(146 days)
syngo.via molecular imaging (MI) workflows comprise medical diagnostic applications for viewing, manipulation, quantification, analysis and comparison of medical images from single or multiple imaging modalities with one or more time-points. These workflows support functional data, such as positron emission tomography (PET) or nuclear medicine (NM), as well as anatomical datasets, such as computed tomography (CT) or magnetic resonance (MR). syngo.via MI workflows can perform harmonization of SUV (PET) across different PET systems or different PET reconstruction methods.
syngo via MI workflows are intended to be utilized by appropriately trained health care professionals to aid in the management of diseases, including those associated with oncology, cardiology, neurology, and organ function. The images and results produced by the syngo.via MI workflows can also be used by the physician to aid in radiotherapy treatment planning.
syngo.via MI Workflows (including Scenium and syngo MBF applications) is a multi-modality postprocessing software only medical device intended to aid in the management of diseases, including those associated with oncology, cardiology, neurology, and organ function. The syngo.via MI Workflows applications are part of a larger syngo.via client/server system which is intended to be installed on common IT hardware. The hardware itself is not seen as part of the syngo.via MI Workflows medical device.
The syngo.via MI Workflows software addresses the needs of the following typical users of the product:
- . Reading Physician / Radiologist – Reading physicians are doctors who are trained in interpreting patient scans from PET, SPECT and other modality scanners. They are highly detail oriented and analyze the acquired images for abnormalities, enabling ordering physicians to accurately diagnose and treat scanned patients. Reading physicians serve as a liaison between the ordering physician and the technologists, working closely with both.
- . Technologist – Nuclear medicine technologists operate nuclear medicine scanners such as PET and SPECT to produce images of specific areas and states of a patient's anatomy by administering radiopharmaceuticals to patients orally or via injection. In addition to administering the scan, the technologist must properly select the scan protocol, keep the patient calm and relaxed, monitor the patient's physical health during the protocol and evaluate the quality of the images. Technologists work very closely with physicians, providing them with quality-checked scan images.
The software has been designed to integrate the clinical workflow for the above users into a serverbased system that is consistent in design and look with the base syngo.via platform and other syngo.via software applications. This ensures a similar look and feel for radiologists that may review multiple types of studies from imaging modalities other than Molecular Imaging, such as MR.
syngo.via MI workflows software supports integration through DIC emission tomography (PET) or nuclear medicine (NM) data, as well as anatomical datasets, such as computed tomography (CT) or magnetic resonance (MR).
Although data is automatically imported into the server based on predefined configurations through the hospital IT system, data can also be manually imported from external media, including CD, external mass storage devices, etc.
The Siemens syngo.via platform and the applications that reside on it, including syngo.via MI Workflows, are distributed via electronic medium. The Instructions for Use is also delivered via electronic medium.
syngo.via MI Workflows includes 2 workflows (syngo.MM Oncology and syngo.MI General) as the Scenium neurology software application and the syngo MBF cardiology software application which are launched from the OpenApps framework within the MI General workflow.
Here's a breakdown of the acceptance criteria and the study information for the Syngo.via MI Workflows, Scenium, and Syngo MBF device, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document primarily focuses on two areas of performance evaluation: Organ Segmentation and Tau Workflow Support. The acceptance criteria for Organ Segmentation are explicitly stated, while for Tau Workflow, the criteria are implied through correlation and agreement with existing methods.
| Feature | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Organ Segmentation | All organs must meet criteria for either the average DICE coefficient or the average symmetric surface distance (ASSD: average surface distance between algorithm result and manual ground truth annotation). | All organs met criteria for either the average DICE coefficient or the ASSD. (Specific numerical values for DICE or ASSD are not provided in this summary). |
| Tau Workflow Support (SUVRs) | Good correlations and agreement with an MR-based method and MR-based segmentations for SUVRs calculated on individual and composite Braak VOIs using the new pipeline and masks. | Comparisons showed good correlations and agreement between the two sets of values (new pipeline vs. MR-based method) on more than 700 flortaucipir images from ADNI. |
2. Sample Size Used for the Test Set and Data Provenance
- Organ Segmentation: Not explicitly stated. The algorithm used was "originally cleared within syngo.via RT Image Suite (K201444) and carried into the reference predicate device (syngo.via RT Image Suite, K220783)." This suggests the data provenance for this algorithm was tied to those previous clearances. The document implies the segmentation quality was assessed, but the specific test set size for this current submission is not provided.
- Tau Workflow Support: "more than 700 flortaucipir images from ADNI".
- Data Provenance (Tau Workflow): "ADNI" (Alzheimer's Disease Neuroimaging Initiative). This is a prospective, multi-center, North American study. The exact countries of origin of the individual images are not specified but ADNI is a U.S. led initiative with international participation.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
- Organ Segmentation: For manual ground truth annotation, the number of experts and their qualifications are not specified.
- Tau Workflow Support: The ground truth for the "MR-based method and MR-based segmentations" used for comparison is from existing methods mentioned in the references. The number and qualifications of experts involved in establishing this historical ground truth are not specified in this document.
4. Adjudication Method for the Test Set
- Organ Segmentation: An adjudication method is not explicitly stated. The process involved "comparing a manually annotated ground truth with the algorithm result." It's common for a single expert or a consensus of experts to establish manual ground truth, but the method for resolving discrepancies or reaching consensus is not detailed here.
- Tau Workflow Support: An adjudication method is not explicitly stated. The comparison was made between the device's calculated SUVRs and those from an "MR-based method and MR-based segmentations." This implies a comparison against a pre-established or validated method rather than a multi-reader adjudication specifically for this study.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No MRMC comparative effectiveness study was done for this submission. The document explicitly states: "Clinical testing was not conducted for this submission." The evaluations focused on standalone performance and agreement with existing methods.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Performance
- Yes, standalone performance was done.
- Organ Segmentation: The segmentation algorithm's performance (DICE coefficient and ASSD) was assessed by comparing its output directly against manually annotated ground truth. This is a standalone evaluation.
- Tau Workflow Support: The "SUVRs calculated on individual and composite Braak VOIs using our pipeline and our masks" were compared to an "MR-based method." This directly assesses the algorithm's standalone quantification capabilities.
7. Type of Ground Truth Used
- Organ Segmentation: Expert consensus (manual annotation) is implied ("manually annotated ground truth").
- Tau Workflow Support: Reference method (MR-based method and MR-based segmentations) and potentially expert consensus that established those reference methods. The references provided suggest established research pipelines for flortaucipir processing and ADNI publications, which would typically involve expert interpretation and validation.
8. Sample Size for the Training Set
- Organ Segmentation: The document states the algorithm is the "same algorithm originally cleared within syngo.via RT Image Suite (K201444) and carried into the reference predicate device (syngo.via RT Image Suite, K220783)." The training set size for this re-used algorithm is not specified in this document, but would have been part of the original clearance.
- Tau Workflow Support: The training set size for the tau quantification workflow is not specified.
9. How the Ground Truth for the Training Set Was Established
- Organ Segmentation: The method for establishing ground truth for the training set of the deep-learning algorithm is not specified in this document. Given it's a deep-learning algorithm, it would typically involve expert-labeled data, but the details are not provided.
- Tau Workflow Support: The method for establishing ground truth for the training set (if applicable) for the tau quantification workflow is not specified. It mentions using the AAL atlas as a basis for defining Braak regions, which is a pre-existing anatomical atlas.
§ 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).