Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists.
Background: Although lesion counting is an evaluation method that effectively analyzes facial acne severity, its usage is limited because of difficult implementation. Objectives: We aimed to develop and validate an automated algorithm that detects and counts acne lesions by type, and to evaluate its...
| Publicado en: | American Journal of Clinical Dermatology Vol. 24; no. 4; pp. 649 - 660 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164579533&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164579533 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11750561 C4Q jtl: American Journal of Clinical Dermatology issn: 11750561 maglogo: N pubinfo: dt: Jul2023 vid: 24 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164579533 163608388 164579533 164579533 10.1007/s40257-023-00777-5 164579533 ppf: 649 ppct: 11 formats: tig: atl: Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists. aug: au: Kim, Dong Hyo Sun, Sukkyu Cho, Soo Ick Kong, Hyoun-Joong Lee, Ji Won Lee, Jun Hyo Suh, Dae Hun affil: Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea sug: subj: Acne Vulgaris Diagnosis Acne Vulgaris Pathology Severity of Illness Evaluation Algorithms Utilization Automation Dermatologists Acne Vulgaris Classification Human Photography Neural Networks (Computer) Pearson's Correlation Coefficient Descriptive Statistics Predictive Value of Tests Funding Source ab: Background: Although lesion counting is an evaluation method that effectively analyzes facial acne severity, its usage is limited because of difficult implementation. Objectives: We aimed to develop and validate an automated algorithm that detects and counts acne lesions by type, and to evaluate its clinical applicability as an assistance tool through a reader test. Methods: A total of 20,699 lesions (closed and open comedones, papules, nodules/cysts, and pustules) were manually labeled on 1213 facial images of 398 facial acne photography sets (frontal and both lateral views) acquired from 258 patients and used for training and validating algorithms based on a convolutional neural network for classifying five classes of acne lesions or for binary classification into noninflammatory and inflammatory lesions. Results: In the validation dataset, the highest mean average precision was 28.48 for the binary classification algorithm. Pearson's correlation of lesion counts between algorithm and ground-truth was 0.72 (noninflammatory) and 0.90 (inflammatory), respectively. In the reader test, eight readers (100.0%) detected and counted lesions more accurately using the algorithm compared with the reader-alone evaluation. Conclusions: Overall, our algorithm demonstrated clinically applicable performance in detecting and counting facial acne lesions by type and its utility as an assistance tool for evaluating acne severity. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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