(13 days)
Agfa's Orthopedic Software is indicated for use with Impax® Workstations in the acquisition, display, digital processing, review, transfer, storage, archiving and printing of medical images and patient demographic information. It is intended for use by physicians to aid in diagnosis, and by medical professionals whenever they require or desire access to medical images and patient demographic information.
The software application allows orthopedic surgeons and specialists to assess images, plan surgical procedures, monitor patient progress and educate patients in a digital environment.
It allows assessments to be made of geometrical skeletal parameters with comparisons against normative references for adults and children in order to draw therapeutic conclusions. It includes modules for the hip, knee, spine, leg, hand, wrist, elbow, shoulder, foot, ankle and fractures (trauma planning). Users can access a library of manufacturers electronic templates intended to assist in the selection and positioning of implants and the marking of tissues prior to surgery.
The new device is nearly identical to the Agfa predicate (OT-3000). It includes tools for performing common hip, knee and spine measurements, and provides access to orthopedic device manufacturers electronic templates. The measurements and tools enable planning of orthopedic procedures, monitoring patient progress and patient communications.
The orthopedic application is compatible with multiple workstations and Impax® software versions. User may purchase a complete set of tools or individual modules depending on their needs.
The basic principles of operation of the new and predicate devices are the same.
This 510(k) summary (K071972) indicates that the "Agfa Orthopedic Software for Impax Workstations" is nearly identical to a previously cleared device (OT-3000, K050751) with the addition of a spine-planning module, which is present in another predicate device (Cedara Software Corporation's I-SoftView Orthopedic Tools Set, K022881). The submission does not contain detailed acceptance criteria or a dedicated study report that explicitly proves the device meets specific performance metrics in a quantitative way. Instead, the focus is on demonstrating substantial equivalence based on technological characteristics and intended use.
However, based on the provided text, we can infer the acceptance criteria and the "study" that supports it:
1. Table of Acceptance Criteria and Reported Device Performance:
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Functional Equivalence: The new device should perform common orthopedic measurements and provide access to electronic templates similar to the predicate devices. | The device "includes tools for performing common hip, knee and spine measurements, and provides access to orthopedic device manufacturers electronic templates." The basic principles of operation are the same as predicate devices. The new spine module is functionally equivalent to the spine module in the Cedara predicate. |
| Compatibility: The revised orthopedic application should be compatible with Agfa's Impax® PACS Systems. | Agfa's Impax® PACS Systems "have been tested for compatibility with the revised orthopedic application." |
| Accuracy of New Spine Measurements: The new spinal measurements should operate correctly and accurately on medical images. | "Correct operation of the new spinal measurements has been confirmed with medical images in laboratory testing." |
| No Modification of Intended Diagnostic Effect: The change (addition of spine module) should not alter the intended diagnostic effect (assisting surgical planning, monitoring patient progress, communication). | "This change in indication does not modify the intended diagnostic effect." |
2. Sample Size Used for the Test Set and Data Provenance:
The document mentions "medical images" were used for testing the new spinal measurements, but does not specify the sample size (number of images or cases) or the data provenance (e.g., country of origin, retrospective or prospective nature) for this testing.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:
The document mentions "medical images" and "laboratory testing" for confirming the correct operation of spinal measurements, but does not specify the number of experts involved or their qualifications (e.g., "radiologist with 10 years of experience") for establishing ground truth for any test set.
4. Adjudication Method for the Test Set:
The document provides no information about an adjudication method (e.g., 2+1, 3+1, none) for any test set. The confirmation of correct operation appears to be based on internal "laboratory testing."
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:
No, the document does not indicate that an MRMC comparative effectiveness study was done. The focus is on demonstrating substantial equivalence to predicate devices, not on quantifying improvement in human reader performance with or without AI assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done:
The testing mentioned for the new spinal measurements ("Correct operation...has been confirmed with medical images in laboratory testing") likely refers to a standalone assessment of the algorithm's functionality. However, the document does not explicitly state it as a "standalone" study in the context of comparing it to human performance. The device itself is a software application intended to be used by physicians, so its performance is inherently human-in-the-loop.
7. The Type of Ground Truth Used:
The document refers to the "correct operation" of spinal measurements being confirmed. This implies that some form of expert knowledge or established anatomical/mathematical correctness was used as the ground truth against which the software's measurements were compared. However, the specific method of establishing this ground truth (e.g., expert consensus, pathology, other validated measurements) is not detailed. It's likely that clinicians or technical experts verified the accuracy of the software's calculated measurements against expected values or manual measurements on the "medical images."
8. The Sample Size for the Training Set:
The submission does not mention a training set or its sample size. This is common for software that is largely a re-implementation or slight modification of existing functional tools, rather than a novel algorithm requiring extensive machine learning training data. The device's operation is based on identifying physiological landmarks and calculating measurements, which implies programmed rules rather than a learned model from a large training dataset.
9. How the Ground Truth for the Training Set was Established:
Since a training set is not mentioned, the method for establishing its ground truth is not applicable.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).