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This software is intended to generate digital radiographic images of the skull, spinal column, extremities, and other body parts in patients of all ages. Applications can be performed with the patient sitting, or lying in the prone or supine position and is intended for use in all routine radiography exams. The product is not intended for mammographic applications.

This software is not meant for mammography, fluoroscopy, or angiography.

The I-Q View is a software package to be used with FDA cleared solid-state imaging receptors. It functions as a diagnostic x-ray image acquisition platform and allows these images to be transferred to hard copy, softcopy, and archive devices via DICOM protocol. The flat panel detector is not part of this submission. In the I-Q View software, the Digital Radiography Operator Console (DROC) software allows the following functions:

• 1. Add new patients to the system; enter information about the patient and physician that will be associated with the digital radiographic images.
• 2. Edit existing patient information.
• 3. Emergency registration and edit Emergency settings.
• 4. Pick from a selection of procedures, which defines the series of images to be acquired.
• 5. Adiust technique settings before capturing the x-ray image.
• 6. Preview the image, accept or reject the image entering comments or rejection reasons to the image. Accepted images will be sent to the selected output destinations.
• 7. Save an incomplete procedure, for which the rest of the exposures will be made at a later time.
• 8. Close a procedure when all images have been captured.
• 9. Review History images, resend and reprint images.
• 10. Re-exam a completed patient.
• 11. Protect patient records from being deleted by the system.
• 12. Delete an examined Study with all images being captured.
• 13. Edit User accounts.
• 14. Check statistical information.
• 15. Image QC.
• 16. Image stitching.
• 17. Provides electronic transfer of medical image data between medical devices.

The provided document is a 510(k) summary for the I-Q View software. It focuses on demonstrating substantial equivalence to a predicate device through bench testing and comparison of technical characteristics. It explicitly states that clinical testing was not required or performed.

Therefore, I cannot provide details on clinical acceptance criteria or a study proving the device meets them, as such a study was not conducted for this submission. The document relies on bench testing and comparison to a predicate device to establish substantial equivalence.

Here's a breakdown of what can be extracted from the provided text regarding acceptance criteria and the "study" (bench testing) that supports the device:

1. Table of Acceptance Criteria and Reported Device Performance

Since no clinical acceptance criteria or performance metrics are provided, this table will reflect the general statements made about the device performing to specifications.

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

• Test Set Sample Size: Not applicable/not reported. The document describes bench testing, not a test set of patient data.
• Data Provenance: Not applicable. Bench testing generally involves internal testing environments rather than patient data from specific countries or retrospective/prospective studies.

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 was used, no experts were needed to establish ground truth for patient data. Bench testing typically relies on engineering specifications and verification.

4. Adjudication method for the test set

• Not applicable. No clinical test set or human interpretation was involved.

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 comparative effectiveness study was not done. The document explicitly states: "Clinical Testing: The bench testing is significant enough to demonstrate that the I-Q View software is as good as the predicate software. All features and functionality have been tested and all specifications have been met. Therefore, it is our conclusion that clinical testing is not required to show substantial equivalence." The device is software for image acquisition, not an AI-assisted diagnostic tool.

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

• Yes, in a sense. The "study" described is bench testing of the software's functionality and its integration with solid-state detectors. This is an evaluation of the algorithm/software itself.

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

• For bench testing, the "ground truth" would be the engineering specifications and expected functional behavior of the software and its interaction with hardware components. It's about verifying that the software performs according to its design requirements.

8. The sample size for the training set

• Not applicable. The I-Q View is described as an image acquisition and processing software, not an AI/machine learning model that typically requires a training set of data.

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

• Not applicable, as there is no mention of a training set or AI/machine learning component.

Summary of the "Study" (Bench Testing) for K203703:

The "study" conducted for the I-Q View software was bench testing. This involved:

• Verification and validation of the software.
• Demonstrating the intended functions and relative performance of the software.
• Integration testing to verify that compatible solid-state detectors performed within specification as intended when used with the I-Q View software.

The conclusion drawn from this bench testing was that the software performs to specification and is "as safe and as functionally effective as the predicate software." This was deemed sufficient to demonstrate substantial equivalence, and clinical testing was explicitly stated as not required.

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The DigitalDiagnost C90 is intended to acquire, process, store, display and export digital radiographic images. The DigitalDiagnost C90 is suitable for all routine radiography examinations, including specialist areas like intensive care, trauma or pediatric work, excluding fluoroscopy, angiography and mammography.

The proposed DigitalDiagnost C90 is same as the 510(K) approved predicate (K182973) as mentioned below. It is a high-end digital radiography system consisting of a height adjustable patient support table and a ceiling suspension consisting of a tube including a control handle used to acquire images with a flat panel fixed RAD detector. Additionally, different vertical stands for the radiography examinations are available. The ceiling suspension can be moved in longitudinal and lateral directions and additionally the tube can be tilted and rotated as well. The proposed DigitalDiagnost C90 is configured with a Philips x-ray generator and a flat panel fixed RAD detector, Pixium 4343RCE, together with the tube these components form the radiography Image Chain. The proposed DigitalDiagnost C90 introduces a new wireless portable detector, pixium 3543DR i.e. SkyPlate E (cleared in reference device 510(K) K191813) and their relevant grids, in addition to the family of SkyPlate detectors (pixium 3543EZ i.e. Large and pixium 2430EZ i.e. Small) that are already integrated in the currently marketed (predicate) DigitalDiagnost C90 (K182973).

The Philips DigitalDiagnost C90 is a stationary x-ray system, and the submission K202564 introduces the SkyPlate E detector and its relevant grids as a modification to the previously cleared DigitalDiagnost C90 (K182973). The acceptance criteria and the study proving the device meets these criteria are primarily based on demonstrating substantial equivalence to the predicate device through non-clinical performance testing. No clinical study was performed.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the Philips DigitalDiagnost C90, specifically regarding the addition of the SkyPlate E detector, are based on demonstrating equivalence or non-inferiority to the predicate device (DigitalDiagnost C90 with existing SkyPlate detectors) in key technological characteristics and the maintenance of clinical image quality, safety, and effectiveness. The reported device performance indicates that the new SkyPlate E detector either meets or is equivalent to the performance of the existing detectors, or that any differences do not negatively impact clinical image quality or safety.

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

The document explicitly states: "The proposed DigitalDiagnost C90 did not require clinical study since substantial equivalence to the currently marketed and predicate device was demonstrated with the following attributes: Design features; Indication for use; Fundamental scientific technology; Non-clinical performance testing; and Safety and effectiveness."

Therefore, there was no clinical test set with human subject data used for this specific 510(k) submission. The evaluation was based on non-clinical performance data.

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, as no clinical test set was used for this submission. The ground truth, in this context, was established through adherence to recognized standards and technical measurements, not expert clinical assessment of a dataset.

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

Not applicable, as no clinical test set 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. This submission is for an x-ray system with a new detector, not an AI-powered diagnostic tool. No MRMC study was performed.

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

Not applicable, as this is not an AI algorithm. The performance evaluation was for the imaging system itself.

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

The "ground truth" for this submission was established through:

• Adherence to international and FDA-recognized consensus standards: This includes technical specifications and performance metrics outlined in standards like IEC 60601 series, IEC 62220-1, and IEC 62304.
• Results of non-standard performance tests: These tests are designed to verify specific technical aspects of the device and confirm its functionality and safety. The report mentions "System Verification Test," "Image Quality Test," and "Human Factors and Usability Engineering Test."
• Comparison to the predicate device: The core of the substantial equivalence claim relies on demonstrating that the new detector either performs the same as, or does not negatively impact the performance of, the previously cleared predicate device based on its technical specifications.

8. The sample size for the training set

Not applicable, as this is not an AI algorithm requiring a training set.

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

Not applicable, as this is not an AI algorithm.

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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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Intended for use in generating radiographic images of human anatomy. This device is intended to replace film/screen systems in all general purpose diagnostic procedures. This device is not intended for mammography applications. This device is intended for use by qualified medical personnel and is contraindicated when, in the physician, procedures would be contrary to the best interest of the patient.

Intended for digital image capture use in general radiographic examinations, wherever conventional screen-film systems may be used, excluding fluoroscopy, angiography and mammography. The kit allows imaging of the skull, chest, shoulders, spine, abdomen, pelvis, and extremities.

This is a Windows 10 based software to be used in conjunction with an FDA cleared digital x-ray receptor panel. It can be used to upgrade film-based systems. This upgrade allows to acquire digital medical diagnostic X-ray images and transfer the images to hardcopy, softcopy, and archive devices on the same network. Some functions allowed with the E-COM DR-2000 DR software:
a. Add new patients to the system, enter information about the patient and physician that will be associated with the digital radiographic images.
b. Edit existing patient information.
c. Emergency registration and edit Emergency settings.
d. Pick from a selection of procedures, which defines the series of images to be taken.
e. Adjust technique settings before capturing the X-ray image.
f. Preview the image, accept or reject the image entering comments or rejection reasons to the image. Accepted images will be sent to the selected output destinations.
g. Save an incomplete procedure, for which the rest of the exposures will be made at a later time.
h. Close a procedure when all images have been captured.
i. Review History images, resend and reprint images.
j. Re-exam a completed patient.
k. Protect patient records from being deleted by the system.
l. Delete an examined Study with all images being captured.
m. Edit user accounts.
n. Check statistical information.
o. Image QC.
p. Image stitching.

This document describes the FDA 510(k) premarket notification for the E-COM DR-2000 DR, a stationary x-ray system. However, it does not provide details regarding specific acceptance criteria, a study proving the device meets those criteria, or information on AI/standalone performance, expert adjudication, or ground truth establishment typically associated with such studies.

Here's a breakdown of the requested information based only on the provided text, highlighting what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not provided in the document. The submission focuses on demonstrating substantial equivalence to a predicate device rather than presenting specific quantitative performance metrics against acceptance criteria.

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

This information is not provided in the document. No specific test set for performance evaluation is mentioned. The submission states "Clinical Testing: Not required for a showing of substantial equivalence," implying a lack of a dedicated clinical test set for new performance evaluation.

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

This information is not provided in the document. Since no clinical testing or performance study is detailed, there's no mention of experts establishing ground truth for a test set.

4. Adjudication Method for the Test Set

This information is not provided in the document. As no test set is described, no adjudication method is mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

This information is not provided in the document. The device is a "Stationary X-ray System" with software for image acquisition and management. There is no indication that it includes AI for interpreting images or assisting human readers. Therefore, an MRMC study related to AI assistance is not applicable and not mentioned.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

This information is not provided in the document. As there is no mention of an algorithm for image interpretation or analysis, a standalone performance study is not applicable and not mentioned. The device's software functions are primarily for image acquisition, processing, and management.

7. The Type of Ground Truth Used

This information is not provided in the document. Since no clinical performance study is described, there's no mention of the type of ground truth used (e.g., expert consensus, pathology, outcomes data).

8. The Sample Size for the Training Set

This information is not provided in the document. The submission pertains to a conventional x-ray system and its control software. There is no discussion of machine learning or AI components that would require a "training set."

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

This information is not provided in the document. As no training set is discussed (see point 8), the establishment of its ground truth is also not mentioned.

Summary of the Document's Content Regarding Testing:

The K193644 submission's primary focus is on demonstrating substantial equivalence to a predicate device (K130883, Sedecal Digital Radiographic Upgrade Model SDRU-T). This is achieved by comparing the new device's indications for use and technological characteristics to the predicate.

The document states:

• "Bench/Performance Testing: The results of bench testing (software validation and risk analysis for a moderate level of concern device) shows that this new device poses no new issues of safety or effectiveness, has essentially the same technological characteristics as the predicate, and is therefore substantially equivalent to the predicate device."
• "Clinical Testing: Not required for a showing of substantial equivalence."

This indicates that the FDA clearance for E-COM DR-2000 DR was based on demonstrating similar technical specifications and safety/effectiveness profiles to an already cleared device, along with adherence to relevant software development and risk management guidance documents. It does not involve a new performance study with specific quantitative acceptance criteria or extensive clinical data as would be required for a novel device or one incorporating advanced AI algorithms.

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