深度学习在非肿瘤性皮肤病中的应用进展

doi:10.35541/cjd.20220660

Abstract

Abstract: 【Abstract】 With the emergence of the era of big data and the development of computer science, the application of deep learning in dermatology has expanded from the differentiation between benign and malignant cutaneous tumors to the assistance in the diagnosis, severity assessment and treatment of various non-neoplastic dermatoses. This review summarizes recent advances in the application of deep learning in the auxiliary diagnosis and treatment of psoriasis, atopic dermatitis/eczema, onychomycosis, acne vulgaris, etc., so as to raise dermatologists′ understanding of this field and promote the development of related research.

Key words: Artificial intelligence, Psoriasis, Dermatitis, atopic, Onychomycosis, Acne vulgaris, Diagnosis, Deep learning, Non-neoplastic dermatoses, Disease severity assessment, Treatment

Wang Yukun, Liu Jie. Application of deep learning in non-neoplastic dermatoses[J]. Chinese Journal of Dermatology,2024,e20220660. doi:10.35541/cjd.20220660

References ［34］

［1］	刘洁, 邹先彪. 实用皮肤镜学［M］. 北京:人民卫生出版社, 2021.
［2］	Liu J, Zou XB. Practical dermoscopy［M］. Singapore: Springer Singapore, 2022.
［3］	Esteva A, Kuprel B, Novoa RA, et al. Dermatologist⁃level classification of skin cancer with deep neural networks［J］. Nature, 2017,542（7639）:115⁃118. doi: 10.1038/nature21056.
［4］	Zhao S, Xie B, Li Y, et al. Smart identification of psoriasis by images using convolutional neural networks: a case study in China［J］. J Eur Acad Dermatol Venereol, 2020,34（3）:518⁃524. doi: 10.1111/jdv.15965.
［5］	Yang Y, Wang J, Xie F, et al. A convolutional neural network trained with dermoscopic images of psoriasis performed on par with 230 dermatologists［J］. Comput Biol Med, 2021,139:104924. doi: 10.1016/j.compbiomed.2021.104924.
［6］	Aijaz SF, Khan SJ, Azim F, et al. Deep learning application for effective classification of different types of psoriasis［J］. J Healthc Eng, 2022,2022:7541583. doi: 10.1155/2022/7541583.
［7］	Meienberger N, Anzengruber F, Amruthalingam L, et al. Observer⁃independent assessment of psoriasis⁃affected area using machine learning［J］. J Eur Acad Dermatol Venereol, 2020,34（6）:1362⁃1368. doi: 10.1111/jdv.16002.
［8］	Pal A, Garain U, Chandra A, et al. Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network［J］. Comput Methods Programs Biomed, 2018,159:59⁃69. doi: 10. 1016/j.cmpb.2018.01.027.
［9］	Schaap MJ, Cardozo NJ, Patel A, et al. Image⁃based automated psoriasis area severity index scoring by convolutional neural networks［J］. J Eur Acad Dermatol Venereol, 2022,36（1）:68⁃75. doi: 10.1111/jdv.17711.
［10］	Okamoto T, Kawai M, Ogawa Y, et al. Artificial intelligence for the automated single⁃shot assessment of psoriasis severity［J］. J Eur Acad Dermatol Venereol, 2022,36（12）:2512⁃2515. doi: 10.1111/jdv.18354.
［11］	Damiani G, Conic R, Pigatto P, et al. Predicting secukinumab fast⁃responder profile in psoriatic patients: advanced application of artificial⁃neural⁃networks （ANNs）［J］. J Drugs Dermatol, 2020,19（12）:1241⁃1246. doi: 10.36849/JDD.2020.5006.
［12］	Guimarães P, Batista A, Zieger M, et al. Artificial intelligence in multiphoton tomography: atopic dermatitis diagnosis［J］. Sci Rep, 2020,10（1）:7968. doi: 10.1038/s41598⁃020⁃64937⁃x.
［13］	Rasheed A, Umar AI, Shirazi SH, et al. Automatic eczema classification in clinical images based on hybrid deep neural network［J］. Comput Biol Med, 2022,147:105807. doi: 10.1016/j.compbiomed.2022.105807.
［14］	Bang CH, Yoon JW, Ryu JY, et al. Automated severity scoring of atopic dermatitis patients by a deep neural network［J］. Sci Rep, 2021,11（1）:6049. doi: 10.1038/s41598⁃021⁃85489⁃8.
［15］	Wang Y, Qin D, Jin L, et al. Caffeoyl malic acid is a potential dual inhibitor targeting TNFα/IL⁃4 evaluated by a combination strategy of network analysis⁃deep learning⁃molecular simulation［J］. Comput Biol Med, 2022,145:105410. doi: 10.1016/j.compbiomed.2022.105410.
［16］	中华医学会皮肤性病学分会特应性皮炎研究中心, 中华医学会皮肤性病学分会儿童学组. 度普利尤单抗治疗特应性皮炎专家共识［J］. 中华皮肤科杂志, 2022,55（6）:465⁃470. doi: 10.35541/cjd.20210885.
［17］	Han SS, Park GH, Lim W, et al. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: automatic construction of onychomycosis datasets by region⁃based convolutional deep neural network［J］. PLoS One, 2018,13（1）:e0191493. doi: 10.1371/journal.pone. 0191493.
［18］	Kim YJ, Han SS, Yang HJ, et al. Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis［J］. PLoS One, 2020,15（6）:e0234334. doi: 10.1371/journal.pone.0234334.
［19］	Decroos F, Springenberg S, Lang T, et al. A deep learning approach for histopathological diagnosis of onychomycosis: not inferior to analogue diagnosis by histopathologists［J］. Acta Derm Venereol, 2021,101（8）:adv00532. doi: 10.2340/00015555⁃3893.
［20］	Zhu X, Zheng B, Cai W, et al. Deep learning⁃based diagnosis models for onychomycosis in dermoscopy［J］. Mycoses, 2022,65（4）:466⁃472. doi: 10.1111/myc.13427.
［21］	Yilmaz A, Göktay F, Varol R, et al. Deep convolutional neural networks for onychomycosis detection using microscopic images with KOH examination［J］. Mycoses, 2022,65（12）:1119⁃1126. doi: 10.1111/myc.13498.
［22］	Shen X, Zhang J, Yan C, et al. An automatic diagnosis method of facial acne vulgaris based on convolutional neural network［J］. Sci Rep, 2018,8（1）:5839. doi: 10.1038/s41598⁃018⁃24204⁃6.
［23］	Lim ZV, Akram F, Ngo CP, et al. Automated grading of acne vulgaris by deep learning with convolutional neural networks［J］. Skin Res Technol, 2020,26（2）:187⁃192. doi: 10.1111/srt.12794.
［24］	Yang Y, Guo L, Wu Q, et al. Construction and evaluation of a deep learning model for assessing acne vulgaris using clinical images［J］. Dermatol Ther （Heidelb）, 2021,11（4）:1239⁃1248. doi: 10.1007/s13555⁃021⁃00541⁃9.
［25］	Seité S, Khammari A, Benzaquen M, et al. Development and accuracy of an artificial intelligence algorithm for acne grading from smartphone photographs［J］. Exp Dermatol, 2019,28（11）:1252⁃1257. doi: 10.1111/exd.14022.
［26］	Wang J, Luo Y, Wang Z, et al. A cell phone app for facial acne severity assessment［J］. Appl Intell （Dordr）, 2023,53（7）:7614⁃7633. doi: 10.1007/s10489⁃022⁃03774⁃z.
［27］	Phan DT, Ta QB, Huynh TC, et al. A smart LED therapy device with an automatic facial acne vulgaris diagnosis based on deep learning and internet of things application［J］. Comput Biol Med, 2021,136:104610. doi: 10.1016/j.compbiomed.2021.104610.
［28］	Binol H, Plotner A, Sopkovich J, et al. Ros⁃NET: a deep convolutional neural network for automatic identification of rosacea lesions［J］. Skin Res Technol, 2020,26（3）:413⁃421. doi: 10.1111/srt.12817.
［29］	Zhao Z, Wu CM, Zhang S, et al. A novel convolutional neural network for the diagnosis and classification of rosacea: usability study［J］. JMIR Med Inform, 2021,9（3）:e23415. doi: 10.2196/23415.
［30］	Wu H, Yin H, Chen H, et al. A deep learning⁃based smartphone platform for cutaneous lupus erythematosus classification assistance: simplifying the diagnosis of complicated diseases［J］. J Am Acad Dermatol, 2021,85（3）:792⁃793. doi: 10.1016/j.jaad. 2021.02.043.
［31］	Yang Y, Ge Y, Guo L, et al. Development and validation of two artificial intelligence models for diagnosing benign, pigmented facial skin lesions［J］. Skin Res Technol, 2021,27（1）:74⁃79. doi: 10.1111/srt.12911.
［32］	Guo L, Yang Y, Ding H, et al. A deep learning⁃based hybrid artificial intelligence model for the detection and severity assessment of vitiligo lesions［J］. Ann Transl Med, 2022,10（10）:590. doi: 10.21037/atm⁃22⁃1738.
［33］	Lee S, Lee JW, Choe SJ, et al. Clinically applicable deep learning framework for measurement of the extent of hair loss in patients with alopecia areata［J］. JAMA Dermatol, 2020,156（9）:1018⁃1020. doi: 10.1001/jamadermatol.2020.2188.
［34］	Gao M, Wang Y, Xu H, et al. Deep learning⁃based trichoscopic image analysis and quantitative model for predicting basic and specific classification in male androgenetic alopecia［J］. Acta Derm Venereol, 2022,102:adv00635. doi: 10.2340/actadv.v101. 564.

[1]	Chen Jiaqi, Zhang Jin, Tang Jun. Relationships between coronavirus disease 2019 and psoriasis [J]. Chinese Journal of Dermatology, 2025, 58(1): 84-88.
[2]	Combination of Traditional and Western Medicine Dermatology, Chinese Society of Dermatology, China Dermatologist Association. Expert consensus on reflectance confocal microscopic features of common non-melanocytic skin tumors （2025 edition） [J]. Chinese Journal of Dermatology, 2025, 58(1): 20-33.
[3]	Chinese Association of Rehabilitation Dermatology, Working Group on Geriatric Dermatology, Combination of Traditional and Western Medicine Dermatology. Expert consensus on the diagnosis, treatment, and rehabilitation of pruritus in the elderly （2025 edition） [J]. Chinese Journal of Dermatology, 2025, 58(1): 1-8.
[4]	Jin Lan, Qiu Yun, Wang Weijia, Kang Xiaojing, Ding Yuan. Clinical efficacy and safety of biological agents in the treatment of moderate-to-severe psoriasis in 124 elderly patients: a retrospective analysis [J]. Chinese Journal of Dermatology, 2025, 58(1): 47-52.
[5]	Wang Bo, Zheng Jie. Considerations in the treatment of elderly patients with psoriasis and atopic dermatitis using biologics and small-molecule drugs [J]. Chinese Journal of Dermatology, 2025, 58(1): 72-75.
[6]	Yang Zijing, Chen Lihong, Ruan Yeping, Wen Wanting, Zhang Jiayi, Wang Hailun, Pan Meng, Zhao Xiaoqing. Efficacy and safety of dupilumab in the treatment of atopic dermatitis in the elderly [J]. Chinese Journal of Dermatology, 2025, 58(1): 65-69.
[7]	Zou Xianbiao, Chen Jinchun, Zeng Yue, Hao Yi. Application of ultrasonography in dermatology: progress and prospects [J]. Chinese Journal of Dermatology, 2024, 57(9): 785-790.
[8]	Zeng Yue, Shao Huihong, Lin Shiwen, Wen Rou, Zou Xianbiao. Application of a wearable teleconsultation device in diagnosis of common skin diseases [J]. Chinese Journal of Dermatology, 2024, 57(9): 797-800.
[9]	He Lan, Ma Ling, Jiang Qian, Chen Liuqing, Chen Hongying. Analysis of dermoscopic and reflectance confocal microscopic characteristics of 57 cases of extragenital lichen sclerosus [J]. Chinese Journal of Dermatology, 2024, 57(9): 791-796.
[10]	Qiao Jiaxi, Xia Ping, Chen Liuqing. Analysis of dermoscopic and reflectance confocal microscopic features of psoriatic lesions before and after treatment with secukinumab [J]. Chinese Journal of Dermatology, 2024, 57(9): 825-829.
[11]	Combination of Traditional and Western Medicine Dermatology, Chinese Society of Dermatology, China Dermatologist Association. Expert consensus on the application of reflectance confocal microscopy in common melanocytic skin tumors（2024） [J]. Chinese Journal of Dermatology, 2024, 57(9): 775-784.
[12]	Lu Qing, Liu Yanqiu, Liu Danping, Zou Yongyi, Yang Bicheng. Variation analysis and prenatal diagnosis in a pedigree with oculocutaneous albinism [J]. Chinese Journal of Dermatology, 2024, 57(9): 842-843.
[13]	Xie yuanyuan, Liu Yuzhen, Zeng Rong, . Application of skin imaging techniques in acne vulgaris [J]. Chinese Journal of Dermatology, 2024, 57(8): 757-760.
[14]	Dou Jinfa, Wang Jianbo, Zhang Shuai, Li Jianguo, Liu Hongwei, Zhang Shoumin. Analysis of changes in disease status and their influencing factors in patients with moderate to severe plaque psoriasis receiving biologic therapy during the coronavirus disease 2019 pandemic: a single-center cross-sectional study [J]. Chinese Journal of Dermatology, 2024, 57(8): 739-742.
[15]	Lin Ziyuan, Pang Tianyi, Wu Jingwen, Jin Hui, . Role of polycyclic aromatic hydrocarbons in the occurrence and development of inflammatory skin diseases [J]. Chinese Journal of Dermatology, 2024, 57(8): 765-769.

Application of deep learning in non-neoplastic dermatoses

PDF

PDF (Mobile)

Knowledge

Abstract

Cite this article

share this article

References ［34］

Related Articles 15

Metrics

Comments

Recommended 10