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For use as a tool for adjustment of the femoral knee implant to reduce instability from flexion gap asymmetry. The force sensor is sterile, for single patient use.

Orthosensor Intra-Operative Load Sensor (IOLS) system provides a means to dynamically balance the knee during knee replacement surgery intraoperatively. The system includes an instrumented trial tibial insert comprising an array of load sensors that measure the forces applied on its surface after insertion into the space between the tibia and the femur.

Here's a breakdown of the acceptance criteria and study information for the OrthoRex Intra-Operative Load Sensor, based on the provided document:

This 510(k) summary does not provide detailed acceptance criteria or extensive study data typical of a clinical trial. It primarily focuses on demonstrating substantial equivalence to a predicate device through bench testing.

Acceptance Criteria and Reported Device Performance

Study Information

1. Sample size used for the test set and the data provenance:

   • The document mentions "a series of bench testing which includes mechanical, engineering, and comparison studies." However, specific sample sizes (e.g., number of units tested, number of measurements taken) for these bench tests are not provided.
   • Data Provenance: The nature of "bench testing" implies the data was generated in a controlled laboratory environment, not from human subjects. The country of origin is not explicitly stated, but the company is based in Sunrise, FL, USA. The testing would be considered prospective for the device's development and regulatory submission.

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

   • Not applicable. As this was bench testing, there was no "ground truth" established by human experts in the way clinical studies requiring image interpretation or diagnosis would. The "ground truth" would be the known physical properties and performance of the test setups (e.g., applied forces, mechanical loads).

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

   • Not applicable. This concept is relevant for clinical studies involving multiple human readers and is not applicable to mechanical bench testing.

4. 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 device is an intra-operative load sensor, not an AI-based diagnostic imaging tool. It does not involve human readers interpreting data, nor does it involve AI assistance in that context.

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

   • Not applicable in the typical AI sense. The device is a sensor and measurement system designed to provide real-time data during surgery for direct human interpretation and action. Its performance is inherent in its ability to accurately measure forces, which is what the bench testing would have evaluated. There isn't an "algorithm only" performance separate from its function as a measurement tool.

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

   • For the bench testing, the "ground truth" would have been established by precisely calibrated measurement instruments (e.g., load cells, force gauges) used in the mechanical and engineering studies. This would represent physical measurements under controlled conditions.

7. The sample size for the training set:

   • Not applicable. This device is not an AI/ML algorithm that requires a training set in the conventional sense. Its "training" would be the engineering design, calibration, and manufacturing process.

8. How the ground truth for the training set was established:

   • Not applicable. (See point 7).

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