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The Omnilux CLEAR acne facemask is an over-the-counter device intended to emit energy in the red and blue region of the light spectrum, specifically indicated to treat mild to moderate acne vulgaris on the face.

The Omnilux CLEAR consists of:

1. Silicon flexible face mask
2. Controller
3. Power supply and country specific adaptors
4. USB C to USB A connector
5. 2 x Velcro Straps
   The Omnilux CLEAR acne facemask is an over-the-counter device intended to emit energy in the red and blue region of the light spectrum, specifically indicated to treat mild to moderate acne vulgaris on the face. The system consists of a flexible silicon mask (1) that contains light emitting diodes (LEDs) and a controller (2). The LEDs generate the light. The mask is worn on the face and is held in place by two adjustable Velcro straps (5). The mask compromises of 2 surfaces. An inner surface that contacts the skin and an outer surface. Both surfaces are constructed of medical grade silicon.
   The controller (2) switches the LEDs ON/OFF and controls power to the mask. The controller contains a rechargeable Lithium-ion polymer battery. The controller uses a visible display comprising of 3 micro-LEDs to show the user the battery charge status of the device.
   The power supply (3) is used to charge the Lithium battery and is connected to a suitable mains outlet via a 2 or 3 pin input socket and wall plug. The power cable is connected to the controller by a standard micro-USB A-C connector (4). The Omnilux CLEAR mask cannot be operated while charging.
   The equipment is not used to make measurements of any sort, or to draw any conclusions regarding the indication to treat. The equipment does not require checks on the light output as the LEDs do not dim with age to any practical extent.

The provided document is a 510(k) premarket notification for the Omnilux CLEAR device, intended for treating mild to moderate acne vulgaris. It focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than providing a clinical study with acceptance criteria and performance data in the typical sense of a comparative effectiveness study for an AI/ML algorithm.

Therefore, many of the requested details about acceptance criteria, ground truth establishment, expert adjudication, MRMC studies, and standalone performance for an AI/ML device are not present in this document because it describes a light therapy device, not an AI/ML system.

However, I can extract information related to the device's performance based on the provided text, particularly concerning its comparison to predicate devices and the non-clinical testing performed.

Here's an analysis of the provided text in the context of your request:

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

The document does not present "acceptance criteria" for clinical performance in the way an AI/ML algorithm might (e.g., target specificity, sensitivity, or AUC threshold). Instead, it demonstrates substantial equivalence to predicate devices by comparing technological characteristics and intended use. The "performance" is implicitly tied to meeting similar specifications as the predicates and demonstrating general safety and functionality through non-clinical testing.

Here's a table summarizing the device's characteristics compared to predicates, which are implicitly its performance against established acceptable characteristics for such devices:

The justification for "equivalence" in parameters like wavelength, intensity, treatment time, and dose is that these differences "can be negated by appropriate clinical or scientific data demonstrating that the proposed device is as safe and effective as the predicate device, and that the proposed device does not raise any different questions of safety and effectiveness than the predicate device for the same intended use." In this case, the document argues that the non-clinical testing (electrical safety, EMC, photobiological safety, biocompatibility, software, risk management) addresses these differences, implying no new safety/efficacy concerns.

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

For the device itself (Omnilux CLEAR, a light therapy mask), there isn't a "test set" in the sense of a dataset of images/cases for an AI algorithm.

However, there was a label comprehension testing study, which can be considered a form of human factors "test set":

• Sample Size: 27 subjects
• Data Provenance: Not explicitly stated, but the study implies it was conducted with participants, presumably in a controlled environment. Retrospective/Prospective is not specified but usually, such studies are prospective. Country of origin not specified, but the submission is to the US FDA.

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

This is not applicable in the context of this 510(k) submission for a light therapy device. There was no medical "ground truth" to establish for a diagnostic AI system's performance.

For the label comprehension study:

• There were no "experts" establishing a ground truth in the medical sense. The "ground truth" for label comprehension was the correct understanding of the device's labeling by lay users, assessed against pre-defined correct answers to comprehension questions. The study assessed user comprehension, not medical ground truth.

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

Not applicable for this type of device. No medical adjudication was performed as there was no diagnostic "test set" requiring it.

For the label comprehension study:

• No adjudication method like 2+1 was described. The study likely involved participants answering comprehension questions based on the labeling, and their answers were scored against a pre-determined correct set.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is relevant for AI systems in diagnostic imaging, not for a light therapy device. The document explicitly states: "Since the Omnilux CLEAR mask raises no new questions in terms of safety and efficacy, clinical data is not required." This means no human-in-the-loop or clinical efficacy study was required for this 510(k) clearance due to the device's substantial equivalence to predicates.

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

Not applicable. This is not an AI/ML algorithm. It is a physical light therapy device.

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

Not applicable for the device's therapeutic function, as clinical data was not required.

For the label comprehension study, the "ground truth" was simply the intended meaning and safe usage instructions contained within the device's labeling.

8. The sample size for the training set

Not applicable. This refers to AI/ML algorithms. The Omnilux CLEAR is a hardware device.

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

Not applicable. This refers to AI/ML algorithms.

Summary of what was presented in the document:

The submission focuses heavily on demonstrating substantial equivalence of the Omnilux CLEAR to predicate light therapy devices (K160691 and K081307) based on:

• Identical intended use.
• Similar technological characteristics (energy type, wavelengths, intensities, doses, treatment times).
• Compliance with relevant non-clinical performance and safety standards (electrical safety, EMC, photobiological safety, biocompatibility, software life cycle, risk management).
• Label comprehension testing to ensure lay users can operate the device safely and effectively without professional medical intervention.

The document argues that because the Omnilux CLEAR is substantially equivalent to existing devices and presents no new questions of safety or efficacy, a full clinical study with human patients for therapeutic effectiveness was not required for its 510(k) clearance.

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The Derma Hood is an accessory for the TotalExam HD Examination Light. The Derma Hood utilizes standard off-the-shelf light filters and lenses. The Derma Hood accessory is designed for use within healthcare facilities (Doctor's offices, clinics, hospitals, etc.).

The Derma Hood is an accessory for the TotalExam HD Examination Light. The Derma Hood utilizes standard off-the-shelf light filters and lenses.

I am sorry, but based on the provided document, there is no information about the acceptance criteria, study details, or performance of the "Derma Hood" device. The document is an FDA 510(k) clearance letter, which primarily focuses on classifying the device and confirming its substantial equivalence to a predicate device. It does not include the detailed technical performance data or specific study results you've requested.

The document mentions:

• Trade/Device Name: Derma Hood
• Regulation Number: 21 CFR 880.6320 (AC-Powered Medical Examination Light)
• Regulatory Class: I
• Product Code: PEQ
• Indications for Use: "The Derma Hood is an accessory for the TotalExam HD Examination Light. The Derma Hood utilizes standard off-the-shelf light filters and lenses. The Derma Hood accessory is designed for use within healthcare facilities (Doctor's offices, clinics, hospitals, etc.)."

However, it does not provide any of the following information requested in your prompt:

1. A table of acceptance criteria and reported device performance.
2. Sample size for the test set or data provenance.
3. Number and qualifications of experts for ground truth.
4. Adjudication method for the test set.
5. MRMC comparative effectiveness study results or effect size.
6. Standalone algorithm performance.
7. Type of ground truth used.
8. Sample size for the training set.
9. How ground truth for the training set was established.

Ask a specific question about this device

CONi provides for the archiving and viewing of medical images from the following DICOM modalities: Computed Tomography (CT), Magnetic Resonance (MR), X-ray (CR), Ultrasound (US), and Visible Light (External Camera (XC) and Other (OT)). Images and information can be viewed and stored via a secure Internet connection.

CONi is not intended for use in mammography.

CONi is not intended for diagnostic use on mobile devices

CONi (Capture Over Network Interface) is a secure cloud-based application for viewing and archiving medical images. The CONi software system is comprised of a Picture Archiving and Communication System (CONiPACS) and an image viewer (CONiView). CONi supports imaging studies from the following DICOM modalities: Computed Tomography (CT), Magnetic Resonance (MR), X-ray (CR), Ultrasound (US), and Visible Light (External Camera (XC) and Other (OT)). Images and information can be viewed and stored via a secure Internet connection.

The provided submission K130624 for GlobalMedia Group, LLC. CONi™ does not contain information about explicit acceptance criteria for device performance or a detailed study proving the device meets said criteria in the way a clinical performance study would.

Instead, this 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (ALZ Web PACS, Version 1.0, K081304) for its intended use, technological characteristics, and safety/effectiveness, without presenting specific performance metrics.

The non-clinical tests listed are related to software development and validation, rather than a performance study with specific acceptance criteria:

• Establishment of Requirements
• Risk Analysis (software and system)
• DICOM Standard Conformance Statement
• HIPAA Compliance Statement
• Software Unit Testing
• Software Integration Testing
• Software System Testing
• Software Hazard Testing

Therefore, it is not possible to fill out the requested table and answer many of the study-specific questions.

Here's an attempt to address the questions based on the available information, noting where information is explicitly absent:

1. Table of acceptance criteria and the reported device performance

A table of acceptance criteria and reported device performance cannot be provided as this information is not present in the 510(k) summary. The submission focuses on demonstrating substantial equivalence, not on specific performance metrics or clinical study results with predefined acceptance criteria. Testing focused on software validation, DICOM conformance, and HIPAA compliance.

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 does not describe a clinical performance test set or data derived from real patient cases. The testing mentioned (Unit, Integration, System, Hazard) implies internal software quality assurance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

This information is not provided. No ground truth establishment for a test set of medical cases is described.

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

This information is not provided. No adjudication method is mentioned as there is no described test set requiring expert adjudication.

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

An MRMC comparative effectiveness study was not done and is not applicable to this device. CONi™ is a Picture Archiving Communication System (PACS) and image viewer, not an AI-assisted diagnostic tool designed to improve human reader performance. It does not provide AI assistance for diagnostic interpretation.

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

A standalone performance study was not done and is not applicable. CONi™ is a PACS and image viewer, which inherently involves human interaction for viewing and archiving images. It does not perform any diagnostic algorithms in a standalone capacity that would require such a study.

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

This information is not provided. No ground truth is mentioned as there are no diagnostic claims or performance analyses on medical images in the context of clinical accuracy.

8. The sample size for the training set

This information is not provided. As CONi™ is a PACS and image viewer (not an AI/CAD algorithm), it does not typically involve a "training set" in the machine learning sense. The software development process likely involved internal testing with various types of DICOM images to ensure proper functionality and rendering.

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

This information is not provided and is not applicable as there is no "training set" in the context of machine learning described for diagnostic purposes.

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