{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:12:36Z","timestamp":1791915156635,"version":"4.3.4"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Acne vulgaris is a common dermatological condition, particularly affecting adolescents during critical developmental stages, which may have lasting psychosocial impacts. Traditional assessments, including global severity grading and lesion counting, are limited by subjectivity and time constraints. Background\/Objectives: This review aims to systematically assess the recent advancements in artificial intelligence (AI) applications for acne diagnosis, lesion segmentation\/counting, and severity grading, highlighting the potential of AI-driven methods to improve objectivity, reproducibility, and clinical efficiency. Methods: A comprehensive literature search was conducted across PubMed, Scopus, arXiv, Embase, and Web of Science for studies published between 1 January 2017 and 1 March 2025. The search strategy incorporated terms related to \u201cacne\u201d and various AI methodologies (e.g., \u201cneural network\u201d, \u201cdeep learning\u201d, \u201cconvolutional neural network\u201d). Two independent reviewers screened 345 articles, with 29 studies ultimately meeting inclusion criteria. Data were extracted on study design, dataset characteristics (including internal and publicly available resources such as ACNE04 and AcneSCU), AI architectures (predominantly CNN-based models), and performance metrics. Results: While AI-driven models demonstrated promising accuracy, as high as 97.6% in controlled settings, the limited availability of large public datasets, the predominance of data from specific ethnic groups, and the lack of extensive external validation underscore critical barriers to clinical implementation. Conclusions: The findings indicate that although AI has the potential to standardize acne assessments, reduce observer variability, and enable self-monitoring via mobile platforms, significant challenges remain in achieving robust, real-world applicability. Future research should prioritize the development of large, diverse, and publicly accessible datasets and undertake prospective clinical validations to ensure equitable and effective dermatological care.<\/jats:p>","DOI":"10.3390\/jpm15060238","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T03:52:28Z","timestamp":1749181948000},"page":"238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Artificial Intelligence in the Assessment and Grading of Acne Vulgaris: A Systematic Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7926-3389","authenticated-orcid":false,"given":"Daniele Omar","family":"Traini","sequence":"first","affiliation":[{"name":"Dermatologia, Dipartimento Universitario di Medicina e Chirurgia Traslazionale, Universit\u00e0 Cattolica del Sacro Cuore, 00168 Rome, Italy"},{"name":"Dermatologia, Dipartimento Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerardo","family":"Palmisano","sequence":"additional","affiliation":[{"name":"Dermatologia, Dipartimento Universitario di Medicina e Chirurgia Traslazionale, Universit\u00e0 Cattolica del Sacro Cuore, 00168 Rome, Italy"},{"name":"Dermatologia, Dipartimento Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Guerriero","sequence":"additional","affiliation":[{"name":"Dermatologia, Dipartimento Universitario di Medicina e Chirurgia Traslazionale, Universit\u00e0 Cattolica del Sacro Cuore, 00168 Rome, Italy"},{"name":"Dermatologia, Dipartimento Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ketty","family":"Peris","sequence":"additional","affiliation":[{"name":"Dermatologia, Dipartimento Universitario di Medicina e Chirurgia Traslazionale, Universit\u00e0 Cattolica del Sacro Cuore, 00168 Rome, Italy"},{"name":"Dermatologia, Dipartimento Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1111\/bjd.20882","article-title":"Magnitude and temporal trend of acne vulgaris burden in 204 countries and territories from 1990 to 2019: An analysis from the Global Burden of Disease Study 2019*","volume":"186","author":"Chen","year":"2021","journal-title":"Br. J. Dermatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1006.e1","DOI":"10.1016\/j.jaad.2023.12.017","article-title":"Guidelines of care for the management of acne vulgaris","volume":"90","author":"Reynolds","year":"2024","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"65","DOI":"10.5021\/ad.23.094","article-title":"A Comprehensive Review of the Acne Grading Scale in 2023","volume":"36","author":"Bae","year":"2024","journal-title":"Ann. Dermatol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s12551-018-0449-9","article-title":"Machine learning: Applications of artificial intelligence to imaging and diagnosis","volume":"11","author":"Nichols","year":"2018","journal-title":"Biophys. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1111\/exd.14022","article-title":"Development and accuracy of an artificial intelligence algorithm for acne grading from smartphone photographs","volume":"28","author":"Khammari","year":"2019","journal-title":"Exp. Dermatol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, J., Du, D., Zhang, J., Liu, W., Wang, J., Wei, X., Xue, L., Li, X., Diao, P., and Zhang, L. (2023). Development and validation of an artificial intelligence-powered acne grading system incorporating lesion identification. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1255704"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"100150","DOI":"10.1016\/j.xjidi.2022.100150","article-title":"Deep Learning in Dermatology: A Systematic Review of Current Approaches, Outcomes, and Limitations","volume":"3","author":"Jeong","year":"2022","journal-title":"JID Innov."},{"key":"ref_8","unstructured":"Van den Akker, O.R., Peters, G.Y., Bakker, C., Carlsson, R., Coles, N.A., Corker, K.S., Feldman, G., Moreau, D., Nordstr\u00f6m, T., and Pickering, J.S. (2025, March 05). Generalized Systematic Review Registration Form. Available online: https:\/\/osf.io\/e8rhd."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wu, X., Wen, N., Liang, J., Lai, Y.-K., She, D., Cheng, M.-M., and Yang, J. (November, January 27). Joint Acne Image Grading and Counting via Label Distribution Learning. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.01074"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5439","DOI":"10.1109\/JBHI.2023.3304727","article-title":"SA-RPN: A Spacial Aware Region Proposal Network for Acne Detection","volume":"27","author":"Zhang","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zein, H., Chantaf, S., Fournier, R., and Nait-Ali, A. (2024). Generative adversarial networks for anonymous acneic face dataset generation. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0297958"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"851","DOI":"10.2147\/CCID.S360450","article-title":"A Deep Learning-Based Facial Acne Classification System","volume":"ume 15","author":"Quattrini","year":"2022","journal-title":"Clin. Cosmet. Investig. Dermatol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shen, X., Zhang, J., Yan, C., and Zhou, H. (2018). An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-24204-6"},{"key":"ref_14","unstructured":"Zhao, T., Zhang, H., and Spoelstra, J. (2019). A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/srt.12794","article-title":"Automated grading of acne vulgaris by deep learning with convolutional neural networks","volume":"26","author":"Lim","year":"2019","journal-title":"Skin Res. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1111\/srt.12865","article-title":"Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images","volume":"26","author":"Santonja","year":"2020","journal-title":"Skin Res. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rashataprucksa, K., Chuangchaichatchavarn, C., Triukose, S., Nitinawarat, S., Pongprutthipan, M., and Piromsopa, K. (2020, January 5\u20137). Acne Detection with Deep Neural Networks. Proceedings of the 2nd International Conference on Image Processing and Machine Vision, 2nd International Conference on Image Processing and Machine Vision, Bangkok, Thailand.","DOI":"10.1145\/3421558.3421566"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1007\/s13555-021-00541-9","article-title":"Construction and Evaluation of a Deep Learning Model for Assessing Acne Vulgaris Using Clinical Images","volume":"11","author":"Yang","year":"2021","journal-title":"Dermatol. Ther."},{"key":"ref_19","first-page":"e423","article-title":"Development and evaluation of an automatic acne lesion detection program using digital image processing","volume":"19","author":"Min","year":"2012","journal-title":"Skin Res. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Ma, T. (2022). Acne Detection by Ensemble Neural Networks. Sensors, 22.","DOI":"10.3390\/s22186828"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, C., Wang, Z., Hounye, A.H., Li, Z., Kong, M., Hou, M., Zhang, J., and Qi, M. (2023). A novel automatic acne detection and severity quantification scheme using deep learning. Biomed. Signal Process. Control., 84.","DOI":"10.1016\/j.bspc.2023.104803"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7614","DOI":"10.1007\/s10489-022-03774-z","article-title":"A cell phone app for facial acne severity assessment","volume":"53","author":"Wang","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Huynh, Q.T., Nguyen, P.H., Le, H.X., Ngo, L.T., Trinh, N.-T., Tran, M.T.-T., Nguyen, H.T., Vu, N.T., Nguyen, A.T., and Suda, K. (2022). Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence. Diagnostics, 12.","DOI":"10.20944\/preprints202206.0384.v1"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, Y., Jiang, J., Ma, Z., Chen, D., Guan, Y., You, H., Cheng, X., Liu, B., and Luo, G. (2022). KIEGLFN: A unified acne grading framework on face images. Comput. Methods Programs Biomed., 221.","DOI":"10.1016\/j.cmpb.2022.106911"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1111\/srt.13166","article-title":"AcneGrader: An ensemble pruning of the deep learning base models to grade acne","volume":"28","author":"Liu","year":"2022","journal-title":"Skin Res. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"143","DOI":"10.3233\/THC-228014","article-title":"Acne detection and severity evaluation with interpretable convolutional neural network models","volume":"30","author":"Wen","year":"2022","journal-title":"Technol. Health Care"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/s40257-023-00777-5","article-title":"Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists","volume":"24","author":"Kim","year":"2023","journal-title":"Am. J. Clin. Dermatol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111305","DOI":"10.1016\/j.knosys.2023.111305","article-title":"Decoupled Sequential Detection Head for accurate acne detection","volume":"284","author":"Wei","year":"2023","journal-title":"Knowledge-Based Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e13492","DOI":"10.1111\/srt.13492","article-title":"Artificial intelligence analysis of over a million Chinese men and women reveals level of dark circle in the facial skin aging process","volume":"29","author":"Li","year":"2023","journal-title":"Skin Res. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"120312","DOI":"10.1016\/j.eswa.2023.120312","article-title":"DED: Diagnostic Evidence Distillation for acne severity grading on face images","volume":"228","author":"Lin","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e13693","DOI":"10.1111\/srt.13693","article-title":"Facial adult female acne in China: An analysis based on artificial intelligence over one million","volume":"30","author":"Li","year":"2024","journal-title":"Skin Res. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kim, S., Yoon, H., and Lee, J. (2024). Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy\u2013Paste. Diagnostics, 14.","DOI":"10.20944\/preprints202404.0591.v1"},{"key":"ref_33","unstructured":"Zhang, J., Zhang, L., Wang, J., Wei, X., Li, J., Jiang, X., and Du, D. (2022). Learning High-quality Proposals for Acne Detection. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Prokhorov, K., and Kalinin, A.A. (2024, January 27\u201330). Improving Acne Image Grading with Label Distribution Smoothing. Proceedings of the 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece.","DOI":"10.1109\/ISBI56570.2024.10635668"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gao, N., Wang, J., Zhao, Z., Chu, X., Lv, B., Han, G., Ni, Y., and Xie, G. (2025). Evaluation of an acne lesion detection and severity grading model for Chinese population in online and offline healthcare scenarios. Sci. Rep., 15.","DOI":"10.1038\/s41598-024-84670-z"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1038\/s41746-023-00914-8","article-title":"Systematic review of deep learning image analyses for the diagnosis and monitoring of skin disease","volume":"6","author":"Choy","year":"2023","journal-title":"npj Digit. Med."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1001\/jamadermatol.2023.5550","article-title":"Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics","volume":"160","author":"Schmitt","year":"2024","journal-title":"JAMA Dermatol."}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/15\/6\/238\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:47:25Z","timestamp":1760032045000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/15\/6\/238"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,6]]},"references-count":37,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["jpm15060238"],"URL":"https:\/\/doi.org\/10.3390\/jpm15060238","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,6]]}}}