Skin Research And Technology[JOURNAL]

BACKGROUND: Picosecond (ps) laser treatments have been widely used for several years to remove unwanted tattoos. It is hypothesized that following the laser-induced fragmentation of tattoo particles, transepidermal clear... BACKGROUND: Picosecond (ps) laser treatments have been widely used for several years to remove unwanted tattoos. It is hypothesized that following the laser-induced fragmentation of tattoo particles, transepidermal clearance occurs as one of the elimination processes alongside with the renewal of the skin. Nevertheless, the precise microscopic details of the tattoo clearance process following laser treatment remain unclear. OBJECTIVE: To analyze the transepidermal clearance of tattoo particles, along with the morphological and metabolic changes in the surrounding tissue, following ps laser treatment. MATERIAL AND METHODS: The study population comprised healthy male and female patients seeking laser-assisted tattoo removal, who were recruited from the Laser Department at the University Medical Center Hamburg-Eppendorf. Each subject underwent a single ps laser treatment session, and follow-up assessments were conducted at 6 and 12 weeks post-treatment using multiphoton tomography with fluorescence lifetime imaging (MPT-FLIM). RESULTS: The study included a total of nine participants with eleven tattoos. In untreated skin, the tattoo particles were observed to be confined to the dermis, situated between collagen bundles. Six weeks following treatment, tattoo particles were observed in both inter- and intracellular spaces across all epidermal and the upper dermal layers. By the 12-week follow-up, particles were still present in the epidermis and dermis, although their quantity appeared to have decreased. In accordance with the aforementioned findings, the mean fluorescence lifetime measurements demonstrated a decrease across the follow-up visits, although they remained elevated at 12 weeks. CONCLUSION: Our in vivo, non-invasive imaging data indicate that the transepidermal clearance of tattoo particles following ps laser treatment can extend over several months. This supports the hypothesis that longer intervals between ps laser treatments may be beneficial. Further prospective clinical studies are required to compare the efficacy and safety of short- and long-term treatment intervals in laser-assisted tattoo removal. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT06431464.

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Concordance in Basal Cell Carcinoma Diagnosis. Building a Proper Standard Reference to Train Artificial Intelligence Tools.

Silva-Clavería F, Serrano C, Matas I … +3 more , Serrano A, Toledo-Pastrana T, Acha B

Skin Res Technol · 2025 Oct · PMID 41127892 · Full text

BACKGROUND: Reliable labels are essential when training Artificial Intelligence (AI) tools. Whereas some diseases allow biopsy-based labeling, others rely on subjective criteria. For the diagnosis of basal cell carcinoma... BACKGROUND: Reliable labels are essential when training Artificial Intelligence (AI) tools. Whereas some diseases allow biopsy-based labeling, others rely on subjective criteria. For the diagnosis of basal cell carcinoma (BCC), dermatologists detect certain dermoscopic criteria, whose presence (or absence) serves as the basis for determining a diagnosis of BCC. Therefore, an AI tool assisting in BCC diagnosis should provide such criteria to explain its output. MATERIALS AND METHODS: This study analyzes the agreement among four dermatologists in detecting dermoscopic criteria and compares the performance of an AI model trained with labels from a single dermatologist versus a consensus-based standard. A total of1230 dermoscopic images, collected in around 60 primary health centers, sent via teledermatology, and diagnosed by four dermatologists, were used to train an AI tool. They were randomly selected from the teledermatology platform (2019-2021). Subsequently, 204 new images were used to test the AI tool prospectively. A standard reference (SR) was built using Expectation Maximization on the four diagnoses. The performance of the AI tool trained using the reference standard of one dermatologist versus the reference standard statistically inferred from the consensus of four dermatologists was analyzed using McNemar's test and Hamming distance. RESULTS: Agreement among dermatologists was high for BCC versus non-BCC (Kappa = 0.9079; PPV = 0.9670), but lower for specific criteria. Statistical differences were found in the performance of AI models trained with individual and consensus labels. CONCLUSION: Deriving an SR from multiple expert opinions mitigates individual bias and enhances AI interpretability, key for its clinical adoption.

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RETRACTION: Hyperspectral Imaging-Based Erythema Classification in Atopic Dermatitis.

Skin Res Technol · 2025 Oct · PMID 41121661 · Full text

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RETRACTION: Identification of Novel Biomarkers for Childhood-Onset Systemic Lupus Erythematosus Using Machine Learning Algorithms and Immune Infiltration Analysis.

Skin Res Technol · 2025 Oct · PMID 41121652 · Full text

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RETRACTION: Bayesian-Edge System for Classification and Segmentation of Skin Lesions in Internet of Medical Things.

Skin Res Technol · 2025 Oct · PMID 41121651 · Full text

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RETRACTION: Parameter-Based Transfer Learning for Severity Classification of Atopic Dermatitis Using Hyperspectral Imaging.

Skin Res Technol · 2025 Oct · PMID 41121648 · Full text

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Skin Research And Technology[JOURNAL]

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