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| Natura: | Artículo Open Access |
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Wiley
2025
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| Accesso online: | https://onlinelibrary.wiley.com/doi/10.1111/cod.70011 |
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| _version_ | 1867013759934922752 |
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| author | Seoyoung Kim Hyunsik Hwang Mihyun Oh Jieun Han Sodam Park Soyoung Lee Goun Kim Sungwon Cho Dong Hun Lee Jae Youl Cho |
| author_facet | Seoyoung Kim Hyunsik Hwang Mihyun Oh Jieun Han Sodam Park Soyoung Lee Goun Kim Sungwon Cho Dong Hun Lee Jae Youl Cho Seoyoung Kim Hyunsik Hwang Mihyun Oh Jieun Han Sodam Park Soyoung Lee Goun Kim Sungwon Cho Dong Hun Lee Jae Youl Cho |
| collection | Wiley Open Access |
| contents | Evaluation of Artificial Intelligence‐Assisted Diagnosis of Skin Erythema in a Patch Test Seoyoung Kim Hyunsik Hwang Mihyun Oh Jieun Han Sodam Park Soyoung Lee Goun Kim Sungwon Cho Dong Hun Lee Jae Youl Cho Contact Dermatitis ABSTRACT Background The patch test evaluates skin erythema, infiltration, papules and vesicles following exposure to various substances, including metals, cosmetics and medicines. Accurate evaluation of these conditions requires consistent skin score assessments, precise visual grading and minimal inter‐expert variability. Objectives This study aimed to develop a skin irritation artificial intelligence model based on the YOLOv5x object detection framework to automatically detect skin irritation from the patch test images for multiple test substances. Methods Patch test images were collected with test sites marked to enable the YOLOv5x algorithm to locate the samples. An expert assigned a score to each sample (0–4) for training and validation. The model was trained using 83 629 data points. Evaluation and validation were performed with 1312 and 1536 data points, respectively. Results The model achieved an overall accuracy of 0.983 at both 24 and 48 h, with an F1 score (harmonic mean of recall and precision) of 0.982. The areas under the curve ( AUCs ) for scores 0, 1 and 2 were 0.914, 0.838 and 0.865, respectively. The sensitivity for a score of 0 was 0.997. Conclusion These findings suggest that this AI model effectively supports and classifies skin irritation, thereby facilitating faster and more accurate dermatological evaluations. 10.1111/cod.70011 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| doi_str_mv | 10.1111/cod.70011 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_cod_70011 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Evaluation of Artificial Intelligence‐Assisted Diagnosis of Skin Erythema in a Patch Test Seoyoung Kim Hyunsik Hwang Mihyun Oh Jieun Han Sodam Park Soyoung Lee Goun Kim Sungwon Cho Dong Hun Lee Jae Youl Cho Contact Dermatitis Evaluation of Artificial Intelligence‐Assisted Diagnosis of Skin Erythema in a Patch Test Seoyoung Kim Hyunsik Hwang Mihyun Oh Jieun Han Sodam Park Soyoung Lee Goun Kim Sungwon Cho Dong Hun Lee Jae Youl Cho Contact Dermatitis ABSTRACT Background The patch test evaluates skin erythema, infiltration, papules and vesicles following exposure to various substances, including metals, cosmetics and medicines. Accurate evaluation of these conditions requires consistent skin score assessments, precise visual grading and minimal inter‐expert variability. Objectives This study aimed to develop a skin irritation artificial intelligence model based on the YOLOv5x object detection framework to automatically detect skin irritation from the patch test images for multiple test substances. Methods Patch test images were collected with test sites marked to enable the YOLOv5x algorithm to locate the samples. An expert assigned a score to each sample (0–4) for training and validation. The model was trained using 83 629 data points. Evaluation and validation were performed with 1312 and 1536 data points, respectively. Results The model achieved an overall accuracy of 0.983 at both 24 and 48 h, with an F1 score (harmonic mean of recall and precision) of 0.982. The areas under the curve ( AUCs ) for scores 0, 1 and 2 were 0.914, 0.838 and 0.865, respectively. The sensitivity for a score of 0 was 0.997. Conclusion These findings suggest that this AI model effectively supports and classifies skin irritation, thereby facilitating faster and more accurate dermatological evaluations. 10.1111/cod.70011 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| title | Evaluation of Artificial Intelligence‐Assisted Diagnosis of Skin Erythema in a Patch Test |
| topic | Contact Dermatitis |
| url | https://onlinelibrary.wiley.com/doi/10.1111/cod.70011 |