(288 days)
The Aspyra AccessNET PACS is a software device designed to operate on specified off-the-shelf hardware and virtual environments. The device is intended to receive digital images and data from various sources including CT scanners, MR scanners, Nuclear Medicine (NM), PET, ultrasound systems, Computed & Direct radiographic devices (CR, DR), secondary capture devices, scanners, imaging gateways, etc. as well as other DICOM compliant modalities. Images and data can be captured, stored, communicated, processed, printed, and displayed within the system and or across computer networks at distributed locations.
Display and processing functions while viewing images include, but are not limited to, adjustment of window and level, rotation, zoom, measurement of anatomical structures, image stitching, image stacking, filtration of color images (e.g. removal of red channel), annotation and measurement of regions of interest, inversion, rotation, flips, Multi-Planar Reformation (MPR), and Maximum intensity projection (MIP). In addition, users can dictate, edit, sign, and print reports.
The Aspyra AccessNET PACS is indicated for the primary review of "For Presentation" DICOM mammographic images. Lossy compressed mammographic images and digitized film screen images must not be used for primary image interpretations. Mammographic images for primary image interpretations may only be interpreted using an FDA approved monitor that offers at least 5 MP resolution and meets other technical specifications reviewed and accepted by FDA.
The Aspyra PACS is a system used for the management of clinical images and reports. It provides Picture Archive and Communications System (PACS) functionality as well as management of clinical workflow.
The provided text is a 510(k) summary for the Aspyra PACS (AccessNET, AccessMED, AccessRAD, MedVIEW). It primarily focuses on demonstrating substantial equivalence to predicate devices rather than providing specific acceptance criteria and detailed clinical study results for novel performance claims.
Based on the provided document, here's a breakdown of the requested information:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state quantitative acceptance criteria for the device's performance. Instead, it relies on demonstrating substantial equivalence to predicate devices, implying that its performance is at least comparable to those existing, approved systems.
| Feature/Function | Acceptance Criteria (Explicitly Stated) | Reported Device Performance (Explicitly Stated) |
|---|---|---|
| Overall System Safety and Effectiveness | As safe and effective as predicate devices | "The results of bench testing (software verification and validation) and clinical evaluation have shown the device to be as safe and effective as the predicate devices, and raise of no new issues of safety or effectiveness." |
| Operating Principle, Technology, Overall Design, Function, and Intended Use | Substantially equivalent to listed predicate devices (ImageACCESS PACS, CMT SmartRAD, Fuji FlashIIp, Fuji Synapse, GE Centricity) | "The Aspyra PACS is substantially equivalent in operating principle, technology, overall design, function, and intended use to ImageACCESS PACS as described in K973805." |
| "Image stitching is substantially equivalent...to CMT SmartRAD [K003438] and Fuji FlashIIp [K013218] [K041990]." | ||
| "Mammography functionality is substantially equivalent...to the Fuji Synapse [K051553] and GE Centricity [K082318]." |
2. Sample Size Used for the Test Set and Data Provenance
The document states that "clinical evaluation" was performed, but does not provide any details regarding the sample size of the test set, the country of origin of the data, or whether it was retrospective or prospective.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not provide this information.
4. Adjudication Method for the Test Set
The document does not provide this information.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done
The document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. The focus is on substantial equivalence to predicate devices, which typically doesn't involve MRMC studies comparing AI vs. human readers directly.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
The device is a Picture Archiving and Communications System (PACS) intended for human review and interpretation. The performance testing section mentions "software verification and validation" and "clinical evaluation." However, it does not explicitly describe a standalone performance study of an algorithm independent of human interaction. The context implies human interaction with the PACS for image display and processing.
7. The type of ground truth used
The document does not explicitly state the type of ground truth used for the "clinical evaluation." Given it's a PACS device for displaying medical images, the ground truth would likely be established by medical experts (e.g., radiologists) providing interpretations, but this is not detailed.
8. The sample size for the training set
The document does not mention a training set sample size. This type of information is typically provided for AI/ML-based devices that undergo direct training. Since this 510(k) is for a PACS system relying on substantial equivalence, specific training data details are not included.
9. How the ground truth for the training set was established
As no training set is mentioned in the context of an AI/ML algorithm, the document does not provide information on how ground truth for a training set was established.
§ 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).