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ORTHOPANTOMOGRAPH™ OP 3D is an x-ray device that is intended to be used for imaging of adult and pediatric patients. The device can be configured to take panoramic, cephalometric, or 3D images of the cranio-maxillofacial complex for use in diagnostic support. The device can also be configured to take carpus images.

ORTHOPANTOMOGRAPH™ OP 3D must only be used and operated by dentist and other qualified professionals.

ORTHOPANTOMOGRAPH™ OP 3D x-ray unit is a software controlled diagnostic dental X-ray equipment for producing panoramic, cephalometric and 3D images of the cranio-dentomaxillofacial complex of patient head. The ORTHOPANTOMOGRAPH™ OP 3D has panoramic programs (adult, pediatric, TMJ, bitewing and partial), cephalometric programs (lateral and PA, also including carpus imaging) and 3D (CBCT) programs with different 3D Field of View configurations. The components of the device include column, carriage, rotating unit (containing tube head, sensors and collimator), upper shelf, patient head support, ceph attachment (containing ceph tube head and patient support) and driver software including image reconstruction. Workstation software for viewing the images is not included in this submission.

The proposed device utilizes cone beam X-ray technology, which generates conical x-ray beams that rotate around the patient's head and incident upon the receptor that generate sufficiently contrasted images. Quality of the images depends on the level and amount of X-ray energy delivered to the tissue. When interpreted by a trained physician, these images provide useful diagnostic information.

The provided text is a 510(k) summary for the ORTHOPANTOMOGRAPH™ OP 3D, a medical device. It focuses on demonstrating substantial equivalence to a predicate device (ORTHOPANTOMOGRAPH™ OP300) rather than presenting a study proving a device meets specific acceptance criteria based on its performance against a defined ground truth.

Therefore, the document does not contain the specific information required to complete the detailed table and answer most of your questions regarding acceptance criteria and performance studies where an AI or algorithm's diagnostic performance is being evaluated. The submission is for an imaging device, and the "performance data" mentioned refers to engineering and quality assurance testing (electrical, mechanical, safety, EMC, biocompatibility, software level of concern), and subjective review of bench test images, not a clinical diagnostic performance study of the images themselves.

Here's what can be extracted and what cannot:

What can be extracted:

• 1. A table of acceptance criteria and the reported device performance: This document does not provide a table of performance-based acceptance criteria for diagnostic accuracy (e.g., sensitivity, specificity, AUC) or human reader improvement with AI. Instead, it compares technical characteristics to a predicate device. The "performance" mentioned refers to engineering and quality assurance tests.
• Conclusion as to Substantial Equivalence: The document states: "The proposed device does not introduce a fundamentally new scientific technology and the nonclinical tests demonstrate that the device is substantially equivalent with regards to safety and effectiveness. All internal verification and validation has been completed successfully." This is the general "acceptance" for the 510(k) submission: proving substantial equivalence, not meeting specific quantitative performance metrics for diagnostic accuracy.

What cannot be extracted (as per the document):

• 2. Sample sized used for the test set and the data provenance: Not applicable in the context of diagnostic performance. The document mentions "bench test images acquired using ORTHOPANTOMOGRAPH OP™ 3D." No sample size or provenance for this set is given, and it's for subjective quality review, not a formal test set for diagnostic accuracy.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: The document states "bench test images acquired using ORTHOPANTOMOGRAPH OP™ 3D was reviewed by qualified clinicians to be of acceptable quality for the proposed Intended Use." It does not specify the number or qualifications of these clinicians, and this is for subjective image quality, not establishing diagnostic ground truth.
• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
• 5. If a multi reader multi case (MRMC) comparative effectiveness study was done: "No clinical images were utilized for this submission. Clinical data was not needed to support substantial equivalence." This means no MRMC study or any clinical performance study was conducted or submitted.
• 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 AI/algorithm for diagnostic interpretation.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable. "Bench test images" were subjectively reviewed for "acceptable quality."
• 8. The sample size for the training set: Not applicable, as this is an imaging device, not an AI/algorithm that requires a training set.
• 9. How the ground truth for the training set was established: Not applicable.

Summary based on the provided text:

This 510(k) pertains to a Computed Tomography X-ray system (ORTHOPANTOMOGRAPH™ OP 3D). The acceptance criteria for this type of submission are primarily demonstrating substantial equivalence to a predicate device (ORTHOPANTOMOGRAPH™ OP300) in terms of intended use, technological characteristics, and safety/effectiveness through non-clinical (engineering, design verification, and validation) testing. It explicitly states that no clinical performance data or images were utilized for this submission and "Clinical data was not needed to support substantial equivalence." Therefore, the document does not describe a study proving the device meets acceptance criteria related to diagnostic performance, AI accuracy, or human reader improvement with AI assistance.

The closest thing to "acceptance criteria" and "performance" described are:

The document does not relate to an AI device or a comparative effectiveness study involving human readers and AI assistance. It describes the regulatory submission for an X-ray imaging system.

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ORTHOPANTOMOGRAPH™ OP 3D is an x-ray device to take panoramic and 3D images of the cranio-maxillofacial complex for use in diagnostic support.

ORTHOPANTOMOGRAPH™ OP 3D must only be used and operated by dentist and other qualified professionals.

ORTHOPANTOMOGRAPH™ OP 3D x-ray unit is a software controlled diagnostic dental X-ray equipment for producing panoramic and 3D images of the cranio-dentomaxillofacial complex of patient head. The ORTHOPANTOMOGRAPH™ OP 3D has panoramic programs (adult, child, TMJ, BW and partial) and 3D (CBCT) programs with different 3D Field of View configurations. The components of the device include column, carriage, rotating unit (containing tube head, sensors and collimator), upper shelf, patient head support and driver software including image reconstruction. Workstation software for viewing the images is not included in this submission.

The proposed device utilizes cone beam X-ray technology, which generates conical x-ray beams that rotate around the patient's head and incident upon the receptor that generate sufficiently contrasted images. Quality of the images depends on the level and amount of X-ray energy delivered to the tissue. When interpreted by a trained physician, these images provide useful diagnostic information.

The provided text is related to a 510(k) premarket notification for a dental x-ray device (ORTHOPANTOMOGRAPH™ OP 3D). It primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study with specific acceptance criteria and detailed performance data often seen in AI/CAD device submissions.

Based on the provided information, I can answer some of your questions, but many of the requested details are not available or applicable to this type of submission.

Here's an analysis based on the document:

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

The document does not explicitly present a table of acceptance criteria for diagnostic performance or clinical effectiveness of the device in the way you might expect for an AI/CAD system. Instead, the "acceptance criteria" are implied by the conformity to various standards and the demonstration of "equivalent diagnostic quality" to a predicate device through non-clinical testing and clinician review of bench test images.

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 primarily discusses "bench test images" and "non-clinical performance data." It explicitly states: "No clinical images were utilized for this submission. Clinical data was not needed to support substantial equivalence." Therefore, no sample size, data provenance, or other details related to a clinical test set are provided. The "test set" for diagnostic quality appears to be composed of "bench test images."

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)

The document mentions "qualified clinicians" reviewed the bench test images for acceptable quality. However, it does not specify:

• The number of clinicians.
• Their specific qualifications (e.g., years of experience, specialty).
• How ground truth was established, beyond their review for "acceptable quality."

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

Not applicable. There's no mention of a formal adjudication method for the "qualified clinicians" reviewing bench test images.

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 study was performed. The device is an imaging system, not an AI/CAD system assisting human readers. The submission relies on demonstrating substantial equivalence to a predicate imaging device through technical and performance comparisons.

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

This is not an AI/CAD algorithm. It is an x-ray imaging device. The performance evaluated is the image quality and physical/electrical safety of the device itself.

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

For the "bench test images" that were reviewed, the "ground truth" seems to be an implicit assessment by "qualified clinicians" that the image quality was "acceptable" for diagnostic support. No formal "ground truth" (e.g., pathology, clinical outcomes) for specific diagnoses is mentioned, as this was not a diagnostic accuracy study.

8. The sample size for the training set

Not applicable. This is an X-ray imaging device, not an AI/ML 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 for an AI/ML algorithm.

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