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Philips Radiology Smart Assistant is intended to provide patient positioning feedback using validated 2D X-Ray systems. Philips Radiology Smart Assistant is a software which informs Healthcare Professionals regarding patient positioning quality in accordance with clinical guidelines. Philips Radiology Smart Assistant is not intended for diagnostic purposes. It is not intended to be used as the basis for repeating an image.

Philips Radiology Smart Assistant is a software package intended to be used by qualified healthcare professionals. The software is used with general purpose computing hardware for the processing, display of images, and patient positioning feedback within a clinical environment. Philips Radiology Smart Assistant software supports receiving and displaying images from X-ray systems.

The system supports receiving, sending, storing, acceptance and displaying of medical images received from the following modality types via DICOM: DX as well as hospital/radiology information systems.

Philips Radiology Smart Assistant includes a post-processing patient positioning feedback function for posterior-anterior (PA) chest X-ray images. The patient positioning assessment is intended to provide a qualified healthcare professional with timely feedback on the quality of acquired X-ray images that do not suffice the positioning quality standards of clinical guidelines. The quality check comprises an assessment of the following parameters:

• Collimation
• Patient Rotation
• Patient Inhalation State

Here's a breakdown of the acceptance criteria and study details for the Philips Radiology Smart Assistant, based on the provided text:

Acceptance Criteria and Device Performance

The document states that the Philips Radiology Smart Assistant provides "patient positioning feedback using validated 2D X-Ray systems" and "informs Healthcare Professionals regarding patient positioning quality in accordance with clinical guidelines." The quality check specifically assesses:

• Collimation
• Patient Rotation
• Patient Inhalation State

However, the provided text does not contain a specific table of acceptance criteria with numerical targets or the reported device performance for these criteria. It generally states that the device "met the acceptance criteria" in the clinical performance study, but the criteria themselves are not quantified.

Table of Acceptance Criteria and Reported Device Performance (Based on available information):

Study Details:

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

• Sample Size for Test Set: Not explicitly stated in terms of a specific number. The document mentions "previously acquired posteroanterior (PA) chest X-ray images."
• Data Provenance: Not explicitly stated. The images were "previously acquired," but the country of origin and whether it was retrospective or prospective are not mentioned. Given they are "previously acquired," it's highly likely to be retrospective.

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

• Number of Experts: Not explicitly stated. The document mentions "the positioning quality assessment of clinicians." It does not specify how many clinicians.
• Qualifications of Experts: Not explicitly stated beyond "clinicians." The qualifications (e.g., radiologist with X years of experience) are not provided.

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

• Adjudication Method: Not explicitly stated. The document refers to "the positioning quality assessment of clinicians," but it does not detail any consensus or adjudication process (e.g., 2+1, 3+1).

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:

• MRMC Study: No, an MRMC comparative effectiveness study was not specifically described in the provided text. The study compared the algorithm's assessment to "clinicians using standard diagnostic metrics," which suggests a comparison of the AI's output against human assessment, but not a study of human readers with vs. without AI assistance.
• Effect Size: Not applicable, as an MRMC comparative effectiveness study was not detailed.

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

• Standalone Performance: Yes, a standalone performance study was conducted. The "algorithm's assessment as to whether or not an image met quality criteria for aspects of patient positioning quality was compared to the positioning quality assessment of clinicians." This indicates the algorithm's decisions were evaluated independently against a human-defined ground truth.

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

• Type of Ground Truth: Expert assessment/consensus. The ground truth was established by "the positioning quality assessment of clinicians using standard diagnostic metrics."

8. The sample size for the training set:

• Sample Size for Training Set: Not mentioned in the provided text. The document refers only to the clinical performance study on "previously acquired posteroanterior (PA) chest X-ray images."

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

• Ground Truth Establishment for Training Set: Not mentioned in the provided text. The document focuses on the validation study and does not describe the training process or how its ground truth was established.

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