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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):

1. 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.
2. 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.
3. 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.
4. Adjudication method for the test set: Not applicable, as detailed performance test set data is not provided.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.

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The Econsole1 software is indicated for use in general radiographic images of human anatomy (excluding fluoroscopic, angiographic, and mammographic applications).

Econsole1 is digital radiography operating console software. Econsole1 provides an integrated solution for X-ray projection. It integrates with the digital detector. Furthermore, Econsole1 acquires and processes images. In addition, it complies with DICOM standards and is able to transmit and receive data with the PACS system, and print images through the DICOM printer.

The provided text is a 510(k) Summary for the Econsole1 device. It contains some information about the device's characteristics and comparison to a predicate device, but it does not contain acceptance criteria for performance, nor does it describe a study that proves the device meets such criteria.

The document explicitly states: "10. Summary of Clinical Data. This section is not applicable." This strongly indicates that no clinical study was performed for this 510(k) submission to demonstrate performance against specific criteria in a real-world or simulated clinical setting.

The "Summary of Non-Clinical Data" mentions compliance with a "FDA guidance document entitled 'Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices,' May 11, 2005". This guidance focuses on software development and validation processes, not on demonstrating device performance against clinical or technical acceptance criteria.

The information provided confirms that this device is a "Picture archiving and communications system" and "Radiological Image Processing System" which acquires and processes images. The comparison table focuses on technological equivalence to the predicate device, not on quantitative performance metrics.

Therefore,Based on the provided text, I cannot provide the requested information.

Here's why:

• No Acceptance Criteria or Performance Data: The document does not list any specific performance acceptance criteria for the Econsole1 device (e.g., image quality metrics, processing speed metrics, accuracy scores for any automated features). Consequently, there is no reported device performance against such criteria.
• No Study Described: The document explicitly states "10. Summary of Clinical Data. This section is not applicable." This means no clinical study (or any study to demonstrate performance against acceptance criteria) was conducted and detailed in this submission. The submission relies on demonstrating substantial equivalence to a predicate device based on intended use and technological characteristics, not on a new performance study.
• No Clinical Ground Truth: Since no performance study was conducted, there's no mention of ground truth establishment, expert adjudication, or human reader performance.
• No Training Set Information: As no new algorithm performance study is described, there is no information about a training set.

In summary, the provided 510(k) summary focuses on demonstrating substantial equivalence based on intended use and technological characteristics compared to a predicate device, rather than on presenting performance data against specific acceptance criteria from a new study.

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