The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging
Abstract The last decades have brought an interest in ultrasound applications in dermatology. Especially in the case of atopic dermatitis, where the formation of a subepidermal low echogenic band (SLEB) may serve as an independent indicator of the effects of treatment, the use of ultrasound is of pa...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-84051-6 |
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| author | Joanna Czajkowska Adriana Polańska Anna Slian Aleksandra Dańczak-Pazdrowska |
| author_facet | Joanna Czajkowska Adriana Polańska Anna Slian Aleksandra Dańczak-Pazdrowska |
| author_sort | Joanna Czajkowska |
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| description | Abstract The last decades have brought an interest in ultrasound applications in dermatology. Especially in the case of atopic dermatitis, where the formation of a subepidermal low echogenic band (SLEB) may serve as an independent indicator of the effects of treatment, the use of ultrasound is of particular interest. This study proposes and evaluates the computer-aided diagnosis method for assessing atopic dermatitis (AD). The fully automated image processing framework combines advanced machine learning techniques for fast, reliable, and repeatable HFUS image analysis, supporting clinical decisions. The proposed methodology comprises accurate SLEB segmentation followed by a classification step. The data set includes 20 MHz images of 80 patients diagnosed with AD according to Hanifin and Rajka criteria, which were evaluated before and after treatment. The ground true labels- clinical evaluation based on Investigator Global Assessment index (IGA score) together with ultrasound skin examination was performed. For reliable analysis, in further experiments, two experts annotated the HFUS images twice in two-week intervals. The analysis aimed to verify whether the fully automated method can classify the HFUS images at the expert level. The Dice coefficient values for segmentation reached 0.908 for SLEB and 0.936 for the entry echo layer. The accuracy of SLEB presence detection results (IGA0) is equal to 98% and slightly outperforms the experts’ assessment, which reaches 96%. The overall accuracy of the AD assessment was equal to 69% (Cohen’s kappa 0.78) and was comparable with the experts’ assessment, ranging between 64% and 70% (Cohen’s kappa 0.73–0.79). The results indicate that the automated method can be applied to AD assessment, and its combination with standard diagnosis may benefit repeatable analysis and a better understanding of the processes that take place within the skin and aid treatment monitoring. |
| format | Article |
| id | doaj-art-19f23705767d433780bc2decd07f59fa |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
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| series | Scientific Reports |
| spelling | doaj-art-19f23705767d433780bc2decd07f59fa2025-01-05T12:22:09ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-024-84051-6The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis stagingJoanna Czajkowska0Adriana Polańska1Anna Slian2Aleksandra Dańczak-Pazdrowska3Faculty of Biomedical Engineering, Silesian University of TechnologyDepartment of Dermatology and Venereology, Poznan University of Medical SciencesFaculty of Biomedical Engineering, Silesian University of TechnologyDepartment of Dermatology, Poznan University of Medical SciencesAbstract The last decades have brought an interest in ultrasound applications in dermatology. Especially in the case of atopic dermatitis, where the formation of a subepidermal low echogenic band (SLEB) may serve as an independent indicator of the effects of treatment, the use of ultrasound is of particular interest. This study proposes and evaluates the computer-aided diagnosis method for assessing atopic dermatitis (AD). The fully automated image processing framework combines advanced machine learning techniques for fast, reliable, and repeatable HFUS image analysis, supporting clinical decisions. The proposed methodology comprises accurate SLEB segmentation followed by a classification step. The data set includes 20 MHz images of 80 patients diagnosed with AD according to Hanifin and Rajka criteria, which were evaluated before and after treatment. The ground true labels- clinical evaluation based on Investigator Global Assessment index (IGA score) together with ultrasound skin examination was performed. For reliable analysis, in further experiments, two experts annotated the HFUS images twice in two-week intervals. The analysis aimed to verify whether the fully automated method can classify the HFUS images at the expert level. The Dice coefficient values for segmentation reached 0.908 for SLEB and 0.936 for the entry echo layer. The accuracy of SLEB presence detection results (IGA0) is equal to 98% and slightly outperforms the experts’ assessment, which reaches 96%. The overall accuracy of the AD assessment was equal to 69% (Cohen’s kappa 0.78) and was comparable with the experts’ assessment, ranging between 64% and 70% (Cohen’s kappa 0.73–0.79). The results indicate that the automated method can be applied to AD assessment, and its combination with standard diagnosis may benefit repeatable analysis and a better understanding of the processes that take place within the skin and aid treatment monitoring.https://doi.org/10.1038/s41598-024-84051-6Atopic dermatitisHigh-frequency ultrasonography20 MHz HFUSComputer-aided diagnosisDeep learning |
| spellingShingle | Joanna Czajkowska Adriana Polańska Anna Slian Aleksandra Dańczak-Pazdrowska The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging Scientific Reports Atopic dermatitis High-frequency ultrasonography 20 MHz HFUS Computer-aided diagnosis Deep learning |
| title | The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging |
| title_full | The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging |
| title_fullStr | The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging |
| title_full_unstemmed | The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging |
| title_short | The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging |
| title_sort | usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging |
| topic | Atopic dermatitis High-frequency ultrasonography 20 MHz HFUS Computer-aided diagnosis Deep learning |
| url | https://doi.org/10.1038/s41598-024-84051-6 |
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