Search Results

The LaserMe is intended for use in dermatological procedures requiring coagulation of soft tissue and skin resurfacing procedures. The LaserMe is further indicated for treatment of benign pigmented lesions, such as, but not limited to lentigines (age spots), solar lentigos (sun spots), melasma, dyschromia, and for treatment of facial wrinkles and fine lines.

LaserME is a non-ablative diode laser. The device is equipped with a diode laser with a wavelength of 1470 nm and a maximum power of 2W, intended for nonablative skin resurfacing. The device works by punctuating the skin (epidermis) with several micro laser beams. The points are evenly spaced with adjustable spacing from 1mm to 4mm. The energy delivered to a single point is regulated from 5mJ to 50mJ.

The provided text is a 510(k) summary for the LaserME device. It details the device's indications for use, specifications, and comparison to a predicate device (Emerge Fractional Laser K111840).

The concept of "acceptance criteria" and "study that proves the device meets the acceptance criteria" as typically applied to performance metrics of an AI/ML medical device (e.g., sensitivity, specificity, AUC) is not directly present in this document. This submission is for a laser surgical instrument, not an AI/ML device, and therefore the "acceptance criteria" relate more to safety, performance against engineering specifications, and biological compatibility rather than statistical performance metrics of a diagnostic or assistive AI.

However, I can extract the relevant "testing" components that demonstrate the device meets its intended use and safety standards, and present them in a way that aligns with your request for how the device proves it meets acceptance criteria, even if those criteria are not presented in a tabular format of "performance metrics" in the AI sense.

Here's an interpretation based on the provided document, addressing the closest equivalents to your requested points:

Acceptance Criteria and Device Performance for LaserME

Given that the LaserME is a laser surgical instrument, the "acceptance criteria" are not framed in terms of traditional AI/ML performance metrics (e.g., sensitivity, specificity, AUC). Instead, they are related to its functional specifications, safety standards, and biological effects, demonstrating substantial equivalence to a predicate device.

The "study that proves the device meets the acceptance criteria" refers to the non-clinical and clinical testing performed to satisfy regulatory requirements for substantial equivalence.

1. Table of Acceptance Criteria and Reported Device Performance

Study Details (As applicable to a laser device submission):

For a laser device, the "study" is primarily focused on demonstrating safety, functional performance, and substantial equivalence to a legally marketed predicate, rather than the statistical performance of an AI model.

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

• Non-Clinical Testing:
  • Ex vivo tissue: Sample size not specified.
  • Provenance: Not specified (but implied to be laboratory-based).
• Clinical Testing:
  • Sample Size: 18 healthy volunteers.
  • Data Provenance: Not specified regarding country of origin, but described as a prospective clinical study.

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

• Non-Clinical (Histological Assessment): The document implies expert assessment of the ex vivo histological samples, but the number of experts and their qualifications are not specified. This would typically be performed by pathologists or dermatopathologists.
• Clinical (Histology Analysis from Human Subjects): Histology analysis was performed, implying expert review (likely pathologists), but the number of experts and their qualifications are not specified.

4. Adjudication Method for the Test Set

• Not Applicable in the AI/Ground Truth Sense: For this type of device, "adjudication" wouldn't be relevant for a test set in the same way it is for diagnostic AI models (e.g., resolving discrepancies in expert labels). The histological and clinical assessments provide direct evidence of tissue response.

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

• No: This type of study (comparing human readers with and without AI assistance) is not applicable as the LaserME is a therapeutic/surgical device, not a diagnostic AI.

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

• Yes, by nature of the device: The non-clinical and certain aspects of clinical testing assess the performance of the device itself (its laser output, tissue interaction, safety, healing properties) independently of direct human interpretive "performance" in a diagnostic context. The device performs its function directly.

7. Type of Ground Truth Used

• Non-Clinical: Histological assessment in ex vivo tissue provided "ground truth" regarding the extent of laser-induced damage.
• Clinical: Histology analysis from human subjects provided "ground truth" on in vivo thermal effects and tissue healing. "Behaved as intended" serves as the primary outcome.

8. Sample Size for the Training Set

• Not Applicable: This is a physical device, not an AI/ML model that requires a "training set" in the computational sense. Development of laser parameters and designs would be based on engineering principles and prior scientific understanding, not a data-driven training set.

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

• Not Applicable: See point 8.

Ask a specific question about this device