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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.

The system is intended to be used with the head mounted ARVIS® Eyepiece display for augmented reality visualization and information, such as visualization of the preoperative plan and display of instrument and implant alignment information. The augmented/ virtual displayed information should not be relied upon solely for absolute positional/alignment information and should always be used in conjunction with the displayed stereotaxic information.

ARVIS® Shoulder is indicated for total shoulder arthroplasty using the Enovis AltiVate, LimaCorporation PRIMA, and LimaCorporation SMR implant systems.

ARVIS® Shoulder is a computer-controlled navigation system for shoulder arthroplasty. It aids the surgeon in making intra-operative measurements and locating anatomical structures of the shoulder joint based on the patient's preoperative imaging to assist with selection and positioning of orthopedic implant components. The system consists of software, electronic hardware and surgical instruments.

The ARVIS® Eyepiece is mounted on the surgeon's head and contains tracking cameras that locate the positions of trackers on the patient and instruments. All system measurements, instructions, prompts, and alerts are shown to the surgeon on the Eyepiece display. The Eyepiece communicates with the Belt Pack which is worn by the surgeon around their waist and houses the computer module that runs the ARVIS® Shoulder application software.

The device's workflow involves CT based preoperative planning followed by intraoperative navigation and execution. The preoperative planning software enables 3D virtual implant positioning based on the patient's CT reconstructed digital bone model and bony landmarks. The shoulder navigation software then matches the patient's digital bone model and landmarks to the intraoperative landmarks registered by the surgeon, allowing an image-based navigation to follow. The surgeon uses the plan data as guidance to navigate and help position shoulder instruments and implants.

The provided text does not contain details about acceptance criteria or a study proving the device meets those criteria for the ARVIS® Shoulder system.

The document is an FDA 510(k) clearance letter and a 510(k) Summary. These documents primarily focus on demonstrating substantial equivalence to a predicate device, rather than providing detailed acceptance criteria and performance study data as would be found in a clinical study report or a more comprehensive technical document.

While the document mentions "AI-based automatic image segmentation and landmarking algorithms" and states that "The algorithms and the data used to train and test these remain unchanged from the original submission," it does not provide the specific acceptance criteria or the reported performance metrics for these algorithms. It only broadly states that the data was "labeled and validated in advance by trained experts."

Therefore, I cannot fulfill your request for the detailed table and study information based solely on the provided text.

Here is a breakdown of what can be extracted or inferred, and what cannot:

What can be extracted/inferred:

• Sample size for the training set: 240 CT scans (from 240 patients).
• Sample size for the test set: 60 CT scans (20% of 300, derived from 300 total scans with 80% for training).
• Data provenance for training and test sets: CT scans from candidates for shoulder joint replacement surgery, acquired from 300 patients. The text doesn't explicitly state the country of origin but implies a single cohort. It's retrospective (pre-existing scans).
• How the ground truth for training and test sets was established: "labeled and validated in advance by trained experts."
• Type of ground truth used: Expert consensus on image segmentation and landmarking.
• Standalone performance: The mention of "AI-based automatic image segmentation and landmarking algorithms" being "unchanged" implies these algorithms have standalone performance characteristics, but the details are not provided.

What cannot be extracted from the provided text:

• A table of acceptance criteria and reported device performance: This crucial information is missing.
• Number of experts used to establish the ground truth: Not specified.
• Qualifications of those experts: Only "trained experts" is mentioned, no specific qualifications (e.g., radiologist with 10 years of experience).
• Adjudication method for the test set: Not specified (e.g., 2+1, 3+1, none).
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not mentioned.
• Effect size of human readers improving with AI vs. without AI assistance: Not mentioned.

In summary, the provided document describes the regulatory clearance process and confirms the device's substantial equivalence, but it does not detail the specific performance study results and acceptance criteria for the AI components. These details would typically be found in a more technical report submitted as part of the 510(k) submission, but not usually in the public-facing summary or clearance letter.

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The 360CAS Knee is intended to be used as a planning and intraoperative guidance system in open or percutaneous image guided surgical procedures. The 360CAS Knee is indicated for patients undergoing orthopaedic surgery and where reference to a rigid anatomical structure, such as the pelvis, femur, or tibia can be identified. The 360CAS Knee is indicated for the following surgical procedures: . Total Knee Arthroplasty (TKA) . For conditions of the knee joint in which the use of computer assisted surgery may be appropriate

The 360 Computer Assisted Surgery (360CAS) is a stereotaxic surgical navigation system designed for orthopedic surgical procedures. The 360CAS device consists of four main components: 360CAS Navigation Software, Surgical Instruments, Spatial Tracking Components, and the Navigation Cart. It is intended to be used as a planning and intraoperative guidance system in open or percutaneous orthopedic surgical procedures. This device utilizes optical tracking technology, enabling surgeons to map patient morphology, navigate surgical instruments, and assess joint conditions throughout the surgery. Optical trackers are attached to navigation instruments, and the Spatial Tracking Components locate the 3D position of these instruments in space. The coordinates are relayed to the 360CAS Navigation Software, which provides the user with relevant orientation and position information. The 360CAS Knee is a specific application of the 360CAS Navigation Software tailored for knee replacement surgery.

The provided text describes a 510(k) premarket notification for the "360CAS Knee" device. However, it does not include detailed acceptance criteria or a comprehensive study report proving the device meets these criteria. The document states that "Design verification and validation testing demonstrated that the 360CAS System meets all design requirements and is as safe and effective as its predicate device (K223927)." It lists the types of performance tests performed but does not provide the specific acceptance criteria or the reported device performance values for these tests.

Based on the provided text, I can infer some general information about the performance tests, but I cannot fill in the table with specific quantitative acceptance criteria or reported performance values as that information is not present.

Here's a breakdown of what can and cannot be answered:

1. A table of acceptance criteria and the reported device performance

Explanation: The document states that these tests were performed and that the system "meets all design requirements" and "achieve the same system accuracy" as the predicate device (K223927). However, the specific numerical acceptance criteria (e.g., accuracy within X mm, recall > Y%) and the actual measured performance values are not disclosed in this summary.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not specified in the provided text. The tests mentioned are "Software Verification and Validation Testing," "Positional Accuracy Testing (ASTM F2554)," "Systems accuracy testing," and "Non-clinical surgical simulations conducted on bone models." These typically involve controlled test environments and/or cadaveric/bone models rather than patient data.
• Data Provenance: Not specified. Given the nature of the tests (software V&V, positional accuracy, non-clinical simulations), it primarily involves engineering and lab data, not patient data in the typical sense of retrospective/prospective clinical studies.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. The tests performed are engineering and lab-based (software, positional accuracy, simulations on bone models), not clinical studies involving expert interpretation of patient data to establish ground truth for diagnostics or treatment efficacy.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable. This is typically relevant for studies involving human interpretation (e.g., image reading) where consensus is needed. The described tests are objective engineering measurements and simulations.

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 describes a computer-assisted surgery system, not an AI-assisted diagnostic imaging system that would typically involve human readers.

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

Yes, in the sense that the "Performance Data" section discusses "Positional Accuracy Testing," "Systems accuracy testing," and "Software Verification and Validation Testing." These would primarily evaluate the algorithm/system's performance independently of a specific human operator for its core functions (tracking, calculations, etc.). While it's a surgical navigation system and human surgeons are the ultimate users, these tests assess the technical accuracy and functionality of the device itself.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

The ground truth would be based on:

• For Software V&V: Defined software requirements and specifications.
• For Positional Accuracy/Systems Accuracy: Precisely measured physical references and known values.
• For Non-clinical surgical simulations: Predefined surgical plans, anatomical landmarks on the bone models, and accurate measurement tools (e.g., CMMs, highly accurate optical trackers).

8. The sample size for the training set

Not applicable. This device is described as a "stereotaxic surgical navigation system" that uses "optical tracking technology." The description does not indicate that it is an AI/machine learning model that requires a "training set" in the conventional sense of supervised learning on a large dataset of patient images or outcomes. It appears to be a rule-based or model-based system, for which the term "training set" is not relevant.

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

Not applicable, as there's no indication of a training set as would be found with an AI/ML model.

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