(141 days)
syngo.CT Dual Energy is designed to operate with CT images which have been acquired with Siemens Dual Source scanners. The various materials of an anatomical region of interest have different attenuation coefficients, which depend on the used energy. Depending on the region of interest, contrast agents may be used. These differences provide information on the chemical composition of the scanned body materials. syngo.CT Dual Energy combines images acquired with low and high energy spectra to visualize this information.
The functionality of the syngo.CT Dual Energy applications is as follows:
- Monoenergetic ●
- Brain Hemorrhage ●
- Gout Evaluation ●
- Lung Vessels ●
- Heart PBV ●
- Bone Removal
- o Lung Perfusion
- Liver VNC ●
- Monoenergetic Plus ●
- Virtual Unenhanced ●
- Bone Marrow ●
- Hard Plaques ●
- o Rho/Z
- Kidney Stones"
*) Kidney Stones is designed to support the visualization of the chemical composition of kidney stones and especially the differentiation between uric acid and non-uric acid stones. For full identification of the kidney stone additional clinical information should be considered such as patient history and urine testing. Only a well-trained radiologist can make the final diagnosis under consideration of all available information. The accuracy of identification is decreased in obese patients.
Dual energy offers functions for qualitative and quantitative evaluations. Dual energy CT can be used to improve the visualization of the chemical composition of various energy dependent materials in the human body when compared to single energy CT.
Depending on the organ of interest, the user can select and modify different application classes or parameters and algorithms. syngo.CT Dual Energy Software Package is a post processing application package consisting of several post processing application classes that can be used to improve visualization of various energy dependent materials in the human body.
Different body regions require specific tools that allow the correct evaluation of data sets. syngo.CT Dual Energy provides a range of application classes that meet the requirements of each evaluation type. The different application classes for the subject device can be combined into one workflow.
The provided text describes syngo.CT Dual Energy, a software package for post-processing CT images. While it mentions non-clinical testing and verification/validation, it does not provide specific acceptance criteria or details of a study that directly proves the device meets such criteria in terms of performance metrics like accuracy, sensitivity, or specificity for its various applications.
The document focuses on substantiating equivalence to predicate devices, primarily through software updates and new application classes (Rho/Z, Hard Plaques, Fat Map for Liver VNC). The testing described is more about demonstrating that the software functions as intended and meets safety standards, rather than providing performance metrics against specific acceptance thresholds for clinical utility.
Therefore, many of the requested details cannot be extracted directly from this document.
Here's an attempt to answer based on the provided text, highlighting where information is absent:
1. Table of acceptance criteria and the reported device performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not explicitly stated in terms of quantitative performance metrics (e.g., accuracy, sensitivity, specificity). The document focuses on functional and safety requirements. | "The testing results support that all the software specifications have met the acceptance criteria." (General statement, no specific metrics provided.) |
| (Implicit) Compliance with recognized safety and performance standards (DICOM, IEC 62304, ISO 14971, IEC 60601-1-6, IEC 60601-1-4). | "syngo.CT Dual Energy is designed to fulfill the requirements of the following safety and performance standards listed in Table 5..." (Compliance indicated). |
| (Implicit) Functionality of new features (Rho/Z, Hard Plaques, Fat Map for Liver VNC). | "Performance tests were conducted to test the functionality of the syngo.CT Dual Energy. Phantom bench testing and retrospective analysis of available patient data was conducted for application classes Rho/Z, Hardplaques, and feature Fat Map." "The results of these tests demonstrate that the subject device performs as intended." |
| (Implicit) Substantial Equivalence to predicate devices. | "The result of all conducted testing was found acceptable to support the claim of substantial equivalence." |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample size for test set: Not specified. The document mentions "retrospective analysis of available patient data" but does not give a number of cases or patients.
- Data provenance: "retrospective analysis of available patient data". No country of origin is mentioned. Whether it was prospective or retrospective is stated as retrospective.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of experts: Not specified.
- Qualifications of experts: Not specified. However, for "Kidney Stones" application, it states, "Only a well-trained radiologist can make the final diagnosis under consideration of all available information," implying radiologists would be involved in ground truth establishment for that specific application.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Adjudication method: Not specified.
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
- MRMC comparative effectiveness study: No, an MRMC comparative effectiveness study is not mentioned. The document describes a "verification/validation testing" and states that "supportive articles that demonstrate the usability" of certain application classes were provided, but it does not detail a study evaluating human reader improvement with AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Standalone performance study: The non-clinical testing included "phantom bench testing," which would be a form of standalone testing on controlled data. However, quantitative performance metrics for the algorithm's standalone performance (e.g., accuracy of material decomposition in phantoms) are not provided. The overall conclusion is that the device "performs as intended," but specific performance values are absent.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- Type of ground truth: Not explicitly stated. For "retrospective analysis of available patient data," the ground truth would likely be based on existing clinical reports or expert interpretation. For "phantom bench testing," the ground truth would be the known composition of the phantom materials.
8. The sample size for the training set
- Sample size for training set: Not applicable/Not specified. The document describes a software package with new and modified application classes, rather than a deep learning AI model that would typically have a distinct training set. The focus is on verifying improvements and new functionalities over existing predicate devices.
9. How the ground truth for the training set was established
- How ground truth for training set was established: Not applicable/Not specified, as a distinct "training set" for an AI model is not described. The document pertains to updates to an existing software package for image post-processing.
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.