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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.

The brAIn™ Shoulder Positioning software is a cloud-based application intended for shoulder surgeons. The software does not perform surgical planning but provides tools to assist the surgeon with planning primary anatomic and reverse total shoulder replacement surgeries using FX Shoulder Solutions implants. The software is accessible via a web-based 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 serve as a foundation for the surgeon's manual planning, which is performed using an interactive 3D viewer that allows for soft tissue visualization. The surgeon positions the glenoid and humerus implants manually within this same 3D interface using 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 and lateralization. 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 post-implantation).

Here's a breakdown of the acceptance criteria and the study details for the brAIn™ Shoulder Positioning device, based on the provided FDA 510(k) clearance letter:

Table of Acceptance Criteria and Reported Device Performance

Study Details

1. Sample Size Used for the Test Set and Data Provenance:

   • Sample Size for Test Set: 173 samples.
   • Data Provenance: Retrospective, with a split based on patient gender, shoulder side, and geographical region of origin.
     • Geographical Origin (Test Set):
       • Left shoulder: 58.2% Europe (46), 41.8% USA (33)
       • Right shoulder: 56.4% Europe (53), 43.6% USA (41)
     • The data corresponds to patients that underwent total shoulder arthroplasty with an FX Shoulder implant, with diversity in gender, imaging equipment, institutions, and study year. The image acquisition protocol was standard for this type of procedure.

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

   • The document does not explicitly state the number of experts used.
   • Qualifications: "Medical professionals" are mentioned for creating manual segmentation labels. For landmark performance, "experts" adjusted final positions, but their specific qualifications are not detailed beyond "medical professionals." For shoulder side detection, a "Clinical Solutions Specialist" performed a manual assessment.

3. Adjudication Method for the Test Set:

   • The document does not specify an explicit adjudication method such as 2+1 or 3+1 for establishing ground truth from multiple experts. It mentions labels created "manually by medical professionals" and "final positions adjusted by experts," implying a consensus or single-expert approach, but no detailed adjudication process is described.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No, a multi-reader multi-case (MRMC) comparative effectiveness study evaluating human reader improvement with AI vs. without AI assistance was not explicitly mentioned or described in the provided information. The studies primarily focus on the standalone performance of the AI for various tasks.

5. Standalone (Algorithm Only) Performance Study:

   • Yes, a standalone performance study was conducted. The "Segmentation Performance Testing," "Shoulder Side Detection performance testing," "Measurement Accuracy performance testing," "Landmark Performance Testing," "Streaming Stability Testing," and "Ruler Performance Testing" sections all describe the evaluation of the brAIn™ Shoulder Positioning software's algorithmic performance against established ground truths or benchmarks, without explicit human-in-the-loop interaction as part of the primary evaluation metrics.

6. Type of Ground Truth Used:

   • Segmentation: Manual segmentation performed by medical professionals.
   • Shoulder Side Detection: Manual assessment performed by a Clinical Solutions Specialist.
   • Measurement Accuracy: Reported accuracy of the predicate device (for comparison when editing positions) and theoretical distances calculated from spatial coordinates (for ruler tool).
   • Landmark Performance: Final positions adjusted by experts.

7. Sample Size for the Training Set:

   • Sample Size for Training Set: 335 samples (corresponding to 65.9% of the total dataset).

8. How the Ground Truth for the Training Set Was Established:

   • The labels (ground truth) for both the training and testing sets were created "manually by medical professionals."

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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.

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).

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

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.

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AVATAR MEDICAL Software V1 is intended as a medical imaging system that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from CT or MR imaging devices. It is also intended as software for preoperative surgical planning, and as software for the intraoperative display of the aforementioned multi-dimensional digital images. AVATAR MEDICAL Software V1 is designed for use by health care professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.

The Avatar Medical Software V1 (AMS V1) is a software-only device that allows trained medical professionals to review CT and MR image data in three-dimensional (3D) format and/or in virtual reality (VR) interface. The 3D and VR images are accessible through the software desktop application and, if desired, through compatible VR headsets which are used by users for preoperative surgical planning and for display during intervention/surgery.

The AMS V1 product is to be used to assist in medical image review. Intended users are trained medical professionals, including imaging technicians, clinicians and surgeons.

AMS V1 includes two main software-based user interface components, the Desktop Interface and the VR Interface. The Desktop Interface runs on a compatible off-the-shelf (OTS) workstation provided by the hospital and only accessed by authorized personnel. The Desktop Interface contains a graphical user interface where a user can retrieve DICOM-compatible medical images locally or on a Picture Archiving Communication System (PACS). Retrieved CT and MR images can be viewed in 2D and 3D formats. Users are able to make measurements, annotations, and apply fixed and manual image filters.

The VR Interface is accessible via a compatible OTS headset to allow users to review the medical images in a VR format. VR formats can be viewed only when the user connects a compatible VR headset directly to the workstation being used to view the Desktop Interface. Additionally. AMS V1 enables the intended users to remotely stream the Desktop Interface to another workstation on the same local area network (LAN).

The 3D images generated using AMS V1 are intended to be used in relation to surgical procedures in which CT or MR images are used for preoperative planning and/or during intervention/surgery.

The intraoperative use of the AMS V1 solely corresponds to the two following cases:

• Display of the AMS V1 Desktop Interface on existing monitors/screens in the operating room
• Use in a non-sterile image review room accessible from the operating room during the procedure (AMS V1 operates on VR headsets which are not approved to be used in the sterile environment of the operating room)

Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text for points 2-9 below is sparse. The information below is limited to what can be directly inferred or is explicitly stated in the document.

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

The document states that tests for linear and diameter measurements were "performed for a series of objects in reference MR and CT phantom images." This indicates that phantom data was used for these specific tests. The exact sample size (number of phantom images or objects within them) for the test set is not specified.

The data provenance is described as "reference MR and CT phantom images," which implies controlled, synthetic data rather than patient data from a specific country or collected retrospectively/prospectively.

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

The document does not specify the number of experts used or their qualifications for establishing ground truth on the test set. For the measurement tests, the ground truth was based on "reference MR and CT phantom images," meaning the inherent dimensions of the phantom objects served as the ground truth. For visual evaluations (display quality, filter functionality), it can be inferred that qualified personnel performed these, but no details are provided.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method. For the measurement tests, the ground truth was objective (phantom dimensions), and for visual assessments, it seems a direct assessment against standards or intended functionality was performed, without mention of a multiple-reader adjudication process.

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 MRMC comparative effectiveness study was done or reported in this document. The study focused on standalone performance evaluation as described below.

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

Yes, a standalone performance evaluation was done. The performance data section describes "Measurement performance testing was conducted by leveraging reference digital phantoms and the comparison with a cleared device (Osirix MD K101342)." This compares the algorithm's measurement capabilities against a reference, which is a standalone assessment. Similarly, the display quality, optical testing, filter technology, and VR fluidity assessments are focused on the device's inherent performance.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The ground truth used for specific tests was:

• phantom images (for linear and diameter measurements) where the actual dimensions of the objects in the phantoms served as the objective truth.
• AAPM guidance recommendation and IEC63145-20-20 standard (for display quality and optical testing).
• Reference DICOM with intended filtering principles (for filter technology functionality).

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size for a training set. This submission focuses on verification and validation testing, suggesting that the core algorithms might have been developed prior, and details about their training data are not included in this 510(k) summary.

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

The document does not provide any information on how the ground truth for a training set was established, as it does not discuss a training set.

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