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The Digital X-ray detector, EXPD-N Series, is designed for use in digital imaging solutions for general radiographic diagnosis of human anatomy. This device is intended for use in all general diagnostic procedures, replacing film or screen-based radiographic systems for both adult and paediatric patients. It is not intended for use in mammography.

In comparison to existing devices, the new detectors incorporate a Flexible a-Si in the TFT material within the panel. The primary difference from the conventional glass a-Si panel is that the electronic circuits, such as silicon, are deposited on a plastic substrate instead of a glass substrate during the manufacturing of the TFT panel. Since only the material of the substrate on which the silicon is deposited changes, the overall image performance remains unaffected. Another difference is the pixel pitch. While existing products feature only a pixel pitch of 140μm, the new models include an option with a pixel pitch of 100um. The resolution of an X-ray detector has a significant impact on MTF (Modulation Transfer Function) and sensitivity.

This medical device submission is for an X-ray detector, not an AI/ML device. Therefore, the typical acceptance criteria and study requirements for AI/ML devices, such as those related to multi-reader multi-case studies, standalone performance, and ground truth establishment with expert consensus or pathology, are not applicable here.

The submission focuses on establishing substantial equivalence to a predicate device based on technical characteristics and physical performance, confirming it is suitable for general radiographic diagnosis.

Here's a breakdown of the provided information, tailored to the context of a non-AI X-ray detector:

1. Table of Acceptance Criteria and the Reported Device Performance

The acceptance criteria are implicitly defined by demonstrating substantial equivalence to the predicate device in terms of technical characteristics and performance metrics relevant to X-ray image quality. The table below compares the subject device's performance to the predicate device, highlighting where performance is similar or improved.

Study Proving Acceptance Criteria:

The study proving the device meets the acceptance criteria is a non-clinical performance testing and expert review study.

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

• Sample Size for Test Set: Not explicitly stated as a number of images. The document refers to "clinical images acquired from various anatomical sites."
• Data Provenance: The document does not explicitly state the country of origin for the clinical images. It indicates the data was retrospective, as it refers to "clinical images acquired."

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

• Number of Experts: Not explicitly stated, but referred to as "qualified expert clinicians."
• Qualifications of Experts: "qualified expert clinicians." (Specific specialties or years of experience are not provided.)

4. Adjudication Method for the Test Set:

• Adjudication Method: "clinical images acquired from various anatomical sites were reviewed by qualified expert clinicians to assess imaging performance." No specific adjudication method like 2+1 or 3+1 is mentioned. It implies a qualitative assessment by experts.

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:

• No, an MRMC comparative effectiveness study was not done. This device is an X-ray detector, not an AI-powered diagnostic tool, so a study on AI assistance for human readers is not relevant to this submission.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• No, a standalone (algorithm only) performance study was not done. This device is an X-ray detector; its performance is evaluated as a physical imaging device, not a standalone algorithm. The "performance" refers to image quality metrics like MTF, DQE, and resolution.

7. The Type of Ground Truth Used:

• Type of Ground Truth: The ground truth for image quality assessment was established through expert qualitative assessment of clinical images. The stated goal was to demonstrate that "the device produces accurate X-ray images suitable for diagnostic purposes."

8. The Sample Size for the Training Set:

• Sample Size for Training Set: Not applicable. This is a hardware device (X-ray detector) and its associated image processing software (EConsole 2). While the software itself might have been developed using some data, the submission does not detail a "training set" in the context of an AI/ML model for diagnostic interpretation. The submission indicates physical performance testing and expert review of acquired images.

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

• How Ground Truth for Training Set Was Established: Not applicable, as this is not an AI/ML device with a defined "training set" requiring ground truth for model development. The focus is on the physical performance characteristics of the detector and the suitability of its output images for diagnostic purposes.

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