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The OrthoHub External Fixator Software is used with Smith & Nephew Taylor Spatial Frame (TSF) rings and struts for the treatment of traumatic or reconstructive tibia deformities. It is used to generate a prescription of strut adjustments to provide to the patient.

The OrthoHub External Fixator Software is used to assist the clinician in adjusting the Smith & Nephew Taylor Spatial Frame (TSF) External Fixator by creating a patient adjustment schedule. The OrthoHub software receives x-ray images of the deformity and installed fixator hardware and produces a prescription recommending adjustments to the fixator that define a correction path for the deformity. The software is used as an accessory to the commercially available hardware as detailed in the Instructions for Use, specifically, Smith and Nephew's Taylor Spatial Frame rings and struts.

The OrthoHub External Fixator Software is a software program used on a Macintosh Computer. The software is used to generate a prescription which details adjustments required for the treatment of traumatic or reconstructive tibia deformities when using the Smith and Nephew Taylor Spatial Frame External Fixator hardware. Users input orthogonal x-ray images of a patient's deformity taken after installation of the fixator hardware on the patient. The software creates a colored graphical representation of the bones and orthopedic fixator hardware shown in the x-rays, and the user adjusts this graphical representation so that it best matches the underlying x-rays. The user then defines a correction rate, and the software generates a prescription of strut adjustments to correct the deformity. This prescription is provided to the patient for strut adjustment during the prescription period.

The provided document, an FDA 510(k) summary for the OrthoHub External Fixator Software, describes the device's intended use and a general verification and validation process. However, it does not contain specific acceptance criteria, detailed study designs, or performance metrics in the way requested by the prompt for an AI/CAD-like system (e.g., sensitivity, specificity, FROC analysis).

Based on the available text, here's an attempt to answer the questions, highlighting where information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

Missing Information: Specific quantitative performance metrics (e.g., accuracy of measurement, deviation from gold standard, success rate of deformity correction) are not provided in this document. The "acceptance criteria" identified are general statements of functionality and equivalence rather than precise, measurable targets.

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

• Sample Size for Test Set: Not specified. The document mentions "inputs (patient x-rays)" but does not quantify how many cases or patients were used for testing.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). It implicitly uses "patient x-rays" which suggests clinical data, but details are absent.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. The document refers to "software verification & validation testing" and "mechanical bench top side by side comparison testing," but does not detail how ground truth or performance assessment was adjudicated.

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

• MRMC Study: No, an MRMC comparative effectiveness study is not indicated as having been done. The document describes "bench testing and software verification and validation process" and comparison to predicate devices, but not a study involving human readers with and without AI assistance to measure improvement.

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

• Standalone Performance: Yes, the described "Software Verification & Validation Testing" and "Mechanical Bench Top Side by Side Comparison Testing" evaluate the software's ability to 'produce recommended adjustments as appropriate for the inputs' and 'function as intended'. This implies a standalone evaluation of the algorithm's output, without explicitly detailing a human-in-the-loop component for the testing itself, though the device's ultimate use is to assist the clinician.

7. The Type of Ground Truth Used

• Type of Ground Truth: Not explicitly stated as "ground truth." However, the implicit ground truth for assessing the software's accuracy would likely be:
  • Validated anatomical measurements/deformity definitions: For the software to correctly "match the underlying x-rays" and "generate a prescription."
  • Engineering/biomechanical principles: Against which the "recommended adjustments" are compared to determine if they are "appropriate."
  • Predicate device outputs: The software's output is compared to the outputs of established predicate devices to demonstrate substantial equivalence.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable/not specified. The document describes the software as performing "mathematical calculations" and creating a "graphical representation" based on user input and x-ray images. It does not suggest the use of machine learning or deep learning that would involve a "training set" in the conventional AI/CAD sense. The software appears to be rule-based or calculation-based, interpreting user inputs (x-rays, fixator hardware, correction rate) rather than learning from a dataset.

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

• Ground Truth for Training Set: Not applicable, as there is no indication of a training set for a machine learning model.

Summary of Missing Information:

This 510(k) summary provides a high-level overview focused on establishing substantial equivalence to predicate devices, rather than a detailed performance study typically associated with AI/CAD systems. Key details regarding specific quantitative acceptance criteria, test set sizes, ground truth establishment methodologies (expert qualifications, adjudication), and the use of training data (as would be relevant for machine learning) are not present in this document. The assessment appears to be based on functional verification and validation and a qualitative comparison to existing technology.

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