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The Computed Oral Radiology System is indicated for patients undergoing an intra-oral dental x-ray examination. It produces instant, digital, intra-oral images of a patient's mouth while reducing the necessary x-ray dosage.

The device and its predicates are small digital imaging receptors that may be used in place of dental x-ray film. The images are displayed on a computer workstation. The modified device uses wireless IEEE 802.11 b/g protocol for image data transfer and control signal transfer to and from the Power and Transceiver (PAT) and the host computer. A rechargeable battery power source is also included in the PAT. A Counter Top Dock (CTD) has been added to this system. The CTD is a support device that provides charging power to the PAT and is used to provide a temporary wired connection, via USB, from the host computer to the PAT to allow initial configuration to the host wireless network.

This 510(k) summary does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and the comprehensive study that proves the device meets those criteria. The provided document is primarily a summary for a 510(k) clearance, which focuses on demonstrating substantial equivalence to a predicate device rather than detailing extensive performance studies with specific acceptance criteria.

However, based on the information provided, here's what can be extracted and inferred:

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

The document does not explicitly state quantitative acceptance criteria or detailed performance metrics. The submission focuses on the modification of an already cleared device (K072134) to incorporate wireless functionality. Therefore, the "acceptance criteria" are mostly implicit in demonstrating that the wireless modification does not degrade the performance or safety of the existing device and maintains substantial equivalence to the predicate.

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

This information is not provided in the 510(k) summary. The document mentions "Testing and design validation," but does not specify sample sizes, data provenance (e.g., country of origin), or whether these tests were retrospective or prospective.

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)

This information is not provided in the 510(k) summary. It's unlikely that such a detailed ground truth establishment would be required or documented in a 510(k) for a modification focused on wireless connectivity, unless there were specific concerns about diagnostic image quality degradation, which are not raised.

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

This information is not provided in the 510(k) summary.

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

This is not applicable to this submission. The device is a digital imaging receptor (X-ray sensor) and associated software for displaying images. It is not an AI-assisted diagnostic tool designed to improve human reader performance.

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

This is not applicable in the context of an AI algorithm. The device, the Computed Oral Radiology System, is an imaging acquisition and display system. Its primary role is to provide images, not to perform independent diagnoses via an algorithm.

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

This information is not provided and likely not relevant for a 510(k) focusing on a wireless connectivity modification for an imaging device. The "ground truth" for the performance of such a device generally relates to objective image quality metrics (resolution, signal-to-noise ratio, contrast) and functional performance, rather than diagnostic accuracy against a clinical ground truth.

8. The sample size for the training set

This is not applicable in the context of a traditional "training set" for machine learning. The device is not described as involving machine learning or AI that would require a distinct training set. The "training" for this type of device would refer to internal development and testing, not an AI training data set.

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

This is not applicable as there is no mention of a training set for an AI algorithm.

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The Computed Oral Radiology System is intended for intra-oral x-ray examinations and indicated for dental patients. It produces instant, digital, intraoral x-ray images of a patient's mouth while reducing the necessary x-ray dosage. This device modification in no way alters the indications for use of this machine beyond what was most recently cleared in K072134.

The Computed Oral Radiography System is indicated for patients undergoing an intra-oral dental X-ray examination. It produces instant digital intra-oral X-ray images of a patient's mouth.

The device and its predicates are small digital imaging receptors that may be used in place of dental x-ray film. A new scintillation material differs from the predicate in that in that it affords higher resolution and lower noise. Direct triggering via an x-ray tube continues to be supported as was cleared in K072134 and K041385. The modification offers an improvement in image quality.

The provided text describes a 510(k) premarket notification for a modification to the Schick Computed Oral Radiology System. This document focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed study report with specific acceptance criteria and performance metrics.

Therefore, much of the requested information, such as a table of acceptance criteria and reported device performance, sample sizes for test sets, number and qualifications of experts, adjudication methods, details of comparative effectiveness studies, and specific ground truth methodologies for training and test sets, is not available in the provided text.

However, I can extract the available information:

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

• Acceptance Criteria: While no explicit numerical or qualitative acceptance criteria are listed, the document states: "All validation activities have demonstrated that the predetermined acceptance criteria were met." The principal risk identified was "unintended x-ray exposure."
• Reported Device Performance:
  • "A new scintillation material differs from the predicate in that that it affords higher resolution and lower noise."
  • "The modification offers an improvement in image quality."
  • "It produces instant, digital, intraoral x-ray images of a patient's mouth while reducing the necessary x-ray dosage." (This statement is about the overall system's capability, not specific performance metrics of the modification).

Since specific metrics and thresholds for "higher resolution," "lower noise," and "improvement in image quality" are not provided, a detailed table cannot be constructed.

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

• Not available in the provided text. The document mentions "validation studies" but does not specify sample sizes or data provenance.

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 available in the provided text.

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

• Not available in the provided text.

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 and not available. This device is an imaging system (hardware modification), not an AI algorithm for interpretation. Therefore, a multi-reader multi-case study comparing human readers with AI assistance would not be relevant to this specific device modification.

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

• Not applicable. This relates to an imaging system, not a standalone algorithm.

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

• Not explicitly stated for the validation study. The document implies that the validation focused on "imaging, software validation, and third-party safety testing" to address the risk of "unintended x-ray exposure" and demonstrate "higher resolution and lower noise." The ground truth for these types of evaluations would likely involve physical measurements (e.g., MTF, DQE for resolution/noise, dosimeter readings for x-ray exposure) rather than clinical expert consensus or pathology, as this is a hardware modification improving image capture, not diagnostic interpretation.

8. The sample size for the training set

• Not applicable and not available. This device represents a hardware modification to an X-ray imager, not an AI model that would require a training set.

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

• Not applicable. As a hardware modification, there is no "training set" in the context of machine learning.

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The Computed Oral Radiology System is intended for intra-oral x-rav examinations and indicated for dental patients. It produces instant, digital, intraoral x-ray images of a patient's mouth while reducing the necessary x-rav dosage. This device modification in no way alters the indications for use of this machine beyond what was most recently cleared in K041385.

The device and its predicates are small digital imaging receptors that may be used in place of dental x-ray film. A new control mechanism differs from the predicate in that image acquisition is initiated via a new hardware trigger residing on the CMOS ASIC. Direct triggering via an x-ray tube continues to be supported as was cleared in K041385. The modification offers an improvement in system-level support. The modification in no way alters the fundamental technology nor intended use.

The provided text is a 510(k) summary for a dental imaging device (Computed Oral Radiology System) and primarily focuses on demonstrating substantial equivalence to a predicate device after a modification. As such, it does not contain the detailed information typically found in a study report proving a device meets specific performance acceptance criteria.

Specifically, the document states: "All validation activities have demonstrated that the predetermined acceptance criteria were met." However, it does not explicitly define these acceptance criteria, nor does it describe the specific studies and their results in enough detail to populate the requested table and answer many of the questions.

Here's a breakdown of what can be inferred and what is missing, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Not specified. The document mentions "validation studies" but does not provide details about sample sizes or data provenance (country of origin, retrospective/prospective). Given that the modification is related to a control mechanism and not a fundamental change in imaging technology or intended use, it's possible that extensive clinical test sets were not deemed necessary for this specific 510(k) rather than for the initial clearance.

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

• Not specified. Ground truth establishment methods, number of experts, and their qualifications are not mentioned.

4. Adjudication method for the test set:

• Not specified.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:

• Not applicable. The device is an imaging sensor, not an AI-assisted diagnostic tool. There is no mention of AI or any MRMC studies involving human readers and AI assistance.

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

• Not explicitly stated how "standalone" performance translates to this device. The device is a digital imaging receptor. Performance would relate to image quality, dose reduction, and overall system function, rather than an "algorithm only" performance separate from the human operator. While "imaging, software validation" is mentioned, no specific standalone performance metrics are provided.

7. The type of ground truth used:

• Not specified.

8. The sample size for the training set:

• Not applicable/Not specified. As there's no mention of AI or deep learning, there isn't a "training set" in the typical sense for an algorithm. If "training set" refers to data used for system development and calibration, it is not detailed.

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

• Not applicable/Not specified.

In summary: The provided 510(k) pertains to a modification of an already cleared device, specifically "a new control mechanism" for image acquisition. The document emphasizes that the modification "in no way alters the fundamental technology nor intended use." This suggests that the validation focused on ensuring the new control mechanism's safety and functionality, and its compatibility with the existing system, rather than re-proving the core diagnostic performance of the imaging technology. Therefore, the detailed performance study information typically required for a novel diagnostic device is not present in this summary.

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Ceph Model 4900 is indicated for individuals who require extra-oral dental radiographic examinations.

The Ceph Model 4900 is intended to replace x-ray film in the acquisition of diagnostic cephalometric x-ray images of the skull as are commonly used in orthodontics to measure skeletal changes and monitor oral growth and development. The device works with a cleared x-ray device, along with a specialized collimator and positioning elements that are bundled together and sold as the PanCeph system.

The provided text describes a 510(k) premarket notification for the Schick Ceph Model 4900, a digital x-ray extraoral source system for cephalometric examinations.

Here's an analysis of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The submission is for a 510(k) for a medical device. For 510(k) submissions, the primary "acceptance criterion" is generally substantial equivalence to a legally marketed predicate device. This means the new device must have the same intended use and be as safe and effective as the predicate, or have different technological characteristics but not raise different questions of safety and effectiveness.

The document does not provide a table with specific, quantifiable acceptance criteria or performance metrics (e.g., sensitivity, specificity, accuracy, resolution, signal-to-noise ratio) and their corresponding reported device performance values. Instead, it makes general claims of equivalence.

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

The document does not specify a sample size for a test set. The testing described focuses on compliance with standards and a radiation survey comparing to a film predicate. No patient data or image dataset testing is explicitly mentioned. The data provenance is not applicable as no test set of clinical data is described.

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

This information is not provided as no test set requiring expert ground truth establishment is described. The submission focuses on device characteristics and comparison to a predicate, not clinical performance evaluation with expert adjudication.

4. Adjudication Method for the Test Set

This information is not provided as no test set requiring adjudication is described.

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

No MRMC study is mentioned in the provided text. The submission focuses on establishing substantial equivalence based on technical characteristics and regulatory compliance, not on comparing reader performance with and without AI assistance.

6. If a Standalone Performance Study was done

A standalone performance study, as typically understood for AI algorithms (i.e., algorithm only without human-in-the-loop performance using a clinically relevant image dataset), was not explicitly described in the provided text. The "Testing" section mentions electrical safety, compliance with FDA guidance for solid-state x-ray imaging devices and software, and a radiation survey. These are device-level safety and performance checks, not a clinical standalone performance study against a ground truth.

7. The Type of Ground Truth Used

The concept of "ground truth" as it relates to clinical accuracy (e.g., expert consensus, pathology, outcome data) is not applicable or mentioned in the context of the testing performed. The "ground truth" here is implied by the predicate device's established safety and effectiveness, and the device's adherence to regulatory standards and performance characteristics (like radiation output).

8. The Sample Size for the Training Set

This information is not applicable and not provided. The device described is a digital x-ray hardware system with integrated software. The submission does not indicate that the device uses machine learning or AI that would require a "training set" in the conventional sense of an AI model. The integrated software is described as "similar to that utilized in the COMPUTED ORAL RADIOLOGY SYSTEM (K041385)," implying re-use or adaptation of existing software, not the training of a new AI model.

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

This information is not applicable and not provided for the same reasons as #8.

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The Computed Oral Radiology System is indicated for patients undergoing an intra-oral dental x-ray examination. It produces instant, digital, intra-oral x-ray images of a patient's mouth while reducing the necessary x-ray dosage.

The device and its predicate are small digital imaging receptors that may be used in place of dental x-ray film. The new control mechanism differs from the predicate in that image acquisition may additionally be triggered through a hardwire to an x-ray tube. This modification allows for a quicker x-ray response time and may improve ergonomics as it eliminates the need for a standalone remote module. The existing firmware has been altered to support the modified and additional hardware. The new remote module may be housed within a specified x-ray source. The modification in no way effects the fundamental technology governing image acquisition.

Based on the provided document, here's an analysis of the acceptance criteria and the study (or lack thereof) to prove the device meets them:

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

The document does not provide specific, quantifiable acceptance criteria or reported device performance in a table format. It states generally:

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

The document does not specify the sample size used for any test set or the data provenance.

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)

The document does not provide information about any experts used to establish ground truth or their qualifications.

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

The document does not describe any adjudication method for a test set.

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

A multi-reader, multi-case (MRMC) comparative effectiveness study was not conducted. The device in question is a digital X-ray imager, not an AI-assisted diagnostic tool.

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

This document describes a modification to a digital dental X-ray imaging system, which is a hardware and firmware update to improve image acquisition and ergonomics. It is not an algorithm-only or AI device, so a standalone performance study in that context is not applicable and was not performed. The "standalone" aspect in this context refers to the elimination of a "standalone remote module."

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

The document does not describe the type of ground truth used, as it doesn't detail specific performance studies where ground truth would be established for diagnostic accuracy. The focus of this 510(k) submission is on demonstrating that a modification to an existing system maintains its safety and effectiveness relative to its predicate, rather than establishing primary diagnostic efficacy with new clinical data. The "ground truth" implicitly would be that the modified system continues to produce images suitable for dental x-ray examination without increased risk of unintended x-ray exposure.

8. The sample size for the training set

The document does not discuss a training set. This is not an AI/machine learning device that would require such data.

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

Not applicable, as there is no training set for an AI/machine learning model mentioned in the document.

Summary of the Document's Focus:

This 510(k) submission focuses on a device modification to an already cleared Computed Oral Radiology System. The modification involves a change in the control mechanism (allowing image acquisition to be triggered via a hardwire to an x-ray tube rather than a standalone remote module) and corresponding firmware alterations.

The document emphasizes demonstrating substantial equivalence to the predicate device. The "studies" mentioned are risk analysis, bench testing, and third-party safety testing, which focused on ensuring the modification did not introduce new risks (specifically unintended X-ray exposure) or alter the fundamental technology or indications for use. The acceptance criteria were therefore primarily related to safety and the maintenance of equivalence to the predicate, rather than new performance benchmarks for diagnostic accuracy.

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The CDR PanX Model 4792 is indicated for individuals requiring extra-oral dental exams. It exposes and acquires radiographic images at the dento-maxillofacial region.

The CDR PanX Model 4792 is a digital panoramic device. A high voltage power supply charges an x-ray tube that emits a pulse which is attenuated by the patient's head. An FDA approved digital receptor receives the resultant image and transmits the data to a computer. The receptor and x-ray source both revolve on a motion stage such that multiple projections may be generated thereby resulting in a panoramic view of the skull. The computer processes and displays the image data on a monitor.

Here's a breakdown of the acceptance criteria and study information based on the provided text, though it's important to note that this 510(k) summary (K031291) from 2003 offers limited detail on performance criteria and specific study methodologies typical of more modern submissions for AI/software devices. The device described is a traditional X-ray system, not an AI/software device.

Acceptance Criteria and Reported Device Performance

Study Information

Given this is a K031291 (approved in 2003) for a digital X-ray machine (not an AI/software device), the depth of information regarding "acceptance criteria" and "study" in the context of performance metrics (like sensitivity, specificity, etc.) is very limited compared to what would be found in a modern AI/software device submission. The "study" here primarily refers to comparative testing against a predicate device to establish substantial equivalence.

1. Sample size used for the test set and the data provenance: Not explicitly stated. The submission mentions "a radiation survey" and "careful analysis and validation studies," but does not provide details on the number of patients, images, or the origin (country, retrospective/prospective) of any data used for these comparisons.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable or not stated. Ground truth, in the context of an X-ray machine demonstrating "similar radiation" or "conforming to electrical standards," would likely refer to engineering measurements and comparisons rather than expert interpretation of images for diagnostic accuracy.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable or not stated.

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: Not applicable. This device is a traditional digital X-ray machine, not an AI-powered diagnostic tool. Therefore, no MRMC study for AI assistance would have been performed or relevant.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm-only device.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc): For the stated criteria (radiation and electrical safety), the ground truth would be based on engineering measurements and established regulatory standards. For the image quality aspect (implied by substantial equivalence to a device producing panoramic radiographs), the ground truth would likely be the visual and diagnostic quality of images produced by the predicate device. Specifics are not provided.

7. The sample size for the training set: Not applicable. This is a hardware device; there is no mention of a "training set" in the context of machine learning.

8. How the ground truth for the training set was established: Not applicable. As above, this is a hardware device.

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The Computed Oral Radiology System is indicated for patients undergoing an intra-oral dental x-ray examination. It produces instant, digital, intra-oral x-ray images of a patient's mouth while reducing the necessary x-ray dosage.

The device is a small digital imaging receptor that may be used in place of dental x-ray film. The proposed device modification alters the method of communication between the sensor and computer from a wired to wireless interface, introducing a radio frequency transmitter. The wireless sensor is powered via a battery. Image acquisition is mediated through hardware rather than software components. The sensor encapsulation material has been modified from aluminum to a thermoplastic resin. The existing firmware has been altered to support the modified and additional hardware, with firmware installed on both the remote module and the sensor. The firmware on the sensor facilitates unidirectional communication of image and sensor status data to the remote module.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Schick Computed Oral Radiology System, structured to answer your questions:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document (K022953) is a 510(k) summary for a modification to an already cleared device. As such, it primarily focuses on demonstrating substantial equivalence to the predicate device, rather than defining new, specific acceptance criteria for performance metrics like sensitivity or specificity. The "acceptance criteria" here implicitly revolve around demonstrating that the modifications did not negatively impact the established performance or safety of the predicate device.

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

The document does not specify a distinct "test set" sample size for evaluating clinical image performance, nor does it provide details on data provenance (country of origin, retrospective/prospective).

The study described is primarily a risk analysis and validation study focused on the changes introduced by the modification. This type of study (for a 510(k) modification) would typically involve engineering tests, bench-top measurements, and consideration of regulatory standards to ensure the modified device functions as intended and remains safe and effective, rather than a large-scale clinical image evaluation comparing diagnostic accuracy against a new, independent test set.

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

Given the nature of the study as presented (risk analysis and demonstration of substantial equivalence for a device modification), there is no mention of experts establishing ground truth for a clinical image test set. The reliance is placed on demonstrating that the modified device's technical characteristics and performance are "substantially equivalent" to the previously cleared predicate.

4. Adjudication Method for the Test Set

As there is no clearly defined clinical "test set" and associated ground truth establishment described, there is no adjudication method mentioned.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not done or described in this document. This document pertains to the sensor technology itself, not an AI-assisted diagnostic tool.

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

No, a standalone algorithm performance study was not done or described. This submission is for a dental x-ray sensor, an imaging device, not an AI algorithm.

7. The Type of Ground Truth Used

For the specific modifications described, the "ground truth" implicitly refers to:

• Engineering specifications and performance characteristics of the predicate device (e.g., imaging parameters as per the "Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices").
• Safety standards and biocompatibility data for materials (thermoplastic resin).
• Risk analysis methodology (ISO9001).

There is no mention of pathology, expert consensus on clinical images, or outcomes data being used as ground truth for evaluating the changes introduced by this specific modification, because the core imaging capability was assumed to be maintained from the predicate device.

8. The Sample Size for the Training Set

This document describes a device modification, not the development of an AI algorithm based on a training set of data. Therefore, there is no concept of a "training set" sample size mentioned.

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

As there is no training set mentioned, there is no description of how its ground truth was established.

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The CDR-PAN Panoramic Dental Digital Imager is indicated for use in acquiring radiographic images at the dento-maxillofacial region. It is intended to replace radiographic film/screen systems in panoramic dental diagnostic procedures.

Not Found

This document is a 510(k) clearance letter for the CDR-PAN Model 4700, an extraoral source x-ray system. The document does not contain information on acceptance criteria or a study proving the device meets acceptance criteria. The information provided is insufficient to answer the request.

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The accuDEXA is a dual-energy x-ray device indicated for use in estimating the bone density of the middle finger of the non-dominant hand (BMD). This BMD value is a relative indicator of bone density elsewhere in the body. accuDEXA BMD estimates can be used as an aid to the physician in determining fracture risk.

The accuDEXA device is a Dual Energy X-Ray Absorptiometer (DEXA) device. The device is intended to estimate bone mineral density in the middle finger of the non-dominant hand. By changing the high voltage on the X-ray tube, two energies are produced. Each of the two settings produces an image of the finger and each image is analyzed using various algorithms to produce a value of bone mineral density (BMD) and bone mineral content (BMC). These values are compared with a normative database, yielding a t-score and a z-score. The t-score is the number of standard deviations that the patient is above or below the mean of a reference sample of young healthy individuals. The z-score is the number of standard deviations that the patient is above or below the mean of a reference sample of individuals of the same age as the patient.

The provided 510(k) summary for the Schick accuDEXA Bone Densitometer does not contain the detailed clinical study information requested to fill out the table and answer all the questions. The submission focuses on demonstrating substantial equivalence to a predicate device (Norland pDEXA and Model 178) based on technological characteristics and indications for use, rather than presenting a new clinical study with acceptance criteria and performance data.

Specifically, the document states:

• "The accuDEXA Bone Densitometer is substantially equivalent to the Norland pDEXA and Model 178 bone densitometer devices with respect to the fracture risk claim."
• "The addition to the indications statement for the accuDEXA and the indications statement cleared by FDA for the predicate device are the same. The accuDEXA Bone Densitometer has the same technological characteristics as the predicate devices."
• "Schick Technologies has demonstrated through its comparison of characteristics with the predicate devices that the accuDEXA Bone Densitomctor is substantially equivalent to the predicate devices."

This means that Schick Technologies did not conduct a new clinical study to prove the device meets specific acceptance criteria for fracture risk determination. Instead, they leveraged the existing clearance of predicate devices that had already established their utility for fracture risk assessment. There is no new performance data or acceptance criteria reported for the accuDEXA itself in this document.

Therefore, most of the requested information cannot be extracted from this specific 510(k) submission.

However, I can populate the table and answer questions based on the information provided about the premise of substantial equivalence, which is that the device should perform "the same" as the predicate.

Device: Schick accuDEXA Bone Densitometer

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not applicable / Not provided. The submission relies on substantial equivalence to predicate devices, not on a new clinical study with a test set.
• Data Provenance: Not applicable / Not provided for the accuDEXA itself. The predicate devices (Norland pDEXA and Model 178) were already on the market, originating from earlier clearances (K973104, K931996 for pDEXA, and pre-1976 for Model 178).

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)

• Number of Experts: Not applicable / Not provided. No new ground truth establishment process for a test set is described. The basis for fracture risk determination for the predicate devices would have been established through a combination of clinical research and expert consensus over time, but these details are not provided here for the accuDEXA.

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

• Adjudication Method: Not applicable / Not provided. No new test set or adjudication process is described in this 510(k) 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

• MRMC Study: No. This submission describes a bone densitometer, which measures bone mineral density, not an AI-assisted diagnostic imaging interpretation device. Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant to this device and was not conducted.

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

• Standalone Performance Study: Not applicable in the context of fracture risk determination as a standalone claim. The device itself (accuDEXA) calculates BMD, t-score, and z-score autonomously. However, these estimates are explicitly stated to be "an aid to the physician in determining fracture risk," indicating a human-in-the-loop model where the physician interprets the device's output within the broader clinical context. The 510(k) does not present a study directly linking the algorithm's output alone to fracture outcomes without physician interpretation.

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

• Ground Truth Type: Not explicitly stated for the accuDEXA. For the general understanding of bone densitometry and fracture risk, the "ground truth" for the predicate devices and the underlying science of BMD would be based on:
  • Clinical Outcomes Data: Longitudinal studies correlating low BMD with increased fracture incidence.
  • Expert Consensus: Guidelines from organizations like the World Health Organization (WHO) and National Osteoporosis Foundation (NOF) which define osteopenia and osteoporosis based on T-scores and relate these to fracture risk. The submission explicitly mentions using these recommendations.

8. The sample size for the training set

• Training Set Sample Size: Not applicable / Not provided. The submission focuses on substantial equivalence based on technological characteristics, not on a machine learning model that requires a discrete training set. The device compares patient values to a "normative database," which serves as a reference, but its size is not specified here.

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

• Ground Truth Establishment for Training Set: Not applicable / Not provided. As above, this is not a machine learning submission. The "normative database" would have been established through large-scale epidemiological studies to define age- and gender-specific bone density distributions, which then form the basis for t-scores and z-scores, but the specifics are not in this document.

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