Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

被引：6
作者：
Li Pomi, Federica [1 ]
Papa, Vincenzo [2 ]
Borgia, Francesco [3 ]
Vaccaro, Mario [3 ]
Pioggia, Giovanni [4 ]
Gangemi, Sebastiano [2 ]
机构：
[1] Univ Palermo, Dept Precis Med Med Surg & Crit Care Me Pre C C, I-90127 Palermo, Italy
[2] Univ Messina, Sch Operat Unit Allergy & Clin Immunol, Dept Clin & Expt Med, I-98125 Messina, Italy
[3] Univ Messina, Dept Clin & Expt Med, Sect Dermatol, I-98125 Messina, Italy
[4] Natl Res Council Italy CNR, Inst Biomed Res & Innovat IRIB, I-98164 Messina, Italy
来源：
LIFE-BASEL | 2024年 / 14卷 / 04期
基金：
英国科研创新办公室;
关键词：
artificial intelligence; machine learning; skin; autoimmune disease; inflammation; atopic dermatitis; psoriasis; vitiligo; alopecia areata; hidradenitis suppurativa; NEURAL-NETWORKS; PSORIASIS; DERMATITIS; IMAGES; SEVERITY; CLASSIFICATION; ERYTHEMA;
D O I：
10.3390/life14040516
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.
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页数：17
相关论文
共 121 条
[1]   Accuracy of Algorithms to Identify People with Atopic Dermatitis in Ontario Routinely Collected Health Databases [J].
Abdalla, Mohamed ;
Chen, Branson ;
Santiago, Robin ;
Young, Jacqueline ;
Eder, Lihi ;
Chan, An-Wen ;
Pope, Elena ;
Tu, Karen ;
Jaakkimainen, Liisa ;
Drucker, Aaron M. .
JOURNAL OF INVESTIGATIVE DERMATOLOGY, 2020, 141 (07) :1840-1843
[2]   Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks [J].
Achararit, Paniti ;
Manaspon, Chawan ;
Jongwannasiri, Chavin ;
Phattarataratip, Ekarat ;
Osathanon, Thanaphum ;
Sappayatosok, Kraisorn .
EUROPEAN JOURNAL OF DENTISTRY, 2023, 17 (04) :1275-1282
[3]   Data augmentation in dermatology image recognition using machine learning [J].
Aggarwal, Pushkar .
SKIN RESEARCH AND TECHNOLOGY, 2019, 25 (06) :815-820
[4]  
Ahuja Kripa, 2023, Pediatr Dermatol, V40, P964, DOI 10.1111/pde.15417
[5]  
Alamdari N, 2016, INT CONF ELECTRO INF, P739, DOI 10.1109/EIT.2016.7535331
[6]   Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection [J].
Allegra, Alessandro ;
Tonacci, Alessandro ;
Sciaccotta, Raffaele ;
Genovese, Sara ;
Musolino, Caterina ;
Pioggia, Giovanni ;
Gangemi, Sebastiano .
CANCERS, 2022, 14 (03)
[7]  
Bang CH, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-85489-8
[8]   Identifying Phenotypes of Atopic Dermatitis in a Longitudinal United States Cohort Using Unbiased Statistical Clustering [J].
Berna, Ronald ;
Mitra, Nandita ;
Hoffstad, Ole ;
Wan, Joy ;
Margolis, David J. .
JOURNAL OF INVESTIGATIVE DERMATOLOGY, 2020, 140 (02) :477-479
[9]   Ros-NET: A deep convolutional neural network for automatic identification of rosacea lesions [J].
Binol, Hamidullah ;
Plotner, Alisha ;
Sopkovich, Jennifer ;
Kaffenberger, Benjamin ;
Niazi, Muhammad Khalid Khan ;
Gurcan, Metin N. .
SKIN RESEARCH AND TECHNOLOGY, 2020, 26 (03) :413-421
[10]   Using Neural Networks for Diagnosing in Dermatology [J].
Bobrova, Margarita ;
Taranik, Maksim ;
Kopanitsa, Georgy .
PHEALTH 2019, 2019, 261 :211-216
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