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bright CT is a computed tomography x-ray system intended to produce 3D, panoramic, and cephalometric diagnostic images of the maxillofacial areas for treatment planning for adult and pediatic patients. The device is operated and used by physicians, dentists, and x-ray technicians.

Rainbow 3D Image Viewer software functions for acquiring, saving, searching, displaying, diagnosing and sending digital X-ray image data in dental practices and clinics.

bright CT is a cone beam CT X-ray device for generating sectional images of dental images such as tooth, nasal cavity and temporomandibular joint. this is a medical diagnostic equipment designed to generate sectional images by placing X-ray source opposite to the imaging detector unit and rotating it around a patient. 2D images of the region of interest are reconstructed using a mathematical algorithm in 3 dimensional volumetric view and displayed on the computer monitor.

The system is composed of X-ray generator, X-ray detector, X-ray collimator, main frame, rotation unit, PC and Monitor, etc. in compliance with US performance standard and regulatory requirement.

Here's an analysis of the acceptance criteria and the study proving the "bright CT" device meets those criteria, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state formal "acceptance criteria" for the performance characteristics in the way one might typically see for a medical device (e.g., "The MTF for CBCT must be greater than X%"). Instead, the performance claims for the "bright CT" are evaluated against those of a predicate device, the "rainbow CT." The key criteria for substantial equivalence appear to be matching or improving upon the predicate's performance.

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

The document does not specify a "test set" in terms of patient images or specific clinical cases. The performance evaluation appears to be based on non-clinical data and performance testing directly on the device's physical components and imaging capabilities.

• Sample Size for performance tests: Not explicitly stated for each test (e.g., how many measurements for MTF/DQE).

• Data Provenance: The document does not describe the use of patient data for performance evaluation in a testing context. The testing instead involved established international and national standards:

  • IEC 60601-1 (Electrical, mechanical, environmental safety)
  • IEC 60601-1-3 (Radiation protection)
  • IEC 60601-1-6 (Usability)
  • IEC 60601-2-63 (Specific requirements for dental x-ray equipment)
  • IEC 60601-1-2 (EMC)
  • NEMA PS 3.1-3.18 (DICOM)
  • FDA Guidance "Guidance for the submissions of 510(k)'s for Solid State X-ray Imaging Devices"
  • IEC 61223-3-4 & IEC 61223-3-5 (Acceptance tests for diagnostic X-ray imaging equipment)

  These tests typically involve physical phantoms and measurement equipment, not clinical patient data. Therefore, questions regarding country of origin or retrospective/prospective nature of data are not applicable to the described performance testing.

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

Not applicable. As noted above, the primary performance evaluation was based on non-clinical, objective measurements of the device's technical specifications against regulatory standards and comparison to a predicate device, not on expert interpretation of clinical images for ground truth.

4. Adjudication Method for the Test Set

Not applicable, as there was no test set requiring expert adjudication.

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

No MRMC comparative effectiveness study was done. The device, "bright CT," is a CT X-ray system for acquiring images, not an AI-powered diagnostic aide. Therefore, the concept of human readers improving with AI assistance is not relevant to this submission.

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

Not applicable. The device is a hardware imaging system. There is no standalone algorithm being evaluated for diagnostic performance.

7. The Type of Ground Truth Used

The "ground truth" for evaluating the technical performance claims (MTF, DQE, pixel resolution, etc.) was established through objective physical measurements using standardized phantoms and measurement techniques as prescribed by the mentioned IEC and NEMA standards. For the safety and efficacy evaluation of the overall device, the ground truth was substantial equivalence to a legally marketed predicate device (rainbow CT), demonstrating that the differences do not raise new questions of safety or effectiveness.

8. The Sample Size for the Training Set

Not applicable. This device is an imaging acquisition system, not a machine learning algorithm that requires a training set of data.

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

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

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SuperLine® implants are indicated for use in surgical and restorative applications for placement in the bone of the upper or lower jaw to provide support for prosthetic devices, such as artificial teeth, in order to restore the patient's chewing function. SuperLine® implants are indicated also for immediate loading when good primary stability is achieved and with appropriate occlusal loading. Single tooth cases on 7 mm length implants are indicated for delayed loading.

The purpose of this submission is to add dental implants to the SuperLine components cleared in K160965. This submission includes two series of implants: SuperLine FXSxxxxB Series and SuperLine FXSxxxx Series.

The FXSxxxB Series has a machined collar height that ranges from 0.03 mm (on the 3.6 mm body diameter implant) to 0.6 mm (on the 5.0 mm body diameter implant). All FXSxxxxB Series implants have an endosseous length of 7 mm, and threaded lengths ranging from of 6.98 mm to 6.4 mm. Other than the endosseous length of 7 mm, the subject device FXSxxxB implants are identical in design and sizes to SuperLine implants cleared in K160965.

The FXSxxxx Series has machined collar 1.5 mm in height for all implant body diameter sizes. For body diameters 4.0 mm to 5.8 mm, the collar has a reverse taper. All FXSxxxx Series implants have an endosseous length of 7 mm, and a threaded length of 5.5 mm. The FXSxxxx series implant design has not been cleared previously by FDA for any body diameter or length.

All subject device implants are manufactured from the same unalloyed titanium conforming to ASTM F67 and have the same surface treatment (S.L.A., Al2O3 blasted and acid etched) as the dental implants cleared in K160965.

All subject device implants are compatible with previously-cleared abutments and prosthetic components from Dentium Co., Ltd., including those cleared in K192436, K141457, and K041368.

The provided document is a 510(k) summary for a medical device (SuperLine dental implants) and does not contain information about acceptance criteria or a study proving the device meets acceptance criteria. The document discusses substantial equivalence to predicate devices based on non-clinical data and design comparisons.

Therefore, I cannot fulfill your request for information regarding acceptance criteria and a study demonstrating device performance against those criteria.

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rainbow MCT is a computed tomography x-ray system intended to produce 3D and panoramic diagnostic images of the maxillofacial areas for treatment planning for adult and pediatric patients. The device is operated and used by physicians, dentists, and x-ray technicians.

Rainbow 3D Image Viewer software functions for acquiring, saving, searching, displaying, diagnosing and sending digital X-ray image data in dental practices and clinics.

rainbow MCT is a cone beam CT X-ray device for generating sectional images of dental images such as tooth, nasal cavity and temporomandibular joint. this is a medical diagnostic equipment designed to generate sectional images by placing X-ray source opposite to the imaging detector unit and rotating it around a patient. 2D images of the region of interest are reconstructed using a mathematical algorithm in 3 dimensional volumetric view and displayed on the computer monitor.

The system is composed of X-ray generator, X-ray detector, X-ray collimator, main frame, rotation unit, PC and Monitor, etc. in compliance with US performance standard and regulatory requirement.

This document describes the premarket notification (510(k)) for the Dentium Co., Ltd rainbow MCT (K200270), a computed tomography x-ray system. The information provided focuses on demonstrating substantial equivalence to predicate devices rather than a standalone clinical study of an AI algorithm. Therefore, many of the requested criteria related to AI performance, such as multi-reader multi-case (MRMC) studies, effect sizes, and specific details about training/test set ground truth establishment for AI, are not applicable or not explicitly detailed in this document, as the device itself is an imaging system and not an AI/ML software.

The document primarily discusses the technical and performance characteristics of the imaging device itself, ensuring it meets standards comparable to existing predicate devices.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a formal "acceptance criteria" table in the way one might see for an AI algorithm's performance metrics (e.g., AUC, sensitivity, specificity targets). Instead, it compares the rainbow MCT's technical specifications and imaging performance metrics against those of predicate devices (K181432, ProVecta 3D Prime with VistaSoft; and K102196, PaX-Zenith3D). The "acceptance" is implied by demonstrating similar or better performance compared to these legally marketed devices.

Summary of differences and claims: The document states, "The MTF, DQE and pixel resolution of the subject device performed similar or better than those of the predicate and reference device. All test results were satisfactory."

2. Sample Size for the Test Set and Data Provenance

The document describes non-clinical testing of the device's physical and technical performance (e.g., electrical, mechanical, environmental safety, EMC, imaging properties per IEC standards). It does not refer to a "test set" in the context of a dataset of patient images used to evaluate an AI algorithm's diagnostic performance. Therefore, sample size and data provenance (country, retrospective/prospective) for a patient image test set are not applicable here.

The tests performed were:

• Electrical, mechanical, environmental safety testing according to IEC 60601-1, IEC 60601-1-3, IEC 60601-2-63.
• EMC testing in accordance with IEC 60601-1-2.
• Non-clinical & Clinical considerations according to FDA Guidance "Guidance for the submissions of 510(k)'s for Solid State X-ray Imaging Devices."
• Acceptance test according to IEC 61223-3-4 and IEC 61223-3-5.

All these refer to technical and performance benchmarks, often using test phantoms or controlled environments, not patient data for diagnostic accuracy assessment.

3. Number of Experts and Qualifications for Ground Truth

Not applicable. As this is a 510(k) for an imaging device, not an AI diagnostic algorithm, there's no mention of experts establishing ground truth from patient images for a diagnostic performance study. The ground truth for the performance characteristics (e.g., MTF, DQE) is inherent to the physical properties of the imaging system and measured using standardized methods and phantoms.

4. Adjudication Method for the Test Set

Not applicable. No diagnostic image test set or human interpretation adjudication is described.

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

No. A MRMC comparative effectiveness study was not done, as the submission is for the imaging device itself, not an AI-assisted interpretation tool. The document explicitly states: "Clinical Data: Not required for a finding of substantial equivalence."

6. Standalone (Algorithm Only) Performance

Not applicable. This submission is for a medical imaging device, not an AI algorithm.

7. Type of Ground Truth Used

The "ground truth" for the device's performance is based on technical specifications and measurements obtained through standardized testing procedures using phantoms and controlled setups (e.g., MTF, DQE measurements, electrical safety tests). It is not based on expert consensus, pathology, or outcomes data from patient cases in a diagnostic context.

8. Sample Size for the Training Set

Not applicable. The device is a hardware imaging system; it does not involve machine learning or training on a dataset of patient images.

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

Not applicable. See point 8.

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rainbow CT is a computed tomography x-ray system intended to produce 3D, panoramic, and cephalometric diagnostic images of the maxillofacial areas for treatment planning for adult and pediatric patients. The device is operated and used by physicians, dentists, and x-ray technicians.

Rainbow 3D Image Viewer software functions for acquiring, saving, searching, displaying, diagnosing and sending digital X-ray image data in dental practices and clinics.

rainbow CT is a cone beam CT X-ray device for generating sectional images of dental images such as tooth, nasal cavity and temporomandibular joint. this is a medical diagnostic equipment designed to generate sectional images by placing X-ray source opposite to the imaging detector unit and rotating it around a patient. 2D images of the region of interest are reconstructed using a mathematical algorithm in 3 dimensional volumetric view and displayed on the computer monitor.

The system is composed of X-ray generator, X-ray detector, X-ray collimator, main frame, rotation unit, PC and Monitor, etc. in compliance with US performance standard and regulatory requirement.

The provided text is a 510(k) summary for the "rainbow CT" device. It describes the device, its indications for use, and a comparison to predicate and reference devices. However, this document does not contain the specific details required to answer your request about acceptance criteria and the study proving the device meets them.

The document states:

• "Performance testing was conducted for the subject device to access whether or not the parameter required for functionalities related to imaging properties of the dental X-ray device meets the designated acceptance criteria. The MTF, DQE and pixel resolution of the subject device performed similar to those of the predicate device. The pixel resolutions of the subject device in CBCT (2x2 binning) and pano mode are superior to that of the reference device. All test results were satisfactory." (Page 8)
• "Non-clinical & Clinical considerations according to FDA Guidance "Guidance for the submissions of 510(k)'s for Solid State X-ray Imaging Devices" were performed. Acceptance test according to IEC 61223-3-4 and IEC 61223-3-5 was performed. All test results were satisfactory." (Page 8)
• "Clinical Data: Not required for a finding of substantial equivalence." (Page 8)

This means the acceptance criteria and study details (like sample size, number of experts, adjudication methods, ground truth, effect sizes) for clinical performance are not present in this 510(k) summary because clinical data was explicitly stated as "Not required for a finding of substantial equivalence."

The performance testing mentioned (MTF, DQE, pixel resolution) refers to technical imaging performance characteristics of the CT system itself, not clinical diagnostic performance of an AI algorithm on patient images. The acceptance criteria for these technical parameters would likely be engineering specifications, and the "study" would be technical measurements in a lab setting, rather than a clinical trial with human readers and patient data.

Therefore, I cannot populate the table or answer the specific questions about clinical performance, human-in-the-loop studies, or ground truth derivation from the provided text. The document focuses on demonstrating substantial equivalence based on technical characteristics and safety standards, rather than a detailed clinical validation study for an AI component.

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iCTmotor (WL-1) is intended for use in dental surgery and implantology. The main control unit is designed to operate a specific dental micro motor that drives dental handpieces to cut hard and soft tissues in the mouth and screw dental implants. iCTmotor (WL-1) is compatible with a handpiece equipped with connection according to ISO 3964.

iCTmotor (WL-1) is a software based driving engine that controls the speed of a specific dental micromotor. This device is optimized for dental implant procedures and user programmable parameters operate and control a dental handpiece for dental implant surgery. iCTmotor (WL-1) consists of a main controller unit, a charger, a foot controller, micro motor, cable, a water holder, tube holder, and micro motor holder. The main control unit operates the speed and torque of a dental micromotor that drives dental handpiece to cut tissues in the mouth and to screw dental implants. The main control unit is operated via a wireless foot pedal. The holders are used for placement of a water bag, a micro motor and a handpiece. The power cord delivers electric power to the main control unit.

The provided text is a 510(k) summary for the iCTmotor (WL-1), a dental device. It does not describe an AI/ML-based medical device or a study involving human readers or AI assistance. Therefore, it's not possible to provide acceptance criteria or a study description related to AI/ML device performance from this document.

The document focuses on demonstrating substantial equivalence to a predicate device (MASTERsurg / EXPERTsurg) through non-clinical performance testing. It highlights the device's intended use in dental surgery and implantology, specifically controlling a dental micromotor for cutting tissues and screwing dental implants.

The non-clinical tests performed are related to the physical and electrical characteristics of the device, as well as software validation for its control functions.

Key points from the document relevant to non-clinical performance testing (not AI/ML):

• Acceptance Criteria & Performance: The document states that "Performance testing was conducted according to ISO 14457:2012 to show that the device meets its design requirements and performs as intended." The specifications for the following parameters were met:

  • Rotating speed of micromotor
  • Torque of micromotor
  • Stop of micromotor
  • Rotating direction of micromotor
  • Irrigation amount

  The exact numerical acceptance criteria and the reported numerical performance for these parameters are not explicitly detailed in a table format within this summary, but the general claim is that they were "met." For example, under "Electrical Specification Motor (Speed)," the iCTmotor (WL-1) has a speed of "400- 40,000rpm," and the predicate has "300 - 40,000 rpm." While these are performance specs, the document doesn't present them as formal "acceptance criteria" vs. "reported performance" in a dedicated test. Instead, it asserts compliance with ISO 14457:2012.

• Sample Size and Data Provenance: Not applicable for an AI/ML context. For the non-clinical tests, the sample size would refer to the number of units tested, which is not specified but is typically a small engineering sample or a representative batch. The data provenance is from the manufacturer's internal testing.

• Experts and Ground Truth: Not applicable in the context of AI/ML interpretation. The "ground truth" for these performance tests is the engineering specification for each parameter (e.g., a specific RPM range, torque value).

• Adjudication Method: Not applicable. Performance testing of physical devices is typically a direct measurement against a specification.

• MRMC Comparative Effectiveness Study: Not applicable. This study focuses on a hardware device, not an AI/ML algorithm.

• Standalone Performance: The "performance testing" described (rotating speed, torque, etc.) represents the standalone performance of the device's control system and motor.

• Type of Ground Truth: The ground truth for this device's performance testing is based on engineering specifications and established international standards (ISO 14457:2012) for dental handpieces/micromotors.

• Training Set Sample Size: Not applicable. This is a hardware device with software control, not an AI/ML model that requires training data. The software was subjected to verification and validation as per FDA guidance for software in medical devices, but this is distinct from AI/ML model training.

• Ground Truth for Training Set: Not applicable, as there is no AI/ML training set in this context. Software "ground truth" for verification and validation typically means that the software functions as designed according to its requirements specification.

In summary, this document is a 510(k) premarket notification for a traditional medical device (iCTmotor (WL-1)) and does not contain information about AI/ML acceptance criteria or a study proving an AI/ML device meets such criteria.

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Intra Oral Sensor (model: IOX 1 / IOX 2) is intended to collect dental x-ray photons and convert them into electronic impulses that may be stored, views and manipulated for diagnostic use by dentists.

Intraoral Sensor (model: IOX 1 / IOX 2) by Dentium is a medical device that acquires digital images by detecting subject information through X-rays and converting them into electrical image signals to identify teeth and tissues in the mouth. The product consists of the Intraoral Sensor, USB Memory, Sensor Holder, Silicon Cover and Quick Guide.

The provided text describes information related to a 510(k) submission for an Intra Oral Sensor (model: IOX 1 / IOX 2) by Dentium Co., Ltd. The document primarily focuses on demonstrating substantial equivalence to a predicate device through bench testing and adherence to relevant standards.

Here's an attempt to extract the requested information, noting that some details typically found in a full study report (e.g., specific sample sizes for test sets, detailed ground truth establishment for training) are not fully elaborated in this summary document.

Acceptance Criteria and Device Performance:

The document states that "Performance testing was conducted for the subject device to assess whether or not the parameter required for functionalities related to imaging properties of the dental X-ray device meets the designated acceptance criteria. All test results were satisfactory." And specifically mentions: "The tests include the MTF(Modulation Transfer Function) and DOE(Detective Quantum Efficiency) of detector. MTF of detector shows the resolution more than 30 % at 6 lp/mm and The DQE of detector shows the resolution more than 40 % at 2.5 lp/mm."

Based on the information provided, the following table can be constructed:

Study Details:

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

   • Sample Size (Test Set): The document does not specify the exact number of images or cases used in the performance bench testing. It refers to "bench testing" and "performance (imaging performance) testing" according to standard IEC 61223-3-4.
   • Data Provenance: This was a non-clinical bench study focused on the technical performance of the device itself (sensor characteristics), not on clinical images from patients. Therefore, data provenance in terms of country of origin or retrospective/prospective clinical data is not applicable here.

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

   • Not applicable. Since this was a non-clinical bench test of technical performance (MTF, DQE), there were no human experts involved in establishing "ground truth" for diagnostic purposes. The ground truth was based on physical measurements of the sensor's technical specifications as per established engineering standards (e.g., IEC 61223-3-4 for imaging performance).

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

   • Not applicable. As this was a non-clinical bench test, there was no adjudication process involving human reviewers.

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

   • No, an MRMC comparative effectiveness study was not done. This submission is for an intraoral sensor, which is a hardware device for capturing X-ray images, not an AI-powered diagnostic software. The study focused on the technical performance equivalence of the sensor to its predicate device. Clinical data and comparative effectiveness studies involving human readers or AI assistance were explicitly stated as "Not required for a finding of substantial equivalence."

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

   • No, this is not applicable as this is a hardware device. This submission is for an intraoral sensor, which is a hardware device that acquires images. There is no standalone algorithm being evaluated for diagnostic performance.

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

   • The ground truth for this study was based on physical and engineering measurements of the sensor's imaging performance characteristics (MTF, DQE) according to established international standards (IEC 61223-3-4). It is a technical ground truth, not a clinical one.

7. The sample size for the training set:

   • Not applicable. This device is a hardware sensor, not an AI algorithm that requires a training set for machine learning.

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

   • Not applicable. As there is no training set for an AI algorithm.

Summary of what the document implies about the study:

The study conducted was a non-clinical bench performance study designed to demonstrate that the new Intra Oral Sensor (IOX 1 / IOX 2) meets technical imaging performance specifications (MTF and DQE) that are substantially equivalent to its predicate device. This type of study focuses on the physical properties and output quality of the imaging hardware itself, rather than evaluating diagnostic accuracy with human readers or AI algorithms on clinical cases. The ground truth was based on objective physical measurements and engineering standards.

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Dentium Prosthetics are intended for use as an aid in prosthetic rehabilitation.

The purpose of this submission is to change the sterilization method of Healing Abutments and Cover Screws. These devices which have been provided non-sterilized by gamma radiation.

Healing Abutments are used provisionally as an accessory to endosseous dental implant during healing period to prepare gingival tissue for acceptance of a final abutment. Healing Abutments are designed to aid in soft tissue contouring during the healing period after implant placement, creating an emergence profile for the final abutment. Cover Screws are used provisionally as an accessory to protect the inner features of the implant.

The Healing Abutments and Cover Screws are prefabricated and made of Ti-6Al-4V ELI (ASTM F136). These devices are sterilized using gamma radiation method and intended for single use only.

The provided document is a 510(k) premarket notification for "Healing Abutments and Cover Screws." This type of submission focuses on demonstrating substantial equivalence to a predicate device, rather than proving a device meets specific performance acceptance criteria through the kind of rigorous, large-scale studies typically associated with AI/ML-based medical devices or novel technologies.

Therefore, many of the requested details, such as sample size for test sets, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance, and training set details, are not applicable to this specific submission. This document describes a change in the sterilization method for existing devices, not a new device with performance metrics that require extensive clinical validation studies beyond sterilization and biocompatibility.

Here's an analysis based on the information available in the document:

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

For a 510(k) submission concerning a change in sterilization method for mechanical components like healing abutments and cover screws, the "acceptance criteria" revolve around demonstrating that the new sterilization method achieves the required sterility assurance level and does not adversely affect the device's material properties or function, maintaining its substantial equivalence to the predicate.

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

• Sample Size: Not explicitly stated as a separate "test set" in the context of clinical performance data typical for AI/ML devices. The "tests" here are primarily engineering and laboratory validations:
  • Sterilization Validation: ISO 11137 standards define the number of units and replicates required for sterility testing (e.g., bioburden determination, dose mapping, sterility testing), which are often small, specific lots. This is standard laboratory validation, not a patient-based test set.
  • Aging Studies: ASTM F1980 dictates sample sizes for accelerated aging, again, laboratory testing of physical samples.
  • Biocompatibility: Relies on a prior submission (K041368) which presumably included its own biocompatibility testing. No new "sample size" for a clinical test set is mentioned for biocompatibility here.
• Data Provenance: The studies are laboratory-based ("Non-clinical testing data"). The manufacturer is located in the Republic of Korea (Dentium Co., Ltd.). The data would be derived from their internal validation processes. It is neither retrospective nor prospective in the sense of patient studies.

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. This submission does not involve image interpretation or clinical diagnosis where expert ground truth would be established. The "ground truth" for sterilization is a defined SAL, verified by microbiological and physical testing per ISO standards. The "ground truth" for material properties is based on ASTM standards and prior biocompatibility data.

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

• Not Applicable. As there is no clinical "test set" based on human interpretation or an AI algorithm's output requiring adjudication, this concept does not apply.

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 traditional medical device (dental implants and accessories), not an AI-assisted diagnostic or therapeutic device. Therefore, no MRMC study, human reader improvement, or AI assistance is relevant.

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

• Not Applicable. This is a physical medical device, not an algorithm.

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

• The "ground truth" for this submission is based on:
  • Standardized Sterility Assurance: Achieving a defined SAL of 10^-6, verified through validated sterilization processes and testing compliant with ISO 11137-1 and ISO 11137-2.
  • Material Standards: Biocompatibility established against Ti-6Al-4V ELI (ASTM F136) as per regulations and previous submissions.
  • Engineering Performance: Shelf-life demonstrated through accelerated and real-time aging studies per ASTM F1980.
  • Substantial Equivalence: Demonstrated by comparing engineering specifications and intended use against legally marketed predicate devices.

8. The sample size for the training set

• Not Applicable. This involved physical device manufacturing and validation, not machine learning model training.

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

• Not Applicable. (See point 8)

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Dentium Prosthetics are intended for use as an aid in prosthetic rehabilitation.

The purpose of this submission is to expand the Dentium Prosthetics to include the Scan Abutments and Comfort Caps.

Scan Abutments are used provisionally as an accessory to endosseous dental implant during healing period to prepare gingival tissue for acceptance of a final abutments are designed to aid in soft tissue contouring the healing period after implant placement, creating an emergence profile for the final abutment.

Comfort Caps are used provisionally as an accessory to protect the dental abutment before final prosthesis.

They have the design feature that enable to transmit position and angulation data of implant when taking a digital impression using an intra-oral scanner.

The Scan Abutments and Comfort Caps are prefabricated and made of Ti-6AI-4V ELI (ASTM F136) or PEEK(ASTM F2026). Scan Abutments are compatible with Dentium Implant(K041368 Implantium and K160965 SuperLine or K153268 NR Line) and Comfort Caps are compatible with Dentium Prosthetics(K052957 Implantium and K172640 SuperLine or K153268 NR Line)

This document is a 510(k) Pre-Market Notification from the FDA regarding dental devices (Scan Abutments and Comfort Caps). It focuses on demonstrating substantial equivalence to previously cleared devices, rather than establishing performance against specific acceptance criteria for a novel AI/ML device.

Therefore, many of the requested details about acceptance criteria and study design (especially those related to AI/ML performance, ground truth, human readers, and training/test set specifics) are not applicable to this type of regulatory submission.

Here's what can be extracted and what cannot:

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

• Acceptance Criteria (Implicit for 510(k) Substantial Equivalence): The primary "acceptance criterion" for a 510(k) is the demonstration of substantial equivalence to a legally marketed predicate device. This is achieved by showing that the new device has the same intended use as a predicate and the same technological characteristics, or different technological characteristics that do not raise different questions of safety and effectiveness and are as safe and effective as the predicate.
• Reported Device Performance (as demonstrated for Substantial Equivalence):
  • Indications for Use: The subject device has the same indication for use as the primary predicates: "Dentium Prosthetics are intended for use as an aid in prosthetic rehabilitation."
  • Technological Characteristics Comparison (Tables provided in Section 7): The document provides detailed comparison tables for Scan Abutments (vs. K172640, K153268, K173374, K172160) and Comfort Caps (vs. K171142, K172160). These tables demonstrate similarities in:
    • Device name, Manufacturer, 510(k) Number (where applicable)
    • Indication for use
    • Materials (Ti-6Al-4V ELI, PEEK – demonstrated to be identical to or commonly used in predicates)
    • Form (Preformed)
    • Sterilization (Non-sterile, similar to primary predicates; note on sterile reference predicates)
    • Use (Prescription)
    • Single Use Only (Yes)
    • Design characteristics: Diameter, Length, Connection type, Scanning feature (for Scan Abutments), Surface treatment.
  • Performance Data (Non-clinical):
    • Steam sterilization validation (ISO 17665-1 and ISO 17665-2), demonstrating a sterility assurance level (SAL) of 10-6. (This is a specific performance metric).
    • Biocompatibility of Ti-6Al-4V ELI (ASTM F136) was demonstrated by referencing a previous submission (K172640) using the same materials and manufacturing processes.
    • Cytotoxicity testing of PEEK (ASTM F2026) was performed according to ISO 10993-5.

N/A (Not Applicable) for this type of submission:

• 2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): No specific "test set" in the context of an AI/ML model for performance evaluation is described. The performance data relates to material properties and sterilization, not diagnostic/AI performance on a dataset.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an AI/ML model is not a concept for these mechanical/material devices.
• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
• 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 not an AI/ML diagnostic tool.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable. The "ground truth" here is compliance with material standards, sterilization standards, and functional equivalence to predicates.
• 8. The sample size for the training set: Not applicable.
• 9. How the ground truth for the training set was established: Not applicable.

In summary, this FDA document is for a medical device (dental abutments and caps) seeking 510(k) clearance based on substantial equivalence. It does not involve AI/ML components or comparative effectiveness studies of human readers, thus many of the questions are not relevant.

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Dentium Implantium® & SuperLine® Prosthetics are intended for use as an aid in prosthetic rehabilitation.

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This document is a 510(k) clearance letter for "Dentium Implantium® & SuperLine® Prosthetics." It states that the device is substantially equivalent to legally marketed predicate devices. However, this document does not contain any information about acceptance criteria or a study proving the device meets them.

The letter primarily focuses on:

• Confirming the substantial equivalence determination.
• Outlining regulatory compliance requirements for the manufacturer.
• Identifying the product code and classification.
• Stating the intended use of the device ("intended for use as an aid in prosthetic rehabilitation").

Therefore, I cannot provide the requested information based on the text provided. This type of regulatory document typically follows the completion of such studies, but does not detail the studies themselves.

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Dentium Ti-Base abutments are intended for use on Dentium endosseous dental implants in the edentulous or partially edentulous maxilla or mandible, as an aid in prosthetic rehabilitation.

All digitally designed abutments for use with Dentium Ti-Base abutments are intended to be sent to a Dentium-validated milling center for manufacture.

Dentium Ti-Base is a patient specific abutment fabricated by Dentium and composed of unalloyed titanium and zirconia ceramic. The Ti-Base has a titanium pre-manufactured precision abutment/implant interface and ceramic superstructure bonded to the post of the titanium base. Ti-Base is used for support of CAD/CAM fabricated zirconia superstructures or for support of hybrid crowns or bridges. Hybrid crowns or bridges and zirconia superstructures are manufactured by a Dentium validated milling center.

Ti-Base Abutments are available in two prosthetic diameters (4.5 and 5.5 mm) and four gingival heights (0.5, 1.0, 1.5, and 2.0 mm). Each of the abutments is available indexed and non-indexed. All subject device abutments are compatible with Dentium and Dentium Superline dental implants.

The provided text describes a 510(k) premarket notification for the Dentium Ti-Base dental abutment, establishing its substantial equivalence to previously marketed predicate devices. This type of submission relies on non-clinical performance data rather than extensive clinical studies with specific acceptance criteria as might be seen for novel devices.

Here's an analysis of the provided information concerning acceptance criteria and supporting studies:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Sizes and Data Provenance

• Test Set Sample Size: Not explicitly stated for any of the non-clinical tests. For ISO 14801, typical sample sizes are small (e.g., n=5 or n=10 per test condition), but this is not specified.
• Data Provenance: The studies are non-clinical (laboratory/mechanical testing). The manufacturer is Dentium Co., Ltd. in the Republic of Korea. Therefore, the data originates from the manufacturer's testing or a contracted testing facility. All data is presumably prospective as it was generated to support the 510(k) submission.

3. Number of Experts and Qualifications for Ground Truth

• This information is not applicable as the studies are non-clinical performance tests for a device seeking substantial equivalence, not a diagnostic algorithm requiring expert-established ground truth. The "ground truth" for these tests is defined by the standards (ISO 17665-2, ISO 10993-5, ISO 14801).

4. Adjudication Method for the Test Set

• This information is not applicable as the studies are non-clinical performance tests and do not involve human adjudication for a "test set" in the context of diagnostic assessment.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The device is a physical dental abutment, not a diagnostic AI system that would assist human readers.

6. Standalone (Algorithm Only) Performance Study

• No, a standalone (algorithm only) performance study was not done. The device is a physical medical device. The "digitally designed abutments" mentioned refer to CAD/CAM processes used in manufacturing, but this is a manufacturing method, not an AI algorithm requiring standalone performance evaluation in the medical sense.

7. Type of Ground Truth Used

• The "ground truth" applied here is primarily established international standards for medical device testing (e.g., ISO 17665-2 for sterilization, ISO 10993-5 for cytotoxicity, ISO 14801 for mechanical strength). For substantial equivalence, the performance is compared against the characteristics of legally marketed predicate devices.

8. Sample Size for the Training Set

• This information is not applicable. The device is a physical product, not an AI/ML algorithm that is "trained" on a dataset. The manufacturing process might involve CAD/CAM software, but this is distinct from an AI training set.

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

• This information is not applicable as there is no "training set" in the context of an AI/ML algorithm.

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