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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 GALILEO™ Spine Alignment Monitoring System is intended to provide intra-operative measurements to a surgeon to aid in the selection and positioning of orthopedic implant system components, relative to anatomical structures and reference axes.

The GALILEO™ Spine Alignment Monitoring System is indicated for patients undergoing orthopedic spine surgery where the use of stereotactic surgery is considered safe and effective, and where a reference to a rigid spinal anatomical structure, such as a vertebral body, can be identified relative to the anatomy. The system aids the surgeon in controlling spinal sagittal alignment.

Example spine surgical procedures include:

• Spinal deformity correction
• Spinal fusion

The GALILEO™ Spine Alignment Monitoring System provides intra-operative real-time measurement of sagittal plane spine alignment parameters such as Lordosis and Kyphosis. The system consists of Orientation Sensing Modules (OSM) that are attached to vertebrae of interest with the patient in prone position. The OSM's are battery-powered devices containing inertial sensors that measure vertebral alignment changes and communicate the information wirelessly to a computer. The computer software calculates sagittal plane spinal alignment parameters in real-time based on the OSM measurements and displays the information on a touch screen tablet.

The provided text describes the GALILEO™ Spine Alignment Monitoring System, but it does not contain the specific details regarding acceptance criteria and the comprehensive study results that would be necessary to fully answer your request.

Here's an analysis of what information is available and what is missing:

Information Present:

• Device Name: GALILEO™ Spine Alignment Monitoring System
• Intended Use/Indications for Use: To provide intra-operative measurements to a surgeon to aid in the selection and positioning of orthopedic implant system components, relative to anatomical structures and reference axes, during orthopedic spine surgery (e.g., spinal deformity correction, spinal fusion) to aid in controlling spinal sagittal alignment.
• Types of Performance Data Performed:
  1. Performance tests under simulated bench conditions to verify software algorithms and overall system accuracy for calibration, sagittal plane registration, vertebral registration, and sagittal plane spine alignment measurements.
  2. Simulated use testing (sawbones and cadaver) to verify and validate overall system performance and compare measurements with clinically accepted standardized radiographic measurements.
  3. Electrical, electromagnetic compatibility, and wireless telecommunication tests (IEC 60601-1-2, FCC 47 CFR part 15).
  4. Software tests for functionalities and features.
  5. Biocompatibility testing (using materials with established biocompatibility).
  6. Sterilization and packaging validation (ISO 14937, ISO 11607 for SAL of 10-9).

Missing Information (Crucial for your request):

• Specific Acceptance Criteria: While performance tests are mentioned, the actual numerical or qualitative acceptance criteria are not provided. For example, what was the acceptable margin of error for sagittal plane alignment measurements?
• Reported Device Performance (Quantitative): The document states that "The results demonstrated that the GALILEO™ Spine Alignment Monitoring System satisfied all user needs and intended use requirements," but it does not provide the actual measured performance values (e.g., accuracy, precision) from the tests.
• Sample Size for Test Set: The number of sawbones or cadaver specimens used for simulated use testing is not specified.
• Data Provenance (Country, Retrospective/Prospective): Not mentioned for any of the performance data.
• Number of Experts & Qualifications for Ground Truth: Not mentioned.
• Adjudication Method: Not mentioned.
• MRMC Comparative Effectiveness Study: The document does not describe any study comparing human readers with and without AI assistance, nor does it mention an effect size.
• Standalone Performance Study: While "Performance tests were performed...to verify the implementation of the software algorithms and overall system accuracy," it's not explicitly stated as a standalone performance study with detailed results for the algorithm only.
• Type of Ground Truth Used: The text mentions comparing system measurements with "clinically accepted standardized radiographic measurements" for simulated use testing, which implies an external reference standard. However, the exact nature (e.g., expert consensus on radiographs, direct physical measurements) is not fully elaborated.
• Sample Size for Training Set: Not mentioned. The device is described as having "inertial sensors" and "computer software calculates sagittal plane spinal alignment parameters," suggesting an algorithm, but training data details are absent.
• How Ground Truth for Training Set Was Established: Not mentioned.

Based on the available information, here's what can be provided with the significant gaps highlighted:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Test Set Sample Size: Not specified. The document only mentions "simulated use testing involving sawbones and cadaver specimen." The number of sawbones or cadavers is not provided.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.
  • Implicit: The ground truth for simulated use testing was compared to "clinically accepted standardized radiographic measurements," implying that these measurements would typically be interpreted or confirmed by radiology or orthopedic specialists, but the specific role and number of experts are not detailed.

4. Adjudication method for the test set

• Adjudication Method: Not specified.

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

• MRMC Study: No, the document does not describe a multi-reader multi-case comparative effectiveness study involving human readers with and without AI assistance. The device is described as providing intra-operative measurements to a surgeon, not as an AI-assisted diagnostic tool for human readers in the traditional sense of image interpretation.

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

• Standalone Performance: Yes, in part. The "Performance tests were performed under simulated bench test conditions to verify the implementation of the software algorithms and overall system accuracy covering all functional steps." These tests would likely assess the algorithm's performance in isolation from a human surgeon interacting with it, focusing on the accuracy of the measurements it produces. However, quantitative results of this standalone accuracy are not provided.

7. The type of ground truth used

• For the simulated use testing, the ground truth was "clinically accepted standardized radiographic measurements." This suggests that the device's output was compared against established radiographic parameters that are considered the standard.

8. The sample size for the training set

• Training Set Sample Size: Not specified. The document describes the device as containing "inertial sensors that measure vertebral alignment changes and communicate the information wirelessly to a computer. The computer software calculates sagittal plane spinal alignment parameters," implying an algorithm, but details about training data are absent.

9. How the ground truth for the training set was established

• Ground Truth for Training Set: Not specified.

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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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