An Overview of Methods to Characterize Skin Type: Focus on Visual Rating Scales and Self-Report Instruments
Abstract
:1. Introduction
2. Skin Type Classification
- Stratum corneum hydration: a stratum corneum with a water content of around 10% has optimal hydration and consequently the skin is resistant, supple, luminous, soft, and smooth. When the water content is lower, the skin has a rough appearance, lacks flexibility, and may present flaking and dehydration lines [8].
- Hydrolipidic film: is made up of a mixture of sweat and sebum. It varies from individual to individual depending on its qualitative and quantitative composition, so the proportion of the aqueous and lipid phases will influence the skin type [8]. Sebum production varies with age, gender, and topographical variations of the skin. Oily skin has a more lipophilic hydrolipidic film because of greater sebum secretion. Skin tightness after washing, pore size and number, daily oiliness, and makeup maintenance are all factors to characterize skin as oily or dry [7].
- Sun reaction: depends on the sensitivity to the sun, tanning ability, and the frequency of appearance of solar erythema, and these characteristics will help determine the phototype [8].
- Skin color: is the combination of melanin (yellowish-brown color) and hemoglobin (red color). Skin color is determined genetically and has to do with the melanin distribution on the epidermis. However, skin color may also be the result of environmental factors such as sun exposure and hormonal factors that lead to an increase in the amount of melanin on the epidermis [9].
- Sensitivity: reflects the appearance of unpleasant sensations such as stinging, burning, pain, pruritus, and tingling, accompanied or not by erythema, due to a stimulus, which in normal skin would not cause these sensations [10].
- Skin aging signs: wrinkles, uneven pigmentation and texture, and lack of elasticity [11].
- Normal skin: Visually, looks uniform, luminous, without excessive shine, and has a smooth and even texture without apparent pores. In a tactile examination, the skin appears fresh and smooth, with normal thickness, hydrated, firm, and flexible.
- Dry skin: On visual examination, looks clear, dull (sebum deficiency), and sometimes flaky. It has no visible pores and may have eczematous, reddish areas and rosacea. On tactile examination, the skin appears cold, thin, rough, with little flexibility, and often with dehydration streaks.
- Oily skin: has a shiny appearance, uneven texture with very large pores. It may have comedones and pimples, acne scars, and skin irritations. On tactile examination, the skin is oily, smooth, hyper-seborrheic, and thick.
- Sensitive skin: Visually, shows seborrheic dermatitis, signs of rosacea, scaling, blisters, edemas, redness, and dryness. It can appear hot and rough.
- Aging skin: Exhibits a pale and dull appearance, uneven texture, presence of wrinkles, enlarged pores, and comedones and dyschromic spots. Upon tactile examination, the skin appears cold, thin, dry, rough, and inelastic.
3. Instruments
3.1. Self-Report Instruments
3.1.1. The Baumann Skin Type System (BSTS)
- Hydration (dry (D) vs. oily (O)): Dry skin (D) is characterized by the lack of water content (lower than 10%) on the stratum corneum and an increase of the TEWL. The dry skin’s symptoms such as rough skin, fissures, and cracks will be questioned on the BSTI. Oily skin (O) is connected to high sebum production. Combination skin may be classified as O (oily skin) or D (dry skin) depending on the characteristics found by the BSTI questionnaire. There are two types of combination skin—Seasonal Skin—dry during winter and in dry climates, and oily during summer and in humid climates—and T-zone skin—oily on the T-zone (forehead, nose, and chin) and dry on both sides of the face. Normal skin can be classified as O1 or D1. When it has an intact barrier with a normal TEWL and a normal production of sebum, it is classified as an O1. When the skin presents an intact barrier, but the sebum production is below normal, it is classified as a D1.
- Skin sensitivity (resistant (R) vs. sensitive (S)): Resistant skin has a strong stratum corneum and in this classification system, it is defined as skin that presents no signs of inflammation. Individuals with this skin type can use any kind of skin care product without developing irritation, acne, or stinging sensation. Resistant skin does not have a score, being that the individual either possesses resistant skin or not. Sensitive skin includes 5 different subtypes: acne subtype (S1), rosacea subtype (S2), stinging subtype (S3), allergic subtype (S4), and seborrheic dermatitis (S5). Inflammation is common among all these sensitive skin subtypes.
- Skin pigmentation (pigmented (P) vs. nonpigmented (N)): This parameter measures the tendency to develop dark spots on the skin (melasma or solar lentigos) as a result of sun exposure. Pigmentation is classified numerically from 1 to 4, according to the probability of developing pigmentation issues.
- Skin elasticity (wrinkled (W) vs. tight (T)): This parameter is influenced by age and ethnicity, as well as lifestyle. Photo-aged skin presents spots on the skin and freckles caused by sun exposure accompanied by wrinkles, being classified as a PW skin type (pigmented and wrinkled).
3.1.2. The Fitzpatrick Skin Phototype Classification (FSPC)
3.1.3. The Roberts Skin Type Classification System
- Fitzpatrick Phototype Scale: a 6-point scale based on skins’ reaction to sun exposure (type I to VI);
- Glogau Scale: A 4-point scale that classifies photoaging skin degree based on wrinkles’ skin examination (type I to IV);
- Roberts Hyperpigmentation Scale: A 7-point scale that determines the post-inflammatory pigmentation and the probability to acquire a pigmentation problem (type 0—hypopigmentation; type I—minimal and transient hyperpigmentation; type II—minimal and permanent hyperpigmentation; type III—moderate and transient hyperpigmentation; type IV—moderate and permanent hyperpigmentation; type V—severe and transient hyperpigmentation; type VI—severe and permanent hyperpigmentation);
- Roberts Scarring Scale: A 6-point scale that identifies the patterns of scarring, by evaluating how the individual’s skin reacts to injury and inflammations (type 0—atrophy; type I—no scar; type II—macule; type III—plaque; type IV—keloid; type V—keloidal nodule).
3.2. Visual Rating Scales
3.2.1. The Glogau Scale
3.2.2. The Griffiths Photonumeric Scale
3.2.3. The Score of Intrinsic and Extrinsic Aging—SCINEXA
3.3. Artificial Intelligence-Based Skin-Type Analysis
3.4. Others
3.4.1. Sensitive Skin—Lactic Acid Stinging Test (LAST)
3.4.2. The Skin Color Charts/Cards/Bars
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Menon, G.K.; Kligman, A.M. Barrier functions of human skin: A holistic view. Skin Pharmacol. Physiol. 2009, 22, 178–189. [Google Scholar] [CrossRef]
- Zaidi, Z.; Lanigan, S.W. Skin: Structure and Function. In Dermatology in Clinical Practice; Lanigan, S.W., Zaidi, Z., Eds.; Springer: London, UK, 2010; pp. 1–15. [Google Scholar] [CrossRef]
- Elias, P.M.; Wakefield, J.S. An integrated view of the epidermal environmental interface. Dermatol. Sin. 2015, 33, 49–57. [Google Scholar] [CrossRef] [Green Version]
- Khavkin, J.; Ellis, D.A. Aging skin: Histology, physiology, and pathology. Facial Plast. Surg. Clin. N. Am. 2011, 19, 229–234. [Google Scholar] [CrossRef] [PubMed]
- Ayer, J.; Griffiths, C.E.M. Chapter 1 Photoaging in Caucasians. In Cutaneous Photoaging; The Royal Society of Chemistry: London, UK, 2019; pp. 1–30. [Google Scholar] [CrossRef]
- Sachs, D.L.; Varani, J.; Chubb, H.; Fligiel, S.E.G.; Cui, Y.; Calderone, K.; Helfrich, Y.; Fisher, G.J.; Voorhees, J.J. Atrophic and hypertrophic photoaging: Clinical, histologic, and molecular features of 2 distinct phenotypes of photoaged skin. J. Am. Acad. Dermatol. 2019, 81, 480–488. [Google Scholar] [CrossRef]
- Youn, S.W.; Kim, S.J.; Hwang, I.A.; Park, K.C. Evaluation of facial skin type by sebum secretion: Discrepancies between subjective descriptions and sebum secretion. Skin Res. Technol. 2002, 8, 168–172. [Google Scholar] [CrossRef] [PubMed]
- Estrade, M.N. Consejos en Cosmetologia; Ars Galenica: Barcelona, Spain, 2002. [Google Scholar]
- Gupta, V.; Sharma, V.K. Skin typing: Fitzpatrick grading and others. Clin. Dermatol. 2019, 37, 430–436. [Google Scholar] [CrossRef]
- Lev-Tov, H.; Maibach, H.I. The sensitive skin syndrome. Indian J. Dermatol. 2012, 57, 419–423. [Google Scholar] [CrossRef]
- Zouboulis, C.C.; Ganceviciene, R.; Liakou, A.I.; Theodoridis, A.; Elewa, R.; Makrantonaki, E. Aesthetic aspects of skin aging, prevention, and local treatment. Clin. Dermatol. 2019, 37, 365–372. [Google Scholar] [CrossRef] [PubMed]
- Monteiro Rodrigues, L.; Fluhr, J.W. EEMCO Guidance for the in vivo Assessment of Biomechanical Properties of the Human Skin and Its Annexes: Revisiting Instrumentation and Test Modes. Skin Pharmacol. Physiol. 2020, 33, 44–60. [Google Scholar] [CrossRef]
- Mercurio, D.G.; Segura, J.H.; Demets, M.B.A.; Maia Campos, P.M.B.G. Clinical scoring and instrumental analysis to evaluate skin types. Clin. Exp. Dermatol. 2013, 38, 302–309. [Google Scholar] [CrossRef]
- Berardesca, E.; Elsner, P.; Wilhelm, K.-P.; Maibach, H.I. Bioengineering of the Skin: Methods and Instrumentation, 1st ed.; CRC Press: Boca Raton, FL, USA, 2020; Volume III. [Google Scholar]
- Giusti, F.; Seidenari, S. Bioengineering Methods and Skin Aging. In Textbook of Aging Skin; Farage, M.A., Miller, K.W., Maibach, H.I., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 677–685. [Google Scholar] [CrossRef]
- Serup, J.; Jemec, G.B.E.; Grove, G. Handbook of Non-Invasive Methods and the Skin, 2nd ed.; CRC Press: Boca Raton, FL, USA, 2006. [Google Scholar] [CrossRef]
- Schneider, S.L.; Kohli, I.; Hamzavi, I.H.; Council, M.L.; Rossi, A.M.; Ozog, D.M. Emerging imaging technologies in dermatology: Part I: Basic principles. J. Am. Acad. Dermatol. 2019, 80, 1114–1120. [Google Scholar] [CrossRef] [PubMed]
- Schneider, S.L.; Kohli, I.; Hamzavi, I.H.; Council, M.L.; Rossi, A.M.; Ozog, D.M. Emerging imaging technologies in dermatology: Part II: Applications and limitations. J. Am. Acad. Dermatol. 2019, 80, 1121–1131. [Google Scholar] [CrossRef] [PubMed]
- Jartarkar, S.R.; Patil, A.; Wollina, U.; Gold, M.H.; Stege, H.; Grabbe, S.; Goldust, M. New diagnostic and imaging technologies in dermatology. J. Cosmet. Dermatol. 2021, 20, 3782–3787. [Google Scholar] [CrossRef]
- Dobos, G.; Lichterfeld, A.; Blume-Peytavi, U.; Kottner, J. Evaluation of skin ageing: A systematic review of clinical scales. Br. J. Dermatol. 2015, 172, 1249–1261. [Google Scholar] [CrossRef] [PubMed]
- Baumann, L. Understanding and Treating Various Skin Types: The Baumann Skin Type Indicator. Dermatol. Clin. 2008, 26, 359–373. [Google Scholar] [CrossRef]
- Baumann, L. Validation of a Questionnaire to Diagnose the Baumann Skin Type in All Ethnicities and in Various Geographic Locations. J. Cosmet. Dermatol. Sci. Appl. 2016, 6, 34–40. [Google Scholar] [CrossRef] [Green Version]
- Baumann, L. The Skin Type Solution; Random House Publishing Group: New York, NY, USA, 2007. [Google Scholar]
- Baumann, L.S.; Penfield, R.D.; Clarke, J.L.; Duque, D.K. A Validated Questionnaire for Quantifying Skin Oiliness. J. Cosmet. Dermatol. Sci. Appl. 2014, 4, 78–84. [Google Scholar] [CrossRef] [Green Version]
- Baumann, L. The Importance of Skin Type: The Baumann Skin Type System. In Cosmeceuticals and Cosmetic Ingredients; McGraw-Hill Education: New York, NY, USA, 2015. [Google Scholar]
- Ahn, S.K.; Jun, M.; Bak, H.; Park, B.D.; Hong, S.P.; Lee, S.H.; Kim, S.J.; Kim, H.J.; Song, D.H.; Min, P.K.; et al. Baumann Skin Type in the Korean Female Population. Ann. Dermatol. 2017, 29, 586–596. [Google Scholar] [CrossRef] [Green Version]
- Lee, Y.B.; Ahn, S.K.; Ahn, G.Y.; Bak, H.; Hong, S.P.; Go, E.J.; Park, C.O.; Lee, S.E.; Lee, W.J.; Ko, H.C.; et al. Baumann Skin Type in the Korean Male Population. Ann. Dermatol. 2019, 31, 621–630. [Google Scholar] [CrossRef] [Green Version]
- Lee, Y.B.; Park, S.M.; Bae, J.M.; Yu, D.S.; Kim, H.J.; Kim, J.W. Which Skin Type is Prevalent in Korean Post-Adolescent Acne Patients?: A Pilot Study Using the Baumann Skin Type Indicator. Ann. Dermatol. 2017, 29, 817–819. [Google Scholar] [CrossRef] [Green Version]
- Perkins, A.C.; Cheng, C.E.; Hillebrand, G.G.; Miyamoto, K.; Kimball, A.B. Comparison of the epidemiology of acne vulgaris among Caucasian, Asian, Continental Indian and African American women. J. Eur. Acad. Dermatol. Venereol. 2011, 25, 1054–1060. [Google Scholar] [CrossRef] [PubMed]
- Kanezawa, S.; Zhu, Y.B.; Wang, Q. Correlation between Chinese Medicine Constitution and Skin Types: A Study on 187 Japanese Women. Chin. J. Integr. Med. 2020, 26, 174–179. [Google Scholar] [CrossRef] [PubMed]
- Roberts, W.E. Skin type classification systems old and new. Dermatol. Clin. 2009, 27, 529–533. [Google Scholar] [CrossRef] [PubMed]
- Falcon, K.; Fors, M.; Palacios Alvarez, S.; Veintimilla, K.; Lasso, N.; Navas, C. Assessment of Predictors of Sun Sensitivity as Defined by Fitzpatrick Skin Phototype in an Ecuadorian Population and Its Correlation with Skin Damage. Dermatology 2019, 235, 400–406. [Google Scholar] [CrossRef]
- Bieliauskiene, G.; Holm-Schou, A.S.; Philipsen, P.A.; Murphy, G.M.; Sboukis, D.; Schwarz, T.; Young, A.R.; Wulf, H.C. Measurements of sun sensitivity in five European countries confirm the relative nature of Fitzpatrick skin phototype scale. Photodermatol. Photoimmunol. Photomed. 2020, 36, 179–184. [Google Scholar] [CrossRef] [PubMed]
- Boldeman, C.; Dal, H.; Kristjansson, S.; Lindelöf, B. Is self-assessment of skin type a valid method for adolescents? J. Am. Acad. Dermatol. 2004, 50, 447–449. [Google Scholar] [CrossRef]
- Fors, M.; González, P.; Viada, C.; Falcon, K.; Palacios, S. Validity of the Fitzpatrick Skin Phototype Classification in Ecuador. Adv. Skin Wound Care 2020, 33, 1. [Google Scholar] [CrossRef]
- González, F.J.; Martínez-Escanamé, M.; Muñoz, R.I.; Torres-Álvarez, B.; Moncada, B. Diffuse reflectance spectrophotometry for skin phototype determination. Skin Res. Technol. 2010, 16, 397–400. [Google Scholar] [CrossRef]
- Sharma, V.K.; Gupta, V.; Jangid, B.L.; Pathak, M. Modification of the Fitzpatrick system of skin phototype classification for the Indian population, and its correlation with narrowband diffuse reflectance spectrophotometry. Clin. Exp. Dermatol. 2018, 43, 274–280. [Google Scholar] [CrossRef]
- Man, I.; Dawe, R.S.; Ferguson, J.; Ibbotson, S.H. The optimal time to determine the minimal phototoxic dose in skin photosensitized by topical 8 methoxypsoralen. Br. J. Dermatol. 2004, 151, 179–182. [Google Scholar] [CrossRef]
- Tylman, M.R.; Narbutt, J.; Fracczak, M.; Sysa-Jedrzejowska, A.; Lesiak, A. Pigment protection factor as a predictor of skin photosensitivity—A Polish study. Acta Dermatovenerol. Croat. 2015, 23, 23–27. [Google Scholar] [PubMed]
- Rajpar, S.F.; Hague, J.S.; Abdullah, A.; Lanigan, S.W. Hair removal with the long-pulse alexandrite and long-pulse Nd:YAG lasers is safe and well tolerated in children. Clin. Exp. Dermatol. 2009, 34, 684–687. [Google Scholar] [CrossRef] [PubMed]
- Holzer, G.; Nahavandi, H.; Neumann, R.; Knobler, R. Photoepilation with variable pulsed light in non-facial body areas: Evaluation of efficacy and safety. J. Eur. Acad. Dermatol. Venereol. 2010, 24, 518–523. [Google Scholar] [CrossRef]
- Gogia, R.; Binstock, M.; Hirose, R.; Boscardin, W.J.; Chren, M.M.; Arron, S.T. Fitzpatrick skin phototype is an independent predictor of squamous cell carcinoma risk after solid organ transplantation. J. Am. Acad. Dermatol. 2013, 68, 585–591. [Google Scholar] [CrossRef] [Green Version]
- Rumpf, J.J.; Schirmer, M.; Fricke, C.; Weise, D.; Wagner, J.A.; Simon, J.; Classen, J. Light pigmentation phenotype is correlated with increased substantia nigra echogenicity. Mov. Disord. 2015, 30, 1848–1852. [Google Scholar] [CrossRef] [PubMed]
- Lin, B.M.; Li, W.Q.; Curhan, S.G.; Stankovic, K.M.; Qureshi, A.A.; Curhan, G.C. Skin Pigmentation and Risk of Hearing Loss in Women. Am. J. Epidemiol. 2017, 186, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Trakatelli, M.; Bylaite-Bucinskiene, M.; Correia, O.; Cozzio, A.; De Vries, E.; Medenica, L.; Nagore, E.; Paoli, J.; Stratigos, A.J.; Del Marmol, V.; et al. Clinical assessment of skin phototypes: Watch your words! Eur. J. Dermatol. 2017, 27, 615–619. [Google Scholar] [CrossRef]
- Mercieca, L.; Aquilina, S.; Calleja, N.; Boffa, M.J. Cutaneous Melanoma More Likely to Be Invasive in Fairer Skin Phototypes: A Retrospective Observational Study. Skinmed 2021, 19, 280–283. [Google Scholar]
- Magin, P.; Pond, D.; Smith, W.; Goode, S.; Paterson, N. Reliability of skin-type self-assessment: Agreement of adolescents’ repeated Fitzpatrick skin phototype classification ratings during a cohort study. J. Eur. Acad. Dermatol. Venereol. 2012, 26, 1396–1399. [Google Scholar] [CrossRef]
- Roberts, W.E. The Roberts Skin Type Classification System. J. Drugs Dermatol. 2008, 7, 452–456. [Google Scholar]
- Glogau, R.G. Aesthetic and anatomic analysis of the aging skin. Semin. Cutan. Med. Surg. 1996, 15, 134–138. [Google Scholar] [CrossRef]
- Oesch, S.; Vingan, N.R.; Li, X.; Hoopman, J.; Akgul, Y.; Kenkel, J.M. A Correlation of the Glogau Scale with VISIA-CR Complexion Analysis Measurements in Assessing Facial Photoaging for Clinical Research. Aesthet. Surg. J. 2022, 42, 1175–1184. [Google Scholar] [CrossRef]
- Özkoca, D.; Aşkın, Ö.; Engin, B. Treatment of periorbital and perioral wrinkles with fractional Er:YAG laser: What are the effects of age, smoking, and Glogau stage? J. Cosmet. Dermatol. 2021, 20, 2800–2804. [Google Scholar] [CrossRef]
- Barrera, J.E.; Adame, M.J.; Lospinoso, J.A.; Beachkofsky, T.M. Efficacy of Laser Resurfacing and Facial Plastic Surgery Using Age, Glogau, and Fitzpatrick Rating. Plast. Reconstr. Surg. Glob. Open 2018, 6, e1740. [Google Scholar] [CrossRef]
- Samadi, A.; Nasrollahi, S.A.; Janani, L.; Moosavi, Z.B.; Hesari, K.K.; Kalantari, A.R.; Firooz, A. Combination of Fractional Radiofrequency and Thermo-Contraction Systems for Facial Skin Rejuvenation: A Clinical and Histological Study. Aesthet. Surg. J. 2018, 38, 1341–1350. [Google Scholar] [CrossRef] [Green Version]
- Cabrera-Ramírez, J.O.; Puebla-Mora, A.G.; González-Ojeda, A.; García-Martínez, D.; Cortés-Lares, J.A.; Márquez-Valdés, A.R.; Contreras-Hernández, G.I.; Bracamontes-Blanco, J.; Saucedo Ortiz, J.A.; Fuentes-Orozco, C. Platelet-Rich Plasma for the Treatment of Photodamage of the Skin of the Hands. Actas Dermosifiliogr. 2017, 108, 746–751. [Google Scholar] [CrossRef]
- Faghihi, G.; Fatemi-Tabaei, S.; Abtahi-Naeini, B.; Siadat, A.H.; Sadeghian, G.; Ali Nilforoushzadeh, M.; Mohamadian-Shoeili, H. The Effectiveness of a 5% Retinoic Acid Peel Combined with Microdermabrasion for Facial Photoaging: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial. Dermatol. Res. Pract. 2017, 2017, 8516527. [Google Scholar] [CrossRef] [PubMed]
- Lim, V.Z.; Yong, A.A.; Tan, W.P.M.; Zhao, X.; Vitale, M.; Goh, C.L. Efficacy and Safety of a New Cosmeceutical Regimen Based on the Combination of Snail Secretion Filtrate and Snail Egg Extract to Improve Signs of Skin Aging. J. Clin. Aesthet. Dermatol. 2020, 13, 31–36. [Google Scholar] [PubMed]
- Buendía-Eisman, A.; Prieto, L.; Abarquero, M.; Arias-Santiago, S. Study of the Exposome Ageing-related Factors in the Spanish Population. Acta Derm. Venereol. 2020, 100, adv00153. [Google Scholar] [CrossRef] [PubMed]
- Yu, S.H.; Baron, E.D. Evaluation and assessment of photoaging. Auswert. Beurteil. Photoaging 2013, 2, 305–314. [Google Scholar] [CrossRef]
- Lee, C.M. Fifty years of research and development of cosmeceuticals: A contemporary review. J. Cosmet. Dermatol. 2016, 15, 527–539. [Google Scholar] [CrossRef] [PubMed]
- Griffiths, C.E.; Wang, T.S.; Hamilton, T.A.; Voorhees, J.J.; Ellis, C.N. A photonumeric scale for the assessment of cutaneous photodamage. Arch. Dermatol. 1992, 128, 347–351. [Google Scholar] [CrossRef] [PubMed]
- Brooke, R.C.; Newbold, S.A.; Telfer, N.R.; Griffiths, C.E. Discordance between facial wrinkling and the presence of basal cell carcinoma. Arch. Dermatol. 2001, 137, 751–754. [Google Scholar] [PubMed]
- Richmond-Sinclair, N.M.; Pandeya, N.; Williams, G.M.; Neale, R.E.; van der Pols, J.C.; Green, A.C. Clinical signs of photodamage are associated with basal cell carcinoma multiplicity and site: A 16-year longitudinal study. Int. J. Cancer 2010, 127, 2622–2629. [Google Scholar] [CrossRef]
- Korgavkar, K.; Lee, K.C.; Weinstock, M.A. Effect of Topical Fluorouracil Cream on Photodamage: Secondary Analysis of a Randomized Clinical Trial. JAMA Dermatol. 2017, 153, 1142–1146. [Google Scholar] [CrossRef]
- Betz-Stablein, B.; Llewellyn, S.; Bearzi, P.; Grochulska, K.; Rutjes, C.; Aitken, J.F.; Janda, M.; O’Rouke, P.; Soyer, H.P.; Green, A.C. High variability in anatomic patterns of cutaneous photodamage: A population-based study. J. Eur. Acad. Dermatol. Venereol. 2021, 35, 1896–1903. [Google Scholar] [CrossRef]
- Vierkötter, A.; Ranft, U.; Krämer, U.; Sugiri, D.; Reimann, V.; Krutmann, J. The SCINEXA: A novel, validated score to simultaneously assess and differentiate between intrinsic and extrinsic skin ageing. J. Dermatol. Sci. 2009, 53, 207–211. [Google Scholar] [CrossRef]
- Vierkötter, A.; Schikowski, T.; Ranft, U.; Sugiri, D.; Matsui, M.; Krämer, U.; Krutmann, J. Airborne particle exposure and extrinsic skin aging. J. Investig. Dermatol. 2010, 130, 2719–2726. [Google Scholar] [CrossRef] [Green Version]
- Li, M.; Vierkötter, A.; Schikowski, T.; Hüls, A.; Ding, A.; Matsui, M.S.; Deng, B.; Ma, C.; Ren, A.; Zhang, J.; et al. Epidemiological evidence that indoor air pollution from cooking with solid fuels accelerates skin aging in Chinese women. J. Dermatol. Sci. 2015, 79, 148–154. [Google Scholar] [CrossRef]
- Oyetakin-White, P.; Suggs, A.; Koo, B.; Matsui, M.S.; Yarosh, D.; Cooper, K.D.; Baron, E.D. Does poor sleep quality affect skin ageing? Clin. Exp. Dermatol. 2015, 40, 17–22. [Google Scholar] [CrossRef]
- Gao, W.; Tan, J.; Hüls, A.; Ding, A.; Liu, Y.; Matsui, M.S.; Vierkötter, A.; Krutmann, J.; Schikowski, T.; Jin, L.; et al. Genetic variants associated with skin aging in the Chinese Han population. J. Dermatol. Sci. 2017, 86, 21–29. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, Y.; Gao, W.; Koellmann, C.; Le Clerc, S.; Hüls, A.; Li, B.; Peng, Q.; Wu, S.; Ding, A.; Yang, Y.; et al. Genome-wide scan identified genetic variants associated with skin aging in a Chinese female population. J. Dermatol. Sci. 2019, 96, 42–49. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Vierkötter, A.; Hüls, A.; Yamamoto, A.; Stolz, S.; Krämer, U.; Matsui, M.S.; Morita, A.; Wang, S.; Li, Z.; Jin, L.; et al. Extrinsic skin ageing in German, Chinese and Japanese women manifests differently in all three groups depending on ethnic background, age and anatomical site. J. Dermatol. Sci. 2016, 83, 219–225. [Google Scholar] [CrossRef]
- Cinotti, E.; Perrot, J.L.; Labeille, B.; Biron, A.C.; Vierkötter, A.; Heusèle, C.; Nizard, C.; Schnebert, S.; Barthelemy, J.C.; Cambazard, F. Skin tumours and skin aging in 209 French elderly people: The PROOF study. Eur. J. Dermatol. 2016, 26, 470–476. [Google Scholar] [CrossRef] [PubMed]
- Fors, M.; Palacios, S.; Falcon, K.; Ventimilla, K.; Simbaña, L.; Lagos, C.; Lasso, N.; Navas, C. Exploratory study of the reproducibility of the SCore for INtrinsic and EXtrinsic skin aging (SCINEXA) scale in “Ruta Escondida de la Mitad del Mundo”, Ecuador, 2017. BMC Dermatol. 2018, 18, 10. [Google Scholar] [CrossRef]
- Li, Z.; Koban, K.C.; Schenck, T.L.; Giunta, R.E.; Li, Q.; Sun, Y. Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends. J. Clin. Med. 2022, 11, 6826. [Google Scholar] [CrossRef]
- Abraham, A.; Sobhanakumari, K.; Mohan, A. Artificial intelligence in dermatology. J. Skin Sex. Transm. Dis. 2021, 3, 99–102. [Google Scholar] [CrossRef]
- Guo, L.; Yang, Y.; Ding, H.; Zheng, H.; Yang, H.; Xie, J.; Li, Y.; Lin, T.; Ge, Y. A deep learning-based hybrid artificial intelligence model for the detection and severity assessment of vitiligo lesions. Ann. Transl. Med. 2022, 10, 590. [Google Scholar] [CrossRef]
- Aggarwal, P. Performance of Artificial Intelligence Imaging Models in Detecting Dermatological Manifestations in Higher Fitzpatrick Skin Color Classifications. JMIR Dermatol. 2021, 4, e31697. [Google Scholar] [CrossRef]
- Georgievskaya, A. Artificial Intelligence Confirming Treatment Success: The Role of Gender- and Age-Specific Scales in Performance Evaluation. Plast. Reconstr. Surg. 2022, 150, 34S–40S. [Google Scholar] [CrossRef]
- Eapen, B.R. Artificial Intelligence in Dermatology: A Practical Introduction to a Paradigm Shift. Indian Dermatol. Online J. 2020, 11, 881–889. [Google Scholar] [CrossRef] [PubMed]
- Vichy Laboratoires. The New Skincare Diagnostic Tool. 2022. Available online: https://www.vichyusa.com/skin-care-analysis-ai.html (accessed on 27 November 2022).
- Diamant, N.; Zadok, D.; Baskin, C.; Schwartz, E.; Bronstein, A.M. Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level. arXiv 2019, arXiv:1902.02593. [Google Scholar]
- Seo, J.I.; Ham, H.I.; Baek, J.H.; Shin, M.K. An objective skin-type classification based on non-invasive biophysical parameters. J. Eur. Acad. Dermatol. Venereol. 2022, 36, 444–452. [Google Scholar] [CrossRef] [PubMed]
- Alagić, A.; Alihodžić, S.; Alispahić, N.; Bečić, E.; Smajović, A.; Bečić, F.; Bećirović, L.S.; Pokvić, L.G.; Badnjević, A. Application of artificial intelligence in the analysis of the facial skin health condition. IFAC-PapersOnLine 2022, 55, 31–37. [Google Scholar] [CrossRef]
- Elder, A.; Ring, C.; Heitmiller, K.; Gabriel, Z.; Saedi, N. The role of artificial intelligence in cosmetic dermatology-Current, upcoming, and future trends. J. Cosmet. Dermatol. 2021, 20, 48–52. [Google Scholar] [CrossRef]
- Loreal Paris. Virtual Try On. 2022. Available online: https://www.loreal-paris.co.uk/virtual-try-on (accessed on 26 November 2022).
- Neutrogena. Neutrogena Skin 360. 2022. Available online: https://www.neutrogena.com/skin360app.html (accessed on 27 November 2022).
- Canfield. VISIA Skin Analysis|Canfield Scientific. Available online: https://www.canfieldsci.com/imaging-systems/visia-complexion-analysis/ (accessed on 27 November 2022).
- Fawkes, N.; Tselenti, E.; Shah, N.; Lappin, V.; Smith, N.; Narasimhan, A.; Smith, A.B. A Survey to Identify Determinants That Influence Self-Perceived Sensitive Skin in a British Population: Clues to Developing a Reliable Screening Tool for Sensitive Skin. Clin. Cosmet. Investig. Dermatol. 2021, 14, 1201–1210. [Google Scholar] [CrossRef]
- Issachar, N.; Gall, Y.; Borell, M.T.; Poelman, M.C. pH measurements during lactic acid stinging test in normal and sensitive skin. Contact Dermat. 1997, 36, 152–155. [Google Scholar] [CrossRef]
- Jeong, S.; Lee, S.H.; Park, B.D.; Wu, Y.; Man, G.; Man, M.Q. Comparison of the Efficacy of Atopalm® Multi-Lamellar Emulsion Cream and Physiogel® Intensive Cream in Improving Epidermal Permeability Barrier in Sensitive Skin. Dermatol. Ther. 2016, 6, 47–56. [Google Scholar] [CrossRef] [Green Version]
- Laquieze, S.; Czernielewski, J.; Baltas, E. Beneficial use of Cetaphil moisturizing cream as part of a daily skin care regimen for individuals with rosacea. J. Dermatol. Treat. 2007, 18, 158–162. [Google Scholar] [CrossRef]
- Lim, H.S.; Lee, S.C.; Won, Y.H.; Lee, J.B. The efficacy of intense pulsed light for treating erythematotelangiectatic rosacea is related to severity and age. Ann. Dermatol. 2014, 26, 491–495. [Google Scholar] [CrossRef] [Green Version]
- Yatagai, T.; Shimauchi, T.; Yamaguchi, H.; Sakabe, J.I.; Aoshima, M.; Ikeya, S.; Tatsuno, K.; Fujiyama, T.; Ito, T.; Ojima, T.; et al. Sensitive skin is highly frequent in extrinsic atopic dermatitis and correlates with disease severity markers but not necessarily with skin barrier impairment. J. Dermatol. Sci. 2018, 89, 33–39. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ye, C.; Chen, J.; Yang, S.; Yi, J.; Chen, H.; Li, M.; Yin, S.; Lai, W.; Zheng, Y. Skin sensitivity evaluation: What could impact the assessment results? J. Cosmet. Dermatol. 2020, 19, 1231–1238. [Google Scholar] [CrossRef] [PubMed]
- Ständer, S.; Schneider, S.W.; Weishaupt, C.; Luger, T.A.; Misery, L. Putative neuronal mechanisms of sensitive skin. Exp. Dermatol. 2009, 18, 417–423. [Google Scholar] [CrossRef] [PubMed]
- Lee, C.H.; Maibach, H.I. The sodium lauryl sulfate model: An overview. Contact Dermat. 1995, 33, 1–7. [Google Scholar] [CrossRef] [PubMed]
- Ma, Y.F.; Yuan, C.; Jiang, W.C.; Wang, X.L.; Humbert, P. Reflectance confocal microscopy for the evaluation of sensitive skin. Skin Res. Technol. 2017, 23, 227–234. [Google Scholar] [CrossRef] [PubMed]
- Misery, L.; Jean-Decoster, C.; Mery, S.; Georgescu, V.; Sibaud, V. A new ten-item questionnaire for assessing sensitive skin: The Sensitive Scale-10. Acta Derm. Venereol. 2014, 94, 635–639. [Google Scholar] [CrossRef] [Green Version]
- Corazza, M.; Guarneri, F.; Montesi, L.; Toni, G.; Donelli, I.; Borghi, A. Proposal of a self-assessment questionnaire for the diagnosis of sensitive skin. J. Cosmet. Dermatol. 2022, 21, 2488–2496. [Google Scholar] [CrossRef]
- Swiatoniowski, A.; Quillen, E.; Shriver, M.; Jablonski, N. Technical Note: Comparing von Luschan skin color tiles and modern spectrophotometry for measuring human skin pigmentation. Am. J. Phys. Anthropol. 2013, 151, 325–330. [Google Scholar] [CrossRef]
- De Rigal, J.; Abella, M.L.; Giron, F.; Caisey, L.; Lefebvre, M.A. Development and validation of a new Skin Color Chart. Skin Res. Technol. 2007, 13, 101–109. [Google Scholar] [CrossRef]
- Bucak, I.H.; Almis, H.; Benli, S.; Turgut, M. The Assessment of Skin Color and Iron Levels in Pediatric Patients with β-Thalassemia Major Using a Visual Skin Color Chart. Hemoglobin 2017, 41, 120–123. [Google Scholar] [CrossRef]
- Treesirichod, A.; Chaithirayanon, S.; Wongjitrat, N.; Wattanapan, P. The efficacy of topical 0.1% adapalene gel for use in t treatment of childhood acanthosis nigricans: A pilot study. Indian J. Dermatol. 2015, 60, 103. [Google Scholar] [CrossRef] [PubMed]
- Nakashima, Y.; Wada, K.; Yamakawa, M.; Nagata, C. Validity of self-reported skin color by using skin color evaluation scale. Skin Res. Technol. 2022, 28, 827–832. [Google Scholar] [CrossRef] [PubMed]
- Yan, L.; Hu, S.; Alzahrani, A.; Alharbi, S.; Blanos, P. A Multi-Wavelength Opto-Electronic Patch Sensor to Effectively Detect Physiological Changes against Human Skin Types. Biosensors 2017, 7, 22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Treesirichod, A.; Chansakulporn, S.; Wattanapan, P. Correlation between skin color evaluation by skin color scale chart and narrowband reflectance spectrophotometer. Indian J. Dermatol. 2014, 59, 339–342. [Google Scholar] [CrossRef] [PubMed]
Oily | Dry | ||||
---|---|---|---|---|---|
Pigmented | Nonpigmented | Pigmented | Nonpigmented | ||
Wrinkled | OSPW | OSNW | DSPW | DSNW | Sensitive |
Tight | OSPT | OSNT | DSPT | DSNT | Sensitive |
Wrinkled | ORPW | ORNW | DRPW | DRNW | Resistant |
Tight | ORPT | ORNT | DRPT | DRNT | Resistant |
Type I | Pale white skin, always burns, never tans |
Type II | White skin, burns easily, minimal tan |
Type III | Medium white skin, sometimes burns, tans slowly |
Type IV | Moderate brown skin, burns minimally, tans easily |
Type V | Brown skin, rarely burns |
Type VI | Dark brown skin, never burns |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Oliveira, R.; Ferreira, J.; Azevedo, L.F.; Almeida, I.F. An Overview of Methods to Characterize Skin Type: Focus on Visual Rating Scales and Self-Report Instruments. Cosmetics 2023, 10, 14. https://doi.org/10.3390/cosmetics10010014
Oliveira R, Ferreira J, Azevedo LF, Almeida IF. An Overview of Methods to Characterize Skin Type: Focus on Visual Rating Scales and Self-Report Instruments. Cosmetics. 2023; 10(1):14. https://doi.org/10.3390/cosmetics10010014
Chicago/Turabian StyleOliveira, Rita, Joana Ferreira, Luís Filipe Azevedo, and Isabel F. Almeida. 2023. "An Overview of Methods to Characterize Skin Type: Focus on Visual Rating Scales and Self-Report Instruments" Cosmetics 10, no. 1: 14. https://doi.org/10.3390/cosmetics10010014
APA StyleOliveira, R., Ferreira, J., Azevedo, L. F., & Almeida, I. F. (2023). An Overview of Methods to Characterize Skin Type: Focus on Visual Rating Scales and Self-Report Instruments. Cosmetics, 10(1), 14. https://doi.org/10.3390/cosmetics10010014