(78 days)
PACScache is an integrated client server software system designed to allow rapid access to radiographic data (specifically high resolution images) through out the radiology department as well as the entire hospital or clinical setting. The product is intended to enable review of images through clinical information systems and allow review of images on a digital picture archiving and communication system (PACS) network using a personal computer or workstation configured for standard internet access.
PACScache is an imaging software program used to view medical images on a personal computer. The software is designed to function with off-the-shelf hardware and software products including standard communications products. Image acquisition is via the industry standard DICOM 3.0 protocol allowing the images to be produced from the digital data originated by the scanner.
The provided document is a 510(k) summary for the PACScache device, an imaging software program for viewing medical images. However, it does not contain information about acceptance criteria, detailed study designs, or performance metrics in the way that would typically be expected for demonstrating the safety and effectiveness of a device using a formal study.
Instead, the document focuses on demonstrating substantial equivalence to previously cleared predicate devices (Articas Web/Intranet Server K970064 and RSTAR's Image Management System K925994). This regulatory pathway for low-risk devices often relies on comparisons to existing technology rather than requiring de novo studies with specific performance targets.
Therefore, many of the requested categories of information cannot be extracted from this document, as they were likely not part of the submission for this particular device.
Here's a breakdown of what can be inferred or directly stated, and where information is missing:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not explicitly defined in terms of quantitative metrics for accuracy, sensitivity, specificity, etc. The submission focuses on substantial equivalence to predicate devices. | The document states PACScache has "Indications for Use similar to other image viewing software products such as AMICAS (510k # K970064)." It also notes it "uses the same target endusers (competent health professionals)" and "is designed to operate with off-the-shelf hardware and systems" like the predicate devices. It "employs JPEG image compression to remove redundant or unimportant information in the original data," similar to predicate devices that use JPEG and wavelet compression. |
2. Sample size used for the test set and the data provenance
- Sample size for test set: Not mentioned.
- Data provenance: Not mentioned. The document states it views "files generated by a medical scanning device and acquired according to the dominant industry standard communications format (DICOM 3.0)," but doesn't detail any specific dataset used for testing. The "test set" in the context of this submission would likely refer to internal verification and validation against software requirements, rather than a clinical dataset for performance evaluation against a gold standard.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- This information is not provided. It's unlikely such ground truth establishment was required or performed for this type of software's 510(k) submission, which emphasizes "viewing" and "imaging software program" functionality rather than diagnostic interpretation.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not mentioned.
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
- No, an MRMC comparative effectiveness study was not done. This device is described as "imaging software program used to view medical images," and explicitly states "It does not provide a diagnosis. It only provides information/data. It is a stand-alone system and not a part of a regulated classified device or accessory to it." The concept of "human readers improve with AI" is not applicable, as this device is a viewing platform, not an AI-powered diagnostic aid. This predates widespread AI in medical imaging.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Yes, in a sense, the device is standalone in its function of displaying images. The document states: "It is a stand- alone system and not a part of a regulated classified device or accessory to it." However, this refers to its independence as a software product, not necessarily an "algorithm only" performance study in the modern sense of a diagnostic AI algorithm. Its performance is related to its ability to correctly display medical images from DICOM data, not to interpret them.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- Not applicable/Not mentioned for performance evaluation in a clinical sense. For this type of software, "ground truth" would likely involve ensuring accurate display of DICOM data as per standards, and verification/validation against specified software requirements.
8. The sample size for the training set
- Not applicable/Not mentioned. This device is described as an "imaging software program" for viewing, operating with "off-the-shelf hardware and software products." It does not appear to involve machine learning or AI models that require a "training set" in the conventional sense.
9. How the ground truth for the training set was established
- Not applicable, as there is no mention of a training set or AI model development.
§ 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).