Laser and Light-Based Therapies for Treating Melasma: An In-Depth Review
Bibliographic record
Abstract
BACKGROUND: Melasma is a chronic, relapsing hyperpigmentation disorder with a multifactorial etiology, posing significant treatment challenges. It is associated with substantial psychological and social burdens, often impairing quality of life. OBJECTIVE: This review comprehensively explores current and emerging laser and light-based treatments for melasma, evaluating their clinical applicability, efficacy, and safety. MATERIALS AND METHODS: The National Library of Medicine database (PubMed) was searched for studies published through April 2024. Studies were selected based on relevance to laser and light-based therapies for melasma, including intense pulsed light devices, Q-switched nanosecond lasers, ablative and nonablative fractionated resurfacing lasers, and picosecond lasers. RESULTS: Current treatments such as intense pulsed light and Q-switched nanosecond lasers offer promising results for treatment of melasma but have risks including disease recurrence and postinflammatory pigmentary changes. Emerging treatments such as picosecond lasers show promise in reducing melanin index and improving skin texture with fewer adverse effects, showing significant reductions in melasma area and severity index scores and high patient satisfaction. CONCLUSION: Laser and light-based therapies continue to evolve, with growing support for multimodal approaches that address the complex pathophysiology of melasma. Their advancement provides a nuanced approach to this challenging condition, emphasizing the need for individualized treatment plans and consideration of psychosocial impacts on patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".