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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 knee 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: Total Knee Arthroplasty (TKA).

The implant systems compatible with the system: Triathlon Total Knee System (CR/CS/PS/PSR cemented and cementless primary); Triathlon Total Knee System (TS inserts cemented primary).

The Mako System with the subject Mako Total Knee Application is a stereotactic instrument that includes a robotic arm, an integrated cutting system, an optical detector, a camera, a computer, dedicated instrumentation, an operating software, and tools and accessories. The system's architecture is designed to support total and partial knee procedures and total hip 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 pre-surgical 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.

Based on the provided FDA 510(k) clearance letter for the Mako Total Knee Application (3.0), here's an analysis of the acceptance criteria and the study that proves the device meets them:

Important Note: The provided document is an FDA 510(k) clearance letter, which determines substantial equivalence to a predicate device. It is not a detailed clinical study report. Therefore, specific numerical acceptance criteria and "performance against criteria" for detailed metrics (like sensitivity, specificity, AUC for AI models) common in AI/ML medical devices are not explicitly stated in this document. Instead, the focus is on whether the modifications to the Mako Total Knee Application (3.0) maintain the safety and effectiveness of the existing predicate device (K241011).

The "acceptance criteria" here are generally inferred from the types of non-clinical performance testing conducted to demonstrate that the modified device functions as intended and is as safe and effective as its predicate.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of the document (510(k) summary), precise numerical acceptance criteria and corresponding reported performance values are not detailed. The performance data section broadly lists the types of tests conducted.

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

The document does not specify the sample sizes (e.g., number of cadavers for validation, number of units for software testing) or the data provenance (e.g., country of origin, retrospective/prospective). The testing listed is primarily non-clinical (lab-based, cadaveric, bench testing) to verify the device's functionality and safety modifications.

3. Number of Experts Used to Establish Ground Truth and Qualifications

This document describes a stereotaxic instrument for orthopedic surgery, not an AI/ML diagnostic or predictive model that requires expert annotation for ground truth. Therefore, the concept of "experts establishing ground truth" in the context of radiological image interpretation, for example, is not applicable here. Ground truth in this context would refer to precisely known anatomical landmarks or simulated bone cuts, which are inherent to the engineering and design validation processes of a surgical robot.

4. Adjudication Method for the Test Set

Adjudication methods (like 2+1, 3+1) are typically used for establishing ground truth in human-annotated datasets, particularly common in AI/ML diagnostic applications. Since this device is a surgical robotic system undergoing non-clinical technical validation, such adjudication methods are not relevant or mentioned. The validation would follow engineering testing protocols, where "ground truth" (e.g., accuracy of a cut relative to a plan) is measured by precise instruments or physical standards.

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

No. The document does not mention any MRMC study. This type of study is primarily relevant for diagnostic AI tools where human readers (e.g., radiologists) interpret images with and without AI assistance to assess the AI's impact on their performance. The Mako Total Knee Application is a surgical assist system, and its validation focuses on the precision and safety of its robotic assistance, not on diagnostic reading improvement.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The device itself is a "human-in-the-loop" system, assisting the surgeon. The "Bone Registration and Resection Accuracy" and "Software Performance Verification" tests can be considered evaluations of the system's (algorithm + hardware) performance in a controlled environment, which includes the interaction of the software with the physical components. The document doesn't explicitly separate "algorithm only" performance for a non-diagnostic device. The system's performance is inherently tied to its function as an assistive tool to the surgeon.

7. The Type of Ground Truth Used

For the performance testing mentioned (e.g., "Bone Registration and Resection Accuracy," "Cadaveric Design Validation"), the ground truth would be established through:

• Precision Metrology: Highly accurate measurement equipment to verify the robotic arm's movements, resections, and landmark registrations against a pre-defined geometric plan or a physical standard.
• CT-based models: The system relies on CT data, so the "ground truth" for anatomical structures would be derived from these high-resolution imaging scans, verified by anatomical experts or engineering specifications.
• Controlled experimental setups: In cadaveric studies, the "ground truth" for ideal surgical outcomes (e.g., correct implant placement, precise bone cuts) would be defined by surgical and engineering standards and then measured on the cadaveric specimens post-procedure.

8. The Sample Size for the Training Set

This document describes a modification to an existing stereotaxic instrument, not a de novo AI/ML model that is "trained" on a dataset in the conventional sense. The "software changes" involve an update to the programming language, UI, features, and an optional planning tool (page 5). These are software updates to an existing system, rather than training a deep learning model. Therefore, the concept of a "training set" for an AI/ML algorithm isn't directly applicable as described here. The system likely uses fixed algorithms based on physics and geometry, possibly with some learned parameters from system calibration and empirical testing, but not in the framework of a large-scale data training process.

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

As noted above, a "training set" in the context of AI/ML model development is not indicated for this device based on the document. The system's "ground truth" for its core functions (registration, resection guidance) would be established by fundamental engineering principles, anatomical studies, and precise metrology during its initial development and subsequent validation.

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