(50 days)
aycan Workstation OsiriX is a software device intended for viewing of images acquired from CT, MR, CR, DR, US and other DICOM compliant medical imaging systems when installed on suitable commercial standard hardware.
Images and data can be captured, stored, communicated, processed, and displayed within the system and or across computer networks at distributed locations.
Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary diagnosis or image interpretation. For primary diagnosis, post process DICOM "for presentation" images must be used. Mammographic images should only be viewed with a monitor approved by FDA for viewing mammographic images.
It is the User's responsibility to ensure monitor quality, ambient light conditions, and image compression ratios are consistent with clinical application.
The aycan Workstation OsiriX provides services for review and post processing of diagnostic medical images and information. It conforms to the DICOM 3.0 standard to allow the sharing of medical information with other digital imaging systems. aycan workstation OsiriX runs on Apple Mac OSX systems and provides high performance review, navigation and post processing functionality for multidimensional and multimodality images.
The provided document is a 510(k) summary for the aycan Workstation OsiriX. It primarily focuses on demonstrating substantial equivalence to a predicate device based on technological characteristics and intended use. The document does not contain the specific details required to answer all parts of your request about acceptance criteria and a detailed study proving performance.
Here's what can be extracted and what is missing:
1. A table of acceptance criteria and the reported device performance
- Acceptance Criteria: The document states, "As required by the risk analysis, designated individuals performed all verification and validation activities and results demonstrated that the predetermined acceptance criteria were met. The system passed all testing criteria."
- Reported Device Performance: The document generally states that the device "provides high performance review, navigation and post processing functionality for multidimensional and multimodality images." However, specific quantitative or qualitative performance metrics against defined acceptance criteria are not provided.
| Acceptance Criteria | Reported Device Performance |
|---|---|
| "Predetermined acceptance criteria were met" (Specific criteria not detailed) | "The system passed all testing criteria." |
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- This information is not provided in the document.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
- This information is not provided in the document.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- This information is 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
- A MRMC study or any information about AI assistance and human reader improvement is not mentioned in the document. The device is a "Picture Archiving Communications System", not an AI-powered diagnostic tool, so such a study would not be expected.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
- The document implies human intervention, stating, "A physician, providing ample opportunity for competent human intervention interarets images and information being displayed and printed." Given that it's a PACS workstation, standalone algorithmic performance for diagnosis is not its primary function, and no such study is mentioned.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- This information is not provided in the document.
8. The sample size for the training set
- This information is not provided in the document. The device is a workstation, and the concept of a "training set" in the context of an AI/ML algorithm doesn't directly apply here.
9. How the ground truth for the training set was established
- This information is not provided in the document, as it's not relevant to the type of device described.
Summary of what the document focuses on:
The 510(k) summary primarily demonstrates the substantial equivalence of the aycan Workstation OsiriX to a predicate device (IQ-SYSTEM PACS SYSTEM K062488). This is achieved by:
- Categorizing the device: As a "Picture Archiving Communications System" and "system, image processing, radiological" (Product code LLZ).
- Describing intended use: For viewing, capturing, storing, communicating, processing, and displaying images from various DICOM-compliant medical imaging systems.
- Highlighting technological characteristics: It's a software device conforming to DICOM 3.0, running on Apple Mac OSX, and providing review and post-processing functionality. It explicitly states it does not contact the patient or control life-sustaining devices.
- Stating compliance: It notes that appropriate verification and validation activities were performed, and predetermined acceptance criteria were met.
- Providing warnings/limitations: Regarding lossy compressed mammographic images, digitized film screen images, and the user's responsibility for monitor quality and ambient light.
§ 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).