(87 days)
3Di is intended for use as an interactive tool for assisting professional Radiologists, Cardiologists and specialists to reach their own diagnosis, by providing tools of communication, clinics networking, WEB Serving, image viewing, image manipulation, 2D/3D image visualization, image processing, reporting and archiving. The 3Di indications for use are processing of Cardiac CT studies, including CT Calcium scoring, CT Cardiac angiography, coronaries analysis, cardiac functional assessment and of CT colonoscopy. The 3Di indications for use have been modified to include viewing of Mammography images.
3Di is a PACS device which enables users to access medical images over a network and to utilize 3Di's image visualization tools to review the images. It provides the following functions: Web server, patient browser, PACS capabilities, multi-modality viewing, CT Cardiac and Colonoscopy clinical applications. The 3Di indications for use have been modified to include viewing of Mammography images.
The provided text describes the 3Di device, a PACS workstation that now includes the viewing of Mammography images. The submission focuses on demonstrating substantial equivalence for this added functionality.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Quality of Mammography imaging is unsubstantially different or equivalent to DICOM source mammographic data (for accurate viewing) | The quality of Mammography imaging was validated by comparing the device imaging output to the DICOM source mammographic data. The comparison results demonstrate that the 3Di and the DICOM source mammographic data are substantial equivalent in terms of image quality. |
2. Sample Size Used for the Test Set and Data Provenance
The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It only mentions "comparison results" without detailing the number of mammography images or studies included in this comparison.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not specify the number of experts used or their qualifications for establishing ground truth for the test set. It only mentions that the device is "intended for use as an interactive tool for assisting professional Radiologists, Cardiologists and specialists to reach their own diagnosis".
4. Adjudication Method for the Test Set
The document does not describe any specific adjudication method (e.g., 2+1, 3+1) used for the test set. The validation was based on a direct comparison of image quality.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No multi-reader multi-case (MRMC) comparative effectiveness study is mentioned in the provided text. The submission focuses on the equivalency of image quality for mammography viewing, not on the impact of the AI on human reader performance or diagnostic accuracy.
6. Standalone Performance Study
No standalone (algorithm only without human-in-the-loop performance) study is explicitly detailed. The validation described is a comparison of image output quality of the device against the DICOM source, rather than an algorithmic diagnostic performance study.
7. Type of Ground Truth Used
The "ground truth" for the validation seems to be the DICOM source mammographic data itself. The device's output was compared directly against this source data to ensure visual fidelity and quality. This indicates a technical ground truth related to image rendering, rather than a clinical ground truth like pathology or expert consensus on disease presence.
8. Sample Size for the Training Set
The document does not provide any information about a training set since the validation focuses on image quality comparison, not on a machine learning model that would require a training set.
9. How the Ground Truth for the Training Set Was Established
Not applicable, as no training set or related ground truth establishment is mentioned in the context of this 510(k) submission.
§ 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).