Skin Research and Technology

Papers
(The H4-Index of Skin Research and Technology is 14. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-04-01 to 2024-04-01.)
ArticleCitations
Methods for evaluating penetration of drug into the skin: A review88
Effect of face mask on skin characteristics changes during the COVID‐19 pandemic58
Line‐field confocal optical coherence tomography—Practical applications in dermatology and comparison with established imaging methods52
Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet27
Clinical evaluation of efficacy, safety and tolerability of cysteamine 5% cream in comparison with modified Kligman’s formula in subjects with epidermal melasma: A randomized, double‐blind clinical tr22
Influence of quarantine mask use on skin characteristics: One of the changes in our life caused by the COVID‐19 pandemic19
Skin measurement devices to assess skin quality: A systematic review on reliability and validity19
The continuous development of a complete and objective automatic grading system of facial signs from selfie pictures: Asian validation study and application to women of three ethnic origins, different19
Kaposi sarcoma of the glans: New findings by line field confocal optical coherence tomography examination18
Biometric changes of skin parameters in using of microneedling fractional radiofrequency for skin tightening and rejuvenation facial18
Characterization of skin aging through high‐frequency ultrasound imaging as a technique for evaluating the effectiveness of anti‐aging products and procedures: A review16
Japanese experiment of a complete and objective automatic grading system of facial signs from selfie pictures: Validation with dermatologists and characterization of changes due to age and sun exposur16
High‐frequency (22‐MHz) ultrasound for assessing the depth of basal cell carcinoma invasion15
Development and validation of two artificial intelligence models for diagnosing benign, pigmented facial skin lesions14
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