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FG Bone Graft B is recommended for:

• Augmentation or reconstructive treatment of the alveolar ridge.
• Filling of infrabony periodontal defects.
• Filling of defects after root resection, apicoectomy, and cystectomy.
• Filling of extraction sockets to enhance preservation of the alveolar ridge.
• Elevation of the maxillary sinus floor.
• Filling of periodontal defects in conjunction with products intended for Guided Tissue Regeneration (GTR) and Guided Bone Regeneration (GBR).
• Filling of peri-implant defects in conjunction with products intended for Guided Bone Regeneration (GBR).

FG Bone Graft B is a sterile, synthetic, multi-porous biocompatible ceramic matrix in granular form for filling bone defects. The material with microporous structure supports rapid ossification with local bone. With its phase purity of >= 99%, the ceramic material complies with US standard specification ASTM F 1088-04. The validated manufacturing process guarantees batch conformity and reproducibility.

The FDA 510(k) clearance letter for FG Bone Graft B indicates that the device is substantially equivalent to a predicate device (CERASORB M DENTAL). The clearance letter references non-clinical tests performed to demonstrate this equivalence, focusing on chemical composition, physical properties, and performance in vivo.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample size for the test set: The document states that the in vivo study used a "Beagle dog" model, and the animals were "divided into groups: test group (FG Bone Graft B), positive control group (Cerasorb, a commercial β-TCP), and a negative control (empty defect)." However, the exact number of animals in each group or total animal count is not specified in the provided text.
• Data provenance: The study was a prospective in vivo animal study performed on Beagle dogs. The location/country of origin of the study is not explicitly stated in the provided text, but the submitter "Full Golden Biotech Co., Ltd." is located in Taiwan.

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

• Number of experts: Not explicitly stated. The histological and radiographic analyses were likely performed by trained professionals (e.g., veterinary pathologists, radiologists), but the number of reviewers or their specific qualifications are not detailed in the provided text.
• Qualifications of experts: Not specified beyond the implied expertise in conducting and analyzing in vivo studies (e.g., histology, micro-CT).

4. Adjudication Method for the Test Set

• Adjudication method: Not explicitly stated. For animal studies, consistency and blinding are typically employed, but a formal "adjudication method" in the sense of multiple human readers for consensus is not described for this non-AI bone graft device. The results are presented as quantitative measurements and observations (e.g., "new bone formation increased," "minimal to mild inflammatory response").

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

• MRMC study: No, an MRMC comparative effectiveness study was not done. This type of study is typically performed for AI/image analysis devices where the AI's impact on human reader performance is being assessed. This document describes a traditional preclinical performance study for a bone graft material.

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

• Standalone performance: N/A. This is a bone graft material, not an algorithm or AI device. The "performance" refers to the biological and physical properties of the material itself, not the output of a software algorithm.

7. The Type of Ground Truth Used

• Type of ground truth: The ground truth for the in vivo study (which is the primary performance study) was established through direct anatomical, histological, and radiographic assessments of the bone defects in the animal model.
  • Histological analysis: Quantified new bone formation, material degradation, and inflammatory response. This involves microscopic examination of stained tissue sections, which is considered a gold standard for assessing tissue regeneration and integration.
  • Radiographic analysis: Used micro-CT to assess bone density and bone volume, providing quantitative structural data.
  • Comparison to predicate: The "ground truth" for showing substantial equivalence was the performance of the established predicate device (Cerasorb) under the same study conditions.

8. The Sample Size for the Training Set

• Sample size for training set: N/A. This device is a bone graft material, not an AI or machine learning algorithm. Therefore, there is no "training set."

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

• Ground truth for training set: N/A. As there is no AI component, there is no training set and no ground truth establishment for such a set.

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1. Direct restorative for Class I, II, III, IV, and V cavities
2. Fissure sealant
3. Sealing hypersensitive areas
4. Repair of (in)direct aesthetic restorations, temporary crown & bridge, defect margins when margins are in enamel
5. Blocking out undercuts
6. Liner or base
7. Core build-up
8. Adhesive cementation of ceramic and composite veneers, inlays and onlays with a thickness (

G-ænial Universal Injectable II is a light-cured, nano-filled radiopaque composite resin filled in syringe. The device is used for the restoration of both anterior and posterior teeth, core build-up, adhesive cementation of ceramic and composite veneer, inlays and onlays, and build-up for transparent removable orthodontic retainers. The device is available in 9 shades.

This document is an FDA 510(k) clearance letter for a dental resin material, G-ænial Universal Injectable II. It is important to note that this is NOT an AI/ML medical device submission. Therefore, the information provided in the document focuses on the material's physical and chemical properties and biocompatibility, as compared to predicate dental materials.

The request asks for information typically found in submissions for AI/ML medical devices, such as acceptance criteria based on diagnostic performance metrics (e.g., sensitivity, specificity, AUC), details about test and training sets, expert consensus for ground truth, MRMC studies, and effect sizes of AI assistance. Since this is a dental material, these types of studies are not relevant and are not present in the provided document.

Therefore, the following response will adapt the requested sections to the context of this dental material, explaining what information is available and what is not, given the nature of the device.

Acceptance Criteria and Device Performance for G-ænial Universal Injectable II

The acceptance criteria for G-ænial Universal Injectable II are based on established ISO standards for dental materials and FDA guidance for composite resin devices. The "study that proves the device meets the acceptance criteria" refers to the Performance Bench Tests and Non-Clinical Performance Testing detailed in Section 7 and 8 of the 510(k) summary. These tests assess the physical, chemical, and biological properties of the material.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are generally "Complies" with specific quantitative or qualitative thresholds defined by the standards (e.g., ISO 4049, ISO 6874). The document reports that the device Complies with all listed requirements.

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

For a dental material, the "test set" refers to the samples of the material manufactured and subjected to the performance bench tests and biocompatibility assessments.

• Sample Size: The document does not specify the exact number of samples (e.g., number of specimens for flexural strength, number of animals for biocompatibility tests). It simply states that "Performance testing includes" and "A biocompatibility assessment was completed."
• Data Provenance: The data provenance is internal to the manufacturer (GC America, Inc.) and derived from laboratory testing of the material according to international ISO standards and FDA guidance documents. There is no indication of "country of origin of the data" in the sense of patient data, nor is it a retrospective or prospective study in the clinical trial sense. These are laboratory-based material characterization tests.

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

This question is not applicable to a dental material submission. The "ground truth" for material properties is established by the standardized test methods themselves (e.g., ISO 4049 defines how depth of cure is measured). There are no "experts" establishing ground truth in the sense of clinical interpretations or diagnoses. The expertise lies in adhering to the established test protocols and analyzing results accurately by qualified laboratory personnel.

4. Adjudication Method for the Test Set

This concept is not applicable for dental material performance testing. Adjudication methods (like 2+1, 3+1 consensus) are used in studies involving human interpretation of images or clinical outcomes, typically for AI/ML device validation. Here, tests are quantitative measurements of physical/chemical properties or biological responses.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, and Effect Size of Human Readers Improving with AI vs Without AI Assistance

This question is not applicable. G-ænial Universal Injectable II is a dental restorative material, not an AI-assisted diagnostic or therapeutic device. There are no "human readers" involved in interpreting its performance, nor does it assist human readers. Therefore, no MRMC study was performed, and no effect size on human reader improvement with AI assistance can be reported.

6. If a Standalone (i.e., Algorithm Only Without Human-in-the-Loop Performance) was Done

This question is not applicable. G-ænial Universal Injectable II is a physical dental material, not an algorithm. There is no "standalone performance" of an algorithm. Its performance is measured directly through laboratory tests of its inherent material properties.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

For dental materials, the "ground truth" is based on:

• Standardized Test Methods: Adherence to established ISO standards (e.g., ISO 4049, ISO 6874, ISO 10993) defines the "truth" for material properties like flexural strength, depth of cure, water sorption, and biocompatibility.
• Predicate Device Comparison: Performance is also evaluated in comparison to predicate devices, where "equivalence" often serves as a benchmark for acceptance.

There is no "expert consensus," "pathology," or "outcomes data" in the sense of human diagnostic performance or clinical trial results, as this is a pre-market notification for a material.

8. The Sample Size for the Training Set

This question is not applicable. There is no "training set" for a dental material in the context of machine learning. The material itself is manufactured, and specific properties are tested for quality control and regulatory submission.

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

This question is not applicable, as there is no training set.

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The Definium Tempo Select is intended to generate digital radiographic images of the skull, spinal column, chest, abdomen, extremities, and other body parts in patients of all ages. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position and the system is intended for use in all routine radiography exams. Optional image pasting function enables the operator to stitch sequentially acquired radiographs into a single image.

This device is not intended for mammographic applications.

The Definium Tempo Select Radiography X-ray System is designed as a modular system with components that include an Overhead Tube Suspension (OTS) with a tube, an auto collimator and a depth camera, an elevating table, a motorized wall stand, a cabinet with X-ray high voltage generator, a wireless access point and wireless detectors in exam room and PC, monitor and control box with hand-switch in control room. The system generates diagnostic radiographic images which can be reviewed or managed locally and sent through a DICOM network for applications including reviewing, storage and printing.

By leveraging platform components/ design, Definium Tempo Select is similar to the predicate device Discovery XR656 HD (K191699) and the reference device Definium Pace Select (K231892) with regards to the user interface layout, patient worklist refresh and selection, protocol selection, image acquisition, and image processing based on the raw image. This product introduces a new high voltage generator which has the same key specifications as the predicate. A wireless detector used in referenced product Definium Pace Select is introduced. Image Pasting is improved with individual exposure parameter adjustable on images on both Table and Wall Stand Mode. Tube auto angulation is added for better auto positioning based on current auto-positioning. Camera Workflow is introduced based on existing depth camera. OTS is changed with 4 axis motorizations. An update was made to the previously cleared Tissue Equalization feature under K013481 to introduce a Deep Learning AI model that provides more consistent image presentations to the user which reduces additional workflow to adjust the image display parameters. The other minor changes including PC change, Wall Stand change and Table change.

The provided FDA 510(k) clearance letter and summary for the Definium Tempo Select offers some, but not all, of the requested information regarding the acceptance criteria and the study proving the device meets them. Notably, specific quantitative acceptance criteria for the AI Tissue Equalization feature are not explicitly stated.

Here's a breakdown of the available information and the identified gaps:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The 510(k) summary does not explicitly list quantitative acceptance criteria for the AI Tissue Equalization algorithm. Instead, it states that "The verification tests confirmed that the algorithm meets the performance criteria, and the safety and efficacy of the device has not been affected." Without specific performance metrics or thresholds, a direct comparison in a table format is not possible for the AI component.

For the overall device, the acceptance criteria are implicitly performance metrics that ensure it functions comparably to the predicate device, as indicated by the "Equivalent" and "Identical" discussions in Table 1 (pages 7-11). However, these are primarily functional and technical equivalency statements rather than performance metrics for the AI feature.

Therefore, this section will focus on the AI Tissue Equalization feature as it's the part that underwent specific verification using a clinical image dataset.

AI Tissue Equalization Feature:

Missing Information:

• Specific quantitative metrics (e.g., AUC, sensitivity, specificity, image quality scores, expert rating differences) that define "more consistent image presentations" are not provided.
• The exact thresholds or target values for these metrics are not stated.

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

• Test Set Sample Size: Not explicitly stated as a number of images or cases. The document refers to "clinical images retrospectively collected across various anatomies...and Patient Sizes."
• Data Provenance: Retrospective collection from locations in the US, Europe, and Asia.

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

Missing Information. The document does not specify:

• The number of experts involved in establishing ground truth.
• Their qualifications (e.g., specific subspecialty, years of experience, board certification).
• Whether experts were even used to establish ground truth for this verification dataset, as the purpose was to confirm the AI met performance criteria rather than to directly compare its diagnostic accuracy against human readers or a different ground truth standard.

4. Adjudication Method for the Test Set

Missing Information. No adjudication method (e.g., 2+1, 3+1) is described for the test set.

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

No. A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or described in the provided document. The verification tests focused on the algorithm meeting performance criteria, not on comparing human reader performance with or without AI assistance.

• Effect Size: Not applicable, as no MRMC study was described.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, implicitly. The "AI Tissue Equalization algorithms verification dataset" was used to perform "verification tests" to confirm that "the algorithm meets the performance criteria, and the safety and efficacy of the device has not been affected." This suggests a standalone evaluation of the algorithm's output (image presentation consistency) against specific, albeit unstated, criteria. While human review of the output images was likely involved, the study's stated purpose was to verify the algorithm itself.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

Implied through image processing improvement, not diagnostic ground truth. For the AI Tissue Equalization feature, the "ground truth" is not in the traditional clinical diagnostic sense (e.g., disease presence confirmed by pathology). Instead, it appears to be related to the goal of "more consistent image presentations" and improving "contrast and visibility in over-penetrated and under-penetrated regions." This suggests the ground truth was an ideal or desired image presentation quality rather than a disease state. It's likely based on existing best practices for image processing and subjective assessment of image quality by experts, or perhaps a comparative assessment against the predicate's tissue equalization.

Missing Information: The precise method or criteria for this ground truth (e.g., a panel of radiologists rating image quality, a quantitative metric for contrast/visibility) is not specified.

8. The Sample Size for the Training Set

Missing Information. The document describes the "verification dataset" (test set) but does not provide any information on the sample size or composition of the training set used to develop the Deep Learning AI model for Tissue Equalization.

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

Missing Information. As the training set size and composition are not mentioned, neither is the method for establishing its ground truth. It can be inferred that the training process involved data labeled or optimized to achieve "more consistent image presentations" by dynamically estimating thick and thin regions, likely through expert-guided optimization or predefined image processing targets.

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