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Brasseler GEM is a USB-driven digital intraoral x-ray sensor which is intended to acquire dental radiographic images. Brasseler GEM must be operated by healthcare professionals who have been trained and are competent using various methods of acquiring radiographic images of dental anatomy. Brasseler GEM can be used with dental positioning devices and holders to assist with aligning an x-ray source beam with the sensor and anatomy. Brasseler GEM can also be aligned by hand with assistance of patient.

Brasseler GEM is a USB-driven digital sensor designed for health care professionals already acquainted with the standard procedures for acquiring dental intraoral radiographs. Digital x-ray imaging is an aide for diagnosis and should always be confirmed by the doctor using appropriate additional diagnostic aides, professional judgement, and experience.

Brasseler GEM10 is for pediatric use and adult periapical use, GEM15 is for pediatric and adult bitewing and periapical use, and GEM20 is for adult use including bitewings. The Brasseler GEM design uses advanced ergonomic principles with four beveled corners, a moderate profile, and a rounded casing providing enhanced comfort for patients. Brasseler GEM is positioned in the patient's mouth in the same manner as intraoral film is positioned.

Brasseler GEM has a CMOS x-ray imager that creates a digital image from x-ray doses perceptible by the sensor. The digital image created is immediately visible on the screen of a personal computer connected to Brasseler GEM through the standard USB port. Image analysis software is not part of the submission. For Brasseler GEM to be used in a dental practice, an optional image analysis software will be necessary. Only with image analysis software can acquired images be optimized for specific diagnostic tasks, archived as image files, and printed out on a suitable printer. Software provides drivers and utilities for x-ray dose optimization, sensor activation and settings. Brasseler GEM capture x-ray images suitable for recognition of normal anatomical structures, dental pathologies, and abnormal conditions. Inadequate images may result in misdiagnosis thereby subjecting the patient to incorrect or unnecessary dental procedures that would present an unacceptable risk to the patient. Functions of the Brasseler GEM detector are controlled by software (firmware). The software of Brasseler GEM is of Moderate Level of Concern and is not based on the software of the predicate, Clio Prime.

Brasseler GEM shall be operated by healthcare professionals who are educated and competent to perform the acquisition of dental intraoral radiographs. Brasseler GEM can be used either in combination with positioners manufactured to facilitate the positioning and alignment with an x-ray beam, or it may also be positioned by hand with the assistance of the patient. Brasseler GEM can be used with patients of any age, providing the correct positioning of the sensor in the patient mouth can be realized. Using Brasseler GEM is a suitable diagnostic method and may offer reduced radiation exposure compared to analog procedures. Available software image enhancement tools may enhance sensitivity and consequently reduce errors introduced by subjective analysis. Brasseler GEM can perform and achieve the same type of two-dimensional images as conventional (traditional) film sizes 0, 1 and 2. Brasseler GEM cannot be used to, or as a substitution for extraoral or other types of dental x-ray. When using Brasseler GEM and software as a diagnostic aide, clinical experience and a combination of the diagnostic aides should be used to form a diagnosis and should not be solely relied upon for diagnosis.

The provided text describes Brasseler GEM, a digital intraoral x-ray sensor, and its equivalence to a predicate device, Clio Prime. The document primarily focuses on demonstrating substantial equivalence through comparison of technical specifications, rather than providing detailed acceptance criteria from a clinical study for diagnostic performance.

Therefore, the response below will focus on the information available in the provided text, and will explicitly state when information requested cannot be found.

Acceptance Criteria and Device Performance Study for Brasseler GEM

The provided document, a 510(k) summary, primarily relies on bench testing and comparison to a predicate device (SOTA Imaging's Clio Prime) to establish substantial equivalence. It does not detail specific diagnostic performance acceptance criteria in the manner one might find for an AI-based diagnostic device (e.g., sensitivity, specificity thresholds). Instead, the acceptance is based on demonstrating that the Brasseler GEM is as safe and effective as the legally marketed predicate device.

The "study that proves the device meets the acceptance criteria" largely refers to the comparative analysis presented through the Comparison Table and the stated equivalence of technical performance metrics like MTF and DQE.

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

Based on the provided document, the "acceptance criteria" are implied by the demonstration of substantial equivalence to the predicate device, Clio Prime. The performance is assessed by comparing technical specifications and affirming identical/equivalent performance in areas relevant to image quality.

Note: The document emphasizes that "Brasseler GEM is not only similar in performance as Clio Prime but is also safe and effective based on performance testing." The performance testing mentioned is primarily bench testing and direct comparison of specifications.

2. Sample sizes used for the test set and the data provenance

The document refers to "extensive bench testing" and "clinical images" for performance testing.

• Test Set Sample Size: The specific sample sizes for bench testing are not explicitly quantified in terms of number of devices or number of test cases run beyond "extensive bench testing." For "clinical images," no specific number of images is provided, nor the number of patients/cases represented.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective). The document only states "RealCloud Imaging has provided clinical images."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable / Not specified. The study described focuses on technical equivalence and image acquisition capabilities, not on diagnostic accuracy requiring ground truth established by experts for clinical images. The text states: "Digital x-ray imaging is an aide for diagnosis and should always be confirmed by the doctor using appropriate additional diagnostic aides, professional judgement, and experience." This implies the device provides images for human interpretation, rather than an AI producing a diagnosis that needs expert ground truth for validation.

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

Not applicable / Not specified. As the study is focused on technical equivalence rather than diagnostic accuracy determined by human readers, an adjudication method for a test set is not detailed.

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. An MRMC comparative effectiveness study was not performed as this is a device for image acquisition, not an AI assisting human readers with diagnosis. There is no AI component described that would augment human diagnostic performance.

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

Yes, in a sense. The "performance testing" described (bench tests, MTF, DQE comparison) reflects the standalone performance of the Brasseler GEM sensor and its ability to acquire images, independent of human interpretation or an AI algorithm. However, this is not an "algorithm only" performance meant for diagnostic output. The text states: "Image analysis software is not part of the submission... only with image analysis software can acquired images be optimized for specific diagnostic tasks, archived as image files, and printed out on a suitable printer." This implies the device itself is an image capture hardware without integrated diagnostic algorithms.

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

Not applicable / Not specified. The "performance testing" detailed focuses on technical specifications (MTF, DQE validation against a predicate device) and basic functionality via "clinical images" to show the system works as intended. There is no mention of "ground truth" for diagnostic accuracy established by expert consensus, pathology, or outcomes data, as the device's role is imaging, not diagnosis.

8. The sample size for the training set

Not applicable / Not specified. This device is an x-ray sensor (hardware), not an AI algorithm that requires a training set. The firmware of the device is mentioned as "software... of Moderate Level of Concern," but this refers to its operational control, not an AI model for image interpretation or diagnosis.

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

Not applicable / Not specified. As this device is a hardware sensor and not an AI algorithm requiring a training set, the concept of establishing ground truth for a training set does not apply here.

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