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The Digiview 250 is indicated for use in generating radiographic images of human anatomy. The device is intended to provide digital x-ray image capture for conventional film/screen radiographic examinations and replace radiographic film/screen systems in all general purpose diagnostic procedures. The device is not intended for mammography applications.

The Kubtec Digital Radiography DIGIVIEW 250 ® is a CMOS based solid state x-ray imager which has a 192 x 246 mm imaging area. The Digital Radiography DIGIVIEW 250® imager intercepts X-ray photons after they pass through anatomy and surrounding air and converts the X-ray photons into electrical signals. These resultant electric signals are converted into digital values which are transmitted for remote viewing. The DIGIVIEW 250 ® system features a DICOM 3.0 compliant software, DIGICOM. The DIGICOM software enables the display and analysis of x-ray images; either live (real time) or previously captured and the storage and transmission of these images to PACS systems.

The provided text describes a 510(k) submission for the Kubtec DIGIVIEW 250, a digital radiography system. The submission focuses on demonstrating substantial equivalence to a predicate device (Canon CXDI-60G) rather than a comprehensive study against specific acceptance criteria for a new medical AI device. Therefore, much of the requested information (e.g., sample sizes for training/test sets, ground truth establishment, expert qualifications, MRMC studies) is not detailed in this document because it pertains to performance studies for an AI or novel diagnostic device, which is not the nature of this 510(k) submission.

Here's the information that can be extracted or inferred from the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in the sense of predefined thresholds for clinical performance metrics (like sensitivity/specificity) against a clinical gold standard. Instead, it compares technical specifications to a predicate device to demonstrate substantial equivalence. The "acceptance criteria" can be inferred as matching or exceeding the predicate device's technical specifications and intended use.

Study Proving Device Meets Acceptance Criteria:

The study proving the device meets the "acceptance criteria" (i.e., substantial equivalence) is the non-clinical testing and design control activities mentioned. The submission states: "The performance data, non-clinical testing, and design process demonstrate that the Kubtec DIGIVIEW 250 ® is as safe and effective as the Canon CXDI-60G, and has no new indications for use, thus rendering it substantially equivalent to the predicate device." This typically involves laboratory testing of the physical and imaging characteristics of the device against industry standards and the specifications of the predicate.

2. Sample Size Used for the Test Set and Data Provenance:

• Test Set Sample Size: Not specified. The document does not detail a clinical test set with patient data. The "testing" referred to is non-clinical, likely engineering and performance measurements.
• Data Provenance: Not applicable in the context of clinical data for a test set. The testing is based on device performance in a lab setting.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

• Not applicable. There is no mention of human experts establishing ground truth for a clinical test set, as this is a device for image capture, not an image interpretation AI.

4. Adjudication Method for the Test Set:

• Not applicable. No clinical test set or human interpretation is detailed.

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

• No. The document makes no mention of an MRMC study. This type of study is typically done for AI-powered diagnostic tools to assess the impact of AI on human reader performance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

• Yes, in a way. The "performance data, non-clinical testing" can be considered a standalone assessment of the device's technical capabilities (e.g., resolution, dynamic range, readout speed) without human intervention in the specific output generation. However, it's a hardware device, not an AI algorithm.

7. Type of Ground Truth Used:

• For technical specifications: Engineering measurements, internationally recognized standards (e.g., for resolution, dynamic range), and the published specifications of the predicate device. There is no "ground truth" for clinical diagnoses as the device itself is not making diagnostic interpretations.

8. Sample Size for the Training Set:

• Not applicable. This device is a hardware digital radiography system, not an AI model that requires a training set of data.

9. How the Ground Truth for the Training Set Was Established:

• Not applicable, as there is no training set for an AI model.

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