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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 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 Philips DuraDiagnost Digital Diagnostic Radiographic System (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 Philips 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.

Here's a breakdown of the acceptance criteria and the study information for the Philips DuraDiagnost, based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than setting specific performance acceptance criteria for the new device as would be found in a clinical trial protocol. Instead, the "acceptance criteria" here relate to the device meeting recognized standards and exhibiting technical characteristics similar to its predicates.

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

• Test Set Sample Size: Not applicable. This 510(k) relies on non-clinical performance testing and comparison to predicate devices, not a clinical test set of patient images.
• Data Provenance: The data provenance is primarily from bench testing, engineering verification, and validation activities against recognized standards and guidance documents. This is not patient-level data. The manufacturer is Philips Healthcare (Suzhou) Co., Ltd in China.

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

• Number of Experts: Not applicable. Since there was no clinical test set of patient images, no experts were used to establish ground truth in this context. The "ground truth" for this submission is related to engineering specifications and compliance with regulatory standards.
• Qualifications of Experts: N/A

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. No test set of patient images requiring adjudication was used.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

• MRMC Study: No, an MRMC comparative effectiveness study was not done. The submission explicitly states: "Clinical study was not warranted to support this 510(k) submission, since substantial equivalence to the currently marketed and predicate devices was demonstrated with the following attributes: Design features; Indication for use; Fundamental scientific technology; and Safety and effectiveness."
• Effect Size: Not applicable, as no MRMC study was performed.

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

• This device is a hardware digital radiographic system with integral software for image acquisition, processing, storage, and viewing. It is not an AI algorithm in the context of standalone performance. Therefore, a "standalone algorithm only" performance study is not applicable. The performance evaluation focuses on the entire system's compliance with standards and equivalence to predicate systems.

7. The Type of Ground Truth Used

• The "ground truth" for this submission is based on:
  • Engineering Specifications and Design Features: Comparing the technical characteristics of the DuraDiagnost to those of the predicate devices.
  • International and FDA-recognized Consensus Standards: Compliance with electrical safety, EMC, radiation protection, software lifecycle, risk management, and usability standards (e.g., IEC 60601 series, IEC 62304, ISO 14971).
  • FDA Guidance Documents: Adherence to specific guidance for solid-state X-ray imaging, software, wireless technology, and pediatric information.
  • Predicate Device Performance: The established safety and effectiveness of the legally marketed predicate devices (GE Brivo XR385, Philips BuckyVision, Swissray ddRVersa™ Motion) serves as a benchmark for substantial equivalence.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This submission is for a conventional digital radiographic system and does not involve an AI algorithm with a training set of images. The "training" for such a system would involve engineering design and software development, not machine learning from a dataset.

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

• Ground Truth for Training Set: Not applicable, as there is no AI algorithm training set involved.

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