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T2 PLUS is digital X-Ray imaging equipment for dental professionals that converts X-Ray signals into digital signals to acquire 2D images and reconstruct them into 3D images, using Panoramic (PANO), Cephalometric (CEPH), and Computed Tomography (CT) technology for the diagnosis of the anatomical structure of the oral and maxillofacial area. T2-CS-P provides all three modes, whereas T2-C-P provides the first two modes and excludes the CEPH mode. The devices are operated and used by physicians, dentist and X-Ray technicians.

Use is contraindicated for patients with a head circumference of less than 48 cm or those aged 2 years or younger.

T2 PLUS is a digital X-ray CT, panoramic, and Cephalo imaging system device composed of X-ray generator, X-ray controller, X-ray supporter, image processing unit (sensor), PC, and software. The apparatus attached to the equipment column is a structure that can be rotated 360 by the system control unit. This system control unit actuates the motor control, X-ray generator, and image processing unit (sensor). The height controlling unit controls the column and adjusts the height of the equipment. The X-ray generator and image processing unit (sensor) are attached to the rotating apparatus. When the rotating apparatus starts the rotation, X-ray is irradiated from the Xray generator (generating unit). This X-ray irradiation penetrates the subject and reaches the image processing unit (sensor), and then is converted into electric signals to secure imagery information. Inside the imaging section of the image processing unit (sensor), real time X-ray input is converted into electric signals and consecutively combined, resulting in imagery information. The combined panoramic imagery information is then sent to the PC and saved in patient management software.

The provided document describes the T2 Plus digital X-ray imaging equipment, focusing on its substantial equivalence to a previously cleared predicate device rather than presenting a detailed study with acceptance criteria for a novel AI or diagnostic algorithm. Therefore, many of the requested elements for a study proving a device meets acceptance criteria are not explicitly stated or applicable in this 510(k) summary.

However, I can extract the information pertinent to the device's performance evaluation and a "study" conducted to support substantial equivalence.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria & Reported Device Performance:

The document does not explicitly state quantitative "acceptance criteria" for diagnostic performance in terms of metrics like sensitivity, specificity, or AUC, as it's a submission for substantial equivalence of imaging equipment, not a new diagnostic algorithm that interprets images. The study's aim was to show equivalent image quality to the predicate device.

Note: The acceptance criteria are inferred from the study's objective: to demonstrate equivalent image quality for diagnostic purposes. No specific quantitative thresholds (e.g., "score must be X or higher") are provided.

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

• Test Set Sample Size: A total of 31 cases were reviewed.
  • CT: 10 cases
  • Cephalometric (Ceph): 10 cases
  • Panoramic (Pano): 11 cases
  • For each case, both the proposed device (T2-CS-P) and the predicate device (T2-CS) images were reviewed, meaning 31 image sets from T2-CS-P and 31 image sets from T2-CS.
• Data Provenance: Not explicitly stated regarding country of origin. The study states images were from "patients of the same gender and similar age group, under identical conditions." It implies retrospective image collection from existing patients, as it refers to "patients" not trial participants.
  • Given the manufacturer is based in the Republic of Korea, the data may originate from there, but this is not explicitly stated.

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

• Number of Experts: "A radiologist" (singular) was used as the "evaluator."
• Qualifications of Experts: The qualification provided is "a radiologist." No further details on years of experience, sub-specialty, or board certification are given.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not applicable/None explicitly described. Since only one radiologist served as the evaluator, there was no need for adjudication among multiple readers. The radiologist's assessment served as the primary evaluation.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done and Effect Size:

• MRMC Study: No. This was not an MRMC study. It involved a single evaluator (radiologist) comparing images from the proposed device and predicate device.
• Effect Size: Not applicable, as no MRMC study was performed and no quantitative diagnostic performance metrics (e.g., sensitivity, specificity, AUC) were reported. The evaluation was qualitative ("scores... were identical").

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

• Standalone Study: Not applicable. The device is imaging equipment. The "study" evaluated the image quality of the equipment as interpreted by a human radiologist, not the performance of an independent AI algorithm. There is no AI component described that would operate in a "standalone" mode for diagnostic interpretation.

7. The Type of Ground Truth Used:

• Type of Ground Truth: The "ground truth" for purpose of this image quality comparison was the expert consensus/opinion of a single radiologist regarding the image quality and its adequacy for diagnosis. This is not a "true" clinical ground truth from pathology or long-term outcomes, but rather a subjective assessment of image utility.

8. The Sample Size for the Training Set:

• This information is not provided. The document focuses on performance testing for substantial equivalence, not on the development of an AI model that requires a training set. The device is X-ray imaging equipment, not an AI diagnostic algorithm, so the concept of a "training set" for the device itself is not applicable in the typical sense of machine learning.

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

• Not applicable, as there is no description of a "training set" for an AI model within the context of this device's performance evaluation.

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