopaedic healthcare professionals for diagnosis and surgical planning in a hospital or clinic environment.

Bonelogic software provides:

Semi-automatic segmentation with manual or assisted input of bony structure ● identification from CT imaging input,
Three-dimensional mathematical models of the anatomical structures of foot and ankle, ●
Measurement templates containing radiographic measures of foot and ankle, and tools ● for manually obtaining linear and angular measurements,
Surgical planning application for foot and ankle using three-dimensional models of the . anatomical structures and radiographic measures.

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

Device Description:

Predicate Device:

Disior submits the following information in this Premarket Notification to demonstrate that, for the purposes of FDA's regulation of medical devices. K223757 is substantially equivalent in indications, design principles, and performance to the following subject device:

	Predicate Device K203290	Subject Device K223757
Information	(Bonelogic)	(Bonelogic 2.2)	Comparison
Classification Name	Medical image managementand processing system	Medical image managementand processing system	Identical
Service Type	Software	Software	Identical
Classification	21 CFR 892.2050	21 CFR 892.2050	Identical
Class	II	II	Identical
Product Code	LLZ	QIH	Identical
Indications for Use	Bonelogic software is intendedto be used by specializedmedical practitioners to assistin the characterization ofhuman anatomy with 3Dvisualization and specificmeasurements. The medicalimage modalities intended tobe used in the software arecomputed tomography (CT)images, cone beam computedtomography (CBCT) imagesand weight-bearing cone beamCT (WBCT) images. Theintended patient population isadults over 16 years of age.Bonelogic software containsthe measurement template	Bonelogic software is to beused by orthopaedic healthcareprofessionals for diagnosis andsurgical planning in a hospitalor clinic environment.Bonelogic software provides:Semi-automatic segmentationwith manual or assisted inputof bony structure identificationfrom CT imaging input,Three-dimensionalmathematical models of theanatomical structures of footand ankle,Measurement templatescontaining radiographicmeasures of foot and ankle,and tools for manually	Removal of wristand hand relatedfunctionality andaddition of anoptionalsemiautomaticsegmentationworkflow withassisted boneidentificationprocess in thesubject device

Substantial Equivalence:

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Information	Predicate Device K203290(Bonelogic)	Subject Device K223757(Bonelogic 2.2)	Comparison
	with a set of distance andangular measures. Themeasurements can be used fordiagnostic purposes. Thethree-dimensional (3D)models are displayed and canbe manipulated in thesoftware. Together, theinformation from themeasurements and the 3Dvisualization can be used fortreatment planning in the fieldof orthopedics (foot and ankle,and hand and wrist). The 3Dmodels can be outputted fromthe software for traditional oradditive manufacturing. Thephysical models generatedbased on the 3D digital modelsare not intended for diagnosticuse.	obtaining linear and angularmeasurements,Surgical planning applicationfor foot and ankle using three-dimensional models of theanatomical structures andradiographic measures.The three-dimensional modelsof the anatomical structurescombined with themeasurements can be used forthe diagnosis of orthopaedichealthcare conditions. Thesurgical planning applicationcontaining the three-dimensional structural modelscombined with themeasurements can be used forthe planning of treatments andoperations to correctorthopaedic healthcareconditions of foot and ankle.
Input	Computed tomographyDICOM images	Computed tomographyDICOM Computedtomography	Identical
Image processing	Segmentation of bonestructures	Segmentation of bonestructures	Identical
Output	3D model of patient anatomy	3D model of patient anatomy	Identical
Measuring andplanning	Perform measurements forpresurgical planning	Perform measurements forpresurgical planning	Identical
Bone identification	Manual process	Manual and an optionalsemiautomatic workflow withassisted input processperformed by artificial neuralnetwork	Optionalsemiautomaticboneidentificationprocess in

Performance Testing Summary:

Software verification and validation were carried out based on the "Guidance for . the Content of Premarket Submissions for Software Contained in Medical Devices" at the unit, integration, and system levels to determine substantial equivalence to the predicate device. The predicate device meets the subject device's established acceptance criteria of 95% model conformance within 1.0mm distance to reference model and 2.0 degrees standard deviation for angular measurements.
Bench testing Software verification and validation for Bonelogic software ● demonstrated its substantial equivalence to the predicate device. An additional clinical data based software performance assessment study was carried out to validate the standalone performance of AI algorithms from a clinical perspective. The testing for 82 CT image series presented 100% correctly identified bones of foot and ankle. The existence of metal was identified correctly for 98.8% of the images (specificity 98%, sensitivity 100%).

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Study subjects:

The AI algorithm for bone identification was developed using 145 CT image studies and metal identification was developed using 130 CT image studies. Testing was carried out using 82 CT image studies. Out of 357 CT image studies, 340 were from individual patients with few studies from same patient with different foot alignments. The CT image series' were collected from various sites across USA and Europe with a minimum of 50% of the images originating from the USA. The CT image studies were from patients with different ages and racial groups, with minimum of 35% male/female within each dataset, with mean age approximately 47 years (SD 15 years), and representatives from White, (Non-)Hispanic. African American, and Native 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 (44% with test data) to pre-/post-operative clinical conditions such as Hallux Valgus, Progressive Collapsing Foot Deformity, fractures, or with metal implants (40% of the test data).

Imaging Systems:
The 357 image studies were collected using CT imaging system made by five (5) manufacturers (7 different models in total). From the test data of 82 images, 61% of the images were acquired using Curvebeam PedCAT, 11% with Planmed Verify, and 26% with Carestream OnSight 3D Extremity. In addition, system test data contains images acquired with Toshiba Somatom. Typical imaging protocol is disclosed within the IFU, however, the test data contains wider range of parameters for generalization (tube voltages between 90-120 kV, tube currents 5-8 mA, and slice thickness/pixel spacing 0.37-1.5mm).
Ground Truth: ●
The ground truths for bone and metal identification were independently established by three (3) U.S. Orthopedic surgeons with a 3rd party software. Each clinicians reviewed each of the DICOM series through axial/sagittal/coronal views and/or 3D reconstruction and marked on a spreadsheet the presence of a bone and metal in the image series. Based on the majority vote of three, two same responses were required to establish a ground truth on each of the DICOM series.
Training, Tuning, and Validation Data Independence:
The Bonelogic 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 CT image study was allowed to be allocated to only data set.

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Conclusion:

The subject device and the predicate devices have intended use and have similar technological characteristics. The data included in this submission demonstrate substantial equivalence to the predicate devices listed above. Bonelogic 2.2 is as safe, as effective, and performs as well as, or better, than the predicate devices.

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).