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The OFIX MIS App software assists healthcare professionals in viewing, storing, and measuring images as well as planning orthopedic surgeries. The app allows service providers to plan surgical procedures by making measurements for the placement of surgical implants. Clinical judgement and experience are required to properly use the software.

The OFIX MIS App will provide a novel mobile phone solution to supplement the use of calipers when selecting rods used in immobilization of pedicle screw type spinal systems. The App will rely on capturing 2D images used in spinal implant procedures through use of mobile phone camera. The App measures the difference between known Orthofix pedicle screw diameters and provides feedback useful in determining appropriate rod length with corresponding Orthofix product number.

The OFIX MIS App is a software designed to assist healthcare professionals in viewing, storing, measuring images, and planning orthopedic surgeries, specifically by measuring anatomical components for the placement of surgical implants. The device helps determine appropriate rod length with corresponding Orthofix product numbers by capturing 2D images used in spinal implant procedures via a mobile phone camera.

Here's an analysis of the acceptance criteria and the study proving the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly detail specific quantitative acceptance criteria (e.g., minimum accuracy percentages, maximum error margins) and their corresponding reported device performance values. Instead, it broadly states that "The testing demonstrated that the App meets the required specifications." and "The OFIX MIS App demonstrated accuracy and usability with bench top and simulated use testing."

However, based on the device's function, the implied acceptance criteria would revolve around the accuracy of measurements and the usability of the app for surgical planning. The provided information indicates compliance with these general requirements.

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

The document does not specify the sample size used for the test set. It also does not explicitly state the data provenance (e.g., country of origin, retrospective or prospective nature of the images used for testing). The testing is described as "bench top and simulated use testing," which implies either synthetic data, controlled clinical images, or a mix, but no details are given.

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

The document does not provide information regarding the number of experts used to establish ground truth for the test set or their specific qualifications (e.g., "radiologist with 10 years of experience").

4. Adjudication Method for the Test Set

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

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

The document does not indicate that an MRMC comparative effectiveness study was done to assess how much human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance and usability of the OFIX MIS App.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, a standalone study was implicitly done. The "Software Verification and Validation Testing" and "Usability Testing" sections describe the evaluation of the App itself to ensure it meets specifications and demonstrates accuracy and usability. The statement "The OFIX MIS App demonstrated accuracy and usability with bench top and simulated use testing" suggests testing of the algorithm's performance and the app's functionality independent of clinical human performance metrics.

7. The Type of Ground Truth Used

The document does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data). Given the nature of the device (measurement and planning), it is highly probable that a "gold standard" of measurement, likely derived from manual measurements by qualified experts or using high-precision instruments on the test images, would have been used as the ground truth.

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set. It mentions the algorithm is "not machine-learning based," which implies that there might not have been a traditional "training set" in the machine learning sense. Instead, the algorithm relies on "information specific to Orthofix implant systems."

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

Since the algorithm is explicitly stated as "not machine-learning based" and uses "information specific to Orthofix implant systems," there wouldn't be a typical training set with corresponding ground truth established in the way machine learning models are trained. Instead, the "ground truth" or foundational data for the algorithm would be the known, precise specifications and dimensions of Orthofix's pedicle screws and rods, likely derived from their engineering and manufacturing data. The algorithm would then use these pre-defined parameters to process the captured images and recommend appropriate rod lengths.

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