(173 days)
EConsole1 is indicated for use in general radiographic images of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures (excluding fluoroscopic, and mammographic applications).
The main features of this software are controlling and interfacing the detector, controlling the x-ray generator acquisition settings, storing acquired images, data management and image processing.
Radiological Image Processing Software, EConsolel is complete digital image processing console software specialized for the digital X-ray detector series developed by DRTECH Corporation.
EConsole1 not only processes the acquired images but also complies with DICOM standards which allow the user to transmit and receive data with the PACS system and print images through the DICOM printer.
The provided text does not contain the detailed information necessary to fully answer the request regarding specific acceptance criteria, study methodologies, and performance results for the EConsole1 device.
The document is a 510(k) summary for a medical image management and processing system (EConsole1). Its primary purpose is to demonstrate substantial equivalence to a predicate device, not to present a comprehensive study proving specific performance metrics against pre-defined acceptance criteria for, for example, diagnostic accuracy of an AI component.
Here's a breakdown of what can be extracted and what is missing, based on the provided text:
What is Present:
- Device Name: EConsole1
- Device Type: Radiological Image Processing Software (Medical Image Management and Processing System)
- Intended Use: General radiographic images of human anatomy, to replace film/screen systems in general-purpose diagnostic procedures (excluding fluoroscopic, angiographic, and mammographic applications). Its main features involve controlling and interfacing the detector, controlling X-ray generator acquisition settings, storing acquired images, data management, and image processing.
- Regulatory Classification: Class II, Product Code LLZ.
- Predicate Device: FEEL-DRCS (K110033) and a reference device EConsole1 (K152172).
- Non-Clinical Data: Mentions compliance with standards (IEC 62304, NEMA PS 3.1-3.20 DICOM, ISO 20417, IEC 62366-1, ISO 14971) and software verification/validation activities (code, module, integration, dynamic tests, risk analysis).
- Cybersecurity: States conformity to FDA guidance for managing cybersecurity.
- Conclusion: The device is substantially equivalent to the predicate, passed V&V testing, and performs as intended without new safety risks.
What is NOT Present (and therefore cannot be answered from this document):
- A table of acceptance criteria and the reported device performance: The document states, "The test results suggest that all software specifications meet the acceptance criteria." However, it does not list what those specific acceptance criteria were (e.g., minimum accuracy, sensitivity, specificity, processing speed, image quality metrics) nor does it provide the reported numerical values for the device's performance against these criteria.
- Sample size used for the test set and the data provenance: No information on the number of images/cases used specifically for a "test set" to evaluate performance. No details on the country of origin or whether the data was retrospective or prospective.
- Number of experts used to establish the ground truth for the test set and their qualifications: No mention of expert review or ground truth establishment for performance data, as specific clinical performance evaluation data is not provided.
- Adjudication method for the test set: Not applicable, as detailed performance test set data is not provided.
- If a multi-reader multi-case (MRMC) comparative effectiveness study was done: The document explicitly states "Clinical studies are unnecessary to validate the safety and effectiveness of the software in EConsole1". This indicates no MRMC study was conducted or deemed necessary for this 510(k) submission.
- If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: While general "verification and validation" and "non-clinical tests" are mentioned, no specific standalone performance metrics for an AI algorithm (e.g., for disease detection) are provided. The device described appears to be an image processing and management system, not an AI for diagnostic interpretation.
- The type of ground truth used: Not applicable, as no specific performance study against a diagnostic ground truth is described. The V&V described are for software functionality, not diagnostic accuracy.
- The sample size for the training set: Not applicable, as no AI model requiring a training set for diagnostic classification/segmentation is described. The software's function is image management and processing.
- How the ground truth for the training set was established: Not applicable for the same reason as above.
In summary, the provided FDA 510(k) summary is for an image management and processing system, not an AI diagnostic algorithm. Therefore, it focuses on software V&V, functional equivalence, and compliance with standards rather than clinical performance metrics, ground truth establishment, or human reader studies typically associated with AI-powered diagnostic devices.
§ 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).