(195 days)
uOmnispace is a software solution intended to be used for viewing, manipulation, communication and storage of medical images. It allows processing and filming of multimodality DICOM images.
It can be used as a stand-alone device or together with a variety of cleared and unmodified software options, and also support to plug in multi-vendor applications which meet interface requirements.
u Omnispace is intended to be used by trained professionals, including but not limited to physicians and medical technicians.
The system is not intended for the displaying of digital mammography images for diagnosis in the U.S.
uOmnispace is a software only medical device, the hardware itself is not seen as part of the medical device and therefore not in the scope of this product.
uOmnispace provides 2D and 3D viewing, annotation and measurement tools, manually and automatically segmentation tools (Rib extraction algorithm is based on Machine Learning) and film and report features to cover the radiological tasks reading images and reporting. uOmnispace supports DICOM formatted images and objects, CT, MRI, PET and DR multimodality are supported.
uOmnispace is a software medical device that allows multiple users to remotely access clinical applications from compatible computers on a network. The system allows processing and filming of multimodality DICOM images. This software is for use with off the-shelf PC computer technology that meets defined minimum specifications.
uOmnispace communicates with imaging systems of different modalities and medical information systems of the hospital using the DICOM3.0 standard.
The system is not intended for the displaying of digital mammography images for diagnosis in the U.S.
The acceptance criteria and the study proving the device meets accepted criteria for the uOmnispace Medical Image Post-processing Software are described below.
1. Table of Acceptance Criteria and Reported Device Performance:
| Validation Type | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Average DICE | The average dice of testing data is higher than 0.8 | The average dice on testing data set is 0.855 |
2. Sample size used for the test set and data provenance:
- Sample Size: 60 chest CTs.
- Data Provenance: The data was collected during product development. The document does not specify the country of origin of the data nor explicitly states whether it was retrospective or prospective, though the context of "product development" often implies some level of retrospective analysis of collected data.
3. Number of experts used to establish the ground truth for the test set and their qualifications:
- Number of Experts: Not explicitly stated as a specific number, but it involved multiple annotators and a "senior clinical specialist".
- Qualifications: "Annotators" and a "senior clinical specialist". Specific details like years of experience or medical certifications (e.g., radiologist) are not provided for the individual annotators or the senior specialist.
4. Adjudication method for the test set:
- Adjudication Method: A multi-step process:
- An initial rib mask was generated using a threshold-based interactive tool.
- Annotators refined the first-round annotation.
- Annotators checked each other's annotations.
- A senior clinical specialist checked and modified annotations to ensure ground truth correctness.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done:
- No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly done to measure human reader improvement with AI assistance. The study focused on validating the standalone performance of the ML-based rib segmentation algorithm against ground truth.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone performance study of the ML-based rib segmentation algorithm was done. The performance was evaluated by comparing its output to established ground truth.
7. The type of ground truth used:
- Type of Ground Truth: Expert consensus, established through a multi-step annotation and refinement process involving annotators and a senior clinical specialist.
8. The sample size for the training set:
- The document states that the training data used was "independent of the algorithm" but does not specify the sample size for the training set.
9. How the ground truth for the training set was established:
- The document does not explicitly describe how the ground truth for the training set was established, only that the training data and testing data were independent.
§ 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).