Search Results

The Nd: YAG Laser Therapy System is indicated for the treatment of: benign cutaneous lesions, such as Warts, Scars, Striae and Psoriasis; benign pigmented lesions, such as Lentigines, Nevus, and Birthmark; and the removal of black or blue tattoos.

The Nd: YAG Laser Therapy System (Model: QN-1) is laser system which delivers laser at a wavelength 1064nm or 532nm and the pulse mode is single pulse.

In laser handpiece, there is one optical cavity containing the Nd:YAG crystal. The laser beam is directed to the treatment zone. When the laser beam contacts human tissue, the energy in the beam is absorbed, resulting in a very rapidly, highly localized temperature increase to the target tissue. The instantaneous temperature increase (thermal effect) causes the cells change of target tissue to achieve laser treatment effect.

The provided FDA 510(k) clearance letter for the Nd: YAG Laser Therapy System (QN-1) does not contain any information about acceptance criteria or a study proving that the device meets those criteria, specifically concerning AI/ML performance.

This document describes a traditional medical device (a laser therapy system) and its substantial equivalence to a predicate device based on non-clinical performance and safety standards. There is no mention of an AI/ML component or any software functionality that would require an AI/ML performance study as described in your prompt.

Therefore, I cannot provide the requested information. The document focuses on:

• Device Type: Nd: YAG Laser Therapy System (QN-1), a physical laser device.
• Regulatory Path: 510(k) pathway, demonstrating substantial equivalence to a predicate laser device (K232716).
• Testing: Non-clinical bench tests confirming compliance with electrical safety, EMC, laser safety, and biocompatibility standards.
• Clinical Studies: Explicitly states "No clinical study is included in this submission."
• Ground Truth: Not applicable in the context of this device's non-clinical submission.

To answer your prompt, an AI/ML medical device submission would typically include information on:

1. A table of acceptance criteria and the reported device performance: This would list specific metrics (e.g., Sensitivity, Specificity, AUC, F1-score) and the required numerical thresholds for device acceptance, along with the actual performance achieved in the study.
2. Sample sized used for the test set and the data provenance: Details on the number of cases (e.g., images, patient records) in the test set, country of origin, and whether the data was collected retrospectively or prospectively.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Information on how many medical professionals (e.g., radiologists, pathologists) annotated or reviewed the data to establish the definitive diagnosis, and their experience levels.
4. Adjudication method for the test set: How disagreements among experts were resolved (e.g., majority vote, senior expert arbitration).
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Whether human readers' performance with and without AI assistance was evaluated, and the measured improvement.
6. If a standalone performance (i.e. algorithm only without human-in-the loop performance) was done: The algorithm's performance without any human interaction.
7. The type of ground truth used: Whether the ground truth was based on expert consensus, pathological confirmation, long-term patient outcomes, etc.
8. The sample size for the training set: The number of cases used to train the AI model.
9. How the ground truth for the training set was established: The methodology for obtaining the labels used during the AI model's training phase.

Since the provided document does not pertain to an AI/ML medical device, none of the above questions can be answered based on the input.

Ask a specific question about this device