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The IODR1717 / IODR1417 / IODR1417-GF (Digital Flat Panel X-Ray Detector) is indicated as a digital imaging solution designed for providing the general radiographic diagnosis of human anatomy targeting both adults and children. It is intended to replace film-based radiographic diagnostic systems. Not to be used for mammography.

The IODR1717 / IODR1417 / IODR1417-GF detectors are wired or wireless digital flat panel detectors that have been designed for faster, more streamlined approach to digital radiography systems. The IODR1717 / IODR1417 / IODR1417-GF detectors utilize a combination of propriety TFT and scintillator (Csl), and those and electronics are housed in one package. The detectors support an auto-trigger signal sensing technology that allows the detectors to be used without generator integration.

The flat panel sensors of The IODR1717 / IODR1417 / IODR1417-GF are fabricated using thin film technology based on amorphous silicon technology. Electronically, the sensors are much like conventional photodiode arrays. Each pixel in the array consists of a light-sensing photodiode and a switching Thin Film Transistor (TFT) in the same electronic circuit. Amorphous silicon photodiodes are sensitive to visible light, with a response curve roughly comparable to human vision. The sensitivity of amorphous silicon photodiodes peaks in green wavelengths, well matched to scintillators such as Csl. The response has the excellent linearity of a charge-integrating-biased photodiode.

Aspen\View software includes the basic functionality: generator control, detector control, firmware, image acquisition, image calibration and correction, image storage.

The Aspen Imaging Healthcare IODR1717 / IODR1417 / IODR1417-GF Digital Flat Panel X-ray Detectors are evaluated against a predicate device (K223930). This submission focuses on demonstrating substantial equivalence based on technological characteristics and performance, rather than a clinical study with specific acceptance criteria on diagnostic accuracy for an AI-powered device.

Here's an analysis of the provided text in relation to your request, with the caveat that this is a hardware device (digital X-ray detector) and not an AI algorithm for diagnosis. Therefore, many of your requested points, especially those related to AI effectiveness, human reader improvement, and expert-established ground truth for a diagnostic test set, are not directly applicable.

1. Table of Acceptance Criteria and Reported Device Performance

The submission establishes substantial equivalence by comparing the proposed device's performance characteristics to a legally marketed predicate device (K223930). The "acceptance criteria" here are implicitly that the proposed device's reported performance metrics are equivalent to or better than the predicate device.

2. Sample size used for the test set and the data provenance

The document states that "Imaging performance test has been conducted according to: IEC 62220-1, Medical Electrical Equipment Characteristics of Digital X-ray Imaging Devices Part . 1-1: Determination of the Detective Quantum Efficiency Detectors Used in Radiographic Imaging." This standard describes methods for laboratory testing of detector performance, using phantoms and controlled X-ray beams. It is not equivalent to a clinical test set with patient data.

Therefore, there is no information about a "test set" in the context of clinical images or patient data. The provenance of such clinical data (e.g., country of origin, retrospective/prospective) is not provided because such a test set was not used for this type of device submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. As this is a hardware device (digital X-ray detector) and not an AI-powered diagnostic algorithm, there was no clinical test set requiring expert interpretation or ground truth establishment in the diagnostic sense. The performance tests (DQE, MTF, resolution) are objective physical measurements.

4. Adjudication method for the test set

Not applicable, for the same reasons as point 3.

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

Not applicable. This is not an AI-powered diagnostic device, and no MRMC study was conducted or mentioned. The submission is for an X-ray detector, which is a component rather than a diagnostic interpretation system.

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

This question is geared towards AI/software. The IODR devices are digital flat panel X-ray detectors. They are hardware components for X-ray imaging. While they include "Aspen\View software" for "generator control, detector control, firmware, image acquisition, image calibration and correction, image storage", this software pertains to the operation and image processing of the detector itself, not diagnostic analysis or algorithms to be used standalone for diagnosis. Therefore, a standalone performance study in the sense of an algorithm for diagnostic interpretation was not done.

7. The type of ground truth used

The "ground truth" for the performance tests (DQE, MTF, resolution) is based on physical measurements using phantoms and standardized protocols as defined by IEC 62220-1-1. This is a technical standard for evaluating the intrinsic image quality characteristics of the detector, not pathological or clinical outcomes data.

8. The sample size for the training set

Not applicable. This is not an AI algorithm that requires a training set. The device itself is hardware.

9. How the ground truth for the training set was established

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

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