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The DENTIOIII series is intended for dental radiographic examination of the teeth and temporomandibular joints, specifically for panoramic and cephalometric examinations. It is to be used only by dental practitioners and/or radiologists

This equipment is a dental X-ray imaging device used for diagnostic purposes in dental examination. The operating principle of this device is obtaining the panoramic images by rotating the arm to get the recombination data, having the X-ray generator and detector rotate around the patient to irradiate the X-ray, and measuring the penetrated X-ray using the detector. When the X-ray irradiates the teeth area for instance, a large amount of X-ray is attenuated because objects such as bones are highly dense. By contrast, the X-ray permeates more through small molecules with low density such as skin or tissue measuring the X-ray is reconstructed by the software for display and analysis, the anatomical structure can be viewed for the diagnosis purposes. In addition, the Cephalo arm uses the scanning method.

This document is a 510(k) summary for the DENTIOIII series, an Extraoral Source X-Ray System. The summary focuses on establishing substantial equivalence to previously cleared predicate devices, rather than presenting a standalone clinical study to prove new acceptance criteria for an AI/CADe device. Therefore, the information required to directly answer your request about acceptance criteria and study proving device meets those criteria (especially regarding AI performance, human reader improvement, and ground truth establishment methods for large datasets) is not present in this document.

The document primarily addresses the following for comparison with predicate devices:

• Technical characteristics: X-ray tube properties, detector types, pixel size, active area, MTF, DQE, geometry, software, and compliance with electrical safety and radiation protection standards.
• Intended use: Dental radiographic examination of teeth and temporomandibular joints for panoramic and cephalometric images.

However, based on the provided document, here's what can be extracted and inferred, along with explicit statements about missing information for your specific request related to AI/CADe systems:

No AI/CADe Performance Details:
This 510(k) application is for an "Extraoral Source X-Ray System," which is a device for acquiring X-ray images. It is not an AI/CADe device that performs analysis or assists human readers. Therefore, the concepts of "acceptance criteria" and "study proving the device meets acceptance criteria" as they relate to AI/CADe performance (e.g., sensitivity, specificity, human reader improvement, ground truth establishment by experts) are not applicable to this document's content. The "acceptance criteria" discussed implicitly refer to meeting technical specifications and safety standards for an imaging device.

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

The document does not present explicit "acceptance criteria" in the format of pass/fail metrics for a clinical study assessing diagnostic performance of an AI/CADe. Instead, it compares technical specifications of the proposed DENTIOIII series to predicate devices to establish substantial equivalence.

Implicit "Acceptance Criteria" (Technical Specifications for an Imaging Device):

The performance of the device is reported through its technical specifications, compared to predicate devices. These essentially serve as the "performance" data that needs to be "accepted" as substantially equivalent.

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The Faxitron VisionCT is a Cabinet x-ray system that is used to provide two and three dimensional digital x-ray images of harvested specimens from various anatomical regions in order to provide rapid verification that the correct tissue has been excised during the biopsy procedure. Doing the verification directly in the same room or nearby enables cases to be completed faster, thus limiting the time the patient needs to be under examination. Specimen radiography can potentially limit the number of patient recalls.

The Faxitron VisionCT is specially designed for high detail radiographic imaging of surgically excised medical specimens. It is a fully shielded Cabinet X- ray System that has been designed to comply with 21 CFR 1020.40. It allows up to 4.0 times geometric magnification of excised specimens with minimal geometric distortion through the use of a focal spot size that is less than 15 microns. With optimized cabinet geometry and the superior contrast available from the low kV capability. the VisionCT provides enhanced image performance. It is configured to acquire high resolution digital images up to 15 x 15 cm in size, through the use an integrated detector and Faxitron Vision Specimen Radiography software. The Faxitron Software supports the DICOM Store, Print and Modality Worklist services.

Here's an analysis of the provided text to extract information about the acceptance criteria and the study proving device performance:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state formal acceptance criteria in a quantitative table format for the device's diagnostic performance (e.g., sensitivity, specificity for detecting specific tissue types). Instead, it focuses on technical specifications and comparability to predicate devices. The "reported device performance" is primarily descriptive of its capabilities rather than a set of performance metrics against specific targets.

However, based on the non-clinical testing data mentioned, we can infer some "performance" aspects:

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

The document states: "The non-clinical performance testing data provided in this submission includes detector Xineos 1515 imaging performance data and sample clinical images to demonstrate the capability of the VisionCT to image various anatomical regions (breast tissue, femoral head, other tissues such as fallopian tube, prostate, nasal polyp, and heart valve)."

It also mentions: "The phantom images include those provided to compare the 2D images from the predicate device and 3D images from the subject device. In addition, a uniform phantom and phantom with embedded test objects with ranges of sizes and contrasts designed to characterize CT x-ray system contrast and detail imaging performance, are also provided."

• Test Set Sample Size:
  • Clinical Images: The exact number of "sample clinical images" is not specified. It's described generally as "sample clinical images... to image various anatomical regions." This implies a limited set of images from different tissue types.
  • Phantom Images: Not specified, but includes a "uniform phantom and phantom with embedded test objects."
• Data Provenance: Not specified in the provided text. It does not mention the country of origin or whether the data was retrospective or prospective for the clinical images. Given the context of a 510(k) summary, these are likely internal validation studies.

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

The document does not specify the number of experts used or their qualifications for establishing ground truth on the "sample clinical images." The ground truth for these images would likely involve pathology or surgical confirmation of tissue excision, but this is not detailed.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method (e.g., 2+1, 3+1, none) for a diagnostic interpretation of the test set. The focus is on the device's ability to provide images and its technical performance, not on a human interpretation study.

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 or described.
• The device is a specimen X-ray system providing 2D and 3D images for rapid verification of excised tissue during biopsy. It is not an AI-assisted diagnostic device, and therefore, no comparison of human readers with or without AI assistance is mentioned or relevant to this submission.

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

• A standalone performance evaluation of the imaging system was done. The "non-clinical performance testing data provided" focuses on the device's technical specifications, imaging capabilities (2D and 3D), image quality using phantoms, and artifact analysis. This testing demonstrates the algorithm's (and hardware's) ability to produce images and reconstruct 3D data independently.
• However, it's crucial to distinguish this from an "algorithm only" performance for a diagnostic task. The device itself provides images; the human still performs the "verification that the correct tissue has been excised." The standalone performance here refers to the system's ability to generate high-quality images, not its ability to make a diagnostic call itself.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• For the "sample clinical images," the implicit ground truth for verifying "correct tissue has been excised" would likely be pathology results or surgical confirmation that the specimen indeed represents the intended tissue. However, this is not explicitly stated.
• For the phantom images, the ground truth is the known characteristics of the phantom (e.g., sizes and contrasts of embedded test objects).
• For electrical safety and EMC, the ground truth is compliance with established international standards (IEC 61010 series, IEC 61326 series).

8. The Sample Size for the Training Set

The document does not mention a training set in the context of machine learning or AI. The Faxitron VisionCT is an imaging device, not an AI-based diagnostic algorithm that requires a separate training set. The "Faxitron Vision Software" mentioned is an operating software for the device, upgraded from a predicate, and was "thoroughly tested," but not in the sense of a machine learning training process.

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

As no training set (for machine learning) is discussed, this question is not applicable.

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