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SMART PCFD software includes AI-powered algorithms and is intended to be used to support orthopedic healthcare professionals in the diagnosis and surgical planning of Progressive Collapsing Foot Deformity (PCFD) in a hospital or clinic environment. The medical image modality intended to be used in the software is weight-bearing CT (WBCT).

SMART PCFD software provides for the user:

• Visualization report of the three-dimensional (3D) mathematical models and measurements of the anatomical structures of foot and ankle and three-dimensional models of orthopedic fixation devices,
• Measurement templates containing radiographic measures of foot and ankle, and
• Surgical planning application for visualization of foot and ankle anatomical three-dimensional structures, radiographic measures, and surgical instrument parameters supporting the following common flatfoot procedures: Medial Displacement Calcaneal Osteotomy (MDCO), Lateral Column Lengthening (LCL), and Cotton Osteotomy (CO).

The visualization report containing the measurements is intended to be used to support orthopedic healthcare professionals in the diagnosis of PCFD. The surgical planning application contains the visualizations of the three-dimensional structural models, orthopedic fixation device models and surgical instrument parameters combined with the measurements is intended to be used to support orthopedic healthcare professionals in surgical planning of PCFD.

The SMART PCFD software is intended to be used in reviewing and digitally processing computed tomography images for the purposes of interpretation by a specialized medical practitioner. The device segments the medical images and creates a 3D model of the bones of the foot and ankle. Measurements, including anatomical axes, are provided to the user and the device allows for presurgical planning.

The device includes the same machine learning derived outputs as the primary predicate SMART Bun-Yo-Matic CT (K240642) device and no new validations were conducted.

Details on the previously performed validation are summarized below. 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%).

Here's a breakdown of the acceptance criteria and study information for the SMART PCFD device, as extracted from the provided FDA 510(k) clearance letter:

1. Table of Acceptance Criteria and Reported Device Performance

The clearance letter does not explicitly state acceptance criteria in a formal table format with specific thresholds for each metric. Instead, it describes performance results. Based on the provided text, the acceptance criteria can be inferred from the reported performance, implying that these levels of performance were deemed acceptable.

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

• Test Set Sample Size: 82 CT image studies.
• Data Provenance:
  • Country of Origin: Various sites across 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 "patients with different ages and racial groups" and "clinical subgroups ranging from control/normal feet to pre-/post-operative clinical conditions" suggests retrospective data collection.
  • Patient Demographics: Different ages and racial groups, minimum of 35% male/female within each dataset, mean age approximately 47 years (SD 15 years), and representatives from White, (Non-)Hispanic, African American, and Native racial groups.
  • Clinical Conditions: Balanced in terms of subjects with different foot alignment, and 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).

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. Specific years of experience are not mentioned.

4. Adjudication Method for the Test Set

• Adjudication Method: Majority vote. "Based on the majority vote of three, two same responses were required to establish a ground truth on each of the DICOM series." This indicates a "2-out-of-3" or "2+1" adjudication where two experts must agree to establish ground truth.

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

• Was an MRMC study done? No. The document describes standalone algorithm performance, and comparison to human readers with or without AI assistance is not mentioned.

6. Standalone Performance Study

• Was a standalone study done? Yes. The "Details on the previously performed validation are summarized below" section describes testing conducted on the algorithm itself, independently of human interaction. The reported device performance for bone and metal identification comes directly from this standalone evaluation.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. The ground truths for bone and metal identification were "independently established by three (3) U.S. Orthopedic surgeons" who "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."

8. Sample Size for the Training Set

• AI algorithm for bone identification: 145 CT image studies.
• Metal identification: 130 CT image studies.

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

The document states that 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 how ground truths for the test set were established by three U.S. Orthopedic surgeons. However, the document does not explicitly describe how the ground truth for the training set was established. It's common practice for training data to also be annotated by experts, but the details of that process are not provided in this specific excerpt.

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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.

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.

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

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.

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SMART Bun-Yo-Matic CT 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 Computed Tomography (CT).

SMART Bun-Yo-Matic CT software provides:

· Visualization report of the three-dimensional mathematical models of the anatomical structures of 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 threedimensional structural models, orthopaedic fixation device models and surgical instrument parameters combined with the measurements can be used for the planning of treatments and operations to correct orthopaedic healthcare conditions of foot and ankle.

The SMART Bun-Yo-Matic CT device is an automatic software tool that segments foot and ankle bones from computed tomography (CT) images and provides a case report showing images of a 3D model of the segmented structures with pre-operative and post-correction measurements. The correction is for hallux valgus through a Lapidus Arthrodesis procedure. The case report also provides parameters of an orthopedic surgical instrument and an example of an implant construct for the procedure.

The device includes machine learning derived outputs. Details on the validation are summarized below. 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%).

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

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

• Test Set Sample Size: 82 CT image studies.
• Data Provenance: The CT image series were collected from various sites across the USA and Europe, with a minimum of 50% of the images originating from the USA.
  • Patient Demographics: Patients of different ages and racial groups, with a minimum of 35% male/female within each dataset. Mean age approximately 47 years (SD 15 years). Representatives from White, (Non-)Hispanic, African American, and Native racial groups.
  • Clinical Conditions: Balanced in terms of subjects with different foot alignment, demographics, imaging devices, and with subjects from clinical subgroups ranging from control/normal feet (44% of test data) to pre-/post-operative clinical conditions such as Hallux Valgus, Progressive Collapsing Foot Deformity, fractures, or with metal implants (40% of test data).

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

• Number of Experts: Three (3).
• Qualifications: U.S. Orthopedic surgeons.

4. Adjudication method for the test set:

• Adjudication Method: Majority vote. Two same responses were required from the three experts to establish a ground truth for the presence of a bone and metal in each DICOM series.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• No MRMC comparative effectiveness study was explicitly mentioned or detailed in the provided text. The study described focuses on standalone algorithm performance against expert-established ground truth.

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

• Yes, a standalone performance assessment was conducted for the SMART Bun-Yo-Matic CT software. The reported performance metrics (100% bone identification, 98.8% metal identification, model conformance, and measurement accuracy) refer to the algorithm's performance without human intervention in the interpretation phase.

7. The type of ground truth used:

• Expert consensus (majority vote of three U.S. Orthopedic surgeons).

8. The sample size for the training set:

• Bone identification algorithm: 145 CT image studies.
• Metal identification algorithm: 130 CT image studies.

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

• The document states that "The AI algorithm for bone identification was developed using 145 CT image studies and metal identification was developed using 130 CT image studies." It does not explicitly detail the method for establishing ground truth for the training data, beyond implying it was part of the algorithm development process. However, given the ground truth methodology for the test set, it is highly probable that a similar expert review or gold standard was used for training data labeling.

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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

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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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

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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