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The RiaspDR software directly controls and acquires general radiographic images of human anatomy (excluding fluoroscopic, angiographic, dental and mammographic applications). The RiaspDR software is designed to work with X-ray images from the Mars1417X detector (K210316).

RIASPDR is Radiographic Imaging Acquisition Software Platform. RIASPDR software directly controls and acquires images from Mars1417X detector(K210316) whose manufacturer is iRay Technology. Furthermore, RIASPDR acquires and processes images. In addition, it complies with DICOM standards and is able to transmit and receive data with the PACS system.

The provided FDA 510(k) clearance letter and supporting documentation for the Shen Zhen Cambridge-hit Digital Radiographic Imaging Acquisition Software - DR (RiasDR) do not contain detailed information about the specific acceptance criteria and the comprehensive study that proves the device meets these criteria.

The document states:

• "Software verification and validation testing were conducted and documentation was provided in this 510(k). Results demonstrated that the predetermined acceptance criteria were met."
• "Software Verification and Validation Testing was performed in accordance with internal requirements, international standards and guidance shown below, the safety and effectiveness of RIASPDR were supported, and the substantial equivalence to the predicate device was demonstrated."
• "Clinical testing: Not applicable."

This indicates that acceptance criteria were defined and met through non-clinical testing, but the specifics of these criteria and the methodology of the study are not included in this extract. The document mainly focuses on comparative equivalence to a predicate device (Econsole1, K152172) and adherence to general software validation guidelines and DICOM standards.

Therefore, I cannot provide a detailed answer to your request based solely on the provided text, as the specific information about the acceptance criteria table, sample sizes, expert involvement, adjudication, MRMC study, standalone performance, and ground truth establishment is not present.

However, I can extract the available information and highlight what is missing based on your request:

Device: Digital Radiographic Imaging Acquisition Software - DR (RiasDR)

Study Type: Non-clinical (Software Verification and Validation Testing)

1. Acceptance Criteria and Reported Device Performance

The document specifies performance deviations for certain measurement functions, which likely serve as a subset of the acceptance criteria. However, a complete table of acceptance criteria and a detailed breakdown of all reported device performance metrics are not provided.

Partial Acceptance Criteria (from "5. Device specification"):

Missing Information: A comprehensive table of all acceptance criteria the device was tested against and the corresponding quantitative results showing the device met these criteria.

2. Sample Size and Data Provenance

• Test Set Sample Size: Not specified.
• Data Provenance: The document states "Software verification and validation testing were conducted." It does not specify the origin of the data used for this testing (e.g., country of origin, retrospective or prospective). Given it's non-clinical software testing, it likely involves simulated data, synthetic data, and/or a collection of de-identified real-world medical images, but this is not specified.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.
  • Since clinical testing was noted as "Not applicable," expert review for clinical ground truth is unlikely to be a primary method for this specific 510(k) submission, unless experts were involved in verifying the non-clinical test data or the accuracy of image processing algorithms against known standards. This is not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. Given the non-clinical nature of the testing described (software verification and validation), traditional expert adjudication methods (e.g., 2+1, 3+1) are unlikely to be detailed here unless specifically applied to the generation of a 'ground truth' for image quality metrics.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? No, implicitly. The document explicitly states "Clinical testing: Not applicable." An MRMC study would fall under clinical testing.

6. Standalone Performance (Algorithm Only)

• Was standalone performance done? The software verification and validation testing likely assessed the algorithm's performance in a standalone context against defined functional and performance requirements. However, specific metrics (e.g., sensitivity, specificity, AUC for a diagnostic task) for algorithm-only performance are not provided in this document. The focus of the "Device specification" section is on measurement accuracy rather than a diagnostic performance.

7. Type of Ground Truth Used

• Type of Ground Truth: The ground truth for the non-clinical testing would relate to the accuracy of image acquisition, processing, and measurement functions. This would likely involve:
  • Known input parameters/standards: For verifying that the software correctly controls the X-ray detector and acquires images.
  • Defined geometric shapes/patterns: For verifying the accuracy of length, area, perimeter, and angle measurements.
  • DICOM compliance: Ground truth related to adherence to DICOM standards for image transmission and storage.
  • The document does not mention ground truth based on expert consensus, pathology, or outcomes data, as clinical testing was not applicable.

8. Training Set Sample Size

• Training Set Sample Size: Not applicable/Not specified. The RiasDR is described as "Digital Radiographic Imaging Acquisition Software - DR." This type of software typically manages image acquisition and processing rather than employing machine learning algorithms that require a "training set" in the conventional sense (i.e., for diagnostic prediction models). Its function is to control the detector and process images (e.g., viewing, search, storage, annotation, measurement, processing). There's no indication it's an AI/ML diagnostic aid requiring a training set.

9. How Ground Truth for Training Set was Established

• Not applicable/Not specified, as there is no indication of a training set for an AI/ML model for diagnostic purposes.

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