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The Mako System is intended to assist the surgeon in providing software defined spatial boundaries for orientation and reference information to anatomical structures during orthopedic procedures.

The Mako System is indicated for use in surgical shoulder procedures in which the use of stereotactic surgery may be appropriate, and where reference to rigid anatomical bony structures can be identified relative to a patient imaging databased model of the anatomy. These procedures include:

• · Reverse Shoulder Arthroplasty (primary joint replacement)
  The implants compatible with the system are:

• · Aequalis PerFORM Reversed Glenoid (cementless)

• · Aequalis PerFORM + Reversed Glenoid (cementless)

The Mako System with the subject Mako Shoulder Application is a stereotactic instrument that includes a robotic arm, an integrated cutting system, an optical detector, a computer, dedicated instrumentation, operating software, a planning application, and tools and accessories. The Mako System uses patient CT data to assist the physician with presurgical implant placement planning and intraoperative tracking of the patient's scapula.

The system's architecture is designed to support reverse shoulder procedures. With application-specific hardware and software, the system provides haptic guidance during orthopedic surgical procedures by using patient CT data to assist a surgeon with presurgical planning, implant placement, and interpretive/intraoperative navigation of the patient's anatomy.

Once configured for a specific application, the Mako robotic arm can serve as the surgeon's "intelligent" tool holder or tool guide by passively constraining the preparation of an anatomical site for an orthopedic implant with software-defined spatial boundaries.

Here's a breakdown of the acceptance criteria and study information for the Mako Shoulder Application (1.0), based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document details various performance tests but does not explicitly state numerical acceptance criteria or quantifiable performance metrics for these tests. The conclusions broadly state that performance testing demonstrates equivalence and that the device is safe and effective.

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

The document does not specify the sample sizes (e.g., number of cases, number of cadavers) used for the software functional testing, cutting system accuracy, reliability, software performance verification, system testing, cadaveric design validation, or summative evaluation.

The data provenance is from non-clinical performance testing, primarily involving software and hardware evaluations, and cadaveric design validation. There is no mention of human clinical data or its provenance (e.g., country of origin, retrospective/prospective).

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 or their qualifications for establishing ground truth for any of the performance tests. The testing appears to be primarily engineering and design validation based, rather than clinical ground truth establishment. For the cadaveric design validation, it's plausible that surgeons or surgical experts were involved, but this isn't explicitly stated, nor are their qualifications.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method (e.g., 2+1, 3+1, none). This type of adjudication is typically used in studies involving human interpretation or subjective assessments, which are not detailed in this section of the submission. The performance testing described suggests objective measurements and validations against predefined specifications.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study is not mentioned or described. Such studies are typically conducted to evaluate the impact of AI or assisted devices on human reader performance in areas like diagnostic imaging. This submission focuses on the safety and effectiveness of a surgical guidance system through engineering and cadaveric validation.

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

The performance testing described (Software Functional Testing, Cutting System Accuracy, Reliability, Software Performance Verification, System Testing) constitutes standalone testing of the algorithm and hardware components. The "Cadaveric Design Validation" also tests the system's performance, albeit in a simulated surgical environment where a "human-in-the-loop" (the surgeon operating the system) is inherent to its use. However, the initial listed tests would primarily assess the underlying algorithmic and mechanical performance without necessarily measuring full human interaction.

7. The Type of Ground Truth Used

The ground truth for the various tests would be based on:

• Design Specifications/Requirements: For software functional testing, reliability, and software performance verification, the ground truth is adherence to predefined engineering and functional specifications.
• Physical Measurements/Engineering Standards: For cutting system accuracy, ground truth would be precise measurements against known physical standards or benchmarks.
• Anatomical Accuracy/Surgical Goals: For cadaveric design validation, the ground truth would relate to the accurate execution of the pre-planned surgical procedure on the cadaveric specimen, evaluated against anatomical landmarks and surgical objectives.

There is no mention of "expert consensus," "pathology," or "outcomes data" as ground truth in this document.

8. The Sample Size for the Training Set

The document does not specify a training set sample size. The Mako Shoulder Application uses patient CT data for pre-operative planning and intraoperative guidance, but the document does not discuss how the underlying algorithms were "trained" using specific datasets, nor the size of any such training data. The mention of BLUEPRINT (K232265) for the preoperative workflow implies use of an established planning software, but details about its training data are not provided in this 510(k) summary.

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

As no training set is discussed or specified in this document, there is no information on how its ground truth was established.

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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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