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The CombiDiagnost R90 is a multi-functional general R/F system. It is suitable for all routine radiography and fluoroscopy exams, including specialist areas like angiography or pediatric work, excluding mammography.

The CombiDiagnost R90 is a multi-functional general Radiography/Fluoroscopy (R/F) system. It is suitable for all routine radiography and fluoroscopy exams, including specialist areas like angiography or pediatric work, excluding mammography.

The CombiDiagnost R90 is a remote-controlled fluoroscopy system in combination with high-end digital radiography. The system is suitable for routine X-ray examinations and special examinations on patients in standing, seated or laying positions. The CombiDiagnost R90 retrieves images by means of a Cesum Iodide flat panel detector.

Philips fluoroscopy systems consist of the following components (standard configuration):

• Basic unit (also called "geometry" or "table unit")
• Workstation Eleva Workspot with integrated generator control, hand switch, keyboard, mouse, touch screen and PC
• Equipped with a dual screen-monitor as standard
• Spot film device (digital camera or flat panel detector)
• X-ray Generator Velara
• X-ray tube assembly mounted in above table mode to be remote controlled
• Receptor: Flat panel detector

Optional components:

• Skyplate wireless portable detectors small and large
• Ceiling Suspension (CSM3)
• Vertical Wall stand (VS2)
• Ceiling Suspension for monitors
• Monitor trolley
• Remote control for RF viewer
• Accessories for "Stitching on the Table"

The CombiDiagnost R90 uses the same workflow from the currently marketed and predicate device, CombiDiagnost R90 (K163210) with only the following modifications:

• additional optional components (like the reference monitor, remote control),
• Eleva Workspot updated to incorporate new imaging features mainly from the previously approved reference device, DigitalDiagnost C90 (K182973) along with functional clusters like Digital Subtraction Imaging and stitching on the table
• updates to improve usability and serviceability.

The Eleva software of the proposed CombiDiagnost R90 is based on a workstation i.e., Eleva Workspot (computer, keyboard, display, and mouse) that is used by an operator to preset examination data and to generate, process and handle digital x-ray images. The Eleva Software system is decomposed into software components. These components are clustered in three component collections like the image handling focused Back-end (BE), the acquisition focused Front-end (FE) and Image Processing (IP). The Eleva software is intended to acquire, process, store, display and export digital fluoroscopy and radiographic images.

The proposed CombiDiagnost R90 is same as the predicate device (K203087) with some modifications as described.

The proposed device complies to 'Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices, dated September 1, 2016'. The solid-state imaging components including the detector in the proposed device have the same physical, functional, and operational characteristics as the predicate device (K203087). Also, other image chain components like X-ray tube and generator, which are used for exposure characteristics and clinical performance evaluation remains the same. Hence all the features and characteristics potentially influencing image quality of the proposed are in accordance with FDA guidance document. Additionally, image quality testing has been performed on the proposed device for the changes that are affecting the image quality.

The provided text is a 510(k) Summary for the Philips Medical Systems DMC GmbH CombiDiagnost R90, which is an X-ray system. This document focuses on demonstrating that the proposed device is substantially equivalent to a previously cleared predicate device, rather than proving a new medical diagnosis or treatment effectiveness. Therefore, the traditional acceptance criteria and study designs typically associated with AI/ML diagnostic devices (e.g., sensitivity, specificity, clinical accuracy, MRMC studies) are not directly applicable or reported in this type of submission.

Instead, the acceptance criteria and studies here are aimed at demonstrating that the modified device maintains the safety and effectiveness of the predicate device and complies with relevant standards and regulations.

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

1. Table of Acceptance Criteria and Reported Device Performance

Image Quality Details (from document):

Modulation Transfer Function (MTF) (according to IEC 62220-1-3 standard)

Detective Quantum Efficiency (DQE) (according to IEC 62220-1-3 standard) at 2 µGy

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

The document does not specify a "test set" in the context of clinical images or patient data for evaluating a diagnostic algorithm. This submission is for an X-ray imaging system, not an AI diagnostic algorithm. The "tests" mentioned are non-clinical engineering and conformity tests.

• Test Set Sample Size: Not applicable/not specified in the context of clinical images. The testing refers to verification and validation of the system's hardware and software components.
• Data Provenance: Not applicable in the context of clinical images or patient data. The tests are focused on the device's technical specifications and compliance with standards.

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

Not applicable. This is not a study requiring expert-established ground truth for diagnostic accuracy, as it's a submission for an imaging device, not an AI diagnostic tool. The "ground truth" for the engineering tests would be the established technical standards and specifications.

4. Adjudication Method for the Test Set

Not applicable. No clinical image test sets requiring adjudication are mentioned. The testing involves compliance with standards and internal system verification.

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

No. This type of study is not mentioned as it is not relevant for demonstrating substantial equivalence of an X-ray imaging system through non-clinical performance testing.

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

Not applicable. The CombiDiagnost R90 is an X-ray imaging system, not a standalone AI algorithm. While it contains "Eleva software" with imaging features, the submission focuses on the system's overall safety and performance, not a new AI algorithm's standalone diagnostic capability.

7. The Type of Ground Truth Used

The "ground truth" for the evaluations performed in this submission are:

• Engineering Specifications: The defined technical parameters and performance metrics for the device components (e.g., MTF, DQE values, electrical safety, EMC).
• Regulatory Standards: The requirements outlined in FDA recognized consensus standards (e.g., IEC 60601 series) and FDA guidance documents.
• Predicate Device Performance: The established performance and safety characteristics of the legally marketed predicate device (CombiDiagnost R90, K203087), against which the proposed device's performance is compared for substantial equivalence.

8. The Sample Size for the Training Set

Not applicable. This is an X-ray imaging device, not an AI algorithm that requires a training set for machine learning.

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

Not applicable, as there is no training set for an AI algorithm mentioned.

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The DigitalDiagnost C50 system is intended for use in generating radiographic images of human anatomy by qualified/ trained doctor or technician. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. This device is not intended for mammographic applications.

The DigitalDiagnost C50 Digital Radiography System (DigitalDiagnost C50) is a flexible digital radiography (DR) system that is designed to provide fast and smooth radiography examinations of sitting, standing or lying patients. The DigitalDiagnost C50 consist of the following components: ceiling suspension with X-ray assembly, wall stand with detector carrier, patient table with detector carrier and floating table top, high voltage generator, and an acquisition and reviewing workstation for post-processing, storage and viewing of images. Images may be transferred via a DICOM network for printing, storage and detailed review.

Here's a breakdown of the acceptance criteria and study information for the DigitalDiagnost C50, based on the provided text.

1. Table of Acceptance Criteria & Reported Device Performance:

Study Proving Acceptance Criteria:

The document states that a non-clinical verification/validation study was performed. The basis for substantial equivalence is that the DigitalDiagnost C50 is considered equivalent to the predicate DigitalDiagnost C50 (K163410) based on:

• Identical Indications for Use.
• Fundamental scientific technology (including largely identical components or components identical to other cleared reference devices).
• Non-clinical performance testing (demonstrating compliance with standards).
• Safety and effectiveness (as supported by the non-clinical testing).

The non-clinical verification and validation tests demonstrated that the DigitalDiagnost C50:

• Complies with the aforementioned international and FDA-recognized consensus standards and FDA guidance documents.
• Meets the acceptance criteria and is adequate for its intended use.

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

The document explicitly states: "The DigitalDiagnost C50 does not require clinical study..." and "Summary of Clinical Data: Based on the information provided above, the DigitalDiagnost C50 is considered substantially equivalent to the currently marketed and predicate DigitalDiagnost C50 (K163410, January 4, 2017) in terms of fundamental scientific technology."

Therefore, there appears to be no clinical test set, sample size, or specific data provenance for a clinical study as part of this 510(k) submission. The evaluation relies on non-clinical performance data and equivalence to predicate/reference devices.

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

Since no clinical study with a test set was performed, this information is not applicable and not provided in the document.

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

Since no clinical study with a test set was performed, this information is not applicable and not provided in the document.

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 mentioned. The device described, DigitalDiagnost C50, is an X-ray system, not an AI-assisted diagnostic device. Therefore, this information is not applicable and not provided in the document.

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

The DigitalDiagnost C50 is an X-ray imaging system. There is no mention of a standalone algorithm-only performance study, as it's not a standalone diagnostic algorithm. Therefore, this information is not applicable and not provided in the document.

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

For the non-clinical verification and validation tests, the "acceptance criteria" were derived from international and FDA-recognized consensus standards and FDA guidance documents (listed on pages 11-12). These standards themselves implicitly define the "ground truth" or acceptable performance ranges for device characteristics, image quality, electrical safety, radiation protection, software lifecycle, usability, and risk management. No other forms of expert consensus, pathology, or outcomes data are mentioned for establishing ground truth for the device's technical specifications.

8. The sample size for the training set:

Since no clinical study or AI algorithm training is described for this device, this information is not applicable and not provided in the document.

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

Since no clinical study or AI algorithm training is described for this device, this information is not applicable and not provided in the document.

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The DuraDiagnost is intended for use in generating radiographic images of human anatomy by qualified/trained doctor or technician. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. This device is not intended for mammographic applications.

The DuraDiagnost is a flexible digital radiography (DR) system that is designed to provide fast and smooth radiography examinations of sitting, standing or lying patients.
The DuraDiagnost consist of the following components: Tube column with X-ray assembly, wall stand with detector carrier, patient table with detector carrier and floating table top, high voltage generator, and acquisition and reviewing workstation for post-processing, storage and viewing of images. Images may be transferred via a DICOM network for printing, storage and detailed review.

The provided text describes a 510(k) premarket notification for the Philips DuraDiagnost, an X-ray system. The submission focuses on demonstrating substantial equivalence to a predicate device (DuraDiagnost K141381), rather than proving the performance of a novel AI algorithm. Therefore, many of the requested details, such as acceptance criteria for AI performance metrics, sample sizes for test sets, expert adjudication methods for AI ground truth, MRMC studies, standalone AI performance, and AI training set details, are not applicable to this submission.

The document primarily evaluates the DuraDiagnost against safety and effectiveness standards applicable to X-ray systems and its equivalence to a previous version of the device.

Here's the information that can be extracted, and an explanation of why other requested information is not present:

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

The document does not provide specific quantitative acceptance criteria for image quality or clinical performance metrics in a readily extractable table format for human or AI performance. Instead, it states that the device meets acceptance criteria by:

• Complying with international and FDA-recognized consensus standards.
• Demonstrating substantial equivalence to its predicate device in terms of design, technology, indications for use, safety, and effectiveness.

The "performance" is reported as compliance with the following standards and internal tests:

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

This information is not provided in the document as it is a 510(k) submission based on comparison to a predicate device and compliance with general safety and performance standards for X-ray systems, not specific clinical performance studies with AI. The non-clinical verification/validation tests performed would typically use test phantoms or specific equipment testing, not a "test set" of patient data in the way an AI algorithm would.

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 is not applicable as the submission is not for an AI algorithm requiring clinical ground truth established by experts.

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

This is not applicable.

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

This is not applicable. The device is an X-ray system, not an AI-powered diagnostic aide. The document explicitly states: "The DuraDiagnost does not require clinical study since substantial equivalence to the primary currently marketed and predicate device was demonstrated..." (Page 15).

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

This is not applicable. The device includes image processing software (UNIQUE 2 and SkyFlow), listed as comparable or updated versions of software present in predicate/reference devices, but it is not presented as a standalone AI diagnostic algorithm.

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

This is not applicable in the context of an AI study. The "ground truth" for this submission refers to the established safety standards and the performance characteristics of the predicate device, against which the new device (DuraDiagnost) is compared. Compliance with engineering standards and performance specifications (e.g., tube voltage, focal spot size, image matrix, pixel size) serves as the "ground truth" for the device's equivalent performance to what is already on the market.

8. The sample size for the training set

This is not applicable. The document does not describe the development or training of a new AI algorithm.

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

This is not applicable.

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The KDR™ AU-DDR System Advanced U-Arm with Dynamic Digital Radiography and KDR™ AU System Advanced U-Arm with Static Digital Radiography is indicated for use by qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic static and serial 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 proposed System is a digital radiography diagnostic system that has the capability of obtaining two modes (static mode and dynamic modes) of radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts. Images may be obtained with the patient sitting, standing, or lying in the prone or supine position. It is not intended for mammographic use. The system is configurable in two options. Both are exactly the same with the exception of the option to select one of two flat panel detectors. One configuration, referred to as KDR™ AU-DDR System Advanced U-Arm with Dynamic Digital Radiography contains a HD/FNB flat panel and the other configuration, referred to as KDR™ AU System Advanced U-Arm with Static Digital Radiography a HQ/KDR panel. The technological feature of each flat panel detector is described below.

The proposed system is a compact, floor and wall mounted radiographic system with proprietary ULTRA software and DICOM 3 connectivity.

The system consists of a combination of several components. The System's hardware consists of the 3 kev components:

1. A floor and wall-mounted Positioner (also referred to as a stand)
2. A generator
3. An off-the-shelf computer with proprietary software (also referred to as an acquisition workstation)

The positioner has a swivel arm that has several rotating and linear movements, and movement controls including an information screen. Mounted on the positioner are:
a) A collimator,
b) An X-ray tube
c) An Automatic Exposure Control (AEC)
d) A flat panel detector (There are 2 configurations available for the end user to select. The KDR™ AU-DDR System Advanced U-Arm with Dynamic Digital Radiography contains a HD/FNB flat panel detector capable of obtaining both static and dynamic images and the KDR™ AU System Advanced U-Arm with Static Digital Radiography, which contains the HQ/KDR flat panel detector capable of obtaining static images only.

Hardware accessories include:

1. A mobile patient table
2. Stitching stand
3. Weight bearing stand

Optional Hardware accessories include:

1. Motorized height adjustable table
2. 3 knob collimator
3. Dose area product meter
4. Advanced weight bearing stand

The proposed system has a proprietary ULTRA software as the central interface of the system. The software for the proposed system enables users to acquire static and dynamic images.

There are two modes within the software package of the proposed system, "static mode," which may be used to generate, a single frame of radiographic images captured at a single time and "dynamic mode" (or "Dynamic Digital Radiography," abbreviated "DDR,") which generates multiple frames in a single series, presenting the physician with a diagnostic view of dynamic density and anatomic motion without using fluoroscopy or cine. The number of images acquired with the proposed system are limited to 300 compared with flouroscopy or cine, which do not limit the number of images (Note: only the configuration with the HD/FNB flat panel detector is capable of obtaining both static and dynamic images. The other configuration may only obtain static images).

The system is also capable of quickly assuming a preprogrammed position when a new exam is selected, saving time when positioning the equipment. This is referred to as "auto positioning," and made possible by the positioner and image processing software working together.

The provided document is a 510(k) summary for the Konica Minolta Healthcare Americas KDR™ AU-DDR System Advanced U-Arm with Dynamic Digital Radiography and KDR™ AU System Advanced U-Arm with Static Digital Radiography.

This document describes the device and its substantial equivalence to predicate devices, focusing on regulatory compliance and technical specifications rather than specific clinical performance data for AI/software components. The primary performance data discussed refers to compliance with safety and performance standards for X-ray systems, not an AI-driven diagnostic or assistive feature.

Therefore, many of the requested points regarding acceptance criteria and study details (like sample size for test/training sets, expert ground truth, adjudication methods, MRMC studies, or standalone performance for an AI component) are not present in this document. The document primarily addresses the safety and effectiveness of the X-ray system hardware and its software for image acquisition, not an AI-based diagnostic tool.

Based on the provided text, here's what can be extracted:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a table of acceptance criteria with specific performance metrics for an AI component. Instead, it refers to compliance with various electrical, safety, and imaging standards for the overall X-ray system.

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

• Not explicitly stated in the provided document. The document discusses compliance with technical standards for an X-ray system, not the performance of an AI algorithm on a specific medical image dataset.

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

• Not applicable / not stated. This document focuses on the technical and safety performance of an X-ray imaging system, not on a machine learning model requiring expert-annotated ground truth for diagnostic accuracy.

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

• Not applicable / not stated. The context of this document does not involve diagnostic interpretations requiring 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:

• Not done / not stated. The device described is an X-ray acquisition system; it does not present itself as an AI-assistive diagnostic tool for human readers in the context of this 510(k) summary.

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

• Not applicable / not stated. The device is an X-ray system, which includes software for image acquisition ("proprietary ULTRA software"), but the document does not describe a standalone AI algorithm for diagnostic interpretation.

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

• Not applicable / not stated. Ground truth, in the context of diagnostic AI models, is not relevant to the compliance testing of an X-ray imaging system described here.

8. The sample size for the training set:

• Not applicable / not stated. The document describes an X-ray system, and there's no mention of a machine learning component requiring a training set in this context.

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

• Not applicable / not stated. As no training set is mentioned for an AI algorithm, ground truth establishment is not relevant to the information provided.

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

The MobileDiagnost wDR 2.2 is a motorized mobile radiographic system consisting of a mobile base unit, a user interface consisting of Eleva Workspot combined with flat solid state X-ray detectors used to operate, generate, process and handle digital X- ray images. The MobileDiagnost wDR 2.2 integrates a new wireless portable detector (SkyPlate E). The family of SkyPlate detectors (Large and Small) have already been integrated into the MobileDiagnost wDR 2.0 based on the K141736 pre-market submission.

The provided text describes a 510(k) premarket notification for the MobileDiagnost wDR 2.2, a mobile X-ray system. The focus of the submission is to demonstrate substantial equivalence to a predicate device, the MobileDiagnost wDR 2.0 (K141895), with several modifications. The document does not contain a clinical study to prove the device meets acceptance criteria in terms of diagnostic effectiveness or a "performance study" in the typical sense of evaluating a new diagnostic algorithm's accuracy. Instead, the submission relies on demonstrating substantial equivalence through non-clinical verification and validation tests against established standards.

Therefore, the requested information needs to be framed within this context of demonstrating substantial equivalence, rather than a traditional diagnostic performance study.

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

Acceptance Criteria and Device Performance (within the context of Substantial Equivalence)

The acceptance criteria are implicitly defined by the compliance with recognized international and FDA consensus standards and the outcome of the comparison to the predicate device, showing that modifications do not raise new questions of safety or effectiveness. The device performance is assessed against these standards and through direct comparison of technical characteristics to the predicate.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a substantial equivalence submission relying on technical changes and compliance with standards, the "acceptance criteria" are compliance with these standards and the "reported device performance" is the verification that these standards are met, and that the technical characteristics of the modified device are acceptably equivalent to the predicate.

• IEC 60601-1 (Edition 3.1)
• IEC 60601-1-2 (Edition 4.0)
• IEC 60601-1-3 (Edition 2.1)
• IEC 60601-1-6 (Edition 3.1)
• IEC 60601-2-54 (Edition 1.1)
• IEC 62304 (Edition 1.0:2006)
• IEC 62366 (Edition 1.0 2015)
• ISO 14971 (Edition 2.0, 2007)
• ISO 10993-1 (Edition 4.0 2009)
• IEC 60601-2-28 (Edition 2.0 2010-03)
• IEC 62220-1 (Edition 1.0 2015-03)
• NEMA PS 3.1 - 3.20 (DICOM)
• "Guidance for the Submission of 510(k)s for Solid State X-Ray Imaging Devices" (Sept 1, 2016)
• "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (May 11, 2005)
• "Pediatric Information for X-ray Imaging Device Premarket Notifications" (Nov 28, 2017) | Meets acceptance criteria and is adequate for its intended use, demonstrating substantial equivalence. Non-clinical information deemed sufficient to support substantial equivalence. |
  | II. Technical Characteristics Equivalence (MobileDiagnost wDR 2.2 vs. MobileDiagnost wDR 2.0) | | |
  | Base Unit Type, X-ray Tube rotational capabilities, Mains Supply, Mode of Exposure, Available Exposure Methods, X-ray Absorber, Installation type, Readout Mechanism, Maximum X-ray Dose for Linear Response, Maximum Usable Dose, Maximum Lifetime Dose, Image Processing (Eleva Workstation), ADC Digitisation, Signal to Electronic Noise Ratio (SENR), Data Interface to Workstation, X-ray Tube type, material, maximum voltage, nominal focal spot, anode type, nominal anode input power, Generator configurations, Collimator operation mode, beam shape, External Connectivity (DICOM), Software Platform (Eleva WorkSpot with SkyFlow). | Same/Identical to predicate. | Identical; thus, demonstrating Substantial Equivalence (SE). |
  | Dimensions (overall, transport, source-floor distance) | Minor differences in mm measurements. | Differences do not impact safety or effectiveness. Thus, demonstrating SE. |
  | Detector Models | Addition of SkyPlate E Large (Trixell 3543DR). Previously cleared SkyPlate Large/Small retained. | Addition of SkyPlate E detector does not impact safety or effectiveness. Thus, demonstrating SE. All technical detector characteristics influencing image quality assessed per FDA guidance. |
  | Detector Weight | Max 3.1 kg (vs. Max 3 kg for predicate). | Difference has no impact on clinical workflow, safety, or effectiveness. Thus, demonstrating SE. |
  | Image Size (X-ray field) | 345.0 mm x 426.0 mm (vs. 344.8 mm x 421.2 mm for predicate). | Difference does not impact clinical Image Quality, safety, or effectiveness. Thus, demonstrating SE. |
  | Pixel Size | 160 µm (vs. 148 µm for predicate). | Difference of 12 µm pixel size does not impact image resolution "to an extent that can impact the clinical image quality," safety or effectiveness. Thus, demonstrating SE. |
  | Image matrix size (Number of pixels) | 2156 x 2662 pixels (vs. 2330 x 2846 pixels for predicate). | "Infinitesimal change" and reduction in number of pixels due to 160 µm pixel size does not impact clinical Image Quality, safety, or effectiveness. Thus, demonstrating SE. |
  | Nyquist Frequency | 3.125 lp/mm (vs. 3.38 lp/mm for predicate). | Difference does not impact clinical Image Quality, safety, or effectiveness. Thus, demonstrating SE. |
  | Modulation Transfer Function (MTF) & Detective Quantum Efficiency (DQE) | Slightly different typical values reported (e.g., MTF at 1 lp/mm: 62% vs. 61%; DQE at 3 lp/mm: 22% vs. 29%). | Differences do not impact clinical Image Quality, safety, or effectiveness. Thus, demonstrating SE. |
  | Grids | Addition of new large grids for SkyPlate E. Previously cleared grids retained. | Addition of new grids has no impact on clinical workflow, safety, or effectiveness. Thus, demonstrating SE. |
  | Column Configuration | Sliding Column (vs. Standard/Short Column for predicate). | Sliding Column provides better viewing; its introduction does not impact safety or effectiveness. Thus, demonstrating SE. |
  | System Power ON/OFF | Keyless user access (vs. Physical Key for predicate). | Keyless user access provides authenticated access; its introduction does not impact safety or effectiveness. Thus, demonstrating SE. |
  | Exposure @ zero degree column position | Exposures allowed (vs. No Exposure allowed for predicate). | Allows examinations in space-constrained situations; difference does not impact safety or effectiveness. Thus, demonstrating SE. |
  | Exposure during Charging | Exposures allowed (vs. Not allowed for predicate). | Allows charging during exam preparation; exposure energy still drawn from generator batteries, not mains. Does not impact safety or effectiveness. Thus, demonstrating SE. |
  | Handles on Collimator | Handles can be used to move both Tube head and Collimator (vs. only Tube head for predicate). | Provides ease of use; difference does not impact safety or effectiveness. Thus, demonstrating SE. |
  | Image Processing Algorithm | UNIQUE 2 (vs. UNIQUE for predicate). | UNIQUE 2 provides improved image processing (reduced noise, improved contrast) and was already cleared under K182973. Upgrading does not alter clinical workflow, safety, or effectiveness. Thus, demonstrating SE. |

Study Details (as applicable for a Substantial Equivalence submission based on non-clinical data)

The provided text clearly states: "The MobileDiagnost wDR 2.2 does not require clinical study since substantial equivalence to the primary currently marketed and predicate device MobileDiagnost wDR 2.0 (K141895- September 18, 2014) was demonstrated with the following attributes: Indication for use; Technological characteristics; Non-clinical performance testing; and Safety and effectiveness."

This means there was no "study" in the sense of a clinical trial or a diagnostic performance study to evaluate accuracy, sensitivity, specificity, etc., with human or AI readers. The "study" here refers to the comprehensive non-clinical verification and validation process against relevant standards.

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

• Sample Size: Not applicable. No clinical test set of patient data was used for a diagnostic performance evaluation. The "tests" were non-clinical verification and validation tests on the device itself (e.g., electrical safety, electromagnetic compatibility, radiation protection, software validation, usability, risk management).
• Data Provenance: Not applicable. No patient data (retrospective or prospective, from any country) was used for evaluating the device's diagnostic performance for the purpose of this 510(k) submission.

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)

• Not applicable. There was no diagnostic test set requiring ground truth established by experts.

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

• Not applicable. There was no diagnostic test set requiring 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. This device is not an AI-assisted diagnostic product but rather an X-ray imaging system with updated components and software functionalities.

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. There was no diagnostic performance study requiring ground truth. Ground truth for the non-clinical tests would be the established scientific/engineering principles, specifications, and regulatory standards.

8. The sample size for the training set

• Not applicable. This device is an X-ray system, not a machine learning model that requires a "training set" in the context of AI. The software (UNIQUE 2) was previously cleared (K182973), indicating its own development and validation, but details of its training are not provided here.

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

• Not applicable, as there was no training set for the device itself. For the incorporated UNIQUE 2 image processing algorithm, its ground truth establishment (if it involved machine learning) would have been part of its original 510(k) (K182973), but those details are not in this document.

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