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Intended for use by a qualified/trained doctor or technician on adult subjects for taking diagnostic radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. Not for mammography.

The Digital Radiography System (MobileApex 60E, MobileApex 60F, MobileApex 60G, MobileApex 60H) is comprised of a High Voltage Generator with a maximum power output of 63kW, the Digital Radiography System (MobileApex 60E, MobileApex 60F, MobileApex 60C, MobileApex 60H) can meet different exposure needs for varying positions and body mass. With the Digital Radiography System's (MobileApex 60E, MobileApex 60F, MobileApex 60G, MobileApex 60H) state-of-the-art design and powerful 63kW generator coupled with the Digital Detector (CareView 1500Cwe, CareView 750Cw, Mars1717X, Mars1417X, Luna 1012X) and Digital Radiography Operator Console, users can obtain clear images quickly and easily.

Based on the provided text, the device in question is a Digital Radiography System (MobileApex 60E, MobileApex 60F, MobileApex 60G, MobileApex 60H). The acceptance criteria and the study proving the device meets these criteria are focused on demonstrating substantial equivalence to a predicate device, the MX40 Mobile Digital X-ray System (K181874), rather than proving a specific, quantitative performance metric for a clinical application (like detection of a disease).

The documentation primarily discusses non-clinical performance tests related to electrical safety, electromagnetic compatibility, radiation protection, software validation, and usability. There is no mention of a clinical study to assess diagnostic performance (e.g., accuracy in detecting specific conditions).

Therefore, the following information is extracted and where information is not provided in the text, it is explicitly stated as "Not provided in the text."

Acceptance Criteria and Device Performance for Digital Radiography System

The acceptance criteria for this device are based on demonstrating substantial equivalence to a predicate device in terms of intended use, technological characteristics, and safety and effectiveness, primarily through compliance with recognized standards.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample size for test set: Not applicable/Not provided. The summary describes non-clinical engineering and performance testing against standards, not a clinical test set with patient data for diagnostic performance.
• Data provenance: Not applicable/Not provided. The testing described is type testing and verification against standards, not clinical data collection.

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

• Not applicable/Not provided. Ground truth in a diagnostic sense (e.g., for disease detection) was not established as part of this submission's described testing, which is focused on technical performance and safety.

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

• Not applicable/Not provided. No clinical test set requiring adjudication of ground truth is described.

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 study was not done. This device is a mobile X-ray system, not an AI-powered diagnostic software.

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

• Not applicable. This is a hardware device (X-ray system), not a standalone algorithm.

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

• Not applicable for diagnostic performance. For the described non-clinical performance tests (e.g., electrical safety, radiation output), the "ground truth" is defined by the requirements and specifications of the relevant international and FDA performance standards (e.g., IEC 60601 series, 21 CFR 1020.30/31).

8. The sample size for the training set

• Not applicable/Not provided. This document describes a traditional medical device (X-ray system) and its non-clinical testing, not an AI/ML product that would require a training set.

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

• Not applicable/Not provided. (See point 8).

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Intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic radiographic exposures of the skull, spinal column, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. Not for mammography.

Aquila 320-D Series: Small, light, easy handling. High Performance, High Power 320 mA, Leading Technology. Capacitive discharge technology, wireless image capture and transmission technology, smartphone image access. Wireless Detector and Workstation on touchscreen notebook or tablet. High frequency generator with micro-processor controls: Power 35.2 KW (The Aquila 320-S comes without the digital panels and workstation)

The provided text is a 510(k) summary for the AQUILA 320 D / AQUILA 320 S mobile x-ray system. It details the device, its intended use, and its substantial equivalence to a legally marketed predicate device. However, it does not describe an AI-powered device or a study where an AI device meets acceptance criteria.

The document explicitly states regarding clinical testing:
"Clinical testing was not required to establish substantial equivalence because all digital x-ray receptor panels have had previous FDA clearance."

Therefore, I cannot provide the requested information about acceptance criteria and a study proving an AI device meets them based on the provided text, as this document pertains to a traditional medical imaging device (mobile x-ray system) and not an AI-powered one, and no clinical study was conducted or described.

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The PowerDR™ Digital X-ray Imaging System is indicated for use as an X-ray imaging modality to acquire, process, display, quality assure and store digital medical X-ray images.

The PowerDR™ Digital X-ray Imaging System is indicated for use in general radiographic and fluoroscopic examinations of any anatomy for adult, pediatric, and neonatal patients. It is not indicated for use in mammography.

The PowerDR™ Console Application is a digital medical X-ray imaging system consisting of an X-Ray detector, computer hardware and the PowerDR™ software. The User supplies the X-Ray generator. The PowerDR™ Console Application is intended to enable a procedure of medical image acquisition, processing, display, quality assurance, and storage. The software interfaces to an X-Ray detector from variety of vendors to acquire raw pixel data. Its image-processing algorithms transform raw pixel data into diagnostic quality images and image sequences to aid the medical professional in diagnosis. For temporary storage, image data can be stored on the local computer. For long term storage, image data can be stored on a portable media device or a remote PACS (Picture Archive and Communication System) server. The PowerDR™ Digital X-ray Imaging System is intended for use in general radiographic and fluoroscopic examinations of any anatomy for adult, pediatric, and neonatal patients. It is not intended for use in mammography. The system can be sold with or without a computer, and with or without a compatible, previously cleared, digital receptor panel.

The provided text is a 510(k) Premarket Notification for the PowerDR™ Digital X-ray Imaging System. This type of submission focuses on demonstrating substantial equivalence to a previously legally marketed device (predicate device), rather than proving the device meets specific performance acceptance criteria through the kind of studies typically seen for novel AI/ML devices.

Therefore, the document does not contain the information requested regarding acceptance criteria and a study proving the device meets those criteria for AI/ML performance.

Specifically:

• No table of acceptance criteria and reported device performance is provided because this is a substantial equivalence submission, not a performance validation against defined metrics for an AI/ML component. The "performance" demonstrated is that the new device operates similarly to the predicate device in terms of image acquisition, processing, display, quality assurance, and storage.
• No sample size for a test set or data provenance is mentioned in the context of an AI/ML performance study. The "test set" here refers to the validation of the system's ability to acquire and process images, not to a diagnostic performance evaluation of an AI algorithm. The document states "image inspection, bench, and test laboratory results" were used, and "Each available digital receptor panel has undergone a rigorous verification and validation procedure."
• No number of experts or qualifications of experts used for ground truth establishment for a test set. This is not an AI/ML diagnostic study.
• No adjudication method is mentioned, as there is no diagnostic ground truth establishment process described for an AI/ML algorithm.
• No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done because there is no AI assistance component to evaluate.
• No standalone (algorithm only) performance study was done; the focus is on the integrated system's functionality.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc.) is not applicable in the context of an AI/ML performance study. The "ground truth" for this device relates to the technical specifications and image quality relative to the predicate device.
• No sample size for the training set is applicable; this is not an AI/ML algorithm that undergoes a training phase as typically understood.
• How the ground truth for the training set was established is not applicable for the same reason.

The core argument for the PowerDR™ system is that it is substantially equivalent to the predicate device (Nexus DRF Digital X-ray Imaging System, K130318) in terms of its intended use, technology, and safety and effectiveness. The evidence provided to support this is:

• Bench testing: "The results of image inspection, bench, and test laboratory results indicates that the new device is as safe and effective as the predicate devices."
• Use of previously cleared components: All compatible digital panels supported by PowerDR™ "have previously received FDA 510(k) clearances" and "undergone a rigorous verification and validation procedure."
• Compliance with FDA guidance documents: Specifically, guidance for software in medical devices, cybersecurity, and pediatric imaging information.
• Comparison chart: A detailed "Substantial Equivalence Chart" (Section 5) outlining similarities in identification, intended use, description, where used, image processing, image storage, image data source, configuration, primary digital panel support (multiple for proposed vs. one for predicate, with all proposed panels being previously cleared), system software, image data format, image presentation, application software, tracking X-ray dose, fluoro image processing, MultiRad image support, dose and processing auto optimization, quality assurance, DICOM 3.0 conformance, IHE Integration profile, power source, and computer platform.

Conclusion stated in the document: "After analyzing bench testing and risk analysis and compliance to the DICOM standard, it is the conclusion of Radiology Information Systems, Inc. that the PowerDR™ Digital X-ray Imaging System is as safe and effective as the predicate device, have few technological differences, and has the same indications for use, thus rendering it substantially equivalent to the predicate device."

In summary, this 510(k) submission does not describe an AI/ML device or a study validating AI/ML performance using acceptance criteria. Instead, it demonstrates substantial equivalence to a predicate device through bench testing and comparison of technical specifications.

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