(119 days)
SenoIris is designed for diagnostic review in diagnostic and screening breast imaging environments through flexible and interactive manipulation of multi-modality, multi-vendor softcopy images.
It provides image review, manipulation, analysis, Post-Processing and printing capabilities for FFDM, DBT, and CESM images. The software also supports the display of CAD, breast density assessments and other breast imaging data from various modalities.
Image routing and compression, as well as centralized workflow steering. including double blind reading and integrated in-image reporting, are part of the solution to support the women's healthcare professionals to enrich existing workflows for breast imaging needs.
Various specific and general interfaces exist to synchronize to other external software on the front- and back-end side. The software also provides functions to directly import data from and export them to mobile storage media or onto the local operating system.
The SenoIris is a medical image review workstation software for diagnostic and screening mammography.
SenoIris has the capability to review Digital Breast Tomosynthesis (DBT) images that are compatible with Breast Tomosynthesis Image Storage.
It is a software product.
The provided text describes information about the SenoIris device, a medical imaging software. Here's a breakdown of the requested information based on the text:
1. A table of acceptance criteria and the reported device performance
The document states: "A clinical reader study has been performed and has shown that the image quality of V-Preview of SenoIris is either better or equivalent to the image quality of V-Preview of the predicate device MammoWorkstation."
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Image quality of V-Preview is better than or equivalent to the predicate device. | The image quality of V-Preview of SenoIris is either better or equivalent to the image quality of V-Preview of the predicate device MammoWorkstation. |
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document does not explicitly state the sample size for the test set or the data provenance (country of origin, retrospective/prospective).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The document does not specify the number of experts or their qualifications. It only refers to a "clinical reader study."
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
The document does not describe the adjudication method used for the test set.
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
The document mentions "A clinical reader study has been performed" for image quality comparison, but it does not specify if it was an MRMC comparative effectiveness study, nor does it provide information on an effect size of human readers improving with AI vs. without AI assistance. The device is primarily a review workstation software, not an AI-assisted diagnostic tool in the typical sense of providing a computer-aided detection/diagnosis score.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The device is a "medical image review workstation software," implying human-in-the-loop performance. The document does not describe a standalone algorithm-only performance study. The statement "V-Preview is an additional image for navigation use only and does not replace FFDM image" further reinforces its role as an aid for human review rather than a standalone diagnostic tool.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The document relates the study to "image quality," which suggests a subjective assessment by readers. It does not explicitly state the type of ground truth used for establishing the image quality, but it implies comparison against a predicate device based on expert perception.
8. The sample size for the training set
The document does not mention the sample size for any training set. The descriptions of "image quality improvement" through "modified generation algorithm" and "lowering the artefacts and the noise" suggest algorithmic improvements, which would typically involve training data, but the details are not provided.
9. How the ground truth for the training set was established
The document does not provide information on how the 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).