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Allengers Wireless/ Wired X-Ray Flat Panel Detectors used with AWS (Acquisition Workstation Software) Synergy DR FDX/Synergy DR is used to acquire/Process/Display/Store/Export radiographic images of all body parts using Radiographic techniques. It is intended for use in general radiographic applications wherever a conventional film/screener CR system is used.

Allengers Wireless/Wired X-ray Flat Panel Detectors are not intended for mammography applications.

The Wireless/ Wired X-Ray Flat Panel Detectors are designed to be used in any environment that would typically use a radiographic cassette for examinations. Detectors can be placed in a wall bucky for upright exams, a table bucky for recumbent exams, or removed from the bucky for non-grid or free cassette exams. These medical devices have memory exposure mode, and extended image readout feature. Additionally, rounded-edge design for easy handling, image compression algorithm for faster image transfer, LED design for easy detector identification, extra protection against ingress of water. This Device is currently indicated for general projection radiographic applications and the scintillator material is using cesium iodide (CsI). The Wireless/ Wired X-Ray Flat Panel Detectors sensor can automatically collect x-ray from an x-ray source. It collects the x-ray and converts it into digital image and transfers it to Desktop computer / Laptop/ Tablet for image display. The x-ray generator (an integral part of a complete x-ray system), is not part of the submission. The sensor includes a flat panel for x-ray acquisition and digitization and a computer (including proprietary processing software) for processing, annotating and storing x-ray images, the personal computer is not part of this submission.

Wireless/ Wired X-Ray Flat Panel Detectors used with Accessory: "AWS (Acquisition Workstation Software) Synergy DR FDX/ Synergy DR", runs on a Windows based Desktop computer/ Laptop/ Tablet as a user interface for radiologist to perform a general radiography exam. The function includes:

1. User Login
2. Display Connectivity status of hardware devices like detector
3. Patient entry (Manual, Emergency and Worklist)
4. Exam entry
5. Image processing
6. Search patient Data
7. Print DICOM Image
8. Exit

This document describes the 510(k) clearance for Allengers Wireless/Wired X-Ray Flat Panel Detectors (K243734). The core of the submission revolves around demonstrating substantial equivalence to a predicate device (K223009) and several reference devices (K201528, K210988, K220510). The key modification in the subject device compared to the predicate is an increased scintillator thickness from 400µm to 600µm, which consequently impacts the Modulating Transfer Function (MTF) and Detective Quantum Efficiency (DQE) of the device.

Based on the provided text, the 510(k) relies on non-clinical performance data (bench testing and adherence to voluntary standards) to demonstrate substantial equivalence, rather than extensive clinical studies involving human subjects or AI-assisted human reading.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the comparison to the predicate device's performance, particularly for image quality metrics (MTF and DQE). The goal is to demonstrate that despite changes, the device maintains diagnostic image quality and does not raise new safety or effectiveness concerns.

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

The document explicitly states that the submission relies on "Non-clinical Performance Data" and "Bench testing". There is no mention of a clinical test set involving human subjects or patient imaging data with a specified sample size. The data provenance would be laboratory bench testing results. The country of origin of the data is not explicitly stated beyond the company being in India, but it's performance data, not patient data. The testing is described as functional testing to evaluate the impact of different scintillator thicknesses.

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

This information is not applicable as the clearance is based on non-clinical, bench testing data (physical performance characteristics like MTF and DQE) rather than clinical image interpretation or diagnostic performance that would require human expert ground truth.

4. Adjudication Method for the Test Set

Not applicable, as there is no mention of a human-read test set or ground truth adjudication process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No. The document does not mention an MRMC study or any study involving human readers, with or without AI assistance. The device is an X-ray detector, not an AI software.

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

Not applicable in the context of an AI algorithm, as this device is an X-ray detector and associated acquisition software. However, the "standalone" performance of the detector itself (MTF, DQE, sensitivity) was assessed through bench testing and measurements, which can be considered its "standalone" performance.

7. The Type of Ground Truth Used

The "ground truth" for the performance claims (MTF, DQE, sensitivity) is based on physical phantom measurements and engineering specifications obtained through controlled bench testing following recognized industry standards (e.g., IEC 62220-1-1). It is not based on expert consensus, pathology, or outcomes data from patient studies.

8. The Sample Size for the Training Set

Not applicable. This submission is for an X-ray flat panel detector, not an AI/ML model that would require a "training set" of data.

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

Not applicable. As stated above, this device does not involve an AI/ML model with a training set.

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Allengers Wireless / Wired X-Ray Flat Panel Detectors used with AWS (Acquisition Workstation Software) Synergy DR FDX/ Synergy DR is used to acquire/ Process/ Display/ Store/ Export radiographic images of all body parts using Radiographic techniques. It is intended for use in general radiographic applications wherever a conventional film/screen or CR system is used

Allengers Wireless/ Wired X-Ray Flat Panel Detector is not intended for mammography applications.

Wireless/ Wired X-Ray Flat Panel Detectors used with AWS (Acquisition Workstation Software) Synergy DR FDX/ Synergy DR is substantially equivalent product of its predicate device, Yushan X-Ray Flat Panel Detector with DROC, K201528, K210988, K220510. There are 8 models in this submission G4336RWC, G4336RWG, G4343RWC, G4343RWG, T4336RWC, T4336RWG are portable (wireless) and G4336RWC, G4336RWG, G4343RWC, G4343RWG, T4336RWC, T4336RWG, G4343RG, G4343RC (wired) Digital are non-portable detectors. The Wireless/ Wired X-Ray Flat Panel Detectors is designed to be used in any environment that would typically use a radiographic cassette for examinations. Detectors can be placed in a wall bucky for upright exams, a table bucky for recumbent exams, or removed from the bucky for nongrid or free cassette exams. These medical devices have memory exposure mode, and extended image readout feature. Additionally, rounded-edge design for easy handling, image compression algorithm for faster image transfer, LED design for easy detector identification, extra protection against ingress of water.

This Device is currently indicated for general projection radiographic applications and the scintillator material is using cesium iodide (Csl) or gadolinium oxy sulfide (GOS).

The Wireless/ Wired X-Ray Flat Panel Detectors sensor can automatically collect x-ray from an x-ray source. It collects the x-ray and converts it into digital image and transfers it to Desktop computer / Laptop/ Tablet for image display. The x-ray generator (an integral part of a complete x-ray system), is not part of the submission. The sensor includes a flat panel for x-ray acquisition and digitization and a computer (including proprietary processing software) for processing, annotating and storing x-ray images, the personal computer is not part of this submission.

Wireless/ Wired X-Ray Flat Panel Detectors used with AWS (Acquisition Workstation Software) Synergy DR FDX/ Synergy DR, runs on a Windows based Desktop computer/ Laptop/ Tablet as a user interface for radiologist to perform a general radiography exam. The function includes:

• 1. User Login
• 2. Display Connectivity status of hardware devices like detector
• 3. Patient entry (Manual, Emergency and Worklist)
• 4. Exam entry
• 5. Image processing
• 6. Search patient Data
• 7. Print DICOM Image
• 8. Exit

The provided text outlines the performance data for the "Wireless/Wired X-Ray Flat Panel Detectors used with AWS (Acquisition Workstation Software) Synergy DR FDX/ Synergy DR." However, it does not specifically present a table of acceptance criteria and reported device performance in a quantitative manner (e.g., sensitivity, specificity, accuracy). Instead, it primarily focuses on compliance with recognized standards and guidance documents, emphasizing non-clinical tests.

Here's an analysis of the information provided, addressing your specific points:

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

The document does not provide a quantitative table of acceptance criteria (e.g., specific metrics like DQE values, spatial resolution, or SNR targets) with corresponding reported device performance values. Instead, it states that the device "confirms to the voluntary standards" and that "the image quality evaluation confirmed that the image quality of the Wireless/ Wired X-Ray Flat Panel Detectors is substantially equivalent to that of the predicate device."

The acceptance criteria are implicitly tied to the successful demonstration of compliance with a long list of international and FDA-recognized consensus standards and guidance documents. The reported performance is that the device "Met all requirements" for each of these standards.

Implicit Acceptance Criteria and Reported Performance (based on document content):

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

The document primarily describes non-clinical performance data (bench testing). There is no mention of a specific "test set" with a sample size of patient data. The provenance of testing (e.g., country of origin, retrospective/prospective) for these non-clinical tests is not detailed, beyond being conducted by Allengers Medical Systems Limited and likely by accredited testing labs for compliance with international standards.

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)

This information is not applicable or not provided since no clinical study with a "test set" requiring expert ground truth establishment for diagnostic performance is described. The evaluation focuses on engineering and regulatory compliance.

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

This information is not applicable or not provided as there was no clinical test set requiring expert adjudication.

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 MRMC comparative effectiveness study was done or reported. The document explicitly states: "Clinical Performance Data: No clinical study has been performed. The substantial equivalence has been demonstrated by non-clinical studies." Furthermore, this device is an X-ray flat panel detector and associated acquisition software, not an AI-powered diagnostic algorithm designed to assist human readers.

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

This question is not directly applicable in the context of an X-ray detector. The device itself is a component for acquiring images. Its "standalone performance" is assessed through engineering metrics and compliance with imaging standards (e.g., DQE, MTF, noise characteristics), which are part of the non-clinical testing. It's not an "algorithm" in the sense of a standalone diagnostic AI.

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

For the non-clinical tests, the "ground truth" is typically defined by the technical specifications of the standards and test methodologies themselves. For example:

• For IEC 62220-1-1 (DQE), the ground truth is the physical properties of the detector under specific X-ray conditions, measured according to the standard's protocol.
• For biocompatibility (ISO 10993), the ground truth is the absence of adverse biological reactions as determined by standardized in vitro and in vivo tests.
• For safety (IEC 60601-1), the ground truth is meeting the safety limits and design requirements outlined in the standard.

There is no mention of clinical ground truth (expert consensus, pathology, outcomes data) as no clinical studies were performed.

8. The sample size for the training set

Not applicable. This device is a hardware component (X-ray detector) and its associated acquisition software. It is not described as involving a machine learning algorithm that requires a "training set" of data for diagnostic purposes. The software mentioned ([AWS] Synergy DR FDX/ Synergy DR) is for image acquisition, processing, display, and storage, not for AI-driven detection or diagnosis.

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

Not applicable for the reasons stated in point 8.

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The DigiX FDX radiographic systems are used in hospitals, clinics and medical practices. DigiX FDX enables radiographic exposure of the whole body including: Skull, chest, abdomen, and extremities and may be used on pediatric, adult and bariatric patients. It can also be used for intravenous, small interventions (like biopsy, punctures, etc.) and emergency (trauma critical ill) applications.

Exposure may be taken with the Patient's sitting, standing, or in the prone/supine position.

The DigiX FDX System is not meant for mammography.

The DigiX FDX uses an integrated or portable or fixed or wi-fi digital detector for generating diagnostic images by converting X-Ray into electronics signals. DigiX FDX is also designed to be used with conventional film/screen or Computed Radiography (CR) Cassettes.

The DigiX FDX system is a diagnostic X-Ray system intended for general purpose radiographic imaging of the human body. It is not intended for mammographic imaging.

The DigiX FDX system is comprised of a combination of devices that include a ceiling mounted X-Ray tube suspension, vertical Bucky stand, fixed or mobile patient Bucky table, X-Ray generator, X-Ray tube, beam limiting device, and a solid-state image receptor.

The DigiX FDX systems are not intended to be operated with any other cleared devices, or to be integrated with other software/hardware devices via direct or indirect connections.

The provided FDA 510(k) summary for the DigiX FDX device details its substantial equivalence to predicate devices, particularly focusing on technical and functional similarities rather than a standalone performance study with specific acceptance criteria related to diagnostic accuracy. Therefore, the response will focus on the details available within the document regarding non-clinical testing and conformity to standards, as a direct "acceptance criteria vs. reported performance" table for diagnostic metrics is not present.

Here's a breakdown of the requested information based on the provided text:

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

The document does not present specific acceptance criteria in terms of diagnostic performance metrics (e.g., sensitivity, specificity for a particular condition) and a corresponding reported device performance with numerical results. Instead, the acceptance criteria are framed in terms of:

• Conformity to recognized standards: The device must comply with various IEC and ISO standards related to electrical safety, electromagnetic compatibility, radiation protection, software life cycle, usability, and risk management.
• Meeting design requirements: Non-clinical verification testing ensures the device's functions and image performance meet its design specifications.
• Substantial equivalence: The primary "acceptance criterion" for this 510(k) submission is to demonstrate substantial equivalence to the predicate devices (DigiX FDX K192541 and Siemens Ysio K081722) without raising new questions of safety or effectiveness.

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

The document explicitly states: "No clinical data is necessary to evaluate safety or effectiveness for purposes of determining substantial equivalence of the proposed device. Hence, clinical testing is not applicable due to the fact that no new clinical applications were introduced to the system. Bench testing was performed to assess the device safety and effectiveness."

Therefore, there was no "test set" in the sense of patient images or clinical data used for this 510(k) submission to prove diagnostic performance. The testing was non-clinical (bench testing, verification, and validation) and focused on engineering criteria, safety, and functionality.

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

Not applicable, as no clinical test set requiring expert ground truth was used for this submission.

4. Adjudication method for the test set

Not applicable, as no clinical test set requiring adjudication was used for this submission.

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. The DigiX FDX is an X-ray imaging system, not an AI-assisted diagnostic device for image interpretation. Its changes primarily involve hardware components (new detectors) and software GUI updates.

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

Not applicable. This device is an X-ray system, not a standalone diagnostic algorithm.

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

Not applicable in the context of diagnostic ground truth. For the non-clinical testing performed, the "ground truth" would be established by engineering specifications, recognized standards, and established testing methodologies for hardware and software functionality and safety.

8. The sample size for the training set

Not applicable. This is not an AI/machine learning device that requires a training set for diagnostic classification.

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

Not applicable.

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The Cardiovascular Angiography System is digital system with high frequency X-Ray generator for application in cardiovascular procedures. This system used in catheterization labs uses high power x-ray pulses and digital imaging system to visualize the vascular structures of human body.

Clinical applications may include (but not limited to) interventional cardiovascular procedures, Neurovascular procedures, pacemaker implantable and high end investigations.

This system should be handled by persons who have been briefed in its professional handling and who have familiarized themselves with the product by means of instructions for use. Intended use also means following the user manual and observing the conditions for inspection and maintenance.

Exclusion: This system is not recommended for Mammography.

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

The Cardiovascular Angiography system is designed to perform fluoroscopic & digital radiographic studies and are used in interventional examinations. This system covers the complete range of angiographic applications, cardiac angiography, neuroangiography, general angiography, surgery and surgical angiography, multipurpose angiography, rotational angiography and digital radiographic/ fluoroscopic procedures. The following components are configured to create the Cardiovascular Angiography system:

• 1. Floor mounted C-arm (Altima FDX Adv) or Ceiling Mounted C-Arm (Proxima FDX) or Mobile C-Arm (Digiscan FDX-VXXYY), X-ray tube assembly and Solid State X-Ray image detector
• 2. Patient Table
• 3. Ceiling suspended display(s)
• 4. Footswitch for releasing radiation
• 5. Control console for controlling the stand, patient table, collimator and imaging system.

The provided FDA 510(k) summary for the "Cardiovascular Angiography System" focuses on demonstrating substantial equivalence to predicate and reference devices, rather than establishing acceptance criteria and providing detailed study results in the manner typically seen for novel AI/ML devices.

This submission is for an X-ray imaging system, not an AI/ML powered device, so the typical AI/ML-specific study components like "number of experts used to establish ground truth" or "MRMC comparative effectiveness study" are not applicable in this context. The evaluation here is based on technical specifications, non-clinical performance testing, and adherence to recognized industry standards.

Here's an analysis based on the provided document, framed within your request:

Acceptance Criteria and Device Performance (for an X-ray system)

The acceptance criteria are implicitly defined by the demonstration of substantial equivalence to legally marketed predicate devices and compliance with established performance standards for X-ray systems. The "reported device performance" is primarily presented as meeting or being comparable to these standards and the predicate device's specifications.

Table of Acceptance Criteria and Reported Device Performance

Since this is a traditional 510(k) for an X-ray system, the "acceptance criteria" are not framed as specific performance metrics of an AI algorithm (e.g., AUC, sensitivity/specificity targets). Instead, they are based on established safety and performance characteristics of such medical devices as outlined in relevant standards and predicate device comparisons. The document indicates that the device meets these, making it "substantially equivalent."

Study Details (for an X-ray system, not AI/ML)

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

   • This document describes "non-clinical performance testing" and "bench testing." It does not specify a "test set" in the context of patient data or images as would be seen for AI/ML validation.
   • The "testing" refers to technical evaluations of the system's components and overall functionality against engineering specifications and industry standards.
   • There is no mention of patient data (retrospective or prospective) for performance evaluation. The data provenance (country of origin) is not applicable or stated for this type of technical performance testing.

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

   • Not applicable. Ground truth for an AI/ML algorithm (e.g., disease presence) is not being established here. The "truth" is whether the system performs according to its design specifications and relevant safety/performance standards. This is determined through engineering and compliance testing.

3. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set:

   • Not applicable. Adjudication methods are relevant for human reader studies, particularly in AI/ML performance evaluation where ground truth might be derived from expert consensus.

4. 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 an X-ray imaging device, not an AI-assisted diagnostic tool. No human reader studies are described.

5. If a Standalone (i.e. algorithm only without human-in-the loop performance) was Done:

   • Not applicable, as this is not an AI/ML algorithm.

6. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.):

   • The "ground truth" here is the adherence to established engineering specifications, safety standards (e.g., IEC 60601 series), and the functional equivalence to predicate devices, verified through non-clinical bench testing. There is no biological or clinical ground truth like pathology or outcomes data used to establish device performance in this type of submission.

7. The Sample Size for the Training Set:

   • Not applicable. There is no mention of a "training set" as this is not an AI/ML device.

8. How the Ground Truth for the Training Set Was Established:

   • Not applicable.

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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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The DigiX FDX radiographic systems are used in hospitals, clinics and medical practices. DigiX FDX enables radiographic exposure of the whole body including: Skull, chest, abdomen, and extremities and may be used on pediatric, adult and bariatic patients. It can also be used for intravenous, small interventions (like biopsy, punctures, etc.) and emergency (trauma critical ill) applications. Exposure may be taken with the Patient's sitting, standing, or in the prone/ supine position.

The DigiX FDX System is not meant for mammography.

The DigiX FDX uses an integrated or portable or fixed or wi-fi digital detector for generating diagnostic images by converting X-Ray into electronics signals. DigiX FDX is also designed to be used with conventional film/screen or Computed Radiography (CR) Cassettes.

The DigiX FDX system is a diagnostic X-Ray system intended for general purpose radiographic imaging of the human body. It is not intended for mammographic imaqing.

The DigiX FDX system is comprised of a combination of devices that include a ceiling mounted X-Ray tube suspension, vertical Bucky stand, fixed or mobile patient Bucky table, X-Ray generator, X-Ray tube, beam limiting device, and a solid-state image receptor.

The DigiX FDX systems are not intended to be operated with any other cleared devices, or to be integrated with other software/hardware devices via direct or indirect connections.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document focuses on demonstrating substantial equivalence to predicate devices rather than defining specific quantitative performance metrics as "acceptance criteria" for a new AI/software feature in the way clinical performance studies usually do. Instead, the "acceptance criteria" are implied to be that the modifications do not negatively impact safety or effectiveness, and that the device performs comparably to the predicate devices and meets relevant safety standards.

For the new features explicitly mentioned (Automatic Stitching and Dual Energy Subtraction), the document states they add functionality without affecting patient safety or effectiveness. For the other components, the criteria are often "Same" or "Similar functionality with same imaging results" or "doesn't affect the safety or effectiveness."

To represent this in a table, we'll extract performance comparisons from the "Functional and Specification Differences" table (Table 4) and the "Justification for Differences" (Table 5).

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

The document states:

• "Non-clinical testing included verification and validation testing, image evaluation, testing, and safety testing."
• "Performance testing included functional testing of all motions of the system(s) with respect to the design specifications. Image performance testing was conducted and results included in the submission."
• "Clinical testing is not applicable due to the fact that no new clinical applications were introduced to the system."

The document does NOT specify a sample size for any test set (clinical or otherwise) in terms of patient data or images used for validation of the radiographic system itself, nor does it mention data provenance (country of origin, retrospective/prospective). It primarily relies on hardware and software equivalence, and compliance with industry standards.

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

Not applicable. The document explicitly states "Clinical testing is not applicable." Therefore, there was no clinical study conducted that would necessitate expert readers to establish ground truth for a test set. The evaluation focuses on technical performance and equivalence to predicate devices.

4. Adjudication Method for the Test Set

Not applicable. As no clinical test set requiring expert adjudication was performed, no adjudication method is mentioned.

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

No MRMC comparative effectiveness study was done. The document states "Clinical testing is not applicable due to the fact that no new clinical applications were introduced to the system." Therefore, there is no information on how much human readers improve with or without AI assistance. The new software features (Automatic Stitching, Dual Energy Subtraction) are presented as additional functionalities that don't affect safety or effectiveness, not as AI-assisted diagnostic tools requiring a comparative effectiveness study.

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

Not applicable. The device is a radiographic system, not an AI algorithm intended for standalone diagnostic performance. While it includes new software features (Automatic Stitching, Dual Energy Subtraction), these are integrated functionalities of the imaging system and not described as standalone diagnostic algorithms requiring independent performance evaluation without human interaction.

7. Type of Ground Truth Used

The "ground truth" for the evaluation is primarily based on:

• Compliance with technical specifications and design requirements: Functional testing, image performance testing.
• Adherence to safety and performance standards: IEC 60601 series, EN ISO 14971, IEC 62366-1, IEC 62304, 21 CFR 1020.30, 21 CFR 1020.31.
• Substantial equivalence to predicate devices: Demonstration that the new device has the same intended use, fundamental technological characteristics, and that any differences do not raise new questions of safety or effectiveness.
• Software documentation assessment: For the software components, including the new features, documentation was provided for a "Moderate Level of Concern" software as per FDA guidance.

No clinical ground truth (e.g., expert consensus, pathology, outcomes data) was used or required given the nature of this submission.

8. Sample Size for the Training Set

Not applicable. The document describes an X-ray imaging system, not an AI/ML device that requires a training set of data. The new software features (Automatic Stitching, Dual Energy Subtraction) are described as functionalities, not adaptive algorithms that learn from data.

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

Not applicable. As there was no training set, there was no ground truth to establish for it.

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The DigiX FDX radiographic systems are used in hospitals, clinics and medical practices. DigiX FDX enables radiographic exposure of the whole body including: Skull, chest, abdomen, and extremities and may be used on pediatric, adult and bariatric patients. It can also be used for intravenous, small interventions (like biopsy, punctures, etc.) and emergency (trauma critical ill) applications. Exposure may be taken with the Patient's sitting, standing, or in the prone position. The DigiX FDX System is not meant for mammography. The DigiX FDX uses an integrated or portable or fixed or wi-fi digital detector for generating diagnostic images by converting X-Ray into electronics signals. DigiX FDX is also designed to be used with conventional film/screen or Computed Radiography (CR) Cassettes.

The DigiX FDX system is a diagnostic x-ray system intended for general purpose radiographic imaging of the human body. It is not intended for mammographic imaging. The DigiX FDX system is comprised of a combination of devices that include a ceiling mounted x-ray tube suspension, vertical Bucky stand, fixed or mobile patient Bucky table, x-ray generator, x-ray tube, beam limiting device, and a solid-state image receptor. The DigiX FDX systems are not intended to be operated with any other cleared devices, or to be integrated with other software/hardware devices via direct or indirect connections.

Here's a summary of the acceptance criteria and the study information for the Allengers Medical Systems Limited DigiX FDX device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily focuses on substantial equivalence to a predicate device (Siemens Ysio K081722) rather than defining explicit, quantitative acceptance criteria for clinical performance. The "acceptance criteria" can be inferred as meeting or being sufficiently similar to the predicate device in terms of functionality and safety, as well as complying with relevant standards.

• G1092: 0.6mm/1.2mm focal spot (Predicate: 0.6mm/1.0mm), 1 MHU (Predicate: 783 kHU), 108 mm target diameter (Predicate: 100 mm).
  G292: 0.6mm/1.2mm focal spot (Predicate: 0.6mm/1.0mm), 600 KHU (Same), 12° target angle (Predicate: 16°), 102 mm target diameter (Predicate: 100 mm). | Similar, providing essentially same imaging resolution and higher loading for some G1092 models. Different target angle for G292 provides full coverage at 40" SID. Differences in heat units and target diameter also noted as "similar" or providing higher loading/instantaneous focal spot loading. |
  | Beam Limiting Device: Construction, compliance with CFR 21 1020.31, automatic feature. | Matches predicate. | Same. |
  | Solid State X-ray Image Detectors: Panel type, active area, pixel pitch, pixel matrix, input scintillator, limiting resolution. | Detectors (e.g., P-E XRPAD 4343F vs. Trixell Pixium 4600):
• Active area: 432x432mm (Predicate: 429x429mm).
• Pixel pitch: 100 µm (Predicate: 143 µm).
• Pixel matrix: 4318x4320 (Predicate: 3001x3001).
• Limiting resolution: 5 lp/mm (Predicate: 3.57 lp/mm).
  Similar differences for other detector models listed. | Essentially the same imaging area. Provides higher resolution for pixel pitch, pixel matrix, and limiting resolution in most cases, which is considered an improvement and not negatively affecting safety or effectiveness. |
  | Viewing Monitors: Size, resolution. | Matches predicate. | Same. |
  | Software Features: DICOM 3.0 compatibility, operating system, user interaction, multi-user, image import/export, acquisition device, image interferences, organization, search, storage, database, viewing, measurement, annotation, operations, security, generator control. | Matches predicate for all listed software features. Also uses previously cleared image processing software. | Same. |
  | Safety Standards Compliance: | Complies with 21 CFR 1020.30, 21 CFR 1020.31, IEC 60601-1, EN 60601-1-2, IEC 60601-1-3, IEC 60601-2-54, EN ISO 14971, EN 62366, EN 62304. | All applicable standards met. |
  | Functional Performance: All system motions, image performance. | All functions met design requirements. Image performance criteria satisfactorily met (details in Section 18 of the original submission, not provided in this extract). | Confirmed. |
  | Software/Firmware Functionality: All functions between DROC software and IntegraX firmware. | All functions passed testing criteria. | Confirmed. |

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

The document explicitly states: "It was determined that clinical evaluation was not required as all imaging devices have been previously cleared by the FDA."
Therefore, there was no clinical test set used for this specific 510(k) submission. The evaluation was based on non-clinical testing and substantial equivalence to previously cleared devices.

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

Not applicable, as no clinical test set requiring expert ground truth was used.

4. Adjudication Method for the Test Set:

Not applicable, as no clinical test set requiring adjudication 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. The device is a diagnostic X-ray system, not an AI-powered diagnostic tool, and no MRMC study was conducted.

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

Not applicable. The device is a diagnostic X-ray system, not an algorithm, and its performance is evaluated as a system used by a human operator.

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

Not applicable for clinical ground truth, as no clinical evaluation was performed. For non-clinical performance (image quality, functional testing, safety), the "ground truth" was compliance with design specifications and relevant international standards.

8. The sample size for the training set:

The document does not describe the use of machine learning or AI models that would require a "training set" in the traditional sense. The device is a traditional X-ray system. The component parts, such as solid-state detectors and image processing software, are stated to have been "previously cleared by the FDA" or "tested and evaluated per Guidance for the Submission of 510(k)s for Solid State X-ray Imaging Devices." Therefore, any implicit training (e.g., for image processing algorithms) would have occurred as part of the development and clearance of those component devices, but details are not provided here for the system submission.

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

Not applicable, as no training set for an AI/ML model is described for this device.

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