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The KneeAlign® 2 System is a computer controlled system intended to assist the surgeon in determining reference alignment axes in relation to anatomical structures during stereotactic orthopedic surgical procedures. The KneeAlign® 2 System facilitates the accurate positioning of implants and instrumentation, relative to these alignment axes. Example orthopedic surgical procedures include but are not limited to: Total Knee Arthroplasty

The KneeAlign® 2 System is an innovative non-invasive computer assisted surgical navigation system for use in knee arthroplasty procedures. The KneeAlign® System is configured to detect, measure, and display angular measurement changes in a triaxial format. The KneeAlign® 2 System utilizes a palm-sized computer module and reference sensor to generate positional information in orthopedic procedures providing a sequence of steps for registration of anatomical landmarks, calculation of mechanical axes, and positioning of instruments relative to the mechanical axes. In knee arthroplasty procedures, the device assists the surgeon in: Establishing the mechanical axis of the femur, determining the varus/valgus angle and the flexion/extension angle of the cutting block relative to femur. Establishing the mechanical axis of the tibia, determining the varus/valgus angle and the posterior slope angle of the cutting block relative to tibia. The KneeAlign® 2 System comprises a single use computer module and reusable instrumentation.

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state specific numerical acceptance criteria for the KneeAlign® 2 System's performance in terms of accuracy (e.g., within X degrees or Y mm). It instead uses broader statements about the device's ability to be used according to its intended purpose and its substantial equivalence to predicate devices.

However, based on the description of its function, the implicit acceptance criteria would revolve around:

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

• Sample Size for Test Set: The document does not specify a numerical sample size for any of the performance tests. It mentions "bench, and cadaver testing" and "simulated use testing."
• Data Provenance: The document does not explicitly state the country of origin for the data. Given the applicant's location (Aliso Viejo, CA) and the FDA submission, it's highly probable the testing was conducted in the USA. The data is retrospective in the sense that it describes tests performed as part of the device development and validation process before submission.

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

The document does not provide information regarding the number of experts, their qualifications, or how ground truth was established specifically for the test sets (bench, cadaver, or simulated use).

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, and Effect Size

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or described. The study focuses on the device's accuracy and functionality, not its comparative effectiveness with or without human-in-the-loop performance or its impact on human reader performance.

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

The testing described (system accuracy, bench, cadaver, simulated use) would inherently involve standalone performance, as it's evaluating the device's ability to measure and display angles. While the device assists a surgeon, the tests would assess the device's output independently of the surgeon's influence on the measurement capabilities themselves. The system "utilizes algorithms to convert sensor outputs into spatial coordinates, providing graphical representation." This implies the core functionality is algorithmic and tested directly.

7. The Type of Ground Truth Used

The document does not explicitly state the specific type of ground truth. However, for "system accuracy testing" and "bench, and cadaver testing" in the context of orthopedic navigation, ground truth would typically be established by:

• Highly accurate measurement systems/reference standards: For bench testing, this would involve precise mechanical fixtures and measurement tools.
• Anatomical landmarks/pathology: For cadaver testing, ground truth for anatomical axes would be derived from direct anatomical measurements or established radiographic landmarks, potentially verified by experienced surgeons or anatomists.

8. The Sample Size for the Training Set

The document does not provide any information about a specific "training set" or its sample size. This device appears to be a direct measurement and navigation system, rather than a machine learning model that requires a distinct training phase. Its algorithms convert sensor outputs, implying a rule-based or model-based approach rather than a learned one from a large dataset.

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

Since no training set is mentioned (see point 8), there is no information on how ground truth for a training set was established.

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The KneeAlign™ System with Reference Sensor is a computer controlled system intended to assist the surgeon in determining reference alignment axes in relation to anatomical structures during stereotactic orthopedic surgical procedures. The KneeAlign™ System with Reference Sensor facilitates the accurate positioning of implants and instrumentation, relative to these alignment axes. Example orthopedic surgical procedures include but are not limited to: Total Knee Arthroplasty.

The KneeAlign System with Reference Sensor is an innovative non-invasive computer assisted surgical navigation system for use in knee arthroplasty procedures. The KneeAlign System is configured to detect, measure, and display angular measurement changes in a triaxial format. The KneeAlign System with Reference Sensor utilizes a palm-sized computer module and reference sensor to generate positional information in orthopedic procedures providing a sequence of steps for registration of anatomical landmarks, calculation of mechanical axes, and positioning of instruments relative to the mechanical axes. In knee arthroplasty procedures, the device assists the surgeon in establishing the mechanical axis of the tibia, determining the varus/valgus angle and the posterior slope angle of the cutting block relative to tibia. The KneeAlign System with Reference Sensor comprises a single use computer module and reusable instrumentation. The KneeAlign™ System with Reference Sensor comprises a single use computer module, a reusable reference sensor, and reusable tibial jig. The device utilizes algorithms to convert sensor outputs into spatial coordinates, providing graphical representation of instruments and anatomy on the user display screen.

The provided 510(k) summary for the KneeAlign™ System indicates that its acceptance criteria and the study proving it meets these criteria are primarily based on demonstrating substantial equivalence to predicate devices through various performance tests. However, the document does not explicitly state specific numerical acceptance criteria for accuracy or performance metrics, nor does it provide a direct table of acceptance criteria versus reported device performance.

Instead, the submission focuses on a comparative approach, asserting that if the device performs comparably to the legally marketed predicate devices, it is considered safe and effective.

Here's a breakdown of the information available and what is not explicitly stated:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not explicitly stated. The document mentions "Simulated Use/Cadaver Testing," implying a test set was used, but the number of cadavers or simulated scenarios is not provided.
• Data Provenance: Not explicitly stated. The document doesn't mention the country of origin or whether the data was retrospective or prospective. Given it involves cadaver testing, it would be prospective data collection for the study itself.

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

• This information is not provided in the document. Ground truth for cadaver studies typically involves direct measurement by a clinician or engineer, potentially verified by multiple individuals, but no details are given here.

4. Adjudication Method for the Test Set

• This information is not provided in the document.

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

• No, a MRMC comparative effectiveness study was not done as described (human readers improving with AI vs. without AI assistance). This device is a surgical navigation system, not an AI diagnostic tool primarily evaluated by human readers interpreting images. Its "assistance" is for the surgeon during the procedure, not a post-hoc diagnostic interpretation by multiple readers.
• The comparison focuses on the device's performance against predicate devices and the standard of care (visual observation and tactile feedback or existing computer-assisted surgery devices), rather than quantifying human reader improvement with or without the device in an MRMC study setup.

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

• Yes, implicitly. The "System accuracy testing" and other bench tests would constitute standalone performance evaluations of the device's algorithms and hardware without a human actively operating it in a surgical scenario. The "Simulated Use/Cadaver Testing" would then involve a human-in-the-loop, but the initial accuracy tests would be standalone.

7. The Type of Ground Truth Used

• For "System accuracy testing" and "Simulated Use/Cadaver Testing," the ground truth would likely be established through direct, precise measurements using highly accurate instruments (e.g., optical trackers, mechanical goniometers, or other measurement tools) independent of the device being tested. In cadaver studies, this often involves disarticulation and direct measurement of bone axes or angles after the device's measurements are recorded.
• The document does not explicitly state the methodology for establishing this ground truth.

8. The Sample Size for the Training Set

• This device is a rule-based or sensor-based system that applies algorithms to convert sensor outputs into spatial coordinates. This is generally not a machine learning/AI system that requires a "training set" in the common sense of supervised learning. Therefore, a training set in the context of machine learning is not applicable or discussed. The algorithms are likely pre-programmed and validated, not trained on a large dataset.

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

• As noted above, a training set (in the machine learning sense) is not applicable here as the device operates on established biomechanical principles and sensor data processing rather than learning from data. The accuracy of the underlying algorithms would be validated through engineering principles and possibly through bench testing with known inputs and expected outputs.

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The KneeAlign "M System is a computer controlled system intended to assist the surgeon in determining reference alignment axes in relation to anatomical structures during stereotactic orthopedic surgical procedures. The KneeAlign™ System facilitates the accurate positioning of implants and instrumentation, relative to these alignment axes.

Example orthopedic surgical procedures include but are not limited to:

• . Total Knee Arthroplasty/tibial resection

The KneeAlign " System is an innovative non-invasive computer assisted surgical navigation system for use in knee arthroplasty procedures. The KneeAlign™ System is configured to detect, measure, and display angular measurement changes in a triaxial format.

The KneeAlign " System utilizes a palm-sized computer module and registration instruments to generate positional information in orthopedic procedures providing a sequence of steps for registration of anatomical landmarks, calculation of mechanical axes, and positioning of instruments relative to the mechanical axes. In knee arthroplasty procedures, the device assists the surgeon in establishing the mechanical axis of the tibia, determining the varus/valgus angle and the posterior slope angle of the cutting block relative to tibia. The KneeAlign " System comprises a single use computer module and reusable instrumentation.

Here's a breakdown of the acceptance criteria and study information based on the provided text for the K091411 (KneeAlign™ System):

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state specific quantitative acceptance criteria or detailed numerical device performance metrics. Instead, it makes a general statement about validation.

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

• Sample Size: Not explicitly stated. The text mentions "bench and cadaver testing." The number of cadavers or specific test cases is not provided.
• Data Provenance: Not explicitly stated. "Bench and cadaver testing" generally implies studies conducted by the device manufacturer or a contracted lab. The country of origin is not specified, nor is whether the data was retrospective or prospective.

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

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: Not explicitly stated.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The document simply mentions "bench and cadaver testing" to confirm the system's intended use and performance. There is no information about expert adjudication for establishing ground truth in a test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

• MRMC Study: No, an MRMC comparative effectiveness study is not indicated. The device is a surgical navigation system, not an imaging analysis AI or diagnostic tool that would typically involve human "readers" interpreting output.
• Effect Size: Not applicable.

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

• Standalone Performance: The core functionality validated is the "algorithm only" in the sense that the device uses "algorithms to convert sensor outputs into spatial coordinates, providing graphical representation of instruments and anatomy." The "bench and cadaver testing" would evaluate the accuracy and reliability of these algorithmic outputs in a controlled environment, which implicitly assesses its standalone performance in providing accurate measurements and guidance. However, it's not described as a "standalone validation" in the typical sense of comparing an AI's output to ground truth without human intervention in a diagnostic context. The system is designed to assist the surgeon, implying a human-in-the-loop during actual surgery.

7. The Type of Ground Truth Used

• Type of Ground Truth: Not explicitly stated. For bench testing, ground truth would likely be established using precise measurement tools (e.g., goniometers, CMMs) or highly accurate reference systems. For cadaver testing, ground truth might involve post-procedure measurements using specialized tools or dissection to verify alignment relative to anatomical landmarks. The document does not specify how these ground truths were established.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This device is a computer-assisted surgical navigation system, and the information provided does not suggest it uses machine learning/AI in a way that would require a separate "training set" for predictive model development as seen in image analysis or diagnostic AI. The algorithms mentioned are likely deterministic (converting sensor outputs to coordinates) rather than machine learning algorithms trained on data.

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

• Ground Truth for Training Set: Not applicable, as no training set for machine learning is indicated.

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