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Dental Sensors, models Tuxdeluxe, 6100B-Size 1, and 6101B-Size 2 are intended to collect dental x-ray photons and convert them into electronic impulses that may be stored, viewed, and manipulated for diagnostic use by dentists. This device must only be used in hospital environments, clinics or dental offices by trained and qualified dental personnel, and not used in the oxygen rich environment. This device is suitable for providing dental radiography imaging for both adult and pediatric patients.

This Intraoral Digital Imaging Sensor employs CMOS (Complementary Metal-Oxide-Semiconductor), protective optical fiber and scintillator. This sensor was developed to obtain a high-quality x-ray image from the human mouth and its structures. The acquisition process is made by positioning the sensor inside the mouth, behind the structure you want to perform the exam. The structure must be exposed to an x-ray dose using an external source. Once exposed, the sensor performs a conversion of the x-ray photons into a digital signal and transfers it to a computer through USB connection (Universal Serial Bus). The x-ray generator (an integral part of a complete dental x-ray system) is not part of the device. Device sensor sizes: Size 1: 24.1 x 36.2 x 5.9mm Size 2: 30.5 x 42.8 x 5.7mm. The I/O sensor is compatible with the following cleared software: Xray Vision / XV Capture (XV Web) / Curve / Sota. This software must be purchased separately by the end user.

The provided document is a 510(k) Premarket Notification from the FDA for the Tuxdeluxe Intraoral Dental Digital Imaging Sensor. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed acceptance criteria study for an AI/CAD system. Therefore, I cannot generate the specific information requested about acceptance criteria and a study proving a device meets them in the context of an AI/CAD system.

The document discusses the physical characteristics and general performance of the dental sensor, comparing it to an already cleared predicate device (NanoPix). The "study" mentioned for this device is primarily non-clinical testing to ensure safety, EMC, biocompatibility, and general performance, and an image quality analysis by a dentist. There is no mention of an AI algorithm or a comparative effectiveness study involving human readers with and without AI assistance.

Here's how to break down why the requested information cannot be fully provided based on the document:

• The Device: The Tuxdeluxe is a dental imaging sensor (hardware), not an AI/CAD software or system. It collects X-ray photons and converts them into electronic impulses for viewing. It does not perform diagnostic algorithms or AI-driven analysis.
• The Goal of the Submission: The 510(k) submission aims to prove "substantial equivalence" to a predicate device. This is a common regulatory pathway for medical devices that are similar in technology and intended use to devices already on the market. It does not require a full clinical trial to prove efficacy in the same way a novel drug or a high-risk AI algorithm might.
• "Acceptance Criteria" for this device: For this hardware device, acceptance criteria revolve around technical specifications (pixel size, line pairs/mm, DQE, operating temperature, etc.), safety standards (IEC 60601-1, EMC, usability), and basic image quality deemed suitable for diagnostic use by a qualified professional. They are not about the performance of an AI algorithm (e.g., sensitivity, specificity for detecting a disease).

However, I can extract the relevant information that is present and explain why other requested information is absent:

Acceptance Criteria and Device Performance (Based on "Comparison with predicate device" and "Non-clinical Testing")

Information Not Applicable or Not Provided in the Document (due to the nature of the device and submission type):

2. Sample size used for the test set and the data provenance: This refers to the number of images/cases used in a study evaluating a diagnostic algorithm. For this hardware device, there wasn't a "test set" of images in that sense for an algorithmic performance evaluation. The "image quality analysis" by the dentist is mentioned, but the number of images reviewed or their provenance is not specified.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Ground truth establishment (often by multiple experts) is for diagnostic algorithm studies. Here, a "USA Board Certified Dentist" reviewed images for general clinical acceptability, but not to establish ground truth for an AI system.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable for this type of hardware device submission.
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: Not applicable. This is a hardware device, not an AI/CAD system designed to assist human readers. The document explicitly states "Clinical testing is not required for a finding of substantial equivalence."
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. There is no diagnostic algorithm in this device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable as there's no diagnostic algorithm performance being evaluated against a ground truth. The "image quality analysis" by a single dentist is simply to confirm the images produced are acceptable.
8. The sample size for the training set: Not applicable. This is a hardware device, not a machine learning model requiring a training set.
9. How the ground truth for the training set was established: Not applicable for the same reason as above.

In summary, the provided document describes the regulatory approval of a dental imaging hardware device based on substantial equivalence to an existing predicate. It does not provide details about an AI/CAD system's acceptance criteria or performance study, as such studies would be required for AI-driven diagnostic software.

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The Intraoral Dental Digital Imaging Sensor is aimed at the acquisition of intra-oral medical images from teeth, jaw and oral structure, only for exclusive dental use and must be handled by trained and qualified health professionals.

This Intraoral Dental Digital Imaging Sensor employs CMOS (Complementary Metal-Oxide-Semiconductor), protective optical fiber and scintillator. There are three names proposed for this device: EAGLE S/ SAEVO SLIM/D700 SLIM. The three models are all identical except for the name. This sensor was developed to obtain a high-quality x-ray image from the human arch and its structures. The acquisition process is made by positioning the sensor inside the mouth, behind the structure you want to perform the exam. The structure must be exposed to an x-ray dose using an external source. Once exposed, the sensor performs a conversion of the x-ray photons into a digital sign and transfers it to a computer through USB connection (Universal Serial Bus). The device performs the acquisition by positioning the sensor inside of the patient's mouth, behind the structure to be examined. The structure should be exposed to a dose of x-rays, using an external source. Once exposed, the sensor performs a conversion of the x-ray photons into a digital signal and then transfers it to a computer via a Universal Serial Bus (USB) connection. The x-ray generator (an integral part of a complete dental x-ray system) is not part of the device.

The provided text describes the marketing submission for an Intraoral Dental Digital Imaging Sensor (Eagle S/Saevo Slim/D700 Slim) and its claim of substantial equivalence to a predicate device. However, it does not contain detailed acceptance criteria or a comprehensive study demonstrating performance against specific numerical or qualitative endpoints. The document primarily focuses on non-clinical testing (safety, EMC, biocompatibility) and a general statement about clinical images.

Here's an analysis of what information is available and what is missing, structured according to your request:

Acceptance Criteria and Reported Device Performance

The document does not explicitly define acceptance criteria as pass/fail thresholds for specific performance metrics. Instead, it presents a comparison table and discusses general improvements over the predicate device.

Study Details

1. Sample size used for the test set and the data provenance:

   • Sample Size: Not explicitly stated for the "clinical images" or "submitted intra-oral radiographs."
   • Data Provenance: Not specified (e.g., country of origin, retrospective or prospective). The document only mentions "submitted intra-oral radiographs" and "adequate clinical images."

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

   • Number of Experts: One expert was mentioned for the image quality analysis.
   • Qualifications: "USA Board Certified Radiologist." No mention of years of experience.

3. Adjudication method for the test set:

   • Adjudication Method: Not specified. It mentions a single "USA Board Certified Radiologist" performed an "image quality analysis" comparing the predicate to the new device. This suggests a sole expert review rather than a multi-reader adjudication process (e.g., 2+1 or 3+1).

4. 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:

   • MRMC Study: No. The device is an imaging sensor, not an AI-assisted diagnostic tool for humans, so an MRMC study related to AI assistance for human readers is not relevant in this context. The clinical evaluation mentioned focused on inherent image quality of the sensor itself.

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

   • Standalone Performance: Yes, in the sense that the sensor's technical performance (resolution, MTF, DQE) was evaluated through bench testing, which is an algorithm/device-only evaluation. The "adequate clinical images" and radiologist review also reflect the standalone performance of the imaging sensor.

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

   • Ground Truth (for image quality): Expert opinion/comparison. The "USA Board Certified Radiologist" compared images from the predicate and the new device. For the technical metrics (MTF, DQE, resolution), these are physical measurements rather than clinical ground truth established by pathology or outcomes.

7. The sample size for the training set:

   • Sample Size for Training Set: Not applicable. This document describes an imaging sensor, not a machine learning or AI algorithm that requires a training set.

8. How the ground truth for the training set was established:

   • Ground Truth for Training Set: Not applicable (as above).

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The MIDWEST® RDH Freedom® Cordless Prophy System with SmartMode™ is a high-performance cordless prophylaxis handpiece with an optional wireless foot pedal for use with NUPRO Freedom® disposable prophy angles in a hygiene operatory to perform cleaning and polishing procedures on teeth.

The proposed device, MIDWEST® RDH Freedom® Cordless Prophy System with SmartMode™ Technology, is a cordless handpiece which is intended for use by a dental clinician during dental cleaning and polishing procedures. The proposed device, MIDWEST® RDH Freedom® Cordless Prophy System with SmartMode™ Technology, is comprised of an electric motor-driven prophylaxis cordless handpiece (consisting of an inner module and outer sheath), a direct current (DC) powered charging base for cordless handpiece, cradle for cordless handpiece, direct current (DC) power supply and cord, and uses proprietary disposable prophy angles (DPAs). The proposed device, MIDWEST® RDH Freedom® Cordless Prophy System with SmartMode™ Technology, uses a USB mini connector to provide battery charging power to the charging base for the cordless handpiece and operates using Bluetooth low energy (BLE) wireless radio frequency. An optional wireless foot pedal is sold separately.

This document describes the Dentsply Sirona MIDWEST® RDH Freedom® Cordless Prophy System with SmartMode™ Technology (K160825), a dental handpiece used for cleaning and polishing teeth. The submission compares this new device to a predicate device, the MIDWEST® RDH Freedom® Cordless System (K110753).

Acceptance Criteria and Device Performance:

The document primarily focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance data. It does not explicitly state acceptance criteria in a quantitative, pass/fail manner for features beyond "Pass," but rather compares performance to the predicate device and internal Dentsply Professional test methods.

Study Information:

1. Sample Size used for the test set and the data provenance:
   The document does not specify exact sample sizes for the non-clinical performance tests. It mentions "multiple professional evaluations with typodonts" for the clinical performance section, but does not quantify the number of typodonts or treatments. The data provenance is internal to DENTSPLY Professional ("DENTSPLY Professional Internal Test Method"). The studies are effectively prospective as they were conducted to test the performance of the proposed device. The country of origin is not explicitly stated but can be inferred as the US, given the FDA submission.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
   The document states that "Professional evaluations were conducted considering the proposed device intended use, users, and use environments. All studies were completed by practicing registered dental hygienists." The number of dental hygienists is not specified, nor are their exact qualifications beyond "practicing registered dental hygienists."

3. Adjudication method for the test set:
   The document does not describe a formal adjudication method (e.g., 2+1, 3+1) for the non-clinical or clinical performance evaluations. The results are presented as "Pass" based on internal test methods, implying a direct comparison to pre-defined technical specifications or successful operation.

4. 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 comparative effectiveness study was done. This device is a dental handpiece, not an AI-powered diagnostic tool, so such a study would not be applicable. The "SmartMode™ Technology" refers to an automated control mechanism based on force applied, not an AI feature involving human "readers" or diagnostic improvement.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
   The non-clinical performance tests described, particularly those for SmartMode™ functionality, speed, and torque, represent a form of standalone performance evaluation of the device's automated capabilities. These tests verify the device's technical specifications and how it responds to various inputs (e.g., applied force, different DPAs).

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
   For the non-clinical tests, the "ground truth" is based on internal engineering specifications and standards (e.g., "meet specifications," "acceptable per design specifications," "fluid does not accumulate"). For the "clinical human factor validations," the "ground truth" seems to be the absence of "unacceptable risks or unacceptable use related hazards related to SmartMode™ Technology" as assessed by practicing registered dental hygienists. This implies an expert assessment of performance and safety on typodonts.

7. The sample size for the training set:
   Not applicable. This is a medical device clearance, not an AI model requiring a training set. The "SmartMode™ Technology" is described as a control mechanism, not a learned AI algorithm.

8. How the ground truth for the training set was established:
   Not applicable, as there is no training set for an AI model.

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