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The TMINI® Miniature Robotic System is indicated as a stereotaxic instrumentation system for total knee replacement (TKA) surgery. It is to assist the surgeon by providing software-defined spatial boundaries for orientation and reference information to identifiable anatomical structures for the accurate placement of knee implant components.

The robotic device placement is performed relative to anatomical landmarks as recorded using the system intraoperatively and based on a surgical plan determined preoperatively using CT based surgical planning tools.

The targeted population has the same characteristics as the population that is suitable for the implant(s) compatible with the TMINI® Miniature Robotic System.

The TMINI® Miniature Robotic System is to be used with the following knee replacement system(s) in accordance with the indications and contraindications:

• Enovis™ EMPOWR Knee System®
• Ortho Development BKS® and BKS TriMax® Knee System
• Total Joint Orthopedics Klassic® Knee System
• United U2™ Knee Total Knee System
• Medacta® GMK® Sphere / SpheriKA Knee Systems
• Zimmer Biomet Anterior & Posterior Referencing Persona® Knee
• b-ONE MOBIO® Total Knee System
• Maxx Orthopedics Freedom® Total & Titan Knee
• LINK® LinkSymphoKnee System

The TMINI® Miniature Robotic System consists of three primary components: a three-dimensional, graphical, Preoperative Planning Workstation (TPLAN® Planning Station) including THINK Case Manager (TCM) the web-based method for surgeons to review, approve and download approved surgical plans, an Optical Tracking Navigation Console (TNav) and a robotically controlled hand-held tool (TMINI Robot) that assists the surgeon in preparing the bone for implantation of TKA components.

The TPLAN Planning Station uses preoperative CT scans of the operative leg to create 3D surface models for case templating and intraoperative registration purposes. The Planning Workstation contains a library of 510(k) cleared knee replacement implant(s) available for use with the system. The surgeon can select an implant model from this library. The planner/surgeon can manipulate the 3D representation of the implant in relation to the bone model to place the implant. The surgeon reviews and approves the case plan using either TPLAN or the TCM web-based application once the surgeon is satisfied with the implant selection, location and orientation. The data from the approved plan is written to a file that is used to guide the robotically controlled hand-held tool.

The hand-held robotic tool is optically tracked relative to optical markers placed in both the femur and tibia and articulates in two degrees-of-freedom, allowing the user to place bone pins in a planar manner in both bones. Mechanical guides are clamped to the bone pins, resulting in subsequent placement of cut slots and drill guide holes such that the distal femoral and proximal tibial cuts can be made in the pre-planned positions and orientations, and such that the implant manufacturer's multi-planer cutting block can be placed relative to drilled distal femoral pilot holes. If the surgeon needs to change the plan during surgery, it can be changed intraoperatively using TNav.

The provided FDA 510(k) clearance letter pertains to the TMINI Miniature Robotic System, a device used to assist surgeons in total knee replacement (TKA) surgery. The submission describes modifications to the system, primarily software enhancements to improve tibial registration performance, along with data logging updates, open-source software report updates, and cybersecurity updates. The application claims substantial equivalence to a previously cleared predicate device (K243481) and focuses on demonstrating that these modifications do not alter the intended use, safety, or effectiveness of the device.

Based on the provided document, here's a breakdown of the acceptance criteria and the study details:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of numerical acceptance criteria for performance metrics (e.g., specific thresholds for accuracy, precision). Instead, it states that "Testing to verify the function of the subject device was conducted following the same test methods and acceptance criteria as those used for the predicate device. The testing demonstrated that the TMINI® Miniature Robotic System met all test criteria and specifications."

The performance tests conducted and their qualitative results are summarized in Table 2: Substantial Equivalence, under the "Performance Testing" section.

* *Note: For Pin & Block Placement Accuracy, System Gap Balance Accuracy, User Needs Validation Testing, and Usability Testing, the document explicitly states "**Passed" and clarifies in a footnote, "These tests did not need to be repeated as a result of the changes to the software included in this submission." This implies that the acceptance criteria were met by the previous testing on the predicate device, and the current modifications did not necessitate re-testing these specific performance aspects.

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

The document does not explicitly state the sample size used for the performance testing. For tests like "Cadaver Lab Validation Testing," while it mentions "Reanalyzed data passed," it does not specify the number of cadavers or cases.

The document does not provide information on the data provenance (e.g., country of origin, retrospective or prospective) for the test sets.

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

The document does not provide information on the number of experts used or their qualifications for establishing ground truth for the test set. Given the nature of a robotic surgical system, ground truth would typically refer to highly accurate measurements obtained from advanced imaging or physical measurements in a controlled environment, likely assisted by surgical and engineering expertise.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1, none) used for the test set.

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

No, an MRMC comparative effectiveness study was not done or reported. This type of study is more common for diagnostic AI algorithms where human interpretation is a key component. The TMINI Miniature Robotic System is a surgical assistance robot, and the study focuses on its performance and accuracy rather than its impact on human reader performance.

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

The performance tests listed, such as "Pin & Block Placement Accuracy," "Cadaver Lab Validation Testing," and "System Gap Balance Accuracy," directly assess the standalone performance of the robotic system in achieving its intended surgical accuracies. While a surgeon operates the system, these accuracy measurements inherent to the robot's capabilities would constitute standalone performance in a sense, as they evaluate the robot's ability to execute pre-planned actions with precision. However, it's important to note that the system is intended to assist the surgeon, so "standalone" in the context of a robotic surgical system usually refers to the accuracy and precision of the robotic movements and tool positioning, which appear to have been tested.

7. The Type of Ground Truth Used

The document implicitly suggests that the ground truth for surgical accuracy tests (e.g., "Pin & Block Placement Accuracy," "System Gap Balance Accuracy") would be established through highly precise measurement techniques in a controlled lab or cadaveric setting, likely using CMM (Coordinate Measuring Machine) data, optical tracking references, or other metrology tools to determine the true positions and orientations relative to the planned surgical targets. For the "Cadaver Lab Validation Testing," the ground truth would be based on anatomical measurements in those cadavers after the robotic intervention.

8. The Sample Size for the Training Set

The document does not provide any information on the sample size for a training set. This submission is for modifications to a previously cleared device, and the focus is on verification and validation of those specific changes rather than the initial development and training of a new AI model for the core robotic functions. While "software enhancements to improve tibial registration performance" are mentioned, it's not specified if this involved a machine learning model that required a distinct training set.

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

As no training set information is provided, there is no information on how ground truth for a training set was established.

Ask a specific question about this device

TMIN® Miniature Robotic System is indicated as a stereotaxic instrumentation system for total knee replacement (TKA) surgery. It is to assist the surgeon by providing software-defined spatial boundaries for orientation and reference information to identifiable anatomical structures for the accurate of knee implant components.

The robotic device placement is performed relative to anatomical landmarks as recorded using the system intraoperatively and based on a surgical plan determined preoperatively using CT based surgical planning tools.

The targeted population has the same characteristics as the population that is suitable for the implant(s) compatible with the TMINI® Miniature Robotic System.

The TMNI® Miniature Robotic System is to be used with the following knee replacement systems in accordance with the indications and contraindications:

• · Enovis™ EMPOWR Knee System®
• · Ortho Development® BKS® and BKS TriMax® Knee System
• · Total Joint Orthopedics Klassic® Knee System
• · United® U2™ Total Knee System
• · Medacta® GMK® Sphere / SpheriKA Knee Systems
• · Zimmer Biomet Anterior & Posterior Referencing Persona® Knee
• b-ONE MOBIO® Total Knee System
• · Maxx Orthopedics Freedom® Total & Titan Knee
• · LINK® LinkSymphoKnee System

The TMINI® Miniature Robotic System like its predicate, the TMINI® Miniature Robotic System consists of three primary components: a three-dimensional, graphical. Preoperative Planning Workstation (TPLAN Planning Station), an Optical Tracking Navigation Console (TNav) and a robotically controlled hand-held tool (TMINI Robot) that assists the surgeon in preparing the bone for implantation of TKA components. In addition, this submission will add a web-based method for surgeons to review, approve and download approved surgical plans generated on the TPLAN Planning Station.

The TPLAN Planning Station uses preoperative CT scans of the operative leg to create 3D surface models for case templating and intraoperative registration purposes. The Planning Workstation contains a library of 510(k) cleared knee replacement implant(s) available for use with the system. The surgeon can select an implant model from this library. The planner/surgeon can manipulate the 3D representation of the implant in relation to the bone model to optimally place the implant. The surgeon reviews and approves the case plan using either TPLAN or the TCM web-based application once the surgeon is satisfied with the implant selection, location and orientation. The data from the approved plan is written to a file that is used to guide the robotically controlled hand-held tool.

The hand-held robotic tool is optically tracked relative to optical markers placed in both the femur and tibia and articulates in two degrees-of-freedom, allowing the user to place bone pins in a planar manner in both bones. Mechanical guides are clamped to the bone pins, resulting in subsequent placement of cut slots and drill guide holes such that the distal femoral and proximal tibial cuts can be made in the pre-planned positions and orientations, and such that the implant manufacturer's multi-planer cutting block can be placed relative to drilled distal femoral pilot holes. If the surgeon needs to change the plan during surgery, it can be changed intraoperatively.

The provided document, an FDA 510(k) summary for the TMINI® Miniature Robotic System, focuses on demonstrating substantial equivalence to a predicate device (K243285) rather than on presenting de novo acceptance criteria and a study proving the device meets those criteria.

The purpose of this 510(k) submission (K243481) is to introduce modifications to the TPLAN Planning station (specifically, an improved segmentation algorithm using a pre-trained and closed machine learning model, enhanced DICOM data importing, updated implant display and selection tools, and improved cybersecurity) and to add the THINK Case Manager (TCM) as a remote method for surgeon review, approval and downloading of approved surgical plans.

Therefore, the document does not contain the detailed information requested regarding specific acceptance criteria and a study that proves the device meets them in the context of a new device's performance validation against novel criteria. Instead, it asserts that the modified device's performance is substantially equivalent to that of the predicate device, which presumably underwent its own performance validation to establish its safety and effectiveness.

However, based on the provided text, I can extract information related to performance testing that was conducted to support the substantial equivalence claim.

Here's an analysis based on the available information:

Key Takeaway from the Document:
The document argues for substantial equivalence to a predicate device (K243285), meaning it explicitly states that no new questions of safety or effectiveness were identified by the modifications, and thus, extensive de novo performance validation against new acceptance criteria was not required or presented. The performance testing mentioned serves to confirm that the modifications did not negatively impact the existing performance characteristics.

Attempt to answer your questions based on the provided 510(k) summary:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly present a table of specific quantitative acceptance criteria or detailed reported device performance values. Instead, it generally states that performance testing was conducted using methods and acceptance criteria similar to those used for the predicate device, and that the device "met all test criteria and specifications."

Here's a summary of the performance testing mentions:

Note: The phrase "Passed" indicates that the device met the (unspecified) acceptance criteria for these tests. The document emphasizes that these tests used "similar test methods and acceptance criteria to those used for the predicate device."

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

The document does not specify the sample sizes for any of the performance tests (e.g., number of test runs, number of cases in cadaver lab validation).

Data provenance is not explicitly mentioned (e.g., country of origin). The studies appear to be pre-clinical (e.g., cadaver lab) or in-house testing, not clinical studies involving patient data for the purpose of this 510(k). The mention of "preoperative CT scans of the operative leg to create 3D surface models" refers to the input data for the system's function, not necessarily the data used for testing its performance in this submission.

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

This information is not provided in the document. Given that it's a 510(k) for device modifications and not a de novo clinical validation, the focus is on engineering and performance testing against internal specifications or predicate performance, rather than establishing clinical ground truth with human experts for an AI component. The software modification mentioned is an "improved segmentation algorithm using a pre-trained and closed machine learning model," which suggests it's an internal algorithm improvement, not a new diagnosis/decision-support AI requiring human expert consensus for ground truth comparison.

4. Adjudication method for the test set

This information is not provided.

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

An MRMC study was not done or mentioned. This type of study is typically performed for AI-driven diagnostic or decision-support tools where human interpretation is involved. The device here is a robotic system for total knee replacement, where the AI component mentioned is an "improved segmentation algorithm" for planning, not a diagnostic imaging AI that assists radiologists.

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

The document mentions an "improved segmentation algorithm using a pre-trained and closed machine learning model." While it implies standalone performance would be measured for such an algorithm (e.g., against ground truth segmentations), the document does not detail the specific standalone performance metrics, acceptance criteria, or results for this algorithm. The overall "Software Testing" passed, but these details are not exposed.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The document does not explicitly state the type of ground truth used for the tests it lists. For "Cutting Accuracy," "Pin & Block Placement Accuracy," and "System Gap Balance Accuracy," it's highly likely that engineering measurements against a known or measured truth (e.g., precise physical measurements in a cadaver lab or benchtop setting) served as the ground truth. For "Software Testing" related to the segmentation algorithm, the ground truth would typically be manually segmented CT scans by experts, but this is not confirmed in the text.

8. The sample size for the training set

This information is not provided. The document states the segmentation algorithm uses a "pre-trained and closed machine learning model," meaning the training was completed before this submission and the model is static. The size and nature of the training data are not disclosed.

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

This information is not provided. For a segmentation algorithm, the ground truth for training would typically involve manual segmentation annotations performed by qualified experts (e.g., orthopedic surgeons or trained annotators with anatomical expertise) on a dataset of CT scans.

Ask a specific question about this device

TMIN® Miniature Robotic System is indicated as a stereotaxic instrumentation system for total knee replacement (TKA) surgery. It is to assist the surgeon by providing software-defined spatial boundaries for orientation and reference information to identifiable anatomical structures for the accurate placement of knee implant components.

The robotic device placement is performed relative to anatomical landmarks as recorded using the system intraoperatively and based on a surgical plan determined preoperatively using CT based surgical planning tools.

The targeted population has the same characteristics as the population that is suitable for the implant(s) compatible with the TMIN® Miniature Robotic System. The TMIN® Miniature Robotic System is to be used with the following knee replacement systems in accordance with the indications and contraindications:

• · Enovis™ EMPOWR Knee System®
• · Ortho Development® BKS® and BKS TriMax® Knee System
• · Total Joint Orthopedics Klassic® Knee System
• · United® U2™ Total Knee System
• · Medacta® GMK® Sphere / SpheriKA Knee Systems
• · Zimmer Biomet Anterior & Posterior Referencing Persona® Knee
• b-ONE MOBIO® Total Knee System
• · Maxx Orthopedics Freedom® Total & Titan Knee
• · LINK® LinkSymphoKnee System

Like its predicate, the TMINI® Miniature Robotic System (Additional Knee System) consists of three primary components: a three-dimensional, graphical, Preoperative Planning Workstation (TPLAN® Planning Station), an Optical Tracking Navigation Console (TNav) and a robotically controlled hand-held tool (TMINI Robot) that assists the surgeon in preparing the bone for implantation of TKA components.

The TPLAN Planning Station uses preoperative CT scans of the operative leg to create 3D surface models for case templating and intraoperative registration purposes. The Planning Workstation contains a library of 510(k) cleared knee replacement implant(s) available for use with the system. The surgeon can select an implant model from this library. The planner/surqeon can manipulate the 3D representation of the implant in relation to the bone model to optimally place the implant. The surgeon reviews and approves the case plan once the surgeon is satisfied with the implant selection and orientation. The data from the approved plan is written to a file that is used to guide the robotically controlled hand-held tool.

The hand-held robotic tool is optically tracked relative to optical markers placed in both the femur and tibia and articulates in two degrees-of-freedom, allowing the user to place bone pins in a planar manner in both bones. Mechanical quides are clamped to the bone pins, resulting in subsequent placement of cut slots and drill guide holes such that the distal femoral and proximal tibial cuts can be made in the pre-planned positions and orientations, and such that the implant manufacturer's multi-planer cutting block can be placed relative to drilled distal femoral pilot holes. If the surgeon needs to change the plan during surgery, it can be changed intraoperatively using TNav.

The provided document is a 510(k) summary for a medical device called the TMINI® Miniature Robotic System (Additional Knee System). This submission is for a modification to an already cleared predicate device (K242264) to add compatibility with three new knee implant systems.

Therefore, the primary focus of the acceptance criteria and study is to demonstrate that the modified device, when used with these new implants, performs equivalently to the predicate device and does not introduce new safety or effectiveness concerns.

Here's the breakdown of the information requested based on the provided text:

1. A table of acceptance criteria and the reported device performance

The document states that "The verification and validation activities were successfully completed, and all pre-determined acceptance criteria were met." It doesn't explicitly list specific numerical acceptance criteria for each test in a table format but rather indicates overall success. The table below summarizes the types of performance testing conducted and implies that the results met the criteria of demonstrating substantial equivalence to the predicate.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not specify the sample size for the test set (e.g., number of cadavers, number of simulated procedures) used in the performance testing. It also does not explicitly state the data provenance (country of origin, retrospective or prospective). It simply mentions "test samples were representative of the production product."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

The document does not provide information on the number of experts used or their qualifications for establishing ground truth in the performance testing. It mentions that surgical planning involves "Technician guided surgical planning with surgeon review and approval on a desktop planning station," implying expert input during surgical planning, but not specifically for establishing ground truth for the test set's performance evaluation.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

The document does not describe any specific adjudication method for the test set.

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

There is no mention of a multi-reader, multi-case (MRMC) comparative effectiveness study in the provided text. The device is a robotic system for surgical assistance, not an AI-assisted diagnostic tool that would typically involve "human readers."

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

The performance testing described ("Full System Run Through Testing," "Cutting Accuracy," "Pin & Block Placement Accuracy," "Cadaver Lab Validation Testing," "System Gap Balance Accuracy") implicitly involves the entire system, which includes the robotic component, navigation system, and software. The device's description highlights its role in "assisting the surgeon," indicating a human-in-the-loop system. Therefore, a standalone (algorithm only) performance assessment, separate from the integrated device, is not explicitly detailed or suggested by the nature of this robotic surgical tool.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

The document implicitly refers to ground truth being established through "pre-planned positions and orientations" of bone cuts and drill guide holes, as determined by the CT-based surgical planning tools and approved by a surgeon. The performance tests ("Cutting Accuracy," "Pin & Block Placement Accuracy," "Cadaver Lab Validation Testing," "System Gap Balance Accuracy") likely compare the achieved surgical outcomes against these pre-planned, ideal targets.

8. The sample size for the training set

The document does not provide information on the sample size for the training set. The device uses "CT scans of the operative leg to create 3D surface models for case templating and intraoperative registration purposes" and contains a "library of 510(k) cleared knee replacement implant(s)," but it does not describe a machine learning training process with a distinct training set.

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

As there is no explicit mention of a "training set" in the context of machine learning, the establishment of its ground truth is not described. The device's operation relies on pre-operative CT scans and surgeon-approved plans, which serve as the intended targets or "ground truth" for each specific surgical case.

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