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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 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 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:

• AltiVate Anatomic (K162024, K173073, K193226 (CS Edge))
• AltiVate Anatomic Augmented (K213387, K222592)
• Turon Shoulder System (K111629, K080402, K123982)
• Reverse Shoulder Prosthesis (K051075, K092873, K111629, K100741, K111061, K111735, K041066, K141006)
• AltiVate Reverse (K141990, K172351, K190290)
• AltiVate Reverse Glenoid (ARG) (K233481)

Lima:

• SMR Shoulder System (K161476, K100858, K101263)
• SMR Reverse Shoulder System (K110598)
• SMR 40MM Glenosphere (K142139)
• SMR 3-Pegs Glenoid (K153722, K130642)
• SMR Modular Glenoid (K113254, K143256)
• SMR TT Metal Back Glenoid (K133349)
• SMR Hybrid Glenoid System (K163397)
• SMR Stemless Anatomic (K221758)
• SMR 140° Reverse Humeral Body (K201905)
• SMR TT Augmented Glenoid System (K191746, K200171)
• SMR Lateralized Connectors with screws (K183042)
• SMR TT Augmented 360 Baseplate (K220792)
• SMR TT Hybrid Glenoid (K220792)
• PRIMA Humeral System and SMR Glenosphere Ø42 (K212800)
• PRIMA TT Glenoid (K222427)

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 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 operation preoperatively. 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 software will create patient-specific Guide CAD file(s) if requested by the surgeon.

• The Precision AI Guides, which are patient-specific guides and models that are based on a pre-surgical plan. This pre-surgical plan is generated using the software component. Patient-specific guides and models will be manufactured using 3D printing by selective laser sintering if the surgeon requests patient-specific guides to transfer the plan to surgery.

The PAI-SPS generates a pre-surgical plan based on medical imaging data using the PAI-SPS Software. The software allows a qualified surgeon to visualize, measure, reconstruct, annotate, edit and approve pre-surgical plan data, which leads to the generation of a case planning report. The PAI-SPS Software allows for the creation of a glenoid and/or humeral pre-operative plan. If requested by the surgeon, PAI-SPS Guides and Models are designed and manufactured based on the approved pre-surgical plan. PAI-SPS Guide and Models are patient specific templates which transfer the pre-operatively determined pin positioning to the patient intraoperatively assisting the surgeon in positioning glenoid/humeral components used with total and reverse shoulder arthroplasty procedures.

The provided 510(k) clearance letter for the PAI-SPS does not contain specific acceptance criteria or details of a study proving the device meets said criteria in the format requested. The document primarily focuses on the regulatory clearance process, device description, and comparison to predicate devices, stating that clinical testing was not required to demonstrate substantial equivalence.

However, based on the information provided, we can infer some aspects and highlight what is missing.

Here's an analysis of the requested information based on the provided text:

1. Table of acceptance criteria and the reported device performance

• Acceptance Criteria: Not explicitly stated in terms of measurable thresholds (e.g., minimum accuracy percentages, error margins). The document implies that the acceptance criteria relate to demonstrating "substantial equivalence" in safety and effectiveness compared to predicate devices.
• Reported Device Performance: No specific numerical performance metrics are reported. The document states that "Software verification and validation" and "Usability validation" were completed to demonstrate substantial equivalence.

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

• Sample Size: Not specified. The document does not mention a specific "test set" in the context of clinical or performance data using patient-specific samples.
• Data Provenance: Not specified. As clinical testing was not required for substantial equivalence, there's no mention of country of origin or whether data was retrospective or prospective.

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

• Not applicable/Not specified. The document does not describe a study involving expert-established ground truth for a test set. The validation efforts mentioned (software V&V, usability validation) do not typically involve this type of ground truth establishment.

4. Adjudication method for the test set

• Not applicable/Not specified. Since no expert-established ground truth test set is described, an adjudication method is not mentioned.

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. The document explicitly states: "Clinical testing was not required to demonstrate substantial equivalence." Therefore, an MRMC comparative effectiveness study was not performed or submitted for this clearance.

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

• Yes, implicitly. The "Software verification and validation" would have involved testing the algorithm's performance in generating the 3D construct and patient-specific guide files. While specific metrics are not provided, this validation inherently assesses the algorithm's standalone capabilities. The software "auto segments via a locked, or static, artificial intelligence algorithm."

7. The type of ground truth used

• For the AI algorithm (segmentation): Implicitly, the ground truth for the segmentation algorithm would have been expert-annotated CT images or established anatomical landmarks against which the AI's segmentation accuracy is measured during its development and internal validation. The document does not specify how this ground truth was established, only that the AI algorithm is "locked, or static."
• For the overall system: The ground truth for the planning software and guides would be the "optimal position" for implant placement as determined by a qualified surgeon. The system's goal is to assist in achieving this optimal position.

8. The sample size for the training set

• Not specified. The document mentions that the AI algorithm for auto-segmentation is "locked, or static," implying it was trained on a dataset, but the size of this training set is not provided.

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

• Not specified. While it's implied that the AI for auto-segmentation was trained using data with established ground truth (likely expert-defined anatomical structures on CT images), the methodology for establishing this ground truth is not detailed in the clearance letter.

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