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The NewTom 5G Computed Tomography X-Ray System is a dedicated X-ray imaging device that acquires a 360-degree rotational X-ray sequence of images for use as diagnostic support in radiology of the dento-maxillo-facial complex and in the field of maxillofacial surgery.

The NewTom 5G accomplishes this task by reconstructing a three-dimensional matrix of the examined volume, producing two-dimensional views of this volume and displaying both two-dimensional images and three-dimensional renderings.

The NewTom 5G is a dedicated X-ray imaging device that acquires a 360-degree rotational X-ray sequence of images. It reconstructs a three-dimensional matrix of the examined volume and produces two-dimensional views of such volume, displaying both two- and three-dimensional images. The NewTom 5G can measure distances and thickness on two-dimensional images. Such images can be printed or exported on magnetic and optical media.

The NewTom 5G hardware, including a scanner unit (comprised of the X-ray source, flat panel detector and the motorized arm) and a motorized patient support, facilitates the acquisition of a full X-ray sequence by the device software. The NewTom 5G software runs on an x86 architecture based workstation. The NewTom 5G reconstructs a three-dimensional model of X-ray images similar to the three-dimensional model obtained using the parent NewTom VG Computed Tomography X-Ray System.

This document describes the NewTom 5G Computed Tomography X-Ray System. The provided text is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing detailed acceptance criteria and a standalone study for a new AI/software component. Therefore, much of the requested information regarding AI device performance, sample sizes for test/training sets, expert consensus for ground truth, and MRMC studies is not available in the provided text.

The submission highlights minor technological differences between the NewTom 5G and its predicate device, the NewTom VG, primarily concerning patient positioning and image quality/reliability improvements through hardware and software modifications. The core imaging principle (360-degree rotational X-ray sequence to reconstruct 3D models for diagnostic support in dento-maxillofacial radiology) remains the same.

Here's the information that can be extracted or inferred from the provided text, along with a clear indication of what is not available:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Sizes for Test Set and Data Provenance

• Sample Size for Test Set: Not specified. The document primarily focuses on demonstrating substantial equivalence through technological comparison and general safety/effectiveness testing, not a detailed clinical performance study with a specific test set for AI.
• Data Provenance: Not specified.

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

• Number of Experts: Not applicable/not specified. The provided text does not describe a clinical study involving experts establishing ground truth for a test set. This type of information is typically part of a performance study for AI/software, which is not the primary focus of this 510(k) summary.
• Qualifications of Experts: Not applicable/not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable/not specified.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• MRMC Study Done? No. The document does not describe an MRMC study comparing human readers with and without AI assistance. This device is an imaging system, and while it produces images that humans interpret, it doesn't describe an AI-powered diagnostic aid that would typically be evaluated with an MRMC study in this context.
• Effect Size of Human Readers with/without AI: Not applicable, as no such study was described.

6. Standalone (Algorithm Only) Performance Study

• Standalone Study Done? No. This submission describes an imaging device, not a standalone algorithm. The "software" mentioned is for image acquisition and reconstruction, not autonomous diagnostic interpretation.

7. Type of Ground Truth Used

• Type of Ground Truth: Not applicable/not specified in the context of a performance study. The "ground truth" for this device's evaluation is primarily its ability to produce diagnostically acceptable images and function safely and effectively, demonstrating substantial equivalence to its predicate device. This is assessed through engineering tests, V&V, and comparison of technical characteristics, not by comparing its outputs against independent clinical ground truth like pathology for specific disease detection in a clinical study.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable/not specified. The device is not described as using machine learning models that require a "training set" in the conventional sense for AI clinical applications. Its software performs image reconstruction, not learned pattern recognition from a large dataset.

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

• How Ground Truth for Training Set Was Established: Not applicable/not specified, as there is no mention of a training set for an AI algorithm.

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