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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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The ARVIS Surgical Navigation System 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 such as changes in leg length in Hip Arthroplasty. The system is compatible with straight acetabular impactors and with specific offset impactors, identified in the instructions for use, for which an adapter has been validated.

Example orthopedic surgical procedures include but are not limited to:

• Total Knee Arthroplasty
• · Unicompartmental Knee Arthroplasty: Tibial Transverse Resection
• · Hip Arthroplasty

The ARVIS® Surgical Navigation System (ARVIS® System) is to be used by orthopedic surgeons and operating room staff who perform hip and knee arthroplasty.

The ARVIS® System uses cameras to measure locations and angles between trackers mounted on the patient and trackers mounted on surgical instruments. By prompting the user through a procedurespecific workflow to register the reference tracker to anatomic landmarks, the ARVIS® Application Software calculates and displays positions of the instruments relative to the patient's anatomy.

The Eyepiece Assembly contains stereo infrared (IR) tracking cameras, a color camera, a stereo display, IR illumination, and IR laser illuminator. The Eyepiece also includes a headlight. The Eyepiece communicates with the Belt Pack via a cable connection.

The Belt Pack houses the computer module, the battery, and the power management board. The Belt Pack supplies power to the eyepiece and computer module. The computer module runs the ARVIS® Application Software.

All system instructions, prompts, alerts, and outputs are displayed to the surgeon on the Eyepiece display. The ARVIS® System Eyepiece is worn on the surgeon's head via a surgical helmet. The ARVIS® Belt Pack is worn on the surgeon's belt or waistband. No electronic hardware is applied to the patient. The surgeon stands adjacent to the patient to operate. The ARVIS® Battery Charger is intended to be used outside the operating room.

The ARVIS® System comprises the major elements listed in Table 1.

The ARVIS® Surgical Navigation System has the following acceptance criteria and study data:

1. Acceptance Criteria and Reported Device Performance

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

The document indicates that "Simulated Clinical Validation Testing" was conducted in a "human cadaveric setting." However, it does not specify the sample size (number of cadavers or simulated cases) used for this test set, nor does it provide details on the country of origin or whether the cadaveric 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

The document does not provide information on the number of experts used or their qualifications for establishing ground truth in the simulated clinical validation testing.

4. Adjudication method for the test set

The document does not describe any adjudication method (e.g., 2+1, 3+1) for the 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 an MRMC comparative effectiveness study or any effect size related to human reader improvement with AI assistance. The testing focused on device performance and accuracy in a simulated clinical setting.

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

The "Simulated Clinical Validation Testing" involved validating "user requirements and navigation accuracy," suggesting human interaction with the system. While the "Safety and Performance Bench Testing" also assessed the system's performance specifications, it's not explicitly stated if a standalone, algorithm-only performance evaluation without any human-in-the-loop was performed. The device is a "Surgical Navigation System" which inherently implies human-in-the-loop operation.

7. The type of ground truth used

For the "Simulated Clinical Validation Testing," the ground truth was likely established through precise measurements or direct observation in the human cadaveric setting to assess navigation accuracy and fulfillment of user requirements. The document does not explicitly state the specific method for establishing this ground truth (e.g., highly accurate physical measurements, known anatomical landmarks).

8. The sample size for the training set

The document does not provide information about a training set since no clinical testing was performed for the determination of substantial equivalence (as stated in Section 9.2). The device is a surgical navigation system, and the provided tests focus on verification and validation of its components and overall system performance rather than an AI model that would require a distinct training set.

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

As no training set is mentioned or implied for an AI model, the method for establishing its ground truth is not applicable or described in this document.

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