{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T18:34:40Z","timestamp":1790793280201,"version":"4.1.0"},"reference-count":20,"publisher":"Mattioli1885","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Introduction: Atopic dermatitis (AD) presents significant diagnostic challenges in darker skin types due to the masking of classical signs like erythema by pigmentation and the lack of diagnostic criteria tailored for this population. The use of machine learning (ML) as a noninvasive diagnostic tool in this context remains underexplored.\u00a0 Objectives: This study aimed to develop and evaluate the accuracy of an ML model in diagnosing AD in darker skin types and distinguishing it from healthy skin.\u00a0\u00a0 Methods: A cross-sectional study using images of AD cases in dark-skinned patients was conducted. An EfficientNet-based convolutional neural network (CNN) model was trained on 1403 labelled images (687 atopic and 716 healthy skin images). Performance metrics including accuracy, precision, recall, and F1 score were assessed. Ethical approval was obtained, and datasets were anonymized for confidentiality. Results: The dataset comprised images from 50 AD patients (age range 1\u201393 years, median 18.0, IQR: 7.0\u201332.0) and 60 healthy volunteers (age range 18\u201350 years, median 27.5, IQR: 23.0\u201333.5), with a slight female predominance in both groups (56.0% and 53.3%, respectively). The model achieved an accuracy of 91.1%, precision of 83.7%, recall of 97.0%, and an F1 score of 89.9%, correctly classifying 103\/106 AD images and 129\/149 healthy skin images. External application programming interfaces (APIs) were integrated into the design to further enhance accuracy and generalizability. Conclusion: This study highlights the potential of ML as a diagnostic tool for AD in darker skin types. Further optimization and large-scale validation could enhance diagnostic accuracy, improving clinical outcomes for populations with darker skin. Iterative refinement and user feedback will ensure its continued efficacy and reliability in clinical practice.<\/jats:p>","DOI":"10.5826\/dpc.1601a5897","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T15:39:33Z","timestamp":1769787573000},"page":"5897","source":"Crossref","is-referenced-by-count":2,"title":["Artificial Intelligence\u2013Assisted Diagnosis of Atopic Eczema in Darker Skin Types: A CNN-Based Study"],"prefix":"10.5826","volume":"16","author":[{"given":"Atinuke","family":"Ajani","sequence":"first","affiliation":[{"name":"Department of Dermatology and Venereology, Obafemi Awolowo University, Ile-Ife, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1289-9266","authenticated-orcid":false,"given":"Ishaya","family":"Gambo","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2130-709X","authenticated-orcid":false,"given":"Oluwasegun","family":"Faniyi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"27750","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"10.5826\/dpc.1601a5897_ref1","doi-asserted-by":"crossref","unstructured":"Fasseeh AN, Elezbawy B, Korra N, et al. Burden of Atopic Dermatitis in Adults and Adolescents: A Systematic Literature Review. Dermatol Ther (Heidelb). 2022;12(12):2653-2668. DOI:10.1007\/s13555-022-00819-6.","DOI":"10.1007\/s13555-022-00819-6"},{"key":"10.5826\/dpc.1601a5897_ref2","doi-asserted-by":"crossref","unstructured":"Chiricozzi A, Maurelli M, Calabrese L, Peris K, Girolomoni G. Overview of Atopic Dermatitis in Different Ethnic Groups. J Clin Med. 2023;12(7):2701. DOI:10.3390\/jcm12072701.","DOI":"10.3390\/jcm12072701"},{"key":"10.5826\/dpc.1601a5897_ref3","doi-asserted-by":"crossref","unstructured":"Premaladha J, Ravichandran KS. Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms. J Med Syst. 2016;40(4):96. DOI:10.1007\/s10916-016-0460-24.","DOI":"10.1007\/s10916-016-0460-2"},{"key":"10.5826\/dpc.1601a5897_ref4","doi-asserted-by":"crossref","unstructured":"Bhadula S, Sharma S, Juyal P, Kulshrestha C. Machine Learning Algorithms based Skin Disease Detection. International Journal of Innovative Technology and Exploring Engineering. 2019;9(2):4044\u20139. DOI: 10.35940\/ijitee.B7686.129219.","DOI":"10.35940\/ijitee.B7686.129219"},{"key":"10.5826\/dpc.1601a5897_ref5","doi-asserted-by":"crossref","unstructured":"Rajeshwari J, Sughasiny M. Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique. AIMS Electronics and Electrical Engineering, 2023, 7(1): 75-99. DOI: 10.3934\/electreng.2023005","DOI":"10.3934\/electreng.2023005"},{"key":"10.5826\/dpc.1601a5897_ref6","doi-asserted-by":"crossref","unstructured":"Mazhar T, Haq I, Ditta A, et al. The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer. Healthcare (Basel). 2023;11(3):415. DOI:10.3390\/healthcare11030415","DOI":"10.3390\/healthcare11030415"},{"key":"10.5826\/dpc.1601a5897_ref7","doi-asserted-by":"crossref","unstructured":"Ahammed M, Mamun M Al, Uddin MS. A machine learning approach for skin disease detection and classification using image segmentation. Healthcare Analytics. 2022;2. DOI:10.1016\/j.health.2022.100122","DOI":"10.1016\/j.health.2022.100122"},{"key":"10.5826\/dpc.1601a5897_ref8","doi-asserted-by":"crossref","unstructured":"Maul L V., Meienberger N, Kaufmann L. Role of artificial intelligence in assessing the extent and progression of dermatoses. Hautarzt. 2020;71(9):677-685. DOI:10.1007\/s00105-020-04657-5","DOI":"10.1007\/s00105-020-04657-5"},{"key":"10.5826\/dpc.1601a5897_ref9","doi-asserted-by":"crossref","unstructured":"Eichenfield LF, Tom WL, Berger TG, et al. Guidelines of care for the management of atopic dermatitis: section 2. Management and treatment of atopic dermatitis with topical therapies. J Am Acad Dermatol. 2014;71(1):116-132. DOI:10.1016\/j.jaad.2014.03.023","DOI":"10.1016\/j.jaad.2014.03.023"},{"key":"10.5826\/dpc.1601a5897_ref10","doi-asserted-by":"crossref","unstructured":"Tschandl P, Rosendahl C, Akay BN, et al. Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks. JAMA Dermatol. 2019;155(1):58-65. DOI:10.1001\/jamadermatol.2018.4378","DOI":"10.1001\/jamadermatol.2018.4378"},{"key":"10.5826\/dpc.1601a5897_ref11","doi-asserted-by":"crossref","unstructured":"Alsaade FW, Aldhyani THH, Al-Adhaileh MH. Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms. Comput Math Methods Med. 2021;2021:9998379. DOI:10.1155\/2021\/9998379","DOI":"10.1155\/2021\/9998379"},{"key":"10.5826\/dpc.1601a5897_ref12","doi-asserted-by":"crossref","unstructured":"Li H, Pan Y, Zhao J, Zhang L. Skin disease diagnosis with deep learning: A review. Neurocomputing. 2021;464. DOI:10.48550\/arXiv.2011.05627","DOI":"10.1016\/j.neucom.2021.08.096"},{"key":"10.5826\/dpc.1601a5897_ref13","doi-asserted-by":"crossref","unstructured":"Wiala A, Ranjan R, Schnidar H, Rappersberger K, Posch C. Automated classification of hidradenitis suppurativa disease severity by convolutional neural network analyses using calibrated clinical images. J Eur Acad Dermatol Venereol. 2024;38(3):576-582. DOI:10.1111\/jdv.19639","DOI":"10.1111\/jdv.19639"},{"key":"10.5826\/dpc.1601a5897_ref14","doi-asserted-by":"crossref","unstructured":"Maulana A, Noviandy TR, Suhendra R, et al. Evaluation of atopic dermatitis severity using artificial intelligence. Narra J. 2023;3(3):e511. DOI:10.52225\/narra.v3i3.511.","DOI":"10.52225\/narra.v3i3.511"},{"key":"10.5826\/dpc.1601a5897_ref15","doi-asserted-by":"crossref","unstructured":"Suaini NHA, Yap GC, Bui DPT, et al. Atopic dermatitis trajectories to age 8 years in the GUSTO cohort. Clin Exp Allergy. 2021;51(9):1195-1206. DOI:10.1111\/cea.13993","DOI":"10.1111\/cea.13993"},{"key":"10.5826\/dpc.1601a5897_ref16","doi-asserted-by":"crossref","unstructured":"Fajar R, Nirmawati RA, Huahua RL, Syailendra Pertiwi EM, Panjaro BL. Machine learning-based differential diagnosis of psoriasis and atopic dermatitis using clinical data and medical images. Journal of Investigative Dermatology. 2023;143(11). DOI:10.1016\/j.jid.2023.09.051","DOI":"10.1016\/j.jid.2023.09.051"},{"key":"10.5826\/dpc.1601a5897_ref17","doi-asserted-by":"crossref","unstructured":"Rasheed A, Umar AI, Shirazi SH, Khan Z, Nawaz S, Shahzad M. Automatic eczema classification in clinical images based on hybrid deep neural network. Comput Biol Med. 2022;147:105807. DOI:10.1016\/j.compbiomed.2022.105807","DOI":"10.1016\/j.compbiomed.2022.105807"},{"key":"10.5826\/dpc.1601a5897_ref18","doi-asserted-by":"crossref","unstructured":"Wu H, Yin H, Chen H, et al. A deep learning, image-based approach for automated diagnosis for inflammatory skin diseases. Ann Transl Med. 2020;8(9):581. DOI:10.21037\/atm.2020.04.39","DOI":"10.21037\/atm.2020.04.39"},{"key":"10.5826\/dpc.1601a5897_ref19","doi-asserted-by":"crossref","unstructured":"Son HM, Jeon W, Kim J, et al. AI-based localization and classification of skin disease with erythema. Sci Rep. 2021;11(1):5350. DOI:10.1038\/s41598-021-84593-z.","DOI":"10.1038\/s41598-021-84593-z"},{"key":"10.5826\/dpc.1601a5897_ref20","doi-asserted-by":"crossref","unstructured":"Guetari R, Ayari H, Sakly H. Computer-aided diagnosis systems: a comparative study of classical machine learning versus deep learning-based approaches. Knowl Inf Syst. DOI:10.1007\/s10115-023-01894-7.","DOI":"10.1007\/s10115-023-01894-7"}],"container-title":["Dermatology Practical &amp; Conceptual"],"original-title":[],"link":[{"URL":"https:\/\/dpcj.org\/index.php\/dpc\/article\/download\/5897\/3731","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dpcj.org\/index.php\/dpc\/article\/download\/5897\/3732","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dpcj.org\/index.php\/dpc\/article\/download\/5897\/3731","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T15:39:37Z","timestamp":1769787577000},"score":1,"resource":{"primary":{"URL":"https:\/\/dpcj.org\/index.php\/dpc\/article\/view\/5897"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,30]]},"references-count":20,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.5826\/dpc.1601a5897","relation":{"has-manifestation":[{"id-type":"other","id":"1-82-5897-g3731","asserted-by":"subject"},{"id-type":"other","id":"1-82-5897-g3732","asserted-by":"subject"}],"is-part-of":[{"id-type":"doi","id":"10.5826\/dpc.1601a%a","asserted-by":"subject"}]},"ISSN":["2160-9381"],"issn-type":[{"value":"2160-9381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,30]]},"article-number":"22"}}