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The BrightHeart View Classifier device is intended to analyze fetal 2D ultrasound images and video clips using machine learning techniques to automatically detect standard views during fetal heart scanning.

The BrightHeart View Classifier device is intended to be used as an adjunct to the acquisition and interpretation of fetal anatomic ultrasound examinations at the second or third trimester of pregnancy performed with transabdominal probes.

BrightHeart View Classifier is a cloud-based software-only device which uses artificial intelligence (AI) to detect standard views during fetal heart scanning in fetal ultrasound images and video clips.

BrightHeart View Classifier is intended to be used by qualified, trained healthcare professional personnel in a professional prenatal ultrasound (US) imaging environment (this includes sonographers, MFMs, OB/GYN, and Fetal surgeons), to help fetal ultrasound examination acquisition and interpretation of 2D grayscale ultrasound by providing automatic classification of video clips and images into standard views, by automatically extracting example frames of standard views from video clips, and by automatically assessing whether the documentation of each standard view in video clips and images satisfies an acquisition protocol defined by the center. Annotated DICOM files generated by the device cannot be modified by the user.

Here's a detailed breakdown of the acceptance criteria and the study proving the BrightHeart View Classifier device meets them, based on the provided FDA 510(k) clearance letter:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state "acceptance criteria" as a set of predefined thresholds. However, it does present objective performance metrics derived from a validation study. For the purpose of this response, we will consider the reported performance metrics as demonstrative of meeting implicit acceptance criteria for clinical utility and safety, especially since the submission states the device "is as safe and effective as the predicate device and supports a determination of substantial equivalence."

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

• Sample Size for Test Set: 2290 clinically acquired images and frames from video clips. These were derived from 579 fetal ultrasound examinations.
• Data Provenance: The data was retrospective, consisting of clinically acquired images and frames. The country of origin for the data includes U.S.A. and France.

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

• Number of Experts: Two experts.
• Qualifications of Experts: One sonographer and one MFM specialist (Maternal-Fetal Medicine) with experience in fetal echocardiography.

4. Adjudication Method for the Test Set

The adjudication method was described as a truthing process where the sonographer and MFM specialist independently determined the presence or absence of standard views. The document doesn't explicitly state a 2+1 or 3+1 method with a third tie-breaker, but it implies a consensus or agreement process between the two experts as they "determined" the presence or absence.

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 reported in this document. The study described focuses on the standalone performance of the AI algorithm. Therefore, there is no reported effect size of how much human readers improve with AI vs. without AI assistance.

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

Yes, a standalone performance study was done. The reported sensitivity and specificity values directly relate to the BrightHeart View Classifier's (algorithm only) ability to identify standard views independently. The document states: "The performance testing demonstrated that BrightHeart View Classifier identifies standard views with a mean standard view recognition sensitivity of 0.939..." This confirms a standalone performance evaluation.

7. The Type of Ground Truth Used

The type of ground truth used was expert consensus / clinical expert interpretation. It was derived through a "truthing process" by a sonographer and an MFM specialist.

8. The Sample Size for the Training Set

The document explicitly states: "The ultrasound examinations used for training and validation are entirely distinct from the examinations used in performance testing." However, the exact sample size for the training set is not provided in the given text.

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

The document states: "The ultrasound examinations used for training and validation are entirely distinct from the examinations used in performance testing." While it confirms distinct data, it does not explicitly describe how the ground truth for the training set was established. It can be inferred that it likely followed a similar expert review process as the test set, but this information is not detailed in the provided text.

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The BrightHeart View Classifier device is intended to analyze fetal 2D ultrasound images and video clips using machine learning techniques to automatically detect standard views during fetal heart scanning.

The BrightHeart View Classifier device is intended to be used as an adjunct to the acquisition and interpretation of fetal anatomic ultrasound examinations at the second or third trimester of pregnancy performed with transabdominal probes.

BrightHeart View Classifier is a cloud-based software-only device which uses artificial intelligence (AI) to detect standard views during fetal heart scanning in fetal ultrasound images and video clips.

BrightHeart View Classifier is intended to be used by qualified, trained healthcare professional personnel in a professional prenatal ultrasound (US) imaging environment (this includes sonographers, MFMs, OB/GYN, and Fetal surgeons), to help fetal ultrasound examination acquisition and interpretation of 2D grayscale ultrasound by providing automatic classification of video clips and images into standard views, by automatically extracting example frames of standard views from video clips, and by automatically assessing whether the documentation of each standard view in video clips and images satisfies an acquisition protocol defined by the center. Annotated DICOM files generated by the device cannot be modified by the user.

Here's a breakdown of the acceptance criteria and the study details for the BrightHeart View Classifier, based on the provided FDA 510(k) Clearance Letter:

1. Table of Acceptance Criteria and Reported Device Performance

The FDA letter does not explicitly state pre-defined acceptance criteria values that the device needed to meet. Instead, it reports the device's performance metrics directly from the validation study. However, based on the performance report, we can infer the achieved performance and understand that these values were deemed sufficient for clearance.

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

• Sample Size: 2290 clinically acquired images and frames from video clips.
• Number of Fetal Ultrasound Examinations: 579
• Country of Origin of Data: U.S.A. and France.
• Retrospective or Prospective: The document implies retrospective data ("clinically acquired images and frames").

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

• Number of Experts: Two experts: "a sonographer and an MFM specialist".
• Qualifications: "with experience in fetal echocardiography". Specific years of experience are not mentioned.

4. Adjudication Method for the Test Set

• Adjudication Method: Independence was maintained in the ground truth establishment. "The reference standard was derived from the dataset through a truthing process in which a sonographer and an MFM specialist with experience in fetal echocardiography determined the presence or absence of standard views on fetal ultrasound images. The truthing process was conducted independently of the BrightHeart View Classifier device." This indicates a consensus or independent review process, but not a specific 2+1 or 3+1 adjudication as those usually imply a tie-breaker. It seems like both experts independently determined the ground truth.

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

• No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly described. The study evaluated the standalone performance of the AI device.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance

• Yes, a standalone performance study was conducted. The reported sensitivity and specificity values are for the BrightHeart View Classifier identifying standard views on its own.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus (from a sonographer and an MFM specialist with experience in fetal echocardiography).

8. Sample Size for the Training Set

• The sample size for the training set is not explicitly stated in the provided document. The document only mentions that "The ultrasound examinations used for training and validation are entirely distinct from the examinations used in performance testing."

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

• The document does not explicitly state how the ground truth for the training set was established. However, given the nature of the device and the ground truth method for the test set, it is highly probable that a similar expert review and annotation process was used for the training data.

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Bright MTA Sealer Plus is used for filling root canals.

Bright MTA Sealer Plus is a ready-to-use, injectable paste-like material for root canal filling, which is hardened and obturated after being injected into the root canal space. The product based on calcium silicate exhibits excellent biocompatibility as well as a low film thickness suitable for easy penetration of lateral and accessory canals.

The provided text describes the acceptance criteria and performance data for a medical device called "Bright MTA Sealer Plus." However, it is not an AI-driven device; it is a root canal filling material. Therefore, some of the requested information, such as details on AI acceptance criteria, expert ground truthing, MRMC studies, or training/test sets for an AI model, are not applicable and thus not present in the document.

The document primarily focuses on the biocompatibility and mechanical performance of the "Bright MTA Sealer Plus" in comparison to a predicate device.

Here's a breakdown of the available information based on your request, with an explanation for elements that are not applicable:

Device Name: Bright MTA Sealer Plus

Device Type: Root canal filling material (Non-AI device)

1. Table of Acceptance Criteria and Reported Device Performance

The closest information available is in the "Mechanical testing" section:

In addition, Biocompatibility testing was performed with the following acceptance criteria and evaluation:

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

• Sample Size: Not explicitly stated in terms of number of samples/units tested for each mechanical or biocompatibility test, but the tests were performed on the device itself.
• Data Provenance: The document implies in-house testing performed by or for Genoss Co., Ltd. The country of origin for the company is South Korea. The studies are assumed to be prospective tests on newly manufactured samples of the device.

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

• Not Applicable. This is not an AI device that requires expert-established ground truth for image interpretation or diagnosis. The ground truth for this device's performance is derived from standardized physical and biological material testing.

4. Adjudication method for the test set

• Not Applicable. As this is not an AI device involving human interpretation, there is no need for an adjudication method for a test set. The results are based on objective measurements from standardized tests.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done

• No. An MRMC study is relevant for AI systems that assist human readers in tasks like radiological interpretation. This device is a material, not an AI system, so an MRMC study would not be performed.

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

• Not Applicable. This is not an algorithm or AI system. Its performance is inherent to its physical and chemical properties.

7. The type of ground truth used

• The ground truth for this device's performance is based on objective measurements from internationally recognized standards (ISO and EN ISO) for dental materials and biocompatibility. For example, specific diameters for flow, time limits for setting, weight differences, and qualitative assessments of physical appearance and biological reactions are used as the "ground truth" against which the device's performance is measured. It's not expert consensus, pathology, or outcomes data in the traditional sense of diagnostic AI.

8. The sample size for the training set

• Not Applicable. This is not an AI device that undergoes machine learning training.

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

• Not Applicable. As it's not an AI device, there is no training set or associated ground truth establishment process.

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Impression of inlay, onlay crown, and bridge preparation

• Crown, bridge impression
• Inlay and onlay impression
• Functional impression
• Denture impression
• Study model impression

Bright Impress impression material is a fast-set of addition-reaction silicone elastomer (Vinyl polysiloxane)-based material for dental professionals, consisting of five types (Light, Medium, Heavy, Bite and Putty) with superior hydrophilicity, dimensional accuracy, high tensile strength, and resistant to deformation. It is designed for versatile impression techniques of crowns, bridges, orthodontics and implants.

The document is a 510(k) Summary for the dental impression material "Bright Impress" by Genoss Co., Ltd. It compares the characteristics of Bright Impress to a predicate device, HySil Impression Materials, and presents performance and biocompatibility data.

Here's an analysis of the provided information regarding acceptance criteria and the study that proves the device meets them:

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

The document provides extensive tables for both biocompatibility and performance for each variant of "Bright Impress" (Light, Medium, Heavy, Putty, Bite). I will consolidate and present a representative sample, focusing on the "Performance Data" which directly addresses how the device meets physical/mechanical acceptance criteria. The "Biocompatibility Data" also uses "Acceptance Criteria" and "P/F" (Pass/Fail) results.

Comprehensive Table of Acceptance Criteria and Reported Device Performance

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1. Base/liner
2. Pit & Fissure sealant

Bright Flow is a light-cured flowable composite resin. It comprises two different types of flowability (Low Flow and High Flow) and 9 shades depending on the intended use, which enables aesthetic and durable outcomes for anterior and posterior composite restorations.

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

Device Name: Bright High Flow
Manufacturer: GENOSS Co., Ltd.

The provided document describes the performance bench testing and biocompatibility testing conducted to demonstrate that the Bright High Flow device meets its acceptance criteria. No clinical study or MRMC effectiveness study is mentioned, as this device appears to be a dental material.

1. Table of Acceptance Criteria and Reported Device Performance

Biocompatibility Testing

Performance Bench Testing

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1. Direct bonding for light-cured composites to tooth surface
2. Bonding of dual-cured core build up composites to tooth surface
3. Intraoral repair of composite, PFM and ceramic restoration using cements
4. Sealing of tooth preparation for indirect restoration

Bright Bond Universal is a light-cured dental adhesive that combines etching, priming, and bonding functions in a single bottle solution. It provides strong and durable bonds between dentin/enamel and light-curable direct/indirect restorative materials.

The provided text describes the acceptance criteria and performance data for a dental bonding agent, "Bright Bond Universal," in the context of its 510(k) premarket notification to the FDA. The study presented here is a bench testing study, not a clinical study involving human patients or multi-reader studies. Therefore, many of the requested details about human expert involvement, MRMC studies, and patient data provenance are not applicable to the information contained in this document.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The device's performance was evaluated against a set of physical and chemical specifications for a dental bonding agent and biocompatibility standards.

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

• Sample Size: The document does not specify exact sample sizes for each bench test. However, these tests are typically conducted on a sufficient number of material samples to ensure statistical validity for the specific test (e.g., multiple specimens for bonding strength, multiple replicates for biocompatibility assays).
• Data Provenance: The tests were conducted by GENOSS Co., Ltd. (South Korea). The data is from laboratory bench testing of the physical properties and biocompatibility of the bonding agent. It is inherently "prospective" in the sense that the tests were specifically performed to demonstrate the device's characteristics for regulatory submission. It does not involve patient data.

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

This information is not applicable. The "ground truth" for these tests is established by industry-standardized test methods (e.g., ISO standards) and measurable physical/chemical properties, not by human expert opinion or interpretation of images. The results are quantitative measurements.

4. Adjudication Method for the Test Set

This is not applicable. The tests involve objective measurements (e.g., weight difference, film thickness, bonding strength in MPa, observation of cell viability or tissue reaction), rather than subjective interpretations requiring adjudication.

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

No, an MRMC study was not done. This type of study is relevant for AI/radiology devices where human readers interpret medical images. This document describes a dental material, not an imaging device or an AI algorithm for image interpretation.

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

This is not applicable. This is not an AI algorithm. The performance described is the inherent performance of the dental bonding material itself.

7. The Type of Ground Truth Used

The ground truth used for these tests is based on:

• Standardized Test Methods: Specific ISO standards (e.g., ISO 10993 for biocompatibility, ISO 4049 and ISO 29022 for physical properties) define how the tests are performed and how results are interpreted.
• Quantitative Measurements: Performance is measured against predetermined quantifiable thresholds (e.g., MPa for bonding strength, µm for film thickness, visual assessment of impurities).
• Biocompatibility Endpoints: For biocompatibility, the ground truth is established by the absence of adverse biological responses as defined by the ISO 10993 series of standards.

8. The Sample Size for the Training Set

This is not applicable. There is no "training set" as this is not an AI/machine learning model. The device's formulation and manufacturing processes are likely developed through research and development, but there isn't a "training set" of data in the AI sense.

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

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

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1. Class I, III, V (non-stress area, minimally invasive restoration)
2. Composite resin repair

Bright Flow is a light-cured flowable composite resin. It comprises two different types of flowability (Low Flow and High Flow) and 9 shades depending on the intended use, which enables aesthetic and durable outcomes for anterior and posterior composite restorations.

This document is a 510(k) summary for the medical device "Bright Low Flow", a light-cured flowable composite resin manufactured by GENOSS Co., Ltd. It declares that the device is substantially equivalent to a predicate device (K091388 G-aenial Universal Flo) and provides performance data to support this claim.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document presents two tables related to performance criteria: Biocompatibility and Performance Bench Testing.

Biocompatibility Acceptance Criteria and Performance:

Performance Bench Testing Acceptance Criteria and Performance:

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• Temporary crowns and bridges

• Temporary inlays and onlays

Not Found

This document is a 510(k) clearance letter for a dental device, "BRIGHT TEMPORARY C&B," and specifies its indications for use. It does not contain information about acceptance criteria, device performance studies, sample sizes, ground truth establishment, or expert involvement as requested in the prompt.

Therefore, I cannot provide the requested information based on the given text. The document focuses on regulatory approval based on substantial equivalence to predicate devices, not on the results of a specific performance study against defined acceptance criteria.

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Bright Guard is a mouth guard intended to protect against grinding and clenching. For over-the-counter use.

Bright Guard is comprised of a mouth guard, spatula and storage case. The mouth guard is an over-the-counter, flexible and moldable one-piece dental mouth guard, which is used as a barrier between teeth for individuals who grind their teeth. Bright Guard mouth guard is intended to be worn while sleeping. The mouth guard material is conformable to wear, and it can be self-fit by the 'boil and bite' method (submerging in hot water to make the material malleable to fit on the upper teeth of the oral cavity). Bright Guard mouth guard is constructed of 100% Thermoplastic resin Propylene Elastomer (TPE) material (ethylene-vinyl-acetate) [Elvax]. When heated it forms to the individual's teeth. It is non-flavored and has no color additives. Bright Guard does not contain Bisphenol A (BPA) or phthalates and is free of natural rubber latex. The spatula and storage case are composed of 100% Polypropylene. The materials in Bright Guard are used in various other 510(k) cleared dental mouth guards. The three components (mouth guard, spatula and storage case) are manufactured by injection molding. Bright Guard mouth guard Is provided in one model/size, is reusable by a single individual at least eighteen (18) years of age and is supplied as non-sterile.

The provided text is a 510(k) Summary for a medical device called "Bright Guard," a mouth guard intended to protect against grinding and clenching. It outlines the device's characteristics, its comparison to predicate devices, and the non-clinical performance data used to demonstrate substantial equivalence.

However, the provided document does not describe acceptance criteria for a study, nor does it detail a study that proves the device meets specific performance criteria in the way typically expected for an AI/ML medical device submission (e.g., accuracy, sensitivity, specificity, or human performance improvement with AI assistance).

Instead, this document focuses on demonstrating substantial equivalence to existing legally marketed predicate devices based on material composition, functional design, intended use, and non-clinical performance tests (biocompatibility and functional tests). There is no mention of an AI algorithm, a test set for AI, ground truth establishment by experts, MRMC studies, or standalone algorithm performance.

Therefore, I cannot fulfill the request to describe the acceptance criteria and the study that proves the device meets the acceptance criteria, as the provided text does not contain such information for an AI/ML device.

The document explicitly states:

• "There were no clinical tests performed or provided in this submission." This means no human subject studies were conducted to assess clinical efficacy or performance.
• The performance data provided relates to "functional non-clinical performance tests" (Hardness, Water Sorption and Solubility, Spatula & Mouth guard Boiling/Cooling Water Testing) and "biocompatibility assessment" (Cytotoxicity, Skin Sensitization, Oral Mucosal Irritation). These are material and safety tests, not performance metrics like accuracy, sensitivity, or human-AI reader improvement.

Based on the provided information, the device is a simple "boil and bite" mouth guard, not an AI-powered device. Therefore, the questions related to AI/ML device testing (e.g., sample size for test/training sets, experts for ground truth, MRMC studies, effect size of AI assistance) are not applicable to this submission.

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