Development and validation of a deep learning image quality feedback system for infant fundus photography
Abstract Retinopathy of prematurity (ROP) is a significant cause of childhood blindness. Many healthcare institutions face a shortage of well-trained ophthalmologists for conducting screenings. Hence, we have developed the Deep Learning Infant Fundus Quality Feedback System (DLIF-QFS) to assess the...
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| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10859-5 |
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| author | Helei Wang Longhui Li Wenjuan Wang Zhiwen Li Tianzi Jian Xueying Yang Boxuan Song Shiqiang Li Fabao Xu Shaopeng Liu Ying Li |
| author_facet | Helei Wang Longhui Li Wenjuan Wang Zhiwen Li Tianzi Jian Xueying Yang Boxuan Song Shiqiang Li Fabao Xu Shaopeng Liu Ying Li |
| author_sort | Helei Wang |
| collection | DOAJ |
| description | Abstract Retinopathy of prematurity (ROP) is a significant cause of childhood blindness. Many healthcare institutions face a shortage of well-trained ophthalmologists for conducting screenings. Hence, we have developed the Deep Learning Infant Fundus Quality Feedback System (DLIF-QFS) to assess the overall quality of infant retinal photographs and detect common operational errors to support ROP screening and diagnosis. Our DLIF-QFS has been developed and rigorously validated using datasets comprising 13,372 images. In terms of overall quality classification, the DLIF-QFS demonstrated remarkable performance. The area under the curve (AUC) values for discriminating poor quality, adequate quality, and excellent quality images in the external validation dataset were 0.802, 0.691, and 0.926, respectively. For most classification tasks related to identifying issues in adequate and poor quality images, the AUC values consistently exceeded 0.8. In expert diagnostic tests, the DLIF-QFS improved accuracy and enhanced consistency. Its capability to identify the causes of poor image quality, enhance image quality and assist clinicians in improving diagnostic efficiency makes it a valuable tool for advancing ROP diagnosis. |
| format | Article |
| id | doaj-art-31b9b92ff45541a79e928fed94199bfa |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-31b9b92ff45541a79e928fed94199bfa2025-08-20T03:05:18ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-10859-5Development and validation of a deep learning image quality feedback system for infant fundus photographyHelei Wang0Longhui Li1Wenjuan Wang2Zhiwen Li3Tianzi Jian4Xueying Yang5Boxuan Song6Shiqiang Li7Fabao Xu8Shaopeng Liu9Ying Li10School of Instrumentation and Optoelectronic Engineering, Beihang UniversityState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen UniversityDepartment of Ophthalmology, Qilu Hospital of Shandong UniversityDepartment of Ophthalmology, Qilu Hospital of Shandong UniversityDepartment of Hematology, Qilu Hospital, Shandong UniversityDepartment of Ophthalmology, Qilu Hospital of Shandong UniversityDepartment of Ophthalmology, Qilu Hospital of Shandong UniversityDepartment of Ophthalmology, Qilu Hospital of Shandong UniversityDepartment of Ophthalmology, Qilu Hospital of Shandong UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversityDepartment of Dermatology, Qilu Hospital of Shandong UniversityAbstract Retinopathy of prematurity (ROP) is a significant cause of childhood blindness. Many healthcare institutions face a shortage of well-trained ophthalmologists for conducting screenings. Hence, we have developed the Deep Learning Infant Fundus Quality Feedback System (DLIF-QFS) to assess the overall quality of infant retinal photographs and detect common operational errors to support ROP screening and diagnosis. Our DLIF-QFS has been developed and rigorously validated using datasets comprising 13,372 images. In terms of overall quality classification, the DLIF-QFS demonstrated remarkable performance. The area under the curve (AUC) values for discriminating poor quality, adequate quality, and excellent quality images in the external validation dataset were 0.802, 0.691, and 0.926, respectively. For most classification tasks related to identifying issues in adequate and poor quality images, the AUC values consistently exceeded 0.8. In expert diagnostic tests, the DLIF-QFS improved accuracy and enhanced consistency. Its capability to identify the causes of poor image quality, enhance image quality and assist clinicians in improving diagnostic efficiency makes it a valuable tool for advancing ROP diagnosis.https://doi.org/10.1038/s41598-025-10859-5Infant fundus imageImage qualityCapturing issuesDeep learningSample balanced strategy |
| spellingShingle | Helei Wang Longhui Li Wenjuan Wang Zhiwen Li Tianzi Jian Xueying Yang Boxuan Song Shiqiang Li Fabao Xu Shaopeng Liu Ying Li Development and validation of a deep learning image quality feedback system for infant fundus photography Scientific Reports Infant fundus image Image quality Capturing issues Deep learning Sample balanced strategy |
| title | Development and validation of a deep learning image quality feedback system for infant fundus photography |
| title_full | Development and validation of a deep learning image quality feedback system for infant fundus photography |
| title_fullStr | Development and validation of a deep learning image quality feedback system for infant fundus photography |
| title_full_unstemmed | Development and validation of a deep learning image quality feedback system for infant fundus photography |
| title_short | Development and validation of a deep learning image quality feedback system for infant fundus photography |
| title_sort | development and validation of a deep learning image quality feedback system for infant fundus photography |
| topic | Infant fundus image Image quality Capturing issues Deep learning Sample balanced strategy |
| url | https://doi.org/10.1038/s41598-025-10859-5 |
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