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Software: The Precision AI Planning Software is intended to be used as a pre-surgical planner for simulation of surgical interventions for shoulder joint arthroplasty. The software is used to assist in the positioning of shoulder components by creating a 3D bone construct of the joint and allows the surgeon to visualize, measure, reconstruct, annotate and edit presurgical plan data. The software leads to the generation of a surgery report along with a pre-surgical plan data file which can be used as input data to design the Precision AI Shoulder Guide and Biomodels.

Hardware: The Precision AI Planning System Guides and Biomodels are intended to be used as patient-specific surgical instruments to assist in the intraoperative positioning of shoulder implant components used with total and reverse shoulder arthroplasty by referencing anatomic landmarks of the shoulder that are identifiable on preoperative CT-imaging scans. The Glenoid Guide is used to place the k-wire and the Humeral Guide is used to place humeral pins for humeral head resection. The Precision AI Guides and Biomodels are indicated for single use only. The Precision AI Surgical Planning System is indicated for use on adult patients that have been consented for shoulder joint arthroplasty. Both humeral and glenoid guides are suitable for a delto-pectoral approach only. The Precision AI Surgical Planning System is indicated for total and reverse shoulder arthroplasty using the following implant systems and their compatible components: Enovis and Lima.

The Precision AI Surgical Planning System is a patient-specific medical device that is designed to be used to assist the surgeon in the placement of shoulder components during total anatomic and reverse shoulder replacement surgery. This can be done by generating a pre-surgical shoulder plan and, if requested by the surgeon, by manufacturing a patient-specific guides and models to transfer the plan to surgery. The subject device is a system composed of the following: The Precision AI Surgical Planning System Software will create a 3D construct/render of the patient's shoulder joint for the surgeon to plan the operatively then create a physical Patient Specific Instrument (or Guide), using 3D printing by selective laser sintering. The patient's CT scan images are the design input for this to be created and are auto segmented via a locked, or static, artificial intelligence algorithm. The surgeon can visualise the deformity of the diseased joint, on this 3D render and CT scan images, and determine the inherent deformity of the joint. They are then able to virtually place the artificial implants in an optimal position to correct the measured deformity for that specific patient. The Precision AI Guides, which are a patient-specific guide and models that are based on a pre-surgical plan. This pre-surgical plan is generated using the software component. Patient-specific guide and models will be manufactured if the surgeon requests patient-specific guides to transfer the plan to surgery.

The provided text is an FDA 510(k) summary for the Precision AI Surgical Planning System (PAI-SPS). It describes the device and its intended use, but it does NOT contain specific acceptance criteria, detailed study designs, or performance results in terms of metrics like sensitivity, specificity, accuracy, or effect sizes for human reader improvement.

The document states that "Software verification and validation were performed, and documentation was provided following the 'Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices.' This includes verification against defined requirements and validation against user needs." It also mentions that "Design verification and validation testing demonstrated that the PAI-SPS meets all design requirements and is as safe and effective as its predicate device (K233992)."

However, it does not explicitly lay out the acceptance criteria (e.g., "model must achieve 90% accuracy") or the results against those criteria. It focuses more on demonstrating substantial equivalence to a predicate device (PAI-SPS K233992) by showing that the technology and intended use are similar, with the main differences being the addition of compatibility with more implant systems.

Therefore, I cannot fully complete the requested table and answer all questions based solely on the provided text. I will provide information based on what is available and indicate where information is missing.

Description of Acceptance Criteria and Study to Prove Device Meets Criteria

Based on the provided FDA 510(k) summary, the PAI-SPS device is being cleared primarily through demonstrating substantial equivalence to an already cleared predicate device (PAI-SPS, K233992). The key claim for equivalence rests on similar intended use, fundamental scientific technology, design, functionality, operating principles, and materials, with the primary difference being expanded compatibility with additional implant systems.

The document implicitly suggests that the "acceptance criteria" are tied to demonstrating that these technological differences "do not raise any different questions of safety and effectiveness." The studies cited are primarily focused on software verification and validation, ensuring the new compatible implant systems do not negatively impact the established safety and effectiveness.

Here is a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The provided document does not explicitly state quantitative acceptance criteria (e.g., minimum accuracy percentages, specific error bounds) or detailed performance metrics. The performance is largely framed as demonstrating that the device "meets all design requirements and is as safe and effective as its predicate device."

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

• The document does not specify the sample size for any test set (e.g., for software validation or hardware accuracy).
• Data Provenance: Not explicitly stated for specific test sets. Given the company is "Precision AI Pty Ltd" in Australia, and the document discusses "previous cadaver testing and composite bone model testing," it's likely a mix of lab-based/simulated data and potentially some retrospective clinical imaging data for initial AI development/testing, but this is not detailed. The document implies that new testing was not extensively conducted for this submission, relying heavily on the predicate device's prior validation and the minor changes to compatibility.

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

• The document does not specify the number of experts or their qualifications used to establish ground truth for any test set.
• It mentions that the software allows a "qualified surgeon" to approve pre-surgical plan data, implying that expert surgical review is part of the workflow.

4. Adjudication Method for the Test Set

• The document does not specify any adjudication method (e.g., 2+1, 3+1) for establishing ground truth or evaluating test results.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No, an MRMC comparative effectiveness study was not explicitly mentioned or described. The focus of this 510(k) is substantial equivalence based on technological similarity and expanded compatibility, not a comparative study against human readers or performance improvement with AI assistance.

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

• The document states that the AI algorithm performs "auto segmentation via a locked, or static, artificial intelligence algorithm." While this indicates a standalone AI component, the document does not provide standalone performance metrics for this AI segmentation. The overall system is described as a "pre-surgical planner" where the surgeon can "visualize, measure, reconstruct, annotate and edit pre-surgical plan data," suggesting a human-in-the-loop workflow.

7. The Type of Ground Truth Used

• For software, the implicit ground truth appears to be expert consensus or approved surgical plans for judging the accuracy of the software's representations and planning capabilities. The document states "The software allows a qualified surgeon to visualize, measure, reconstruct, annotate, edit and approve pre-surgical plan data."
• For hardware, "previous cadaver testing and composite bone model testing" were used, implying physical measurements against a known standard or "true" position established in these models.

8. The Sample Size for the Training Set

• The document does not specify the sample size used for the training set for the AI segmentation algorithm.

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

• The document does not specify how the ground truth for the AI training set was established. It only mentions that the AI algorithm for auto-segmentation is "locked, or static," implying it was trained previously.

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Software

The Precision Al Planning Software is intended to be used as a pre-surgical planner for simulation of surgical interventions for shoulder ioint arthroplasty. The software is used to assist in the positioning of shoulder components by creating a 3D bone construct of the joint and allows the surgeon to visualize, measure, reconstruct, annotate and edit pre-surgical plan data. The software leads to the generation of a surgery report along with a pre-surgical plan data file which can be used as input data to design the Precision Al Shoulder Guide and Biomodels.

Hardware

The Precision Al Planning System Guides and Biomodels are intended to be used as patientspecific surgical instruments to assist in the intraoperative positioning of shoulder implant components used with total and reverse shoulder arthroplasty by referencing anatomic landmarks of the shoulder that are identifiable on preoperative CT-imaging scans.

The Glenoid Guide is used to place the k-wire and the Humeral Guide is used to place humeral pins for humeral head resection.

The Precision Al Guides and Biomodels are indicated for single use only.

The Precision AI Surgical Planning System is indicated for use on adult patients that have been consented for shoulder joint arthroplasty. Both humeral and glenoid guides are suitable for a deltopectoral approach only.

The Precision Al Surgical Planning System is indicated for total and reverse shoulder arthroplasty using the following Enovis implant systems and their compatible components:

• AltiVate Anatomic Shoulder System (K162024)
• AltiVate Anatomic Augmented Glenoid (K213387)
• Turon Shoulder System (K111629)
• Reverse Shoulder Prosthesis (K051075, K092873, K111629)

Precision Al Surgical Planning System (PAI-SPS) is a patient-specific medical device that is designed to be used to assist the surgeon in the placement of shoulder components during total anatomic and reverse shoulder replacement surgery. This can be done by generating a pre-surgical shoulder plan and, if requested by the surgeon, by manufacturing a patient-specific guide and models to transfer the surgical plan to surgery.

The device is a system composed of the following:

• a software component, Precision Al Surgical Planning Software which will create a 3D construct of the patient's joint for the surgeon to plan the operatively. The surgeon will be able to visualise the movement of the diseased joint and determine mechanical failings. They will then be able to place the virtual shoulder replacement in different positions and decide which position gives the patient the best result. Once the surgeon has decided on the best position, the software will generate a CAD file for a Patient Specific Guide.
• Precision Al Surgical Guides, which are patient-specific guides and models will be manufactured if the surgeon requests patient-specific guides to transfer the surgical plan to surgery. Once the CAD model is generated from the planning software, the model is sent to a 3D printer which will then print the guide out of a biocompatible medical grade Nylon material for sintering (Polyamide-12) which has an established usage for similar application. The specific design of the guide will be customised to the individual patient as well as depending on the particular anatomy it will be applied to. Precision Al Patient Specific Guides are intended for single use only.

The Precision AI Surgical Planning System (PAI-SPS) is a patient-specific medical device comprised of software and physical surgical guides, designed to assist in the placement of shoulder components during shoulder replacement surgery.

Here's an analysis of its acceptance criteria and the supporting study information:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state a table of acceptance criteria with specific numerical targets. However, based on the Performance Data section, the overall acceptance criterion is that the device is "as safe, as effective, and performs as well as the predicate device." The performance reported primarily focuses on the successful completion of various non-clinical and a clinical study.

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

For the clinical study, the sample size used was 35 subjects.
The data provenance for this clinical study was Australia, and it was a post-market evaluation of a clinical case series, implying retrospective data collection or analysis, though the exact nature (e.g., only post-operative CT analysis from existing records versus a direct follow-up) isn't specified beyond "post-market evaluation." The study was conducted under ethics committee approval and according to GCP.

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 to establish ground truth for the clinical test set or their qualifications. It only states that the measurement accuracy was evaluated "via post-operative CT." Assuming post-operative CT scans were the ground truth, their interpretation would typically involve radiologists or orthopedic surgeons, but this is not detailed.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for the clinical test set.

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

The document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance. The clinical study focused on the measurement accuracy of the device itself.

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

Yes, a standalone performance assessment was done for the software component of the PAI-SPS. The Performance Data section mentions "Software Verification and Validation Testing." Additionally, the clinical study evaluating "measurement accuracy of the subject device via post-operative CT" implicitly assesses the standalone accuracy of the planning output (which is generated by the software) as compared to the actual outcome. The software generates "a pre-surgical plan data file" and "[allows] the surgeon to visualize, measure, reconstruct, annotate and edit pre-surgical plan data." The accuracy of these measurements would be a standalone performance metric.

7. The Type of Ground Truth Used

For the clinical study, the ground truth used for evaluating measurement accuracy was post-operative CT scans. For the non-clinical tests (e.g., biocompatibility, dimensional stability), established laboratory test standards and methods define the ground truth.

8. The Sample Size for the Training Set

The document does not provide the sample size for the training set used for the "non-adaptive machine or deep learning algorithms trained for the purpose of semi-automatic segmentation and landmark identification of image scans."

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

The document does not specify how the ground truth for the training set was established for the machine/deep learning algorithms. It only states that the algorithms are trained for "semi-automatic segmentation and landmark identification." Typically, this would involve expert annotation of images, but this detail is not provided.

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