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For light curing polymerization of small areas of dental materials capable of curing in the 385 – 425 nm wavelength range.

Fibercure is a handheld dental curing light intended for light curing polymerization of small areas of dental materials capable of curing in the 385 – 425 nm wavelength range. Fibercure functions by delivering light energy to photocurable dental materials, which utilize the light energy to induce a photopolymerization reaction.

Fibercure consists of a pen-shaped handpiece, that is powered with a detachable, rechargeable battery. The handpiece features a user interface containing two push buttons and three status LEDs. The hand-piece is equipped with a solid-state laser diode to generate light energy at a wavelength of 405 ± 5mm and a power of 40 mW. The laser diode is coupled to single-use cure tips containing an optical fiber of 200 µm diameter. The cure tip emits light with an irradiance of 1800 - 2200 mW/cm2 and allows the light to be precisely targeted on the area to be photocured. Fibercure has 2 operation modes to provide either 10 or 20 second irradiation. Fibercure is delivered with a charging stand that is connected to the mains power using a USB power supply. It provides two charging stations and a photometer to confirm the intensity of the emitted light.

Fibercure's handpiece is manufactured from anodized aluminum and houses the electronic assembly. The cure tip consists of a silica-ETFE optical fiber, a stainless-steel fiber guide, a plastic housing and a ceramic ferrule. The detachable battery, charging base and power supply are manufactured from injection-molded plastic.

This is a 510(k) summary for the device Fibercure, an ultraviolet activator for polymerization used in dentistry. The document focuses on establishing substantial equivalence to a predicate device, rather than proving a device meets specific acceptance criteria through a study with statistical measures.

The information provided is primarily a comparison between the subject device (Fibercure) and predicate devices, detailing various technical specifications and indicating that non-clinical tests were performed. However, it does not include the typical acceptance criteria format (e.g., sensitivity, specificity, AUC with thresholds) or a detailed study description to demonstrate that the device "meets" those criteria in the way one would expect for a diagnostic or AI-driven device.

Here's an attempt to answer your questions based on the provided text, while acknowledging the limitations of this type of regulatory submission in addressing your specific points:

Since the document provided is a 510(k) summary for a dental curing light, it focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with acceptance criteria in the manner of a diagnostic or AI-driven device. Therefore, many of your questions, particularly those related to sample size, expert ground truth, MRMC studies, and training sets, are not applicable or cannot be answered from this type of document.

However, I can extract the "performance parameters" and the general conclusion that these parameters meet requirements as a proxy for "acceptance criteria."

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

The document doesn't explicitly state "acceptance criteria" with specific numerical thresholds for aspects like irradiance or depth of cure as pass/fail conditions. Instead, it presents the device's performance parameters and compares them to predicate devices and general standards. The "acceptance criteria" here are implied to be "meeting the requirements of recognized standards and guidance" and being "comparable" to predicate devices.

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 describes non-clinical performance testing but does not detail the methodology, sample sizes, or provenance of the data in the way you would expect for a clinical study or AI algorithm validation. The testing appears to be laboratory-based physical measurements rather than human-data-driven.

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 applicable/provided. The device is a dental curing light, not an AI or diagnostic device that requires expert ground truth for evaluating performance on a test set of cases. The "ground truth" for its performance relates to physical measurements and adherence to technical standards.

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

This information is not applicable/provided. Adjudication methods are relevant for consensus among experts, typically in diagnostic or AI performance studies on clinical data. For this device, performance is evaluated against technical standards and physical measurements.

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 information is not applicable/provided. This type of study is specifically for AI-assisted diagnostic devices involving human readers. The Fibercure device is a dental curing light, a tool used by dental professionals, not an AI-driven diagnostic system.

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

This information is not applicable/provided. This question is relevant for AI algorithms. The Fibercure device is a physical instrument for light curing, not an algorithm.

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

The "ground truth" for this device's performance would be derived from physical measurements and adherence to recognized industry and regulatory standards. For example, irradiance is measured with a photometer, wavelength with a spectrometer, and depth of cure according to ISO 4049. It is not based on expert consensus, pathology, or outcomes data in the context of clinical disease.

8. The sample size for the training set

This information is not applicable/provided. The Fibercure device is a physical product (a dental curing light), not a software or AI algorithm that requires a "training set."

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

This information is not applicable/provided. As above, there is no "training set" for this device.

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Endofill (1) is indicated for the permanent obturation of root canals following root canal treatment

Endofill is a hydrophilic, radiopaque material intended for permanent obturation of the root canals following root canal treatment. It consists of a liquid, flowable material that is injected into the prepared and cleaned root canal and converted into a solid polymer by photocuring. The cured material remains within the root canal to obturate (seal) it.
Endofill is provided ready-to-use in single-use plastic syringes which are sealed inside aluminum-laminate pouches.
The Endofill material is the only part of the device that directly contacts the tissue. The syringe in which the material is provided comes in contact with Endofill material, so it contacts the tissue indirectly.

The provided text describes the 510(k) premarket notification for Lumendo AG's "Endofill" device, a root canal filling resin. It outlines the device's intended use, technological characteristics, and comparison to predicate devices, along with the non-clinical testing performed. However, it does not contain information about acceptance criteria, the specific study conducted to prove the device met those criteria, or details regarding expert involvement, ground truth establishment, or sample sizes related to AI/human reader performance.

Therefore, I cannot provide a table of acceptance criteria and reported device performance or information about a study proving the device meets these criteria, as the requested details are not present in the provided document.

The document primarily focuses on demonstrating substantial equivalence to predicate devices through non-clinical bench performance testing and compliance with recognized standards.

Here's what can be extracted from the document regarding the non-clinical testing:

Non-Clinical Test Summary & Conclusions (as reported in the document):

• Acceptance Criteria & Reported Performance: The document states that Endofill complies with the performance requirements set out in ISO 6876:2012. The specific measured values for each criterion are not provided in this summary, but the general compliance serves as the "reported device performance" in relation to the standards' acceptance criteria.

  Acceptance Criteria (from ISO 6876:2012)	Reported Device Performance
  Flow (as per ISO 6876:2012)	Complies with ISO 6876:2012
  Film thickness (as per ISO 6876:2012)	Complies with ISO 6876:2012
  Solubility & Disintegration (as per ISO 6876:2012)	Complies with ISO 6876:2012
  Radiopacity (as per ISO 6876:2012)	Complies with ISO 6876:2012
  Biocompatibility (ISO 10993-1:2018 & ISO 7405:2018)	Complies with ISO 10993-1:2018 & ISO 7405:2018

• Sample size used for the test set and data provenance: Not specified in the provided text for the non-clinical bench testing.

• Number of experts used to establish the ground truth for the test set and their qualifications: Not applicable, as this was non-clinical bench testing, not a human reader or AI performance study.

• Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable, as this was non-clinical bench testing.

• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No, not mentioned. The study described is non-clinical bench testing.

• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: No, this is a physical dental material, not an algorithm.

• The type of ground truth used: For the non-clinical bench testing, the "ground truth" are the standardized test methods and limits defined in ISO 6876:2012 for the physical-chemical properties, and ISO 10993-1:2018 and ISO 7405:2018 for biocompatibility.

• The sample size for the training set: Not applicable, as this is a physical device, not an AI/ML algorithm.

• How the ground truth for the training set was established: Not applicable.

In summary, the document details a 510(k) submission for a medical device (root canal filling resin), demonstrating its substantial equivalence through mechanical and chemical property testing against international standards (ISO 6876:2012, ISO 10993-1:2018, ISO 7405:2018). It does not involve AI, human reader studies, or complex clinical study designs typically associated with software as a medical device (SaMD) or AI-enabled technologies.

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