Development and evaluation of a deep learning system for screening real-world multiple abnormal findings based on ultra-widefield fundus images Article Swipe
Haodong Xiao
,
Lie Ju
,
Zupeng Lu
,
Shiguang Zhang
,
Yan Jiang
,
Yuan Yang
,
Xuerui Zhang
,
Wenting Zhang
,
Huanyu Liu
,
Tingyi Liang
,
Jianing Ren
,
Jiawei Yin
,
Xiaoyu Liu
,
Tianjiao Ma
,
Lin Wang
,
Wei Feng
,
Kaimin Song
,
Yuzhong Chen
,
Zongyuan Ge
,
Qian Shao
,
Jie Peng
,
Jili Chen
,
Peiquan Zhao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fmed.2025.1584378
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fmed.2025.1584378
Purpose To develop and evaluate a deep learning system for screening multiple abnormal findings including hemorrhages, drusen, hard exudates, cotton wool spots and retinal breaks using ultra-widefield fundus images. Methods The system consisted of three modules: (I) quality assessment module, (II) artifact removal module and (III) lesion recognition module. In Module III, a heatmap was generated to highlight the lesion area. A total of 4,521 UWF images were used for the training and internal validation of the DL system. The system was evaluated in two external validation datasets consisting of 344 images and 894 images from two other hospitals. The performance of the system in these two datasets was compared with or without Module II. Results In both external validation datasets, the deep learning system made better performance when recognizing lesions on processed images after Module II than on original images without Module II. Module II-enhanced preprocessing improved Module III’s five-lesion recognition performance by an average of 6.73% and 14.4% areas under the curves, 14.47% and 19.62% accuracy in the two external validations. Conclusion Our system showed reliable performance for detecting MAF in real-world UWF images. For deep learning systems to recognize real-world images, the artifact removal module was indeed helpful.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmed.2025.1584378
- https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1584378/pdf
- OA Status
- gold
- Cited By
- 1
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411006173
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411006173Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fmed.2025.1584378Digital Object Identifier
- Title
-
Development and evaluation of a deep learning system for screening real-world multiple abnormal findings based on ultra-widefield fundus imagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-03Full publication date if available
- Authors
-
Haodong Xiao, Lie Ju, Zupeng Lu, Shiguang Zhang, Yan Jiang, Yuan Yang, Xuerui Zhang, Wenting Zhang, Huanyu Liu, Tingyi Liang, Jianing Ren, Jiawei Yin, Xiaoyu Liu, Tianjiao Ma, Lin Wang, Wei Feng, Kaimin Song, Yuzhong Chen, Zongyuan Ge, Qian Shao, Jie Peng, Jili Chen, Peiquan ZhaoList of authors in order
- Landing page
-
https://doi.org/10.3389/fmed.2025.1584378Publisher landing page
- PDF URL
-
https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1584378/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1584378/pdfDirect OA link when available
- Concepts
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Fundus (uterus), Computer science, Artificial intelligence, Deep learning, Optometry, Medicine, Ophthalmology, Computer visionTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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33Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2761604622, https://openalex.org/W3007579635, https://openalex.org/W4211199473, https://openalex.org/W4313622121, https://openalex.org/W2886801379, https://openalex.org/W2748890376, https://openalex.org/W2991326607, https://openalex.org/W3127972858, https://openalex.org/W2898036114, https://openalex.org/W2899508414, https://openalex.org/W2918612085, https://openalex.org/W4382393818, https://openalex.org/W3185043567, https://openalex.org/W4225329856, https://openalex.org/W4313214568, https://openalex.org/W3190176307, https://openalex.org/W4385627246, https://openalex.org/W3108303213, https://openalex.org/W2922217454, https://openalex.org/W3016955851, https://openalex.org/W2892773404, https://openalex.org/W2982083293, https://openalex.org/W4300424419, https://openalex.org/W4289170405, https://openalex.org/W2964121744, https://openalex.org/W6908809, https://openalex.org/W2616247523, https://openalex.org/W4312128942, https://openalex.org/W4294597159, https://openalex.org/W4317685894, https://openalex.org/W6846363418, https://openalex.org/W3126778796, https://openalex.org/W3102564565 |
| referenced_works_count | 33 |
| abstract_inverted_index.A | 61 |
| abstract_inverted_index.a | 5, 52 |
| abstract_inverted_index.DL | 77 |
| abstract_inverted_index.II | 136 |
| abstract_inverted_index.In | 49, 116 |
| abstract_inverted_index.To | 1 |
| abstract_inverted_index.an | 154 |
| abstract_inverted_index.by | 153 |
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| abstract_inverted_index.of | 33, 63, 75, 89, 101, 156 |
| abstract_inverted_index.on | 131, 138 |
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| abstract_inverted_index.to | 56, 190 |
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| abstract_inverted_index.344 | 90 |
| abstract_inverted_index.894 | 93 |
| abstract_inverted_index.For | 186 |
| abstract_inverted_index.II. | 114, 143 |
| abstract_inverted_index.MAF | 181 |
| abstract_inverted_index.Our | 174 |
| abstract_inverted_index.The | 30, 79, 99 |
| abstract_inverted_index.UWF | 65, 184 |
| abstract_inverted_index.and | 3, 22, 44, 72, 92, 158, 165 |
| abstract_inverted_index.for | 9, 69, 179 |
| abstract_inverted_index.the | 58, 70, 76, 102, 121, 162, 169, 194 |
| abstract_inverted_index.two | 84, 96, 106, 170 |
| abstract_inverted_index.was | 54, 81, 108, 198 |
| abstract_inverted_index.(II) | 40 |
| abstract_inverted_index.III, | 51 |
| abstract_inverted_index.both | 117 |
| abstract_inverted_index.deep | 6, 122, 187 |
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| abstract_inverted_index.than | 137 |
| abstract_inverted_index.used | 68 |
| abstract_inverted_index.were | 67 |
| abstract_inverted_index.when | 128 |
| abstract_inverted_index.with | 110 |
| abstract_inverted_index.wool | 20 |
| abstract_inverted_index.(III) | 45 |
| abstract_inverted_index.14.4% | 159 |
| abstract_inverted_index.4,521 | 64 |
| abstract_inverted_index.6.73% | 157 |
| abstract_inverted_index.after | 134 |
| abstract_inverted_index.area. | 60 |
| abstract_inverted_index.areas | 160 |
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| abstract_inverted_index.three | 34 |
| abstract_inverted_index.total | 62 |
| abstract_inverted_index.under | 161 |
| abstract_inverted_index.using | 25 |
| abstract_inverted_index.14.47% | 164 |
| abstract_inverted_index.19.62% | 166 |
| abstract_inverted_index.Module | 50, 113, 135, 142, 144, 148 |
| abstract_inverted_index.better | 126 |
| abstract_inverted_index.breaks | 24 |
| abstract_inverted_index.cotton | 19 |
| abstract_inverted_index.fundus | 27 |
| abstract_inverted_index.images | 66, 91, 94, 133, 140 |
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| abstract_inverted_index.lesion | 46, 59 |
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| abstract_inverted_index.showed | 176 |
| abstract_inverted_index.system | 8, 31, 80, 103, 124, 175 |
| abstract_inverted_index.III’s | 149 |
| abstract_inverted_index.Methods | 29 |
| abstract_inverted_index.Purpose | 0 |
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| abstract_inverted_index.average | 155 |
| abstract_inverted_index.curves, | 163 |
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| abstract_inverted_index.drusen, | 16 |
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| abstract_inverted_index.images, | 193 |
| abstract_inverted_index.images. | 28, 185 |
| abstract_inverted_index.lesions | 130 |
| abstract_inverted_index.module, | 39 |
| abstract_inverted_index.module. | 48 |
| abstract_inverted_index.quality | 37 |
| abstract_inverted_index.removal | 42, 196 |
| abstract_inverted_index.retinal | 23 |
| abstract_inverted_index.system. | 78 |
| abstract_inverted_index.systems | 189 |
| abstract_inverted_index.without | 112, 141 |
| abstract_inverted_index.abnormal | 12 |
| abstract_inverted_index.accuracy | 167 |
| abstract_inverted_index.artifact | 41, 195 |
| abstract_inverted_index.compared | 109 |
| abstract_inverted_index.datasets | 87, 107 |
| abstract_inverted_index.evaluate | 4 |
| abstract_inverted_index.external | 85, 118, 171 |
| abstract_inverted_index.findings | 13 |
| abstract_inverted_index.helpful. | 200 |
| abstract_inverted_index.improved | 147 |
| abstract_inverted_index.internal | 73 |
| abstract_inverted_index.learning | 7, 123, 188 |
| abstract_inverted_index.modules: | 35 |
| abstract_inverted_index.multiple | 11 |
| abstract_inverted_index.original | 139 |
| abstract_inverted_index.reliable | 177 |
| abstract_inverted_index.training | 71 |
| abstract_inverted_index.consisted | 32 |
| abstract_inverted_index.datasets, | 120 |
| abstract_inverted_index.detecting | 180 |
| abstract_inverted_index.evaluated | 82 |
| abstract_inverted_index.exudates, | 18 |
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| abstract_inverted_index.highlight | 57 |
| abstract_inverted_index.including | 14 |
| abstract_inverted_index.processed | 132 |
| abstract_inverted_index.recognize | 191 |
| abstract_inverted_index.screening | 10 |
| abstract_inverted_index.Conclusion | 173 |
| abstract_inverted_index.assessment | 38 |
| abstract_inverted_index.consisting | 88 |
| abstract_inverted_index.hospitals. | 98 |
| abstract_inverted_index.real-world | 183, 192 |
| abstract_inverted_index.validation | 74, 86, 119 |
| abstract_inverted_index.II-enhanced | 145 |
| abstract_inverted_index.five-lesion | 150 |
| abstract_inverted_index.performance | 100, 127, 152, 178 |
| abstract_inverted_index.recognition | 47, 151 |
| abstract_inverted_index.recognizing | 129 |
| abstract_inverted_index.hemorrhages, | 15 |
| abstract_inverted_index.validations. | 172 |
| abstract_inverted_index.preprocessing | 146 |
| abstract_inverted_index.ultra-widefield | 26 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 23 |
| citation_normalized_percentile.value | 0.87705537 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |