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The Digiscan FDX Family, a Mobile C-Arm X-Ray System, is intended to provide Fluoroscopic images of the patient during diagnostic, surgical procedures.

Clinical applications may include (but not limited to) orthopedic, Fertility studies (HSG), Gl procedures like endoscopy , neurology, urology, critical care and emergency room procedures.

Digiscan FDX C-Arm is indicated for visualization in real time and/or recording of surgical region of interest and anatomy, using X-Ray imaging technique.

Exclusion: Digiscan FDX Family is not recommended for Mammography.

Contraindications: Exposure of X-Ray should be avoided during pregnancy.

The Digiscan FDX Family (Digiscan FDX-V, Digiscan FDX-R and Digiscan FDX-S) are mobile X-Ray C-Arm fluoroscopic device used by radiation experts. Digiscan FDX family is a digital fluoroscopic imaging system with Solid State X-Ray Image Detetors (FPD) used in diagnostic. The device is designed in such a way that it can moved around and can be positioned for the required be anatomical/clinical/procedural position.

Digiscan FDX family composed of C-Arm, X-Ray generating equipment (X-Ray controller, high voltage generator, X-Ray tube), FPD, and workstation (Console computer and Monitor(s)). C-Arm unit with generator is capable of movements which are essential for patient positioning, like horizontal travel, orbital movement, vertical movement, wig-wag movement and C rotation. The X-Ray generator, X-Ray control system and collimator controls are housed in the C-Arm unit.

Synergy FDX-CR imaging software is a Digital Imaging System (DIS) designed for C-arm Fluoroscopic Mobile X-Ray System. Synergy FDX-CR imaging software provides useful functions to manage X-Ray images obtained from Digiscan FDX family FPD Fluoroscopic Mobile X-Ray System.

The provided text does not describe acceptance criteria for an AI/ML powered device, nor does it present a study proving the device meets particular acceptance criteria. The document is a 510(k) summary for the Allengers Medical Systems Limited's Digiscan FDX, a Mobile C-Arm X-Ray System. It focuses on demonstrating substantial equivalence to predicate and reference devices, rather than establishing direct performance against specific acceptance criteria for an AI component.

However, based on the provided text, I can infer some aspects of performance testing and equivalence argument:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a quantitative table format for performance. Instead, it compares detailed technical specifications of the "Digiscan FDX (Subject Device)" against a "Ziehm Vision RFD (Predicate Device)" and "OEC Elite" and "Cios Fusion" (Reference Devices). The "Discussion of Difference" column informally indicates whether the subject device's performance characteristics are considered "Same," "Similar," or have "No impact on safety or efficacy."

Here's a summary of the comparisons, which serve as the de-facto "performance evaluation" in this substantial equivalence submission:

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

• Test Set Sample Size: Not explicitly stated as a number of "cases" or "patients" in the context of a dataset for algorithmic evaluation.
• Data Provenance: Not applicable as this is a traditional X-ray system, not an AI/ML device that processes data. The "clinical tests" involved "independent views" on "acquired images" from procedures, but these are not described as a formal 'test set' for an AI algorithm.

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

• Number of Experts: "Independent views of Urologist, orthopedic, Gastroenterologists, Neurologist were obtained." The specific number of experts is not quantified (e.g., 2, 3, or more).
• Qualifications: "Urologist, orthopedic, Gastroenterologists, Neurologist." No details on their years of experience or board certification are provided. They are referred to as "radiation experts" within the device description.
• Ground Truth Establishment: Their views confirmed that "the acquired images were of adequate quality for the indicated use." This indicates a qualitative assessment of image quality, not a ground truth for a diagnostic outcome that an AI would predict.

4. Adjudication Method for the Test Set:

• Not applicable in the context of an AI/ML device with an adjudication process. The experts provided "independent views," suggesting no formal adjudication method (like 2+1 or 3+1 consensus) was explicitly described for discrepancy resolution.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size of Human Improvement with AI vs. without AI Assistance:

• No, an MRMC comparative effectiveness study involving AI assistance was not conducted or described. This submission is for a conventional X-ray system, not an AI-powered diagnostic tool. The document establishes substantial equivalence based on technical specifications and safety standards, and qualitative clinical assessment of image quality.

6. If a Standalone (Algorithm Only) Performance Study was done:

• No, a standalone (algorithm only) performance study was not done. The device is a fluoroscopic X-ray system, not an AI algorithm.

7. The Type of Ground Truth Used:

• The "ground truth" for the device's performance was the qualitative assessment of image adequacy by medical experts. The "acquired images were of adequate quality for the specific view and procedures identified in the IFU," as determined by Urologists, Orthopedists, Gastroenterologists, and Neurologists. This is not a pathology report, an outcome measure, or an expert consensus for a diagnostic AI task.

8. The Sample Size for the Training Set:

• Not applicable. This is not an AI/ML device, so there is no "training set."

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

• Not applicable. As there is no training set for an AI/ML component, no ground truth was established for it.

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