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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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ARVIS® Shoulder is indicated for assisting the surgeon in the positioning and alignment of implants relative to reference alignment axes and landmarks in stereotactic orthopedic surgery. The system aids the surgeon in making intraoperative measurements and locating anatomical structures of the shoulder joint based on the patient's preoperative imaging. ARVIS® Shoulder is indicated for total shoulder arthroplasty using the Enovis AltiVate implant system.

ARVIS® Shoulder is a computer-controlled surgical navigation system intended to provide intra-operative measurements to the surgeon to aid in selection and positioning of orthopedic implant components. The subject device is the equivalent shoulder system of the predicate ARVIS® Surgical Navigation System used for indicated knee and hip arthroplasties. ARVIS® Shoulder combines software, electronic hardware and surgical instruments to intraoperatively track tools and locate anatomical structures based on the patient's preoperative imaging. The navigation platform uses the same electronic hardware, mounted on the surgeon's head and waist, as the predicate device. A new equivalent navigation application and a new equivalent surgical instrument set are provided to allow surgeons to navigate instruments in shoulder arthroplasty procedures. The ARVIS® Shoulder workflow involves CT based reconstruction of the patient's shoulder anatomy and preoperative planning to enable image-based navigation. The surgeon uses the plan data as guidance to navigate and help position shoulder instruments and implants. The preoperative planning platform uses Al-based automatic image segmentation and landmarking algorithms. The data used to train and test the algorithms was labeled and validated in advance by trained experts. The total data consisted of 300 CT scans (from 300 patients) acquired from candidates for shoulder joint replacement surgery. The cohort was partitioned into two disjoint subsets through random sampling, with 80% producing a training dataset and 20% constituting the test dataset. The training dataset consisted of 240 CT scans (from 240 patients). Patient ages ranged from 36 to 89 years (mean age of 70), with 46% male and 54% female. All CT scans were acquired using FDA cleared CT scanners. The navigation system is intended to be used with the Enovis AltiVate implant system. ARVIS® Shoulder displays measurements as described in Performance Claims.

Here's a 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

Device: ARVIS® Shoulder
Study Type: Validation of AI algorithms for automatic image segmentation and landmarking.

2. Sample Size and Data Provenance for Test Set

• Sample Size: 60 CT scans (from 60 unique patients)
• Data Provenance: The CT scans were acquired from patients who were candidates for shoulder joint replacement surgery. The scans were acquired using FDA cleared CT scanners (Toshiba, Siemens, Philips, GE Medical Systems, Canon). The specific country of origin is not specified.
• Retrospective/Prospective: The text describes the data as having been used to train and test algorithms, and the cohort was partitioned into disjoint subsets. This suggests the data was retrospective (collected prior to the study for the purpose of algorithm development and validation).

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Total of 3 experts.
  • 1 trained engineer
  • 2 orthopedic surgeons
• Qualifications:
  • Trained Engineer: More than 2 years' experience with medical image processing.
  • Orthopedic Surgeons: Subspecialty qualifications in upper limb surgery.

4. Adjudication Method for Test Set

The adjudication method described is: None (single review - approval).
The reference (ground-truth) label for each CT volume was obtained by a manual process, reviewed, and approved by the consensus of the trained engineer and the two orthopedic surgeons. This implies a single, agreed-upon ground truth rather than a process of resolving disagreements between multiple independent assessments.

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

There is no mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study being done to measure the effect of AI assistance on human readers. The validation focuses solely on the standalone performance of the AI algorithms against expert-established ground truth. Clinical testing was explicitly stated as "not required".

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was done.
The study compared the algorithm-generated outputs for segmentation (Dice Similarity Coefficient, Mean Absolute Distance, Hausdorff Distance) and landmarking (Euclidean Distance, Angular Separation, Successful Point and Angular Classification Rates) against manually established ground truth.

7. Type of Ground Truth Used

The ground truth used was expert consensus.
It was established through a manual process, reviewed, and approved by a trained engineer with medical image processing experience and two orthopedic surgeons with subspecialty qualifications in upper limb surgery.

8. Sample Size for Training Set

• Sample Size: 240 CT scans (from 240 unique patients)
• Total Data Pool: 300 CT scans (80% used for training, 20% for testing).

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

The text states that "The data used to train and test the algorithms was labelled and validated in advance by trained experts." While it details the process for the test set's ground truth, it implies a similar method was used for the training set's ground truth by "trained experts", without providing specific numbers or identical qualification details as for the test set. Given the context, it's reasonable to infer a process of expert labeling, likely by similar qualified individuals, but the exact expert composition for the training set ground truth isn't explicitly detailed with the same specificity as for the test set.

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