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Intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography.

AcuityDRe components into a complete digital x-ray system upgrade kit, including software and digital radiography panels. The customer selects one of the following digital x-ray receptor panels: AcuityDRe 1417w, AcuityDRe 1717w, AcuityDRe 1717t. The "w" indicates wireless wi-fi while the "t" indicates tethered. The indications for use remains unchanged: Intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography. So the only difference between this submission and the predicate submission is the generator/tubestand combination. Each system consists of the following items: Customer supplies: Diagnostic x-ray generator (HF) Class I Code IZO. + Tubehead: Class I Code ITY + Tube Mount: Class I Code IYB + Attached Collimator, Manual (IZX) Class II 510(k) Exempt We supply: Digital X-Ray Receptor Panel 892.1680 Class II Code MQB. Digital X-ray Software 892.2050 Class II Code LLZ. The software offered for sale with this system has received previous 510(k) clearance in K201058.

The provided text describes the 510(k) premarket notification for the AcuityDRe device, which is an upgrade kit for digital x-ray systems. It primarily focuses on demonstrating substantial equivalence to a predicate device (Accuvue+, K201058), rather than detailing a clinical study with specific acceptance criteria and performance against those criteria in a typical sense of an AI/ML medical device.

The "study" cited here is non-clinical testing, specifically clinical image evaluation performed by a Board Certified Radiologist, and bench testing for physical performance characteristics. It's not a comparative effectiveness study involving human readers with and without AI assistance, nor a standalone AI performance study.

Here's an attempt to extract and present the information based on the provided text, acknowledging that many requested fields regarding AI/ML device studies are not applicable or detailed in this 510(k) summary for an x-ray hardware upgrade kit.

Device: AcuityDRe (Digital X-ray System Upgrade Kit)

Description of Testing (Non-Clinical and Image Evaluation)

The primary goal of the testing was to demonstrate substantial equivalence of the AcuityDRe system (comprising new digital x-ray receptor panels, software, and generator compatibility) to a legally marketed predicate device (Accuvue+, K201058). This was achieved through:

• Bench testing for physical performance characteristics of the new digital x-ray panels.
• Clinical image evaluation by a Board Certified Radiologist to assess image quality.
• Confirmation of compliance with relevant medical device standards and FDA guidance documents.

1. Table of Acceptance Criteria and Reported Device Performance

Given that this 510(k) is for an X-ray system upgrade kit rather than an AI/ML algorithm, the "acceptance criteria" are not framed as typical AI performance metrics (e.g., sensitivity, specificity, AUC). Instead, the key "acceptance criteria" are based on demonstrating substantial equivalence to the predicate device in terms of:

• Same Indications for Use
• Similar Technological Characteristics (especially panel performance: DQE, MTF, and panel sizes)
• Compliance with safety and performance standards.
• "Excellent quality" of clinical images as assessed by an expert.

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

• Test Set Sample Size: Not explicitly stated numerically for the clinical image evaluation. The text only mentions "Clinical image evaluation was performed on the proposed new panels."
• Data Provenance: Not specified. It's likely retrospective, as it's an evaluation of images generated by the new panels. Country of origin not mentioned; presumed to be images from typical clinical settings where such panels would be used.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of Experts: One.
• Qualifications: A "Board Certified Radiologist." Further details like years of experience are not provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable/None, as only a single expert was used for the qualitative assessment of image quality.

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

• MRMC Study: No. This type of study (AI vs. human-assisted AI) was not performed as the device is an x-ray system upgrade, not an AI diagnostic algorithm.
• Effect Size of Human Reader Improvement: Not applicable.

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

• Standalone Study: No, as the device itself is a component of an x-ray system, not a standalone AI algorithm. The image quality assessment was of the images produced by the hardware, which would then be interpreted by humans.

7. Type of Ground Truth Used

• Ground Truth Type: Expert Consensus / Qualitative Expert Assessment. The "ground truth" for the image quality was the subjective assessment by the Board Certified Radiologist that the images were "of excellent quality." This is distinct from, for example, pathology-confirmed diagnoses or patient outcomes data.

8. Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This device is a hardware component (digital x-ray panels) and associated software, not an AI/ML algorithm that requires a "training set" in the context of machine learning model development. The software mentioned (AccuVue/AccuVue+) has received previous clearance for control of generators.

9. How the Ground Truth for the Training Set was Established

• Ground Truth Establishment for Training Set: Not applicable, as there is no "training set" in the context of an AI/ML algorithm for this device.

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