(39 days)
The LEONARDO syngo Cardiology Workstation is a medical diagnostic workstation for viewing, manipulation, communication, and storage of medical images and data on exchange media.
The LEONARDO syngo Cardiology Workstation can be configured as a stand-alone diagnostic review and post-processing workstation with a variety of syngo or Windows based software options, which are intended to assist the physician in diagnosis or treatment planning. This includes commercially available post-processing techniques.
This premarket notification covers Siemens LEONARDO syngo Cardiology Workstation syngo is a universal imaging platform based on Windows XP. LEONARDO syngo Cardiology offers a comprehensive cardiology solution to view, optimize, post-process diagnostic information and aid the doctors in the evaluation of digital cardiological and radiological examinations and patient information.
Due to special customer requirements based on the modality image type and the clinical focus, the LEONARDO syngo Cardiology Workstation can be configured with different combinations of clinical applications. syngo applications can be added to the LEONARDO syngo Cardiology Workstation either individually or as clinical focus packages.
The LEONARDO syngo Cardiology Workstation is a medical diagnostic workstation for viewing, manipulation, communication, and storage of medical images and data on exchange media.
The LEONARDO syngo Cardiology Workstation can be configured with a variety of syngo- or Windows based software options, which are intended to assist the physician in diagnosis or treatment planning. This includes commercially available post-processing techniques.
Here's an analysis of the provided text regarding the acceptance criteria and study proving the device meets them:
The provided document (K042203 for Siemens LEONARDO syngo Cardiology) is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than outright performance against specific acceptance criteria for a new device.
Therefore, the information typically found in an acceptance criteria study (like detailed performance metrics, sample sizes for test/training sets, expert qualifications, and specific ground truth methodologies) is largely absent in this type of submission. The primary "study" is the comparison to the predicate device.
Let's address each point based on the available information:
1. A table of acceptance criteria and the reported device performance
Since this is a substantial equivalence submission, there are no explicit quantitative "acceptance criteria" presented in the document itself with corresponding "reported device performance" in the way a de novo or PMA submission would have for a novel device.
The acceptance criteria for a 510(k) is generally that the new device has the same intended use and similar technical characteristics to a legally marketed predicate device, and does not raise new questions of safety and effectiveness.
| Acceptance Criteria (Implied for 510(k) Equivalence) | Reported Device Performance (Summary of Equivalence Claim) |
|---|---|
| Intended Use: Same as predicate device. | The LEONARDO syngo Cardiology Workstation has the same intended use as the predicate LEONARDO (K040970). |
| Technical Characteristics: Similar to predicate device, no new safety/effectiveness questions. | The LEONARDO syngo Cardiology Workstation has similar technical characteristics (Windows XP-based, DICOM support, software-only or complete workstation, post-processing techniques) as the predicate, and Siemens is of the opinion that it does not introduce any new potential safety risks. |
| Safety and Effectiveness: Does not raise new questions of safety and effectiveness. | Risk management (risk analysis, software development, verification and validation testing) is performed. Adheres to recognized industry practices and standards. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not provided in the 510(k) summary. A 510(k) for a workstation like this typically relies on predicate device equivalence and internal verification/validation, rather than a clinical "test set" in the sense of patient data used for performance comparison.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
This information is not provided in the 510(k) summary.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided in the 510(k) summary.
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
An MRMC study was not mentioned or performed as part of this 510(k) submission. This device is a workstation for viewing and post-processing, not an AI-powered diagnostic aid that would directly impact human reader performance through assistance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This device is described as a "medical diagnostic workstation" with various "software options" and "commercially available post-processing techniques" intended to "assist the physician." It is not an algorithm performing a standalone diagnostic function without human involvement. Therefore, a standalone algorithm-only performance study as typically understood for AI-based devices was not applicable or performed.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The document does not detail specific "ground truth" establishment methods because it's focused on workstation functionality and equivalence rather than a diagnostic algorithm's accuracy against a clinical truth. Any internal verification and validation would be against functional specifications and existing image data, but not typically characterized as "ground truth" in this context.
8. The sample size for the training set
This workstation does not appear to employ machine learning or AI in a way that would require a "training set" in the context of deep learning models. It primarily provides tools for physicians. Therefore, a training set as understood for AI development was not applicable or mentioned.
9. How the ground truth for the training set was established
As there's no mention of a training set for machine learning, this information is not applicable.
In summary:
This 510(k) pertains to a medical workstation that provides tools for viewing and post-processing cardiological and radiological examinations. Its clearance is based on substantial equivalence to an existing predicate device (LEONARDO K040970), meaning it has the same intended use and similar technological characteristics without raising new questions of safety and effectiveness. The document does not contain the detailed performance study information typically associated with novel diagnostic algorithms or AI-driven devices that would have specific acceptance criteria, test/training sets, or ground truth methodologies for performance evaluation.
§ 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).