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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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