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The iASSIST Knee System is a computer assisted stereotaxic surgical instrument system intended to assist the surgeon in preparing the bone surfaces for the positioning of orthopedic implant system components intra-operatively. It involves surgical instruments and inertial sensors to determine alignment axes in relation to anatomical landmarks and to precisely position alignment instruments and cut guides relative to these axes.

The present iASSIST Knee System is designed for Total Knee Arthroplasty.

As in the predicates, the iASSIST Knee System consists of Pods (tracking sensors), a computer system, software, and surgical instruments designed to assist the surgeon in the placement of Total Knee Replacement components. The Pods combined with the surgical instruments provide positional information to help orient and locate the main femoral and tibial cutting planes as required in knee replacement surgery. This includes means for the surgeon to determine and thereafter track each of the bones' alignment axes relative to which the cutting planes are set. The computer system and software components control and sequence the functions of the Pods per the applicable knee surgery steps via wireless communication.

The provided text does not contain information about specific acceptance criteria or an associated study for the iASSIST Knee System that would allow me to populate all the requested fields. The document primarily describes the regulatory submission (510(k)) and its general performance testing, but lacks the detailed quantitative data, sample sizes, and expert information typically found in a study proving acceptance criteria.

However, based on the information available, I can extract the following:

1. Table of Acceptance Criteria and Reported Device Performance

Not available in the provided text. The text generally states that the device "is safe and effective and that the performances of the iASSIST Knee System are acceptable," but does not list specific quantitative criteria or performance metrics (e.g., accuracy, precision) with corresponding reported values.

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

The text mentions "full simulated use on cadaveric specimens" for the Validation Lab. It does not specify the number of cadaveric specimens used (the sample size) or their country of origin. The study appears to be prospective in nature, as it involves actual testing on specimens.

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

Not available in the provided text.

4. Adjudication Method for the Test Set

Not available in the provided text.

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

Not applicable. The iASSIST Knee System is described as a computer-assisted surgical instrument system, not an AI diagnostic or interpretive tool that would involve "human readers" or AI assistance in the sense of image interpretation for diagnosis. Its purpose is to assist the surgeon in bone surface preparation and positioning of orthopedic implants.

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

The device is inherently designed for "human-in-the-loop" performance, assisting a surgeon intra-operatively. Therefore, a standalone performance study in the absence of a surgeon would not be relevant or meaningful for this device's intended use. The validation lab involves "full simulated use on cadaveric specimens," implying the system is used as intended with human involvement.

7. The Type of Ground Truth Used

For the "Validation Lab" testing on cadaveric specimens, the ground truth would likely be established through:

• Direct measurement: Using highly accurate external instruments or established anatomical reference points to measure the actual alignment axes and cutting planes achieved by the system and surgical instruments on the cadaver bones.
• Expert surgical assessment: Evaluation by experienced orthopedic surgeons to determine if the achieved bone preparation and alignment match the surgical plan and accepted orthopedic standards.

The text does not explicitly state which method was used, but given the nature of the device, it would involve objective measurements against defined surgical parameters.

8. The Sample Size for the Training Set

Not applicable. The iASSIST Knee System is a stereotaxic surgical instrument system, not a machine learning model that requires a distinct "training set" in the conventional sense of AI/ML development. The software capabilities are likely developed and refined through engineering, modeling, and iterative testing, rather than being "trained" on a large dataset of patient images or outcomes.

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

Not applicable, as described in point 8. The "ground truth" for the development of such a system would involve engineering specifications, biomechanical principles, and established surgical techniques for total knee arthroplasty.

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The iASSIST Knee System is a computer assisted stereotaxic surgical instrument system to assist the surgeon in the positioning of orthopedic implant system components intra-operatively. It involves surgical instruments and position sensors to determine alignment axes in relation to anatomical landmarks and to precisely position alignments and implant components relative to these axes. Example orthopedic surgical procedures include but are not limited to: Total Knee Arthroplasty.

As in the predicates, the iASSIST Knee System consists of Pods (tracking sensors), a computer system, software, and surgical instruments designed to assist the surgeon in the placement of Total Knee Replacement components. The Pods combined with the surgical instruments provide positional information to help orient and locate the main femoral and tibial cutting planes as required in knee replacement surgery. This includes means for the surgeon to determine and thereafter track each of the bones' alignment axes relative to which the cutting planes are set. The computer system and software components control and sequence the functions of the Pods per the applicable knee surgery steps via wireless communication.

The provided text describes the iASSIST Knee System, a computer-assisted stereotaxic surgical instrument system. While it outlines the system's purpose, design, and general testing, it does not include acceptance criteria or detailed results of a study demonstrating the device meets specific performance criteria.

Therefore, I cannot provide a table of acceptance criteria and reported device performance from the given text.

However, I can extract the information that is present regarding the studies and ground truth:

General Information on Performance Data:

• Device Performance Testing:

  • Physical/Performance Tests: Conducted to ensure performance of implemented features and verify related design inputs.
  • Engineering Analysis: Conducted to ensure performance of implemented features and verify related design inputs.
  • Usability Engineering: Addressed user interactions with the iASSIST Knee System.
  • Validation Lab: Performed to validate that using the iASSIST Knee System is safe and effective and that its performances are acceptable under full simulated use on cadaveric specimens.

• Software Verification and Validation Testing:

  • Conducted to satisfy requirements of the FDA Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices and IEC 62304.
  • The software is considered a "moderate" level of concern.
  • Testing demonstrates that the iASSIST Knee System does not raise any new issues of safety and effectiveness compared to predicate devices.

Specific Study Details (based on the provided text's limitations):

1. Table of Acceptance Criteria and Reported Device Performance: Not provided in the text.
2. Sample Size used for the test set and data provenance:
   • Test Set Sample Size: "cadaveric specimens" were used in the Validation Lab. The exact number is not specified.
   • Data Provenance: The Validation Lab study involved "full simulated use on cadaveric specimens." No country of origin is specified for the cadavers or data. It appears to be a prospective simulation study.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not specified in the text.
4. Adjudication method for the test set: Not specified in the text.
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: Not specified in the text. The device is a surgical instrument system, not an imaging interpretation AI.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The text mentions "computer assisted stereotaxic surgical instrument system to assist the surgeon". The validation lab involved "full simulated use on cadaveric specimens," implying human interaction is part of its intended use and testing. A "standalone" performance without human input is not described.
7. The type of ground truth used:
   • For the "Validation Lab" testing, the ground truth would likely refer to the actual anatomical alignments or target positions in the cadaveric specimens, which would then be compared against the system's measurements and guidance. However, the exact method for establishing this ground truth is not detailed (e.g., whether it relied on pre-defined anatomical markers, precise physical measurements independent of the device, or other methods).
8. The sample size for the training set: Not applicable and not mentioned. This device is a surgical navigation system, not a typical machine learning algorithm that requires a separate training set in the conventional sense. The software is verified and validated, but does not appear to be "trained" on a dataset in the way an AI diagnostic tool would be.
9. How the ground truth for the training set was established: Not applicable (see point 8).

In summary, the provided document focuses on the regulatory submission, equivalence to predicate devices, and general categories of testing performed. It lacks the specific quantitative performance metrics, acceptance criteria, and detailed study methodologies typically required to fill out the requested table for an AI/device performance study.

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The iASSIST Knee System is a computer assisted stereotaxic surgical instrument system to assist the surgeon in the positioning of orthopedic implant system components intra-operatively. It involves surgical instruments and position sensors to determine alignment axes in relation to anatomical landmarks and to precisely position alignments and implant components relative to these axes.

Example orthopedic surgical procedures include but are not limited to: Total Knee Arthroplasty.

As in the predicate, the iASSIST Knee System consists of tracking sensors ('pods'), a computer system, software, and surgical instruments designed to assist the surgeon in the placement of Total Knee Replacement components. The pods combined with the surgical instruments provide positional information to help orient and locate the main femoral and tibial cutting planes as required in knee replacement surgery. This includes means for the surgeon to determine and thereafter track each of the bones' alignment axes relative to which the cutting planes are set.

The provided document is a 510(k) Pre-Market Notification for the iASSIST™ Knee System. It details the device, its intended use, and comparisons to a predicate device. However, it does not explicitly provide a table of acceptance criteria with reported device performance statistics in the way that would typically be seen for AI/ML device performance.

Instead, the document states that "Non-clinical tests were performed to assess that no new safety and efficacy issues were raised in the device." The performance data section describes the types of tests conducted, rather than specific quantitative acceptance criteria and results against those criteria.

Therefore, I cannot fulfill all parts of your request with the provided information. I will, however, extract all available information about the study and acceptance criteria as described.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

As noted above, a detailed table with specific quantitative acceptance criteria and corresponding reported device performance (e.g., accuracy, sensitivity, specificity, or specific error margins with numerical results) is not provided in this document. The document focuses on demonstrating that modifications to an existing device (predicate) did not introduce new safety or efficacy issues and that the device still meets its intended functionality.

The closest to "acceptance criteria" are implied by the types of tests described, indicating that the system must maintain its required functionality, robust performance, and compatibility.

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

The document describes "Non-clinical tests" and "simulated bench test conditions," and "Full use simulations tests using sawbones." This indicates the tests were conducted in a controlled, non-human, and likely retrospective or simulated environment, rather than on patient data.

• Sample Size: Not explicitly mentioned.
• Data Provenance: Simulated bench tests and sawbone simulations. This is not patient data; therefore, "country of origin" is not applicable in the typical sense. It implies lab or manufacturing environment testing.
• Retrospective/Prospective: Not applicable, as it's not patient-level data. The tests would have been performed prospectively during the development and modification phases.

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

This information is not provided. Since the tests were primarily engineering and functional verification on simulated or bench setups (e.g., software, hardware, mechanical components, sawbones), "ground truth" would likely be established by engineering specifications, calibration standards, and comparison to the predicate device's known performance, rather than clinical expert consensus.

4. Adjudication Method for the Test Set

Not applicable, as "adjudication" typically refers to resolving discrepancies between human readers or ground truth experts for clinical data. The tests described are engineering validations.

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

No, an MRMC comparative effectiveness study is not mentioned. The document primarily focuses on verifying that device modifications do not introduce new safety or efficacy concerns compared to its own predicate, rather than comparing its performance against humans or quantifying human improvement with AI assistance.

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

The device is described as a "computer assisted stereotaxic surgical instrument system to assist the surgeon." This inherently implies a "human-in-the-loop" system. While the software and hardware components were tested individually ("Software system tests," "Performance tests were performed...to verify the implementation of the performance of the bone registration related functionalities"), these tests were to ensure the components functioned correctly for the purpose of assisting a surgeon. A standalone performance without a human interaction is not the intended use and therefore not explicitly evaluated in isolation as a primary performance metric in the way an AI diagnostic tool might be.

7. The Type of Ground Truth Used

For the engineering tests:

• Software tests: Likely against defined software requirements and specifications.
• Performance tests (bone registration): Likely against known, calibrated physical measurements or established mathematical models for bone alignment.
• Robustness/Compatibility/Battery life/ESD: Against engineering specifications, industry standards, and predicate device performance.
• Sawbone simulations: Likely against established surgical techniques and expected outcomes for total knee arthroplasty, possibly with objective measurements of alignment.

8. The Sample Size for the Training Set

This device is not described as an AI/ML device that undergoes "training" in the typical sense of a deep learning model. It's a computer-assisted surgical instrument system using predefined algorithms and sensors. Therefore, a "training set" as understood in machine learning is not applicable here. The software development would involve traditional software engineering and testing cycles.

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

As explained above, there isn't a "training set" in the AI/ML context for this device. The algorithms are likely based on biomechanical principles, geometry, and surgical protocols, with "ground truth" derived from engineering specifications and clinical understanding of proper implant positioning.

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