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The iFuse TORQ TNT Implant System is indicated for fracture fixation of the pelvis, including acute, non-acute and nontraumatic fractures.

The iFuse TORO TNT Implant System is indicated for:

• Sacroiliac joint fusion for Sacroiliac joint dysfunction including sacroiliac joint disruption and degenerative sacrollitis. - Augmenting immobilization and stabilization of the sacroiliac joint in skeletally mature patients undergoing sacropelvic fixation as part of a lumbar or thoracolumbar fusion.

The iFuse TORO TNT Navigation instruments are intended to be used with the iFuse TORO TNT Implant System to assist the surgeon in precisely locating anatomical structures in iFuse TORQ TNT Implant System procedures, in which the use of stereotactic surgery may be appropriate, and where reference to a rigid anatomical structure, such as the pelvis or vertebra, can be identified relative to the acquired image (CT, MR, 2D fluoroscopic image reconstruction) and/or an image data based model of the anatomy. iFuse TORQ TNT Navigation instruments are intended to be used with the Medtronic StealthStation System.

iFuse TORQ TNT System consists of a fully threaded, 3D-printed porous implant with optional washers along with instruments used to place the implant under either fluoroscopic guidance or with certain navigation systems. The implant is made from titanium alloy (Ti-6Al-4V ELI) and manufactured using 3D printing. The implant has features specific to pelvic anatomy. The subject device is very similar to its primary predicate, iFuse TORQ implant system, except that it is longer in length (up to 170 mm) and smaller in diameter. The longer length enables placement in the transiliac transsacral trajectory.

This is not a medical device with an AI/ML component. The information provided in the document focuses on the mechanical, material, and performance characteristics of the iFuse TORQ TNT™ Implant System and its navigation instruments, primarily through bench testing and comparison to predicate devices, rather than an AI/ML driven diagnostic or assistive tool.

Therefore, many of the typical acceptance criteria and study details relevant to AI/ML devices, such as specific performance metrics like sensitivity/specificity, sample sizes for training/test sets, human expert involvement in ground truth establishment, MRMC studies, or standalone algorithm performance, are not applicable or detailed in this document.

The document discusses "iFuse TORQ TNT Navigation instruments" which are intended to "assist the surgeon in precisely locating anatomical structures," and uses "image data based model of the anatomy," but this navigation system is intended to be used with the Medtronic StealthStation System, a known image-guided surgery platform, and the focus of the submission is on the implant system and its mechanical performance, not the algorithmic performance of an AI/ML component for diagnosis or prediction.

Here's a summary of the available information based on the prompt's structure, highlighting where information is absent due to the device type:

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

Since this is a mechanical implant and navigation instrument system, the "acceptance criteria" are generally related to mechanical properties and functional equivalence to predicate devices, as demonstrated through various ASTM standards. Specific quantitative acceptance criteria or reported device performance metrics (e.g., specific thresholds for fatigue cycles, tensile strength) are not detailed within this 510(k) summary document. The document states that "The test results demonstrate that the device is substantially equivalent to the predicate device," indicating that the performance met the requirements for substantial equivalence.

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

• Sample Size for Test Set: Not explicitly stated in this 510(k) summary. Performance data refers to "testing" and "evaluation" against ASTM standards, which typically involve a specified minimum number of samples per test type.
• Data Provenance: The document implies benchtop testing of the physical device or its components. There is no mention of patient data (retrospective or prospective), nor country of origin for such data.

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

• This is not applicable as the evaluation is based on benchtop mechanical and material testing against established ASTM standards and substantial equivalence to predicate devices, not on diagnostic or AI performance requiring expert-established ground truth from medical images or clinical data.
• The Predetermined Change Control Plan (PCCP) mentions "review with experienced surgeons" for new trajectories, but this is for clinical application review, not for establishing ground truth for a test set in an AI/ML context.

4. Adjudication method for the test set

• Not applicable. This pertains to consensus among experts, which is not relevant for the type of testing described (benchtop mechanical and material performance).

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. This is not an AI/ML diagnostic or assistive device that would participate in an MRMC study. The "navigation instruments" are for surgical guidance, not for interpretation of medical images by human readers.

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

• Not applicable. This is an implant system and navigation instruments, not a standalone algorithm.

7. The type of ground truth used

• The "ground truth" for this device's performance is derived from established engineering standards (ASTM and ISO) for mechanical properties, material composition, and functional characteristics, validated through benchtop testing. The concept of "expert consensus, pathology, or outcomes data" as ground truth is not relevant in this context. Substantial equivalence to predicate implant systems serves as a primary benchmark.

8. The sample size for the training set

• Not applicable. There is no AI/ML component described that would require a training set.

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

• Not applicable. There is no AI/ML component described that would require a training set or its associated ground truth establishment.

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