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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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DC-Air™, Athlos-1, Athlos-Air are intended to be used for a radiographic examination by a dental professional to assist in the diagnosing of diseases of the teeth, jaw, and oral structures.

DC-Air™, Athlos-1, Athlos-Air are suitable for general populations.

The subject device DC-Air™ (also known as Athlos-Air) is a wireless intraoral digital X-ray system that comprises of three (3) main components:

• (1) An intraoral X-ray image detector (sensor) with rechargeable battery for capturing X-ray images and which connects to the docking station via a wireless communications protocol (Bluetooth 5.0),
• (2) A docking station that acts as the receiver of the data (X-ray image) sent by the detector and which forwards the data to the operator's personal computer (PC) via USB connection. Also, the docking station functions as a charging station of the detector, and
• (3) An Imaging Software package.

The provided text describes the regulatory filing for the DC-Air™ device, focusing on its substantial equivalence to a predicate device (QuickRay HD). While it mentions performance data, it does not detail a comprehensive study with specific acceptance criteria, corresponding performance metrics, sample sizes, expert ground truth establishment, or MRMC studies that would typically be required to prove device performance against predefined acceptance criteria. Instead, it provides a general statement that images were "examined by doctors... and found to be diagnostically relevant and reliable" (Page 12, Performance Data 8).

Therefore, I cannot fulfill all parts of your request with the provided input. I will highlight what information is present and indicate what is missing.

Missing Information:

• Specific Acceptance Criteria: The document does not list quantitative acceptance criteria (e.g., minimum sensitivity, specificity, or AUC).
• Reported Device Performance against Acceptance Criteria: Since acceptance criteria are not stated, there is no direct table showing the device meeting these criteria. The document states images were "diagnostically relevant and reliable" but provides no quantitative metrics.
• Sample Size for the Test Set: No specific number of images or patients in a test set is provided.
• Data Provenance (Country of Origin, Retrospective/Prospective): This information is not explicitly stated for the "examined images."
• Number of Experts and Qualifications for Ground Truth: While three doctors are named (Robert Sachs D.D.S., John M. Steinberg D.D.S., and Steven R. Gluck D.D.S.), their specific qualifications beyond "D.D.S." (Doctor of Dental Surgery) and their experience levels are not detailed.
• Adjudication Method: No method for resolving discrepancies among experts (e.g., 2+1, 3+1) is described.
• Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: The document does not mention an MRMC study or any comparison of human readers with vs. without AI assistance. The device itself is an X-ray sensor, not an AI diagnostic aid.
• Standalone Performance: The performance data mentioned (Page 12, Performance Data 8) describes human examination of images from the device, not a standalone algorithm's performance.
• Type of Ground Truth Used: The ground truth appears to be expert consensus (the three D.D.S. doctors), but it's not explicitly labeled as such, nor are specifics on how this consensus was reached.
• Sample Size for the Training Set: This is a performance study, not an AI training study. The device is a hardware sensor.
• Ground Truth Establishment for Training Set: Not applicable as it's a hardware device, not an AI model.

Here's a summary of the available information regarding the device's performance assessment:

Device: DC-Air™, Athlos-1, Athlos-Air (Intraoral Digital X-ray Sensor)

Study Purpose (Implied): To demonstrate the diagnostic relevance and reliability of images produced by the DC-Air™ sensor.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: Not specified. The text mentions "X-ray images taken using the DC-Air™," implying an unspecified number of images.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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

• Number of Experts: Three.
• Qualifications: Doctors Robert Sachs D.D.S., John M. Steinberg D.D.S., and Steven R. Gluck D.D.S. (Doctor of Dental Surgery). No further detail on experience (e.g., years in practice, specialization) is provided.

4. Adjudication Method for the Test Set:

• Method: Not specified. It's only stated that the doctors "examined" the images and "found" them to be diagnostically relevant and reliable, implying a consensus or individual findings without detailing the agreement process.

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

• No, a MRMC comparative effectiveness study was not explicitly mentioned or performed. This device is an imaging sensor, not an AI-based diagnostic tool. The performance claim relates to the quality of the generated images for human interpretation, not an AI's impact on human reading.

6. Standalone Performance:

• Not directly applicable in the AI context. The performance mentioned refers to the quality of the images produced by the sensor for human interpretation, not an algorithm performing a diagnostic task independently.

7. The Type of Ground Truth Used:

• Type: Expert consensus (from the three D.D.S. doctors). No mention of pathology or outcomes data as ground truth.

8. The Sample Size for the Training Set:

• Not applicable. This document describes the testing of a hardware device's image output, not the training of a machine learning model.

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

• Not applicable. (See point 8).

Summary of Device Features and Technical Benchmarking (from Table 6-1):

While not directly "acceptance criteria" for a study proving clinical performance in the AI sense, the document does list various technical specifications and comparisons to the predicate device. These are more akin to engineering and imaging performance metrics rather than clinical outcome measures.

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The IC-WHCD100 (Inspire) is intended to be used as an aid in the detection and diagnosis of dental caries.

The IC-WHCD100 is a toothbrush-sized handpiece used for diagnosis of caries. A USB cable is used to connect the handpiece to a personal computer with a dental imaging software. After a camera cover is placed over the end, the handpiece is positioned over the tooth to be examined. The camera takes images by illuminating the tooth surface with a white LED light for regular tooth image. With fluoresced light, the device can show bacteria on the surface of tooth. With infrared light, the device can show tooth cavity by highlighting enamel. The user can view the images on 510k cleared dental imaging software such as Apteryx vision (K983111), Romexis (K171385), Sidexis (K132773), etc.

The provided text details the FDA 510(k) summary for the IC-WHCD100 (Inspire) device, which is an intraoral camera intended as an aid in the detection and diagnosis of dental caries. However, the document primarily focuses on demonstrating substantial equivalence to predicate devices and provides limited information regarding specific acceptance criteria and detailed study results. Critical information needed to fully answer the request, such as a precise table of acceptance criteria and reported device performance with numerical metrics (e.g., sensitivity, specificity for caries detection), detailed sample size for the test set, number and qualifications of experts for ground truth, adjudication methods, details of comparative effectiveness studies (MRMC), or a comprehensive standalone performance study report are not explicitly present in the provided text.

Based on the available information, here's what can be extracted and what remains unknown:

1. Table of Acceptance Criteria and Reported Device Performance

The document mentions "Performance Test for imaging (Image Sharpness, Image Size, Image Resolution, tooth Caries Detection)" as a non-clinical test. However, it does not provide a specific table of acceptance criteria or the reported device performance metrics (e.g., sensitivity, specificity, accuracy) for caries detection. It only broadly states that "the performance test results of the subject device supports that the transillumination mode works well despite this difference." and "the performance test result supports that the subject device is substantially equivalent to the predicate devices."

Unfortunately, a specific table with numerical acceptance criteria and corresponding performance data for caries detection is not found in the provided text.

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

The document states "performance test results of the subject device supports that the transillumination mode works well despite this difference." and refers to "Performance Tests (Non-clinical)". However, the specific sample size used for the test set (number of teeth, lesions, or patients) and the data provenance (e.g., country of origin of the data, retrospective or prospective nature of data collection) are not disclosed in this 510(k) summary.

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

The provided text does not specify the number of experts used to establish ground truth or their qualifications. It only refers to "Performance Tests" related to caries detection, implying some form of ground truth was used, but details are absent.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method (e.g., 2+1, 3+1, none) used for establishing the ground truth for the test set.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done

The provided text does not mention or describe a Multi Reader Multi Case (MRMC) comparative effectiveness study where human readers' performance with and without AI assistance was evaluated. The focus is on the device's performance relative to predicate devices, not human-AI collaboration.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

The document broadly states "Performance Test for imaging... tooth Caries Detection)". While the device has a caries detection aid capability, the summary does not explicitly detail a standalone algorithm-only performance study with specific metrics (e.g., sensitivity, specificity of the algorithm itself). The device is described as an "aid in the detection and diagnosis," implying human involvement.

7. The Type of Ground Truth Used

The document refers to "tooth Caries Detection" as one of the performance tests. However, the specific type of ground truth used (e.g., expert consensus, pathology/histology, clinical outcomes data, or a combination) is not explicitly stated.

8. The Sample Size for the Training Set

The 510(k) summary does not provide any information regarding a training set sample size. This type of document typically focuses on performance testing for regulatory clearance rather than details of model development. Given that the device is an "intraoral camera with Caries Detection Aid" using specific light sources (405nm and 940nm) to highlight bacteria and cavities, it's possible its "detection aid" might be based on optical properties rather than a complex AI model requiring extensive training data in the traditional sense, but this is speculative given the lack of detail.

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

Since there is no mention of a training set, there is no information on how its ground truth was established.

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R-Sensor is used for a radiographic examination by a dental professional to assist in the diagnosing of diseases of the teeth, jaw and oral structures.

Radiographic examination to assist with diagnosis of diseases of the teeth, jaw, and oral structure.

The R-Sensor dental sensor is intended to replace film and to capture an intraoral x-ray image, when exposed to X-rays, for dental diagnostic purposes.

The subject R-Sensor is an intraoral digital x-ray system comprised of two components: (1) an intraoral detector which connects to a PC via a USB port; and (2) an Image Management Software package.

The subject devices comes in two sizes: Size 1 is 600mm² and Size 2 is 884mm².

R-Sensor, Size 1 is also known as factory code S11684-12; R-Sensor, Size 2 is also known as factory code S116845-12.

The provided text describes the "R-Sensor," an intraoral digital x-ray sensor, and its substantial equivalence to a predicate device, the "QuickRay HD." The document primarily focuses on demonstrating that the R-Sensor is identical to the predicate device, rather than presenting a standalone study with specific acceptance criteria and performance metrics for the R-Sensor itself.

Therefore, the information below is derived from the comparison to the predicate and the general statements about the device's characteristics, as the document states the two devices are identical in all aspects.

1. Table of Acceptance Criteria and Reported Device Performance

Since this submission argues for substantial equivalence based on the R-Sensor being identical to the predicate device (QuickRay HD), the "acceptance criteria" are implicitly met by matching the predicate's specifications and performance where specified. The document does not explicitly state unique acceptance criteria for the R-Sensor but rather focuses on demonstrating its identical characteristics to the cleared predicate.

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

The document mentions "Clinical images were provided" for the R-Sensor to demonstrate that "the complete system works as intended." However, it explicitly states these images "were not necessary to establish substantial equivalence based on the modifications to the predicate device but they provide further evidence in addition to bench testing data." This suggests the primary "test set" for equivalence relies on the established performance of the predicate device and bench testing of the R-Sensor's components.

• Sample size for clinical images: Not specified.
• Data provenance: Not specified, but a qualified practitioner in Ormond Beach, FL reviewed them. It's unclear if these were retrospective or prospective, or from what country of origin beyond the U.S. location of the reviewing practitioner.

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

• Number of experts: One expert is explicitly mentioned.
• Qualifications of experts: Dr. Parham, a "qualified practitioner in Ormond Beach, FL." Specific specialty (e.g., dentist, radiologist) or years of experience are not provided.

4. Adjudication method for the test set

Not applicable/Not specified. The clinical images were "examined by Dr. Parham" and "found to be diagnostically relevant and reliable," suggesting a single-reader assessment rather than an adjudication process involving multiple experts.

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. This document does not mention an MRMC comparative effectiveness study or any AI component. The R-Sensor is described as a hardware device (intraoral sensor) with associated image management software, performing as a receiver of X-rays to capture images.

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

A standalone performance assessment of the R-Sensor hardware was performed through bench testing conforming to IEC 62220-1 (for DQE, influencing image quality) and other standards (IEC 60601-1, IEC 60601-1-2, IEC 60529). The "algorithm" here refers to firmware and driver, which are part of the sensor's electronics, and image management software. The entire system's performance, including software, is presented as being identical to the predicate.

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

For the "clinical images" section, the ground truth was established by the assessment of a single "qualified practitioner" who deemed them "diagnostically relevant and reliable." For the direct comparison, the "ground truth" for substantial equivalence is the previously cleared predicate device, QuickRay HD, and its established performance and adherence to standards.

8. The sample size for the training set

Not applicable. The document describes a medical device seeking 510(k) clearance based on substantial equivalence to an existing predicate. It does not describe an AI/ML device that requires a training set. The firmware and drivers were provided by Hamamatsu, and the image management software (Xray Vision) is an off-the-shelf package cleared previously (K983111).

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

Not applicable, as there is no mention of a training set for an AI/ML algorithm.

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ODI HD Dental Sensor is intended to be used by dentists and other qualified professionals for providing diagnostic x-ray radiographs of dentition, jaws and other oral structures.

The ODI HD Dental Sensors are electronic medical device intra-oral radiographic images. The sensor can be operated by Radiologists, Dental Assistants and other healthcare professionals, who are both trained and competent to take Dental X-ray radiographs. Intra-oral positioning of the sensor is accomplished by the use of dedicated intra-oral positioning devices that facilitate the accurate alignment of the sensor may also be aligned with the assistance of the patient. The ODI HD Dental Sensors is an indirect light converting digital x-ray detector. A scintillating device composed of Cesium lodide (Csl) converts incident x-rays into visible light that is optically coupled to a light detection imager based on CMOS technology. The ODI HD Dental Sensors allow for automatic detection of such incident x-rays in order to generate data. Software interprets this data into images used for dental applications. The ODI HD Dental Sensors support USB 2.0 direct connectivity to personal computers and or laptops with dedicated electronics and a sensor software driver. The subject device does not control the generator, it is only a receiver. The X-ray system and the software used are not a part of this submission.

This document, K190234, is a 510(k) Premarket Notification for a dental sensor. The key argument for substantial equivalence relies on the fact that the device (ODI HD Dental Sensor) is essentially identical to a previously cleared predicate device (Apex Dental Sensor K163282), with the main components sourced from the same supplier. Therefore, the "study" proving the device meets the acceptance criteria is primarily a comparison to the predicate device and adherence to relevant electrical and performance standards.

Here's the information requested, based on the provided text:

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

The acceptance criteria are implicitly defined by the technical specifications and performance characteristics of the predicate device, as well as adherence to recognized international standards for medical electrical equipment and X-ray detectors. The device performance is demonstrated by its identical (or highly similar) specifications to the predicate and its conformity to these standards.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document explicitly states: "Clinical data is not needed to characterize performance and establish substantial equivalence. The non-clinical test data characterizes all performance aspects of the device based on well-established scientific and engineering principles. Clinical testing has not been conducted on the ODI HD Dental Sensors."

Therefore, there was no "test set" in the sense of a clinical dataset used for performance evaluation that would require a sample size or data provenance details. The evaluation was based on engineering and performance testing against standards and comparison to a predicate device.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

Not applicable. As stated above, no clinical data or test set requiring a ground truth established by experts was used. The focus was on engineering performance and substantial equivalence to a predicate device.

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

Not applicable, as no clinical test set requiring adjudication was used.

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 done. This device is a dental X-ray sensor, not an AI-powered diagnostic tool. The submission is for hardware that captures the image, not software that assists in interpretation.

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

Not applicable. This is a hardware device (dental sensor), not an algorithm or AI.

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

Not applicable, as no clinical test set requiring ground truth was used. The "ground truth" for this submission are the established technical specifications and performance characteristics of the predicate device and the relevant international standards.

8. The sample size for the training set

Not applicable. This device is a passive sensor, not an AI model that requires a training set.

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

Not applicable. No training set was used.

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Aurora is an intra-oral sensor used by dental professionals for the purpose of acquiring x-ray images to be used for the diagnosis of diseases of the mouth and for evaluating general dental health.

Aurora is an intraoral digital x-ray system used to acquire digital x-ray images when used with an external x-ray source. The sensor is composed of a scintillation plate that converts incident x-ray light into visible light, which is coupled to a CMOS detector via a fiber optic plate (FOP) collimator. It connects to a PC via a 2-meter cable with USB 2.0 interface. A trained dental professional (e.g. Dentist, Dental Assistant, etc.) will operate the sensor by aligning the device inside the mouth using a positioning device, such as ring and bar holder, and then using an external x-ray source (i.e. dental x-ray tube head) to expose the sensor to radiation with the oral tissue of interest between. A software package will control the acquisition of the x-ray image from the sensor itself and will interpret the data to create an image on a computer screen for the dentist to use for diagnosis. Neither the software package nor the x-ray sensor controls the x-ray generating source in any fashion. The software package used by Sodium Dental is Xray Vision® from Apteryx, Inc (K983111). The Xray Vision® software is a network-based image acquisition and management software used to acquire x-ray/camera images and store them in a patient database.

This 510(k) summary report describes the Aurora intra-oral digital x-ray sensor, which is stated to be identical in hardware and uses identical software to its predicate device, the QuickRay HD (K151926). Therefore, the performance data presented primarily focuses on demonstrating this equivalence rather than establishing new performance criteria or conducting extensive effectiveness studies.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Given the claim of an identical device to the predicate, the acceptance criteria and performance are inherently tied to those of the QuickRay HD. The submission does not explicitly list "acceptance criteria" but rather presents a comparison of specifications between the Aurora and the predicate device. The "reported device performance" is the statement that these specifications are identical.

Note: The "Differences" column in the original table states "None" or "N/A" for all technical specifications, and "Equivalent" for Indications for Use.

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

The document does not explicitly state a specific "test set" sample size for a clinical performance evaluation in the traditional sense, as the primary argument is substantial equivalence to a predicate device.

• Clinical images: "Clinical images were provided" for review by a qualified practitioner. The specific number of images is not stated.
• Data Provenance: The general nature of "clinical images" and their examination by a practitioner in Clarkston, MI, suggests retrospective use of existing images, likely from the practitioner's local patient pool. The country of origin would be the USA based on the practioner's location.

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

• Number of Experts: One (1) expert.
• Qualifications: Dr. Tiffany Danyal, D.D.S., a qualified practitioner of Clarkson Village Dental, Clarkston, MI. No further details on years of experience or specialization beyond "qualified practitioner" are provided.

4. Adjudication Method for the Test Set

The document mentions that "Clinical images were examined by Dr. Tiffany Danyal... The images were determined by Dr. Danyal to be of diagnostic quality and usefulness for evaluation of all relevant oral structures." This indicates a single-reader assessment, with no explicit adjudication method stated (e.g., 2+1, 3+1).

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

No. The document does not describe a Multi Reader Multi Case (MRMC) comparative effectiveness study. The submission focuses on demonstrating substantial equivalence through direct comparison of hardware and software specifications with a predicate device, along with bench testing.

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

Yes, in spirit, for technical and safety aspects, but not for "diagnostic" performance as a standalone AI. The submission argues the Aurora device is identical hardware and software to the predicate. The "Performance Data" section discusses:

• Bench tests: Performed in accordance with IEC 60601-1, IEC 60601-1-2, IEC 62220-1 (which includes DQE, MTF, NNPS measurements), and IEC 60529. These are objective engineering measurements of the device's physical and imaging characteristics, which are "standalone" in nature. The graphs of DQE, MTF, and NNPS versus spatial frequency (pages 7-8) are evidence of this.
• Software Verification and Validation Testing: This also assesses the algorithm/firmware only.

However, these standalone tests are primarily to confirm the device's technical specifications and safety profile match the predicate, not to evaluate its standalone diagnostic accuracy as an AI in interpreting images. The closest to diagnostic performance is the resolution (Real ≥ 20 lp/mm) and grey levels (14 bits), which are technical specifications rather than diagnostic outcomes.

7. The Type of Ground Truth Used

• For technical specifications (e.g., resolution, DQE): The ground truth is generally derived from physical measurements and adherence to international standards like IEC 62220-1.
• For the "clinical images" review: The "ground truth" was established by the expert opinion/assessment of a single dentist, Dr. Tiffany Danyal, who determined the images were of "diagnostic quality and usefulness." This is a subjective expert assessment rather than a definitive, independently verified ground truth like pathology.

8. The Sample Size for the Training Set

Not applicable. This submission focuses on demonstrating substantial equivalence to a predicate device, which is already a cleared medical device. There is no mention of a "training set" for an AI or machine learning algorithm. The imaging software (XRayVision) is stated to be "previously cleared by the FDA". The device itself is an image acquisition sensor, not an image interpretation AI.

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

Not applicable. As no training set for an AI was used, no ground truth needed to be established for it.

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The Apex Dental Sensors is intended to be used for a radiographic examination by a dental professional to assist in the diagnosing of diseases of the teeth, jaw and oral structures.

The Apex Dental Sensors is an electronic medical device used to acquire intra-oral radiographic images. The sensor can be operated by Radiologists, Dental Assistants and other healthcare professionals, who are both trained and competent to take Dental X-ray radiographs. Intra-oral positioning of the sensor is accomplished by the use of dedicated intra-oral positioning devices that facilitate the accurate alignment of the x-ray beam. The sensor may also be aligned with the assistance of the patient. The Apex Dental Sensors is an indirect light converting digital x-ray detector. A scintillating device composed of Cesium Iodide (Csl) converts incident x-rays into visible light that is optically coupled to a light detection imager based on CMOS technology. The Apex Dental Sensors allow for automatic detection of such incident x-rays in order to generate data. Software interprets this data into images used for dental applications. The Apex Dental Sensors support USB 2.0 direct connectivity to personal computers and or laptops with dedicated electronics and a sensor software driver. The subject device does not control the generator, it is only a receiver. The X-ray system and the software used are not a part of this submission.

The provided document is a 510(k) premarket notification for a medical device called "Apex Dental Sensors". The purpose of this notification is to demonstrate substantial equivalence to a legally marketed predicate device, QuickRay HD Intraoral Sensor (K151926).

The document does not describe a study involving patient data, expert evaluation, or MRMC comparative effectiveness to establish the device's performance in a clinical context or against specific acceptance criteria related to diagnostic accuracy. Instead, the performance data presented focuses on engineering and safety standards to show that the new device performs equivalently to its predicate.

Therefore, many of the requested items (sample size, data provenance, number of experts, adjudication method, MRMC study, standalone performance, training set details) are not applicable or cannot be extracted from this specific 510(k) summary.

Here's a breakdown based on the information available:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria provided are primarily based on equivalence to the predicate device and conformity to various engineering and safety standards. There are no clinical performance acceptance criteria (e.g., sensitivity, specificity for detecting a specific disease) reported in this document.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This document describes a premarket notification based on technical and performance equivalence to a predicate device and adherence to industry standards, not a clinical study with a test set of patient data. Therefore, this information is not applicable and not provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable, as no clinical test set requiring expert ground truth establishment is described.

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

Not applicable, as no clinical test set is described.

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 device is an intraoral digital x-ray sensor, a hardware component for image acquisition, not an AI-powered diagnostic software. Therefore, an MRMC study related to AI assistance for human readers is not relevant or described.

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

Not applicable. This is a hardware sensor, not an algorithm, so standalone AI performance is not relevant.

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

Not applicable, as no clinical study requiring ground truth is described. The "ground truth" in this context is the predicate device's established performance and the requirements of various engineering and safety standards.

8. The sample size for the training set

Not applicable. This is a hardware device; thus, there is no AI model requiring a training set.

9. 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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The SunilQ Digital Radiography system is used to collect dental x-ray photons and convert them into electronic impulses that may be stored, viewed and manipulated for diagnostic use by dentists.

The SuniIQ Digital Radiography System reduces x-ray exposure by producing real time digital intra-oral images. The System accomplishes this by replacing X-ray film with a reusable electronic sensor that captures the X-ray photons and converts them to an electronic signal which, in turn, is captured in a computer for viewing, manipulating, storing, and output.

The acceptance criteria and study information for the SuniIQ Digital Radiography System are outlined below:

1. Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria for specific performance metrics. However, it indicates a general acceptance criterion that the device must be "as safe, as effective, and performs as well as or better than the predicate device."

The study concludes that "Testing confirms that the SuniIQ System is as safe, as effective, and performs as well as or better than the predicate device," and that "The identified technological differences between the subject and predicate devices were assessed through bench performance testing data and software V&V testing to demonstrate that they are substantially equivalent."

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

The document does not specify a distinct "test set" in terms of patient data. The testing described focuses on bench testing and software verification and validation (V&V).

• Bench Testing: The "tested functions" listed cover various technical aspects of the device, likely using controlled laboratory conditions and specific testing protocols (e.g., test pattern images, dental phantom images). The sample sizes for these tests are not explicitly stated, but are implied to be sufficient for verifying the functional requirements.
• Data Provenance: The document does not refer to clinical data or country of origin for a test set. The validation is primarily technical.

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

No information is provided regarding experts establishing ground truth for a clinical "test set," as the device's performance was evaluated through bench testing and software V&V, not a clinical study involving diagnostic interpretations by experts.

4. Adjudication Method for the Test Set

Not applicable, as no clinical test set requiring expert adjudication is described in the document.

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

No MRMC comparative effectiveness study is mentioned. The document primarily focuses on demonstrating substantial equivalence through technical performance comparisons with a predicate device, rather than assessing human reader improvement with AI assistance. The device is a "digital radiography system," which captures images; it is not presented as an AI-powered diagnostic aide.

6. Standalone (Algorithm Only) Performance Study

The document describes the performance of the "SuniIQ Digital Radiography System" itself, which is a hardware and software system for capturing and displaying dental x-ray images. The "software components" within the SuniIQ handle sensor control, data transfer, and include an "optional filter algorithm" for image correction. The performance reported in the "Bench Testing" and "Software V&V" sections can be considered the standalone performance of this system, demonstrating its ability to acquire and process images. There is no mention of a separate "AI algorithm" being evaluated independently.

7. Type of Ground Truth Used

The ground truth for the evaluations conducted was primarily based on:

• Functional Requirements/Specifications: The device's performance was compared against defined functional requirements for items like relative error, X-ray saturation, image size, etc.
• Predicate Device Performance: Direct comparison to the performance of the predicate SuniRay II System (K070219) was used as a benchmark for many functional tests.
• Industry Standards: Compliance with standards like IEC 62304 for software, ISO 10993-1 for biocompatibility, IEC 60601-1 for safety, and IEC 60601-1-2 for EMC served as ground truth for regulatory and quality aspects.
• Test Pattern Images and Dental Phantom Images: These are controlled inputs with known characteristics, used to assess image quality and system response under controlled conditions.

There is no mention of pathology, expert consensus on clinical findings, or outcomes data being used as ground truth.

8. Sample Size for the Training Set

The document does not mention a "training set." The device is a digital radiography system, not a machine learning or AI algorithm in the context of typical AI device approvals that involve training datasets. The "software components" are described as drivers, control software, and an optional filter algorithm, implying traditional software development and verification rather than machine learning training.

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

Not applicable, as no training set is mentioned or implied for this device.

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The Tuxedo Digital Dental Sensor is a CMOS sensor for the capturing of digital diagnostic x-ray images on a patient for evaluation by an appropriately trained oral healthcare professional.

The TUXEDO Dental Digital Sensor is used in the detection and diagnosis of anomalies in dental anatomy, as well as for the evaluation of performed treatment in dental care. The primary use is by general dental practitioners to detect the presence and extent of carious legions in the dentin and enamel of a tooth. Two different sized sensors (size 1 and size 2) are utilized to image different anatomy and different patient sizes. The TUXEDO Dental Digital Sensor functions by being placed in a patient's mouth lingually by a licensed dental practitioner, and is designed to capture collimated radiation which is converted into a digital image for viewing by a licensed dental practitioner. The capturing of the radiation is done within the TUXEDO sensor casing which contains a scintillator used to convert the radiation into visible light, and this visible light is immediately captured by the internal CMOS sensor. The digital image is transferred to a computer via USB 2.0 and can be viewed in most common imaging software programs, including LED Imaging Software. A software driver is available from LED Dental which will allow the TUXEDO sensor to be used in these software programs. The software supplied with the Tuxedo Digital Dental Sensor was cleared separately by FDA by Apteryx, Inc. The sensor is intended to be used with a disposable barrier sheath that should be replaced between patients. This is to reduce cross contamination between patients. The sensor is also sealed in a way that the portion of the device that is placed in the patient's mouth can be sterilized with liquid without the device being damaged.

Here's a breakdown of the acceptance criteria and study information for the Tuxedo Digital Dental Sensor, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't present explicit "acceptance criteria" in a typical table format with pass/fail thresholds. Instead, it compares the Tuxedo Digital Dental Sensor's technological characteristics and performance against a legally marketed predicate device (RIO Sensor (RIS 500), K143000) to demonstrate substantial equivalence. The "Comparison Results" column effectively acts as the performance assessment against the predicate.

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

• Test Set (Clinical Images):
  • Sample Size: A single "Phantom equivalent to a 51-year-old male" (DXTTR III Dental X-Ray Phantom (Human Skull)) was used.
  • Data Provenance: This was a simulated, prospective test using a phantom in a laboratory setting. The origin of the phantom itself is not specified as a country.

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

• The document states, "All images acquired demonstrated excellent diagnostic imaging quality on both the size #1 and the size #2 sensor." However, it does not specify the number or qualifications of experts who made this determination for the phantom images. It implies an internal assessment.

4. Adjudication Method for the Test Set

• The document does not describe an explicit adjudication method for the phantom images. The assessment of "excellent diagnostic imaging quality" appears to be a general conclusion rather than a formal adjudication process involving multiple readers.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size

• No, an MRMC comparative effectiveness study was not done. The study's purpose was to demonstrate substantial equivalence to a predicate device, not to compare AI-assisted performance with unassisted human readers or to quantify an effect size for human improvement with AI. The device itself is a digital dental sensor, not an AI diagnostic tool.

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

• Yes, in essence, standalone performance was assessed though not in the context of an "algorithm." The device's imaging performance (MTF, DQE, theoretical resolution, stability of sensitivity, dark output, offset value) was tested independently of a human interpreter, using standardized methodologies (IEC6220-1). The phantom imaging also represents a standalone assessment of the device's image capture capabilities.

7. The Type of Ground Truth Used

• For the technical performance aspects (MTF, DQE, resolution), the ground truth is based on physical measurements and standardized testing protocols (IEC6220-1).
• For the "clinical images" using the phantom, the ground truth is implied to be the known anatomical structures and potential anomalies within the DXTTR III Dental X-Ray Phantom, against which the captured images were visually assessed for diagnostic quality. This is a form of expert assessment of image quality against a known physical standard, though detailed methodology is not provided.

8. The Sample Size for the Training Set

• Not Applicable (N/A). The Tuxedo Digital Dental Sensor is a hardware device (CMOS sensor) for image capture, not an AI algorithm that requires a training set of data. The document does not mention any machine learning or AI components that would necessitate a training set.

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

• Not Applicable (N/A). As mentioned above, there is no training set for this device in the context of AI/machine learning.

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Edlen Imaging's Gemini DUSB sensor is a USB-driven digital X-ray sensor that acquires dental intra-oral X-ray images. The sensor will be operated by trained dental and related healthcare professionals to acquire dental intra-oral radiographs.

The Gemini sensor can be used either in combination with special positioning devices, to facilitate alignment with the x-ray beam, or it may also be positioned manually.

The Gemini sensor is an indirect light converting digital X-ray detector. Incident X-rays are converted to visible light by a scintillating device, (material) such as Csl (Cesium lodide), the light is coupled optically to a light detection imager which is based on CMOS technology.

The design of the sensor allows for automatic detection of incident X-rays to generate a digital data which when coupled with a software program will display images used for dental intra-oral applications.

The Gemini Digital x-ray sensor supports USB 2.0 Direct connectivity to personal computers and or laptops, hence the name Gemini DUSB.

The Gemini sensors employ built-in dedicated electronics and sensor software driver, to allow connectivity to a variety of software packages. The Gemini sensors are coupled with the currently marketed software, Apteryx XrayVision, K983111.

The provided text describes a 510(k) premarket notification for the Edlen Imaging Gemini DUSB Digital X-ray sensor, which demonstrates substantial equivalence to predicate devices rather than providing a detailed clinical study with acceptance criteria and specific performance metrics. Therefore, several requested categories cannot be directly extracted or are not applicable from the provided document.

Here's a breakdown of what can be inferred and what is not available:

1. Table of Acceptance Criteria and Reported Device Performance

Not available. The submission focuses on establishing substantial equivalence to legally marketed predicate devices (Schick Technologies CDR and Suni Medical Imaging Suni Ray II) based on shared indications for use, materials, design, and operational/functional features. It states the device is "as safe, as effective, and performs as well as or better than the predicate devices," but does not provide specific acceptance criteria or quantitative performance results in the format requested.

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

Not applicable. This was a 510(k) submission for substantial equivalence based on technical characteristics and safety standards, not a clinical study with a test set of patient data.

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

Not applicable, as no clinical test set requiring ground truth establishment is described.

4. Adjudication Method for the Test Set

Not applicable, as no clinical test set requiring adjudication is described.

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

Not applicable. The document does not describe an MRMC study comparing human readers with and without AI assistance. The Gemini DUSB is a digital X-ray sensor, not an AI or CAD system designed to assist human readers.

6. Standalone Performance Study (Algorithm Only)

Not applicable. The device is a digital X-ray sensor, which is a hardware component for image acquisition, not an algorithm with standalone performance.

7. Type of Ground Truth Used

Not applicable, as no clinical test set requiring ground truth is described.

8. Sample Size for the Training Set

Not applicable, as the device is a digital X-ray sensor, not an AI or machine learning algorithm that requires a training set in this context.

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

Not applicable, as there is no training set for an AI/machine learning algorithm described.

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