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Li J, Du D, Zhang J, Liu W, Wang J, Wei X, Xue L, Li X, Diao P, Zhang L, Jiang X. Development and validation of an artificial intelligence-powered acne grading system incorporating lesion identification. Front Med (Lausanne) 2023; 10:1255704. [PMID: 37869155 PMCID: PMC10587552 DOI: 10.3389/fmed.2023.1255704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 09/12/2023] [Indexed: 10/24/2023] Open
Abstract
Background The management of acne requires the consideration of its severity; however, a universally adopted evaluation system for clinical practice is lacking. Artificial intelligence (AI) evaluation systems hold the promise of enhancing the efficiency and reproducibility of assessments. Artificial intelligence (AI) evaluation systems offer the potential to enhance the efficiency and reproducibility of assessments in this domain. While the identification of skin lesions represents a crucial component of acne evaluation, existing AI systems often overlook lesion identification or fail to integrate it with severity assessment. This study aimed to develop an AI-powered acne grading system and compare its performance with physician image-based scoring. Methods A total of 1,501 acne patients were included in the study, and standardized pictures were obtained using the VISIA system. The initial evaluation involved 40 stratified sampled frontal photos assessed by seven dermatologists. Subsequently, the three doctors with the highest inter-rater agreement annotated the remaining 1,461 images, which served as the dataset for the development of the AI system. The dataset was randomly divided into two groups: 276 images were allocated for training the acne lesion identification platform, and 1,185 images were used to assess the severity of acne. Results The average precision of our model for skin lesion identification was 0.507 and the average recall was 0.775. The AI severity grading system achieved good agreement with the true label (linear weighted kappa = 0.652). After integrating the lesion identification results into the severity assessment with fixed weights and learnable weights, the kappa rose to 0.737 and 0.696, respectively, and the entire evaluation on a Linux workstation with a Tesla K40m GPU took less than 0.1s per picture. Conclusion This study developed a system that detects various types of acne lesions and correlates them well with acne severity grading, and the good accuracy and efficiency make this approach potentially an effective clinical decision support tool.
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Affiliation(s)
- Jiaqi Li
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Clinical Institute of Inflammation and Immunology, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Dan Du
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Clinical Institute of Inflammation and Immunology, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jianwei Zhang
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Wenjie Liu
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Junyou Wang
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Xin Wei
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Li Xue
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Clinical Institute of Inflammation and Immunology, Sichuan University, Chengdu, China
| | - Xiaoxue Li
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Clinical Institute of Inflammation and Immunology, Sichuan University, Chengdu, China
| | - Ping Diao
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Clinical Institute of Inflammation and Immunology, Sichuan University, Chengdu, China
| | - Lei Zhang
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Xian Jiang
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Clinical Institute of Inflammation and Immunology, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
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Lim ZV, Akram F, Ngo CP, Winarto AA, Lee WQ, Liang K, Oon HH, Thng STG, Lee HK. Automated grading of acne vulgaris by deep learning with convolutional neural networks. Skin Res Technol 2019; 26:187-192. [DOI: 10.1111/srt.12794] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Accepted: 09/05/2019] [Indexed: 12/25/2022]
Affiliation(s)
| | - Farhan Akram
- Bioinformatics Institute Agency for Science, Technology and Research (A*STAR) Singapore Singapore
| | - Cuong Phuc Ngo
- Bioinformatics Institute Agency for Science, Technology and Research (A*STAR) Singapore Singapore
- Hwa Chong Institution Singapore Singapore
| | - Amadeus Aristo Winarto
- Bioinformatics Institute Agency for Science, Technology and Research (A*STAR) Singapore Singapore
- Hwa Chong Institution Singapore Singapore
| | - Wei Qing Lee
- School of Computing National University of Singapore Singapore Singapore
| | - Kaicheng Liang
- Bioinformatics Institute Agency for Science, Technology and Research (A*STAR) Singapore Singapore
| | | | - Steven Tien Guan Thng
- National Skin Centre Singapore Singapore
- Skin Research Institute Singapore A*STAR Singapore Singapore
| | - Hwee Kuan Lee
- Bioinformatics Institute Agency for Science, Technology and Research (A*STAR) Singapore Singapore
- School of Computing National University of Singapore Singapore Singapore
- Image and Pervasive Access Lab CNRS Singapore Singapore
- Singapore Eye Research Institute Singapore Singapore
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Degitz K, Ochsendorf F. Akne. J Dtsch Dermatol Ges 2018; 15:709-722. [PMID: 28677193 DOI: 10.1111/ddg.30_13278] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Accepted: 05/26/2017] [Indexed: 01/09/2023]
Abstract
Akne ist eine chronische Erkrankung mit hoher Prävalenz unter Jugendlichen. Pathogenetische Hauptfaktoren (und ihre klinischen Korrelate) sind gesteigerte Talgproduktion (Seborrhoe), follikuläre Hyperkeratose (Komedonen) und perifollikuläre Entzündungsvorgänge (Papulopusteln). Die Krankheit wird von endogenen (Androgene, IGF-1, neuroendokrine Faktoren) und exogenen (Propionibacterium acnes, Diät, mechanische Irritation, Inhaltsstoffe medizinischer oder kosmetischer Externa) Einflüssen moduliert. Akne geht mit zum Teil hoher Morbidität einher und kann bereits bei leichter Ausprägung eine erhebliche Verschlechterung der Lebensqualität bewirken. Zu Therapie stehen wirksame topische und systemische Behandlungsverfahren zur Verfügung. Eine optimale Behandlung erfordert eine stadiengerechtes Management und kontinuierliche ärztliche Begleitung der Patienten über den erforderlichen Behandlungszeitraum.
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Affiliation(s)
- Klaus Degitz
- Dermatologische Gemeinschaftspraxis, München, Klinik und Poliklinik für Dermatologie und Allergologie der Ludwig-Maximilians-Universität München
| | - Falk Ochsendorf
- Klinik für Dermatologie, Venerologie und Allergologie, Universitätsklinikum, Frankfurt am Main
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5
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Zaenglein AL, Pathy AL, Schlosser BJ, Alikhan A, Baldwin HE, Berson DS, Bowe WP, Graber EM, Harper JC, Kang S, Keri JE, Leyden JJ, Reynolds RV, Silverberg NB, Stein Gold LF, Tollefson MM, Weiss JS, Dolan NC, Sagan AA, Stern M, Boyer KM, Bhushan R. Guidelines of care for the management of acne vulgaris. J Am Acad Dermatol 2016; 74:945-73.e33. [PMID: 26897386 DOI: 10.1016/j.jaad.2015.12.037] [Citation(s) in RCA: 714] [Impact Index Per Article: 89.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Accepted: 12/15/2015] [Indexed: 02/06/2023]
Abstract
Acne is one of the most common disorders treated by dermatologists and other health care providers. While it most often affects adolescents, it is not uncommon in adults and can also be seen in children. This evidence-based guideline addresses important clinical questions that arise in its management. Issues from grading of acne to the topical and systemic management of the disease are reviewed. Suggestions on use are provided based on available evidence.
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Affiliation(s)
| | | | | | | | | | - Diane S Berson
- Weill Cornell Medical College, New York, New York; New York Presbyterian Hospital, New York, New York
| | - Whitney P Bowe
- SUNY Down State Medical Center-Brooklyn, New York, New York
| | - Emmy M Graber
- Boston University School of Medicine, Boston, Massachusetts; Boston Medical Center, Boston, Massachusetts
| | | | - Sewon Kang
- Johns Hopkins Medicine, Baltimore, Maryland
| | - Jonette E Keri
- University of Miami Health System, Miami, Florida; Miami VA Hospital, Miami, Florida
| | | | - Rachel V Reynolds
- Harvard Medical Faculty Physicians, Boston, Massachusetts; Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Nanette B Silverberg
- Mount Sinai Health System-Beth Israel, New York, New York; St. Lukes-Roosevelt, New York, New York
| | | | | | | | | | | | | | - Kevin M Boyer
- American Academy of Dermatology, Schaumburg, Illinois
| | - Reva Bhushan
- American Academy of Dermatology, Schaumburg, Illinois.
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