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iPACS ORTHO™is intended for the manipulation, displaying, and distribution of medical images. It can show images from different modalities and interfaces to medical images. It one and printing devices using DICOM or similar interface standards.

The device assists orthopedic surgeons when doing preoperative planning and post-operative follow-up.

Typical users of this system are trained professionals, for example orthopedic surgeons, physicians, and radiologists.

iPACS ORTHO™ handles and displays various objects in a Picture Archive and Communication System (PACS) environment and is intended to assist orthopedic surgeons when doing preoperative planning and post-operative follow-up. The device is used to overlaying prosthesis templates on radiological images, tools for repositioning the templates, and tools for measurements in the images.

This 510(k) submission (K041366) does not contain a detailed study proving the device meets specific acceptance criteria in the manner of a clinical performance study for an AI/ML device. This submission is for a Picture Archiving Communications System (PACS) software, specifically iPACS ORTHO™, intended for image manipulation, display, and distribution, and to assist orthopedic surgeons with pre-operative planning and post-operative follow-up.

The primary demonstration of "acceptance" in this document is the claim of substantial equivalence to an existing predicate device (Sectra orthopedic package, K031590). The acceptance criteria for such a device are typically related to its functional performance, safety, and effectiveness compared to the predicate, rather than statistical performance metrics like sensitivity or specificity.

Therefore, many of the requested sections (e.g., sample size for test set, number of experts, adjudication method, MRMC study, ground truth for training set) are not applicable or documented in this type of 510(k) submission for a PACS system from 2004. This submission pre-dates the rigorous clinical validation standards often applied to AI/ML devices today.

Here's an attempt to answer the questions based on the provided document, acknowledging the limitations:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Not applicable/Not provided in the document. This type of 510(k) submission for a PACS system primarily relies on demonstrating functional equivalence and safety rather than a quantitative performance evaluation with a specific test set. There is no mention of a test set with patient data.

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

• Not applicable/Not provided in the document. No specific test set or ground truth establishment process is described as part of the submission for substantial equivalence. The "experts" are the "trained professionals" (orthopedic surgeons, physicians, radiologists) who use the system in practice.

4. Adjudication method for the test set

• Not applicable/Not provided in the document.

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/Not provided in the document. This is a PACS software, not an AI-assisted diagnostic tool. No MRMC study was conducted or reported.

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

• Not applicable/Not provided in the document. The device is explicitly stated to require human intervention: "A physician, providing ample opportunity for competent human intervention interprets images and information being displayed and printed."

7. The type of ground truth used

• Not applicable/Not provided in the document. The device isn't making a diagnosis; it's a tool for manipulating and displaying images for manual planning and follow-up by a human expert. The "ground truth" implicitly relies on the physician's expertise and the accuracy of the underlying medical images.

8. The sample size for the training set

• Not applicable/Not provided in the document. This document describes a software system, not a machine learning algorithm that requires a training set.

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

• Not applicable/Not provided in the document.

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