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The purpose of Acuity is to acquire, store, communicate, display and process medical X-ray images. These radiographic systems are intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography, angiography, interventional, or fluoroscopy use.

RadmediX Acuity combines components into a complete stationary x-ray system, including software, tube stands, tube heads, collimators, generators, tables, and digital radiography panels.

Here's a breakdown of the acceptance criteria and the study information based on the provided text:

Acceptance Criteria and Device Performance:

The document describes the Acuity SDR Standard, Acuity SDR Plus, and Acuity FDR Standard as stationary x-ray systems. The core claim for substantial equivalence is that the device performs the same functions using the same technological methods as the predicate device (Visaris Vision®) to produce diagnostic x-ray images.

Study Information:

1. Sample Size and Data Provenance:

   • Test Set Sample Size: Not explicitly stated as a numerical sample size of clinical cases for a formal clinical trial. Instead, the non-clinical testing involved acquiring DICOM images from "all major body structures" using one AcuitySDR system configuration.
   • Data Provenance: The document does not specify the country of origin for the image acquisition or whether it was retrospective or prospective. It was a "field tested" system, implying real-world or simulated real-world scenarios.

2. Number of Experts and Qualifications (for Ground Truth):

   • Number of Experts: Not specified.
   • Qualifications of Experts: Not specified, beyond the statement that images were generated for "diagnostic x-rays" and assessed as "clinically acceptable." This implies evaluation by trained personnel (e.g., radiologic technologists, radiologists), but their specific qualifications or number are not detailed.

3. Adjudication Method:

   • No formal adjudication method is described. The assessment of image quality was based on whether images were "of high quality and contrast and clinically acceptable."

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

   • No MRMC comparative effectiveness study was performed or described. The submission focuses on demonstrating substantial equivalence through technical characteristics and non-clinical image quality assessment, not on human reader performance improvement with or without AI assistance.

5. Standalone Performance Study (Algorithm Only):

   • Yes, a standalone study (non-clinical testing) was performed. The algorithm (software) alongside the hardware components was tested to acquire and process images. The output (DICOM images) was then evaluated for inherent quality and acceptability. The assessment "all images were of high quality and contrast and clinically acceptable" refers to the output of the integrated system.

6. Type of Ground Truth Used:

   • Expert Consensus / Clinical Acceptability: The ground truth for image quality was established through subjective expert assessment of "high quality and contrast and clinically acceptable." It does not mention pathology or outcomes data.

7. Training Set Sample Size:

   • Not applicable. This device is an X-ray system, not an AI/ML algorithm that requires a training set in the typical sense for image interpretation or diagnosis. The "software" components mentioned (AccuVueMED, AccuVue) are image acquisition and processing software, which are generally deterministic and not "trained" on data in the way a diagnostic AI would be. They are "previously cleared software supplied with the system."

8. How Ground Truth for Training Set Was Established:

   • Not applicable, as there is no specific "training set" for an AI/ML algorithm in this submission. The software components were previously cleared by the FDA, implying their performance was established through other means at the time of their original clearance (K152172 and K141440).

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Mars 1717XF-GSI Wireless Digital Flat Panel Detector is indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures. This device is not intended for mammography or dental applications, pediatric, pregnant women and fluoroscopy.

Mars1717XF-GSI Wireless Digital Flat Panel Detector is a kind of wireless digital flat panel detector. It supports the single frame mode, with the key component of TFT (Thin Film Transistor)/PD (Photo Diode) image sensor flat panel of active area: 42.48cm×42.54cm. The sensor plate of Mars1717XF-GSI Wireless Digital Flat Panel Detector is coated with Gd2O2S (GOS) scintillator to achieve the conversion from X-ray to visible photon. The visible photons are transformed to electron signals by diode capacitor array within TFT panel, which are composed and processed by connecting to scanning and readout electronics, consequently to form a panel image by transmitting to PC through the user interface. The major function of the Mars1717XF-GSI Wireless Digital Flat Panel Detector is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. This detector is the key component of DR system, enables to complete the digitalization of the medical X-ray imaging with the DR system software.

The provided document is a 510(k) summary for the iRay Technology Taicang Ltd. Wireless Digital Flat Panel Detector (Mars1717XF-GSI). It compares the proposed device to a predicate device (Mars1417XF-GSI, K182550) to demonstrate substantial equivalence.

Based on the content, here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly present a table of "acceptance criteria" for clinical performance as would be seen for an AI-powered diagnostic device. Instead, it focuses on demonstrating substantial equivalence by comparing the technical specifications of the proposed device to the predicate device. The implicit "acceptance criteria" here are that the performance metrics of the proposed device are comparable to or better than the predicate device, or within acceptable tolerances for the intended use.

Here's a table focusing on the comparative technical specifications presented, which serve as the "performance" data for establishing equivalence:

Note: The primary difference highlighted and addressed in the document is the "panel dimension" which impacts "Image Matrix Size," "Effective Imaging Area," "MTF," "DQE", "Power Consumption", and "Dimensions". The submission argues that non-clinical information is sufficient to support substantial equivalence despite these changes.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document describes non-clinical testing rather than a clinical study with a "test set" of patient data in the typical sense of AI/diagnostic device trials (i.e., analyzing images of patients to assess diagnostic accuracy). The "study" here refers to:

• Electrical Safety and EMC testing: Performed according to IEC/ES 60601-1 and IEC/EN 60601-1-2.
• Biological Evaluation: Evaluated with ISO 10993-1 for materials contacting operators' skin.
• Non-clinical Considerations: Performance comparisons, likely quantitative measurements of detector characteristics (MTF, DQE, spatial resolution), and electromagnetic compatibility. This would involve a sample of the manufactured devices, not patient data.
• Clinical Consideration: The submission states that a clinical consideration (study with patient data) was not necessary because the modification from the predicate device to the proposed device is primarily the "panel dimension." It claims "non-clinical information is sufficient to support the substantial equivalence."

Therefore, there is no patient-based "test set" sample size or data provenance (country, retrospective/prospective) relevant to clinical performance assessment mentioned because a clinical study was explicitly deemed unnecessary by the manufacturer, and presumably accepted by the FDA for this direct predicate comparison.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Since no clinical study with a patient imaging test set was conducted (as per point 2), there were no experts used to establish ground truth for a clinical test set. The ground truth in this submission is established through:

• Standardized technical measurements: For parameters like MTF, DQE, spatial resolution, which are objective physical properties of the detector.
• Compliance with international standards: For electrical safety, EMC, and biological compatibility.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable, as no patient-based clinical "test set" requiring adjudication of diagnoses was used.

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 device is a digital flat panel detector (hardware), not an AI-powered diagnostic software. Its purpose is to capture X-ray images, not to interpret them or assist human readers in diagnosis. Therefore, no MRMC study or AI assistance evaluation was performed.

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

Not applicable, as this is a hardware device (X-ray detector), not a standalone algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

As explained in point 3, the "ground truth" for the performance evaluation in this 510(k) submission is based on:

• Objective physical measurements (e.g., MTF, DQE, spatial resolution) using phantoms and calibrated equipment.
• Adherence to international safety and performance standards (e.g., IEC/ES 60601-1, IEC/EN 60601-1-2, ISO 10993-1).
• Demonstration of equivalence to a legally marketed predicate device based on these technical and safety characteristics.

8. The sample size for the training set

Not applicable. This is a hardware device, 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 a hardware device.

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