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510(k) Data Aggregation
(358 days)
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.
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.
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.
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.
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(88 days)
Bonelogic software is intended to be used by specialized medical practitioners to assist in the characterization of human anatomy with 3D visualization and specific measurements. The medical image modalities intended to be used in the software are computed tomography (CT) images, cone beam computed tomography (CBCT) images and weight-bearing cone beam CT (WBCT) images. The intended patient population is adults over 16 years of age.
Bonelogic software contains the measurement template with a set of distance and angular measurements can be used for diagnostic purposes. The three dimensional (3D) models are displayed and can be manipulated in the software. Together, the information from the measurements and the 3D visualization can be used for treatment planning in the field of orthopedics (foot and ankle, and hand wrist). The 3D models can be outputted from the software for traditional or additive manufacturing. The physical models generated based on the 3D digital models are not intended for diagnostic use.
Bonelogic product is a software tool to be used by specialized medical practitioners. The software tool is aimed to help the user in the characterization of human anatomy, and identifying possible trauma or deformities, the diagnose and the treatment planning should always be based on the professional skills of the specialist doctor. The medical image modalities intended to be used in the software are computed tomography (CT) images, cone beam computed tomography (CBCT) images and weight-bearing cone beam CT (WBCT) images. Bonelogic software has got a modular architecture. The software includes following functionality:
- Importing medical images in DICOM format
- Viewing of DICOM data
- Selecting a region of interest using generic segmentation tools
- Segmenting specific anatomy using dedicated semi-automatic tools or fully automatic algorithms
- Verifying and editing a region of interest
- Calculating a digital 3D model and editing the model
- Measuring on 3D models
- Exporting images, measurements, and 3D models to third-party packages
- Planning treatments on the 3D models
- Interfacing with packages for Finite Element Analysis
The Bonelogic software aims to assist specialized medical practitioners in characterizing human anatomy using 3D visualization and specific measurements. The device processes CT, CBCT, and WBCT images to create 3D models and perform measurements for diagnostic and treatment planning purposes, particularly in orthopedics (foot and ankle, hand and wrist).
Here's a breakdown of the acceptance criteria and the study proving the device meets them:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Implicit) | Reported Device Performance |
|---|---|
| Geometric Accuracy of 3D Models: The subject device's 3D models should be geometrically accurate when compared to models from a predicate device. | The geometric accuracy of 3D virtual models created in the subject device (Bonelogic) was assessed against similar virtual models created with the predicate device (Mimics Medical, K183105). The study concluded that performance testing demonstrated device performance and substantial equivalence to the predicate device. Although no specific metrics (e.g., mean absolute difference, Dice similarity coefficient) are provided, the general statement supports the criterion. |
| Measurement Accuracy: Manual measurements of radiological parameters performed by clinicians should be comparable to measurements generated by the subject device. | The measurement accuracy in the subject device (Bonelogic) was assessed by comparing manual measurements of radiographical parameters against the same measurements created in the subject device. Manual measurements were performed by clinicians. The study concluded that performance testing demonstrated device performance and substantial equivalence to the predicate device. Again, specific quantitative error metrics are not reported in this summary. |
| Performance Testing against Defined Requirements: The device should meet its defined requirements. | Verification against defined requirements via performance testing was conducted. This included testing on measurement repeatability. The study concluded that all performance testing conducted demonstrated device performance and substantial equivalence. No specific details about the defined requirements or quantitative results of the repeatability testing are included. |
| Clinical Validation (Usability/Accuracy): The accuracy of the 3D virtual models generated by the device should be validated against original DICOM imaging data, and the usability in a clinical setting should be validated. | Validation for the subject device against user needs via clinical validation for the usability of 3D models in a clinical setting was performed. Clinical validation for the accuracy of 3D virtual models against original DICOM imaging data was also performed. The general conclusion is that performance testing demonstrated device performance and substantial equivalence to the predicate device. However, specific details about the clinical validation results (e.g., user satisfaction, quantitative accuracy metrics) are not provided. |
| Overall Substantial Equivalence: The device should be as safe and effective and perform as well as the predicate device. | The summary explicitly states: "A comparison of intended use and technological characteristics combined with performance data demonstrates that Bonelogic software is substantially equivalent to the predicate device Mimics Medical (K183105). Minor differences in intended use and technological characteristics exist, but performance data demonstrates that Bonelogic software is as safe and effective and performs as well as the predicate device." |
2. Sample Size Used for the Test Set and Data Provenance
- Geometric Accuracy Study (3D Models): The test set included "DICOM images containing foot and ankle anatomy." The specific sample size (number of cases or patients) is not provided.
- Measurement Accuracy Study: The test set included "DICOM images containing hand and wrist anatomy." The specific sample size is not provided.
- Provenance: The document does not specify the country of origin of the data or whether the data was retrospective or prospective.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
- For the measurement accuracy study, "manual measurements were performed by clinicians." The number of clinicians is not specified. Their specific qualifications (e.g., "radiologist with 10 years of experience") are also not detailed beyond "clinicians."
- For the accuracy of 3D virtual models, the validation was against "original DICOM imaging data." It is implied that human experts would interpret this raw data, but the number and qualifications of these experts are not explicitly stated.
4. Adjudication Method for the Test Set
The document does not describe any specific adjudication method (e.g., 2+1, 3+1) used to establish ground truth for the test set. For the measurement accuracy, it mentions "manual measurements were performed by clinicians," implying these measurements served as a ground truth comparison, but how consensus was reached if multiple clinicians were involved is not detailed.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The document does not mention an MRMC comparative effectiveness study that directly quantifies human readers' improvement with AI vs. without AI assistance. The performance studies primarily focus on comparing the device's output to manual measurements or predicate device outputs, not on the interaction of the device with human readers and its impact on their diagnostic performance.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study
Yes, standalone performance was assessed.
- The "geometric accuracy of 3D virtual models created in the subject device Bonelogic software" was compared against the predicate device's models. This tests the algorithm's output directly.
- The "measurement accuracy in the subject device Bonelogic software" was assessed by comparing the device's generated measurements against manual measurements. This also evaluates the algorithm's standalone capability.
- Verification via "performance testing on measurements repeatability" assesses the algorithm's consistency.
7. Type of Ground Truth Used
- Expert Consensus: Implied for the "manual measurements performed by clinicians" in the measurement accuracy study.
- Comparison to Predicate Device Output: For the geometric accuracy of 3D models, the ground truth was essentially the output of the predicate device (Mimics Medical).
- Original DICOM Imaging Data: For the accuracy of 3D virtual models, they were validated against "original DICOM imaging data," which serves as the foundational ground truth.
8. Sample Size for the Training Set
The document does not provide any information about the sample size used for the training set for the Bonelogic software's algorithms (e.g., for segmentation, measurement automation).
9. How Ground Truth for the Training Set Was Established
The document does not provide details on how the ground truth for any potential training set was established. Given the nature of the device (image processing for measurements and 3D models), it is likely that expert-annotated CT/CBCT/WBCT images would be used for training, but this is not mentioned.
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