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PreXion3D Expedition is intended to produce two-dimensional digital x-ray images including panoramic and cephalometric image, and three-dimensional digital x-ray images of the dental, oral, maxillofacial region, ENT (Ear, Nose and Throat) and neck region at the direction of healthcare professionals as diagnostic support for adult and pediatric patients. Cephalometric imaging also includes the hand and wrist to obtain carpus images for growth and maturity assessment.

This device is not intended for use on patients less than approximately 21 kg (46 lb) in weight and 113 cm (44.5 in) in height: these height and weight measurements approximately correspond to that of an average 5 year old.

PreXion3D Expedition consists of a scanner, which is used for generating X-ray and detecting image data, and a console, which is used for operating the scanner and managing the data. The scan data acquired by the scanner will be transferred to the Console. PreXion3D Explore Image Analysis System will then perform the image analysis (2D/3D) or image edition (creating crosssection diagram, etc.), and output the image to a printer.

X-ray image data is acquired while the rotation arm is rotating around the secured "patient's head" at a constant speed. X-rays, which are emitted from X-ray generator (built in one side of rotation arm), pass through a patient and are detected by the flat panel detector (built in the other side of rotation arm). The detected X-ray absorption data is used to process image reconstruction on the Console to create the 3D image (CT scan), the tomographic image (CT scan, Panoramic scan) and Cephalometric Scan.

The provided text describes the PreXion3D Expedition, a computed tomography x-ray system. However, it does not contain the specific acceptance criteria for the device's performance in terms of diagnostic accuracy, nor does it detail a study proving the device meets such criteria.
The document focuses on non-clinical performance tests for safety and efficacy, and establishing substantial equivalence to a predicate device.

Here's a breakdown of the information that is available based on your request, and what is missing:

1. Table of acceptance criteria and the reported device performance:

• Acceptance Criteria for Diagnostic Performance: Not explicitly stated in the document. The document refers to "internal requirements, national standards, and international standards" for various technical and safety aspects, but not for diagnostic accuracy (e.g., sensitivity, specificity for specific conditions).
• Reported Device Performance: The document states, "We conducted the clinical image validation and as a result, it was confirmed that they are clinically valid." However, no specific metrics, data, or results from this clinical image validation are provided.

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

• Sample Size for Test Set: Not specified.
• Data Provenance (e.g., country of origin, retrospective or prospective): Not specified. The document only mentions "clinical image validation."

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.
• Method of establishing ground truth: Not specified.

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

• Not specified.

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:

• No mention of an MRMC study or AI assistance. The device is described as an imaging system, not an AI-powered diagnostic tool for human readers.

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

• Not applicable, as this is an imaging device, not an algorithm being tested for standalone performance. The device produces images for diagnostic support by healthcare professionals.

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

• Not specified. The document only generically mentions "clinical image validation."

8. The sample size for the training set:

• Not applicable. This document describes an imaging device, not a machine learning model that undergoes a training phase with a distinct training set.

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

• Not applicable, for the same reason as point 8.

In summary, the provided FDA 510(k) summary focuses on the technical, safety, and regulatory aspects of establishing substantial equivalence for the PreXion3D Expedition device. It does not provide detailed information about specific diagnostic acceptance criteria, quantitative performance metrics from clinical studies, or the methodology of any clinical validation involving human readers or ground truth establishment. The statement for clinical validity is very general and lacks specifics.

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PreXion3D Expedition is intended to produce two-dimensional digital x-ray images including panoramic and cephalometric image, and three-dimensional digital x-ray images of the dental, oral, maxillofacial region, ENT (Ear, Nose and Throat) and neck region at the direction of healthcare professionals as diagnostic support for adult and pediatric patients. Cephalometric imaging also includes the hand and wrist to obtain carpus images for growth and maturity assessment.

This device is not intended for use on patients less than approximately 21 kg (46 lb) in weight and 113 cm (44.5 in) in height; these height and weight measurements approximately correspond to that of an average 5 year old.

PreXion3D Expedition consists of a scanner, which is used for generating X-ray and detecting image data, and a console, which is used for operating the scanner and managing the data. The scan data acquired by the scanner will be transferred to the Console. PreXion3D Explore Image Analysis System will then perform the image analysis (2D/3D) or image edition (creating crosssection diagram, etc.), and output the image to a printer.

X-ray image data is acquired while the rotation arm is rotating around the secured "patient's head" at a constant speed. X-rays, which are emitted from X-ray generator (built in one side of rotation arm), pass through a patient and are detected by the flat panel detector (built in the other side of rotation arm). The detected X-ray absorption data is used to process image reconstruction on the Console to create the 3D image (CT scan), the tomographic image (CT scan, Panoramic scan) and Cephalometric Scan.

The FDA 510(k) summary for the PreXion3D Expedition describes the device and its claimed substantial equivalence to predicate devices, but it does not contain the detailed information necessary to answer the questions about acceptance criteria and the study that proves the device meets them. This document is a regulatory filing, primarily focused on demonstrating substantial equivalence based on technical characteristics and intended use, rather than providing a detailed performance study with specific acceptance criteria and outcome metrics for AI/ML components.

Specifically, the document states: "No clinical testing was performed and that the non-clinical testing provided in this submission supports the substantial equivalence of the device." This confirms that there was no performance study of the kind typically used to evaluate AI model performance (e.g., assessing diagnostic accuracy against ground truth).

Therefore, I cannot extract the requested information from the provided text. The tables and sections related to "Technological Characteristics and Performance" are comparing device specifications (e.g., X-ray generation, detector types, physical dimensions) to predicate devices, not reporting on the performance of an AI component against specific diagnostic acceptance criteria.

To answer your questions, one would typically need access to a detailed clinical validation study report for an AI-powered diagnostic device, which is not present in this 510(k) summary.

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