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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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PreXion3D Explorer PRO 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 Explorer PRO 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.

This FDA 510(k) summary does not contain information about an AI/ML-based medical device. Instead, it describes a Computed Tomography X-Ray System (PreXion3D Explorer PRO) which is a hardware device used for generating X-ray images, and the accompanying software primarily performs image analysis and reconstruction, not AI-driven diagnostic assistance.

Therefore, the requested information regarding acceptance criteria and study proving device meets acceptance criteria, including details about sample sizes, expert qualifications, ground truth establishment, MRMC studies, and standalone performance for an AI/ML-based system, cannot be extracted from the provided text.

The document details the technical specifications of the PreXion3D Explorer PRO and compares it to a predicate device (an earlier model of the same product). The performance data mentioned are non-clinical hardware and software verification tests against established standards (e.g., electrical safety, electromagnetic compatibility, radiation safety, software verification and validation, biocompatibility), not clinical performance data related to diagnostic accuracy validated through expert reads or ground truth.

Summary of what is present:

• Device: PreXion3D Explorer PRO, a Computed Tomography X-Ray System.
• Purpose: Produces 2D (panoramic, cephalometric) and 3D (CT) digital x-ray images of dental, oral, maxillofacial, ENT, and neck regions for diagnostic support.
• Predicate Device: PreXion3D Explorer PRO (Model: P03A), K203784.
• Evidence for Substantial Equivalence: Primarily non-clinical performance data demonstrating compliance with various national and international standards (e.g., IEC 60601 series, ISO 10993 series, IEC 62304 for software).
• Clinical Performance Data: "There was no human clinical testing required to support the medical device as the indications for use is equivalent to the predicate device." This explicitly states that no clinical studies were performed for this submission to prove performance against specific diagnostic objectives, as it relies on the predicate's established safety and efficacy.

Information NOT present (and therefore cannot be provided as requested):

• Acceptance criteria related to AI/ML diagnostic performance (e.g., sensitivity, specificity, AUC).
• Any details about a study demonstrating the device meets such AI/ML performance acceptance criteria.
• Sample sizes for test sets where ground truth for AI performance is established.
• Data provenance for AI/ML test sets.
• Number/qualifications of experts for ground truth establishment.
• Adjudication methods.
• MRMC comparative effectiveness study results or effect sizes for human readers with/without AI assistance.
• Standalone (algorithm-only) performance.
• Type of ground truth used (expert consensus, pathology, outcomes data).
• Sample size for AI/ML training set.
• How ground truth for an AI/ML training set was established.

Ask a specific question about this device

PreXion3D Explorer PRO 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.

PreXion3D Explorer PRO 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, CT-Panoramic scan) and Cephalometric Scan.

The provided text is a 510(k) Premarket Notification summary for the PreXion3D Explorer PRO, a Computed Tomography X-Ray System. It primarily focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance data and does not contain information about the acceptance criteria or a study proving that the device meets specific performance criteria for an AI/CADe/CADx algorithm.

The document details the device's technical specifications, compared to predicate devices, and lists various standards it complies with (e.g., electrical safety, radiation safety, software validation, biocompatibility). It explicitly states: "There was no human clinical testing required to support the medical device as the indications for use is equivalent to the predicate device."

Therefore, based on the provided text, I cannot extract the information required to populate the fields related to acceptance criteria, clinical study design, expert involvement, or AI/software performance metrics (such as MRMC studies, standalone performance, or effect sizes of AI assistance). The device is a hardware imaging system, and its approval is based on equivalence to existing hardware, not on the performance of a novel AI algorithm that requires specific clinical validation metrics.

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PreXion3D Explorer EX is intended to produce two-dimensional digital x-ray images including panoramic 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.

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. Use of equipment and exposure settings designed for adults of average size can result in excessive radiation exposure for a smaller patient. Studies have shown that pediatic patients may be more radiosensitive than adults (i.e., the cancer risk per unit dose of ionizing radiation is higher), and so unnecessary radiation exposure is of particular concern for pediatric patients.

PreXion3D Explorer EX 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 Explorer EX Image Analysis System will then perform the image analysis (2D/3D) or image edition (creating cross-section 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) and the tomographic image (CT scan, CT-Panoramic scan, Panoramic Scan).

This document is a 510(k) Summary for the PreXion3D Explorer EX, a Computed Tomography X-ray System. It describes the device, its intended use, and its substantial equivalence to a predicate device (PreXion3D Explorer, K190320) and a reference device (PreXion3D Excelsior, K181983).

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

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

The document does not present a formal table of "acceptance criteria" in the sense of specific performance metrics that the device had to achieve for clearance. Instead, it details modifications to a predicate device and then states that system tests and imaging performance tests were conducted to confirm the validity and diagnostic quality of these modifications, ensuring compliance with relevant standards.

The closest to "acceptance criteria" are the standards the device complies with, and the "reported device performance" is the confirmation that it passed these tests or was deemed equivalent.

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 focuses on non-clinical performance data and a "substantial equivalence" argument rather than clinical or AI algorithm performance studies with defined test sets.

• Sample size: Not applicable in the context of typical AI algorithm test sets. The "sample" referred to here are individual modifications to the device and the associated technical tests.
• Data provenance: Not explicitly stated as "data provenance" for a test set. The document refers to internal requirements and compliance with international/national standards, suggesting in-house testing or third-party laboratory testing. The radiologist who confirmed diagnostic quality for the detector change is described as "American board-certified," implying a U.S. context for that specific assessment. The cybersecurity information for the predicate device mentions six units installed in the US.
• Retrospective or prospective: Not applicable. These are verification and validation activities for device modifications, not a study on an AI algorithm's performance on a dataset of patient cases.

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)

Only one expert is explicitly mentioned for a specific aspect:

• Number of experts: One.
• Qualifications: "An American board-certified radiologist." No information on years of experience is provided.
• Role: Confirmed that images with the detector modification retained "diagnostic quality." This serves as a form of expert assessment for a specific technical change, rather than establishing ground truth for a test set in an AI study.

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

Not applicable. The document describes engineering and performance tests for hardware and software modifications, not a review of clinical cases by multiple experts. The single expert review mentioned for the detector change does not involve an adjudication method as there wasn't a panel.

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. The device is a Computed Tomography (CT) X-ray system, not an AI-powered diagnostic aide. The document only references "diagnostic support" as the general purpose of the imaging system. There is no mention of AI capabilities or human reader improvement with AI assistance.

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

Not applicable. This device is an imaging system, not an AI algorithm.

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

For the specific point where an expert was involved (detector's effective area change), the "ground truth" was the expert opinion of an American board-certified radiologist confirming "diagnostic quality." This is not a clinical ground truth like pathology for specific disease detection but an assessment of image quality for diagnostic purposes. For other technical aspects, the "ground truth" was primarily compliance with established national and international standards.

8. The sample size for the training set

Not applicable. The document describes a medical imaging device (hardware and software), not an AI algorithm that requires a training set.

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

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

Ask a specific question about this device

PreXion3D Explorer 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.

PreXion3D Explorer 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 Explorer Image Analysis System will then perform the image analysis (2D/3D) or image edition (creating cross-section diagram, etc.), and output the image to a printer.

X-ray image data is acquired while the rotation 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, CT-Panoramic scan) and Cephalometric Scan.

This document is a 510(k) premarket notification for the PreXion3D Explorer, a Computed Tomography X-Ray System. It primarily focuses on demonstrating substantial equivalence to a predicate device (PreXion3D Excelsior) rather than providing detailed clinical efficacy study results for an AI-powered device. Therefore, much of the requested information regarding acceptance criteria for AI performance and detailed study methodologies (like MRMC studies, ground truth establishment for AI, sample sizes for training/test sets for AI models, etc.) is not present in this document, as the device is not described as having an AI component in the manner of a diagnostic AI algorithm.

However, I can extract the acceptance criteria and performance data for the device itself based on the provided tables and text, treating "performance" as the device's physical capabilities and measured output rather than AI diagnostic performance.

Here's the closest interpretation of your request based on the provided document:

Device Name: PreXion3D Explorer

1. Table of Acceptance Criteria and Reported Device Performance

The document compares the PreXion3D Explorer (Subject Device) to its predicate device (PreXion3D Excelsior) to demonstrate substantial equivalence. The "acceptance criteria" here are implicitly meeting or improving upon the predicate's performance and complying with relevant standards. The "reported device performance" is the measured characteristics of the PreXion3D Explorer.

Study Proving Device Meets Acceptance Criteria:

The key study mentioned is "Non-Clinical Performance Data."

• Study Type: Non-clinical bench testing, electrical safety testing, software validation, biocompatibility testing, and compliance with various international and national standards. This is a device validation study, not a clinical study on diagnostic accuracy or AI performance.

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

• Test Set Sample Size: The document does not specify a "sample size" in terms of patient cases or images for testing the device's performance. The testing refers to engineering tests performed on the device and its components, often using phantoms or controlled environments, not clinical image sets as one would use for an AI diagnostic algorithm.
• Data Provenance: Not applicable in the context of clinical images for an AI study. The "data" here refers to measurements from bench tests performed by PreXion Corporation. The company is based in Tokyo, Japan. The testing is reported as a "non-clinical" effort, not involving human subjects or clinical data in the traditional sense.

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

• Not applicable. This device is an imaging system itself, not an AI diagnostic algorithm that requires expert-established ground truth on clinical images for its performance evaluation. The "ground truth" for this submission are the technical specifications, measured physical parameters, and compliance with safety and performance standards.

4. Adjudication Method for the Test Set:

• Not applicable. No expert adjudication process for image interpretation (as would be typical for an AI diagnostic device) is described.

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

• No, an MRMC study was not done. The document explicitly states: "There was no human clinical testing required to support the medical device as the indications for use is equivalent to the predicate device." This means no study was performed to assess human reader performance with or without the device, as is common for AI diagnostic devices.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:

• Not applicable. This device is the imaging equipment itself, not a separate diagnostic algorithm. Its "performance" refers to its ability to acquire images meeting technical specifications and safety standards, not a diagnostic output from an algorithm.

7. The Type of Ground Truth Used:

• The ground truth for this submission is based on engineering specifications, physical measurements (e.g., dose, spatial resolution on phantoms), and compliance with established national and international standards for medical electrical equipment, radiation safety, and imaging performance (e.g., IEC 60601 series, ISO 10993). It is not based on expert consensus, pathology, or outcomes data from human subjects.

8. The Sample Size for the Training Set:

• Not applicable. This document describes the clearance of an imaging device, not an AI algorithm that undergoes training.

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

• Not applicable for the same reason as #8.

Ask a specific question about this device

PreXion 3D Excelsior is intended to produce two dimensional digital panoramic and cephalometric images, and three dimensional digital X-ray images of the dental (oral), maxillofacial, and ENT (Ear, Nose and Throat) region at the direction of healthcare professionals as diagnostic support for adult and pediatric patients. Cephalometric imaging also includes the hand wrist to obtain carpus images for growth and maturity assessment.

PreXion 3D Excelsior 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 Excelsior Image Analysis System will then perform the image analysis (2D/3D) or image edition (creating cross-section diagram, etc.), and output the image to a printer. During scanning, X-rays are generated from the x-ray tube head mounted in the arm of the scanner and the x-rays passing through a patient are then detected by the flat panel detector of the scanner under the control of the firmware inside and the console software installed on the qualified Computer. The detected x-ray absorption data is processed by the console software to reconstruct the diagnostic images. The PreXion3D Excelsior has three operation modes, CT scan, Panoramic scan and Cephalometric exposure.

The provided text is a 510(k) Summary for the PreXion 3D Excelsior device. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting detailed acceptance criteria and a study proving device performance against those criteria in the context of an AI-driven diagnostic aid.

Specifically, the document discusses:

• Device Description: The PreXion 3D Excelsior is a dental cone-beam computed tomography (CT) device.
• Modifications from Predicate: The subject device adds a "CT-panoramic" mode (reconstructing panoramic images from CT raw data) and an "Airway measurement function" (calculates volume and cross-section area of airways).
• Non-Clinical Performance Data: Conformance to various harmonized standards (e.g., IEC 60601-1, ISO 14971) and specific testing for 3D imaging performance (MTF for three image orientations).
• Software Quality Activities: Compliance with software moderate level concern based on risk management and usability evaluation, including bench tests and user evaluations.
• Conclusion: The device is substantially equivalent to the predicate device.

However, the document does NOT provide the requested information regarding acceptance criteria related to AI/Machine Learning performance (e.g., sensitivity, specificity, AUC) or a clinical study proving AI-driven diagnostic aid performance. The "Airway measurement function" is described as an additional supplemental function, but no performance metrics for this automated measurement function are presented, nor is it explicitly stated to be an AI-driven diagnostic aid in the sense of pattern recognition or disease detection.

Therefore, I cannot extract the information required to formulate the table and answer the detailed questions about acceptance criteria for an AI device and the study proving it meets those criteria. The provided submission is for a medical imaging device (CT scanner) and its software, not an AI diagnostic software that requires assessment of diagnostic accuracy.

Ask a specific question about this device

PreXion3D Excelsior is intended to produce two dimensional digital panoramic and cephalometric images, and three dimensional digital x-ray images of the maxillofacial, and ENT (Ear, Nose and Throat) region at the direction of healthcare professionals as diagnostic support for adult and pediatric patients.

Cephalometric imaging also includes the hand wrist to obtain carpus images for growth and maturity assessment.

PreXion3D Excelsior 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 Excelsior Image Analysis System will then perform the image analysis (2D/3D) or image edition (creating cross-section diagram, etc.), and output the image to a printer.

During scanning, X-rays are generated from the x-ray tube head mounted in the arm of the scanner and the x-rays passing through a patient are then detected by the flat panel detector of the scanner under the control of the firmware inside and the console software installed on the qualified Computer. The detected x-ray absorption data is processed by the console software to reconstruct the diagnostic images. The PreXion3D Excelsior has three operation modes, CT scan, Panoramic scan and Cephalometric exposure.

The provided text describes a 510(k) premarket notification for the PreXion3D Excelsior device, a dental Cone-beam Computed Tomography (CT) system. The submission aims to establish substantial equivalence to a predicate device (K161881). The key modification is the addition of a "Complete mode" for CT scans, which simulatively creates a wider Field of View (FOV) by extending the FOV height.

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Assessment: The document states that the 3D MTF results for the Complete mode met the predicate device criteria, implying that the spatial resolution in the extended FOV is comparable. However, no specific numerical acceptance threshold or performance value for 3D MTF is provided in the document. The general acceptance criteria are implicit: the modified device should perform as well as the predicate device in relevant aspects.

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

The document does not explicitly state the sample size used for the performance testing.

• It mentions "the stitching area of the volume data of Complete mode" was assessed, but the number of cases or images is not quantified.
• Data Provenance: Not specified. It can be inferred that the testing was performed by the manufacturer, PreXion Corporation, likely in Japan, given their address. There is no information on whether the data was retrospective or prospective.

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

The document does not provide information on the number of experts used or their qualifications to establish ground truth for the test set. The assessment appears to be a technical measurement (3D MTF) rather than relying on expert radiographic interpretation as ground truth.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method. Since the primary performance testing mentioned is a technical measurement (3D MTF), an adjudication method by experts would not be applicable in this context.

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

The document does not indicate that an MRMC comparative effectiveness study was performed. The focus is on technical equivalence and performance of the device itself, not on human reader performance with or without AI assistance.

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

The performance testing mentioned ("assessment of 3D MTF") is inherently a standalone (algorithm only) evaluation, as it objectively measures image quality characteristics of the device's output without human intervention for interpretation.

7. The Type of Ground Truth Used

The ground truth for the performance testing (3D MTF) appears to be based on technical standards and predicate device criteria for image quality measurements. It is not based on expert consensus, pathology, or outcomes data.

8. The Sample Size for the Training Set

The document does not provide information on a training set sample size. This device is a CT imaging system (hardware and associated software for image reconstruction), not an AI/ML algorithm that typically requires a separate training set. The "software modifications" refer to changes that allow for the expanded FOV and reconstruction, not necessarily a machine learning model that needs a training set for diagnostic tasks.

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

Since there is no mention of a training set for an AI/ML algorithm, this information is not applicable/not provided. The "ground truth" concerning the device's functionality and image reconstruction capabilities would have been established through engineering design, simulations, and empirical testing against known physical phantoms or established imaging standards.

In summary:

This 510(k) submission primarily focuses on demonstrating technical equivalence for a modification to an existing CT device. The performance testing described is a technical assessment of image quality (3D MTF) in the newly introduced "Complete mode" to ensure it meets established (predicate device) criteria. It does not involve diagnostic performance studies with human readers, expert consensus on images, or AI/ML training data.

Ask a specific question about this device

PreXion3D Excelsior is intended to produce two dimensional digital panoranic and cephalometric images, and three dimensional digital x-ray images of the maxillofacial, and ENT (Ear, Nose and Throat) region at the direction of healthcare professionals as diagnostic support for adult and pediatric patients.

Cephalometric imaging also includes the hand wrist to obtain carpus innages for growth and maturity assessment.

PreXion3D Excelsior 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 Excelsior Image Analysis System will then perform the image analysis (2D/3D) or image edition (creating cross-section diagram, etc.), and output the image to a printer.

During scanning, X-rays are generated from the x-ray tube head mounted in the arm of the scanner and the x-rays passing through a patient are then detected by the flat panel detector of the scanner under the control of the firmware inside and the console software installed on the qualified Computer. The detected x-ray absorption data is processed by the console software to reconstruct the diagnostic images. The PreXion3D Excelsior has three operation modes, CT scan, Panoramic scan and Cephalometric exposure.

The provided text describes the PreXion3D Excelsior, a dental cone-beam computed tomography device, and its substantial equivalence to predicate devices, but it does not contain the detailed acceptance criteria or a specific study proving the device meets those specified acceptance criteria in the way requested by your prompt.

Specifically, the document states:

• Non-clinical performance data: Conformance to harmonized standards (listed in section 5.7), testing for 3D imaging performance to assess MTF (Modulation Transfer Function) for three image orientations (x, y, z) for 3D modes, and non-clinical considerations according to FDA Guidance "Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices."
• Clinical performance data: Clinical considerations according to FDA Guidance "Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices" were performed by qualified clinical assessors.

However, the document lacks specific quantitative acceptance criteria (e.g., minimum MTF values, specific accuracy thresholds for diagnostic tasks) and detailed results from a study that would demonstrate the device met those specific criteria. The evaluation focuses on substantial equivalence based on conformance to standards and general considerations rather than a detailed performance study with defined acceptance criteria and statistical analysis.

Therefore, I cannot populate the table and answer the following questions with the provided text:

1. A table of acceptance criteria and the reported device performance: The document lists standards it conforms to but does not quantify specific acceptance criteria (e.g., for image quality, diagnostic accuracy) with corresponding performance values.
2. Sample sizes used for the test set and the data provenance: Not specified.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not specified. It mentions "qualified clinical assessors" for clinical considerations but no further detail.
4. Adjudication method: Not specified.
5. Multi-reader multi-case (MRMC) comparative effectiveness study: Not mentioned.
6. Standalone (algorithm only) performance study: Not mentioned. The device is a hardware imaging system, not an AI algorithm in the typical sense for standalone performance.
7. Type of ground truth used: Not specified.
8. Sample size for the training set: Not applicable as this is a hardware device submission, not an AI algorithm requiring a training set in the machine learning sense.
9. How the ground truth for the training set was established: Not applicable.

The document's purpose is to establish substantial equivalence based on technological characteristics and general safety/effectiveness considerations, not to provide a detailed performance study against specific, quantified acceptance criteria for a novel diagnostic algorithm.

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