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The Robotic Arm Interactive Orthopedic System (RIO) is intended to assist the surgeon in providing software defined spatial boundaries for orientation and reference information to anatomical structures during orthopedic procedures.

The RIO is indicated for use in surgical knee and hip 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 CT based model of the anatomy. These procedures include:

• Unicondylar knee replacement and/or patellofemoral knee replacement
• Total hip arthroplasty (THA)

MAKOplasty Total Hip Application is an upgrade to RIO-THA (K121064). The features of this application are to improve overall performance of the system in supporting total hip arthroplasty. MAKOplasty Total Hip Application is used with RIO which includes an optical detector, robotic arm, and guidance module. In addition, the application is designed to be used with a pre-operative planning laptop, as well as both reusable and disposable instrumentation.

The main RIO platform includes an optical detector, computer, dedicated instrumentation, operating software, tools and accessories, cutting system, and a robotic arm. The system's architecture is designed to support two main surgical applications: total hip procedures and partial knee procedures. With application specific hardware and software, it provides stereotactic guidance during minimally invasive orthopedic surgical procedures by using patient CT data to assist a surgeon with presurgical planning and interpretive/intraoperative navigation.

RIO's robotic arm, once configured for a specific application, can serve as 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 an analysis of the provided text regarding the acceptance criteria and study information for the MAKOplasty Total Hip Application, based on the requested format.

It's important to note that the provided document is an FDA 510(k) K141989 summary which often has specific limitations in detailing study methodology, especially for performance data. Much of the requested information (e.g., specific sample sizes for test/training, number and qualifications of experts, adjudication methods, MRMC studies, specific ground truth types with details) is not explicitly stated in this type of document. The summary focuses on demonstrating substantial equivalence through non-clinical performance testing.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a formal table of quantitative acceptance criteria alongside corresponding reported device performance values in the way one might expect from a detailed study report. Instead, it lists "Non-clinical Performance Testing" and states a general conclusion.

Explanation: The document lists several "non-clinical performance tests" and concludes that "The results of testing indicated the device performed within the intended use and did not raise any new safety and efficacy issues." This implies that the acceptance criteria for each of these tests were met, demonstrating the device's functionality and safety. However, the specific quantitative pass/fail criteria for each test (e.g., what constitutes "working as intended" for a communication test, or a specific tolerance for a functional test) are not detailed.

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

• Sample Size for Test Set: Not explicitly stated. The document refers to "non-clinical performance testing" which typically involves bench testing, simulated use, and potentially cadaveric studies, rather than a "test set" in the context of clinical data.
• Data Provenance: Not applicable in the context of clinical data. The tests described are non-clinical, likely conducted in a lab or simulated environment.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: Not applicable/not stated. For non-clinical functional and performance tests, "ground truth" is typically established by engineering specifications, calibration standards, or simulated outcomes, rather than expert consensus on patient data.
• Qualifications of Experts: Not applicable/not stated.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. Given the non-clinical nature of the tests, an adjudication method for conflicting expert opinions on a data set is not relevant. Performance is measured against predetermined engineering or functional specifications.

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

• MRMC Study: No. The document does not mention an MRMC comparative effectiveness study, nor does it discuss human reader performance with or without AI assistance. The MAKOplasty Total Hip Application is a robotic surgical assistance system, not an AI diagnostic tool that human readers would interpret.
• Effect Size of Human Readers Improvement with AI vs. Without AI Assistance: Not applicable, as no such study is described.

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

• Standalone Study: Yes, in the sense of the defined "non-clinical performance testing." The tests listed (e.g., RIO Arm and MICS communication, functional tests) evaluate the device's components and system functionality in a controlled, non-human-in-the-loop environment. The device assists a surgeon; these tests determine if the assistance system itself works as intended algorithmically and mechanically.

7. The Type of Ground Truth Used

• Ground Truth Type: For the non-clinical performance tests, the "ground truth" is likely based on:
  • Engineering Specifications: Whether components communicate correctly, threads connect properly, and mechanical parts function within defined tolerances.
  • Simulated Outcomes: Verification that the system performs its intended function (e.g., provides correct spatial boundaries, achieves desired reaming accuracy in a simulated environment) as per design goals.
  • Design Requirements: Conformance of the system's output to its specified design requirements.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable/not stated. The document describes a robotic surgical assistance system. While such systems involve complex algorithms, the development process (including feature training or calibration) would be part of the overall design and verification/validation, but a specific "training set" in the context of a machine learning-based diagnostic algorithm on a dataset is not described or implied for this device's 510(k) submission. Pre-operative planning uses patient CT data, but that's for individual patient planning, not for training a general algorithm.

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

• Ground Truth for Training Set: Not applicable/not stated, for the reasons outlined in point 8.

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