Abstract
Objective
To develop a computer-aided diagnostic system for retinopathy of prematurity (ROP) disease using retinal vessel morphological features.
Methods
A total of 200 fundus images from 136 preterm infants with stage 1 to 3 ROP were analysed. Two methods were developed to measure vessel tortuosity: the peak-and-valley method and the polynomial curve fitting method. Correlations between temporal artery tortuosity (TAT) and temporal vein tortuosity (TVT) with ROP severity were investigated, and vessel tortuosity relationships with vessel angles (TAA and TVA) and vessel widths (TAW and TVW). A separate dataset from Japan containing 126 images from 97 preterm patients was used for verification.
Results
Both methods identified similar tortuosity in images without ROP and mild ROP cases. However, the polynomial curve fit method demonstrated enhanced tortuosity detection in stages 2 and 3 ROP compared to the peak and valley method. A strong positive correlation was revealed between ROP severity and increased arterial and venous tortuosity (P < 0.0001). A significant negative correlation between TAA and TAT (r = –0.485, P < 0.0001) and TVA and TVT (r = –0.281, P < 0.0001), and a significant positive correlation between TAW and TAT (r = 0.204, P value = 0.0040) were identified. Similar results were found in the test dataset from Japan.
Conclusions
ROP severity was associated with increased retinal tortuosity and retinal vessel width while displaying a decrease in retinal vascular angle. This quantitative analysis of retinal vessels provides crucial insights for advancing ROP diagnosis and understanding its progression.
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Data availability
Data relevant to the study are available upon reasonable request.
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Acknowledgements
This study was supported by the Chang Gung Memorial Hospital Research Grants (CORPG3L0131, CMRPG3M0131–2, and CMRPG3L0151–3) and the Ministry of Science and Technology Research Grant (MOST 109-2314-B-182A-019-MY3). The sponsors had no role in the design or conduct of the study. The authors also thank the Neonatal Intensive Care Unit faculty of Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan. The funder had no role in study conduction and result interpretation. All authors declared no conflict of interest regarding this study.
Funding
This study was supported by the Chang Gung Memorial Hospital Research Grants (CORPG3L0131, CMRPG3M0131–2, and CMRPG3L0151–3) and the Ministry of Science and Technology Research Grant (MOST 109-2314-B-182A-019-MY3). The sponsors had no role in the design or conduct of the study. The authors also thank the Neonatal Intensive Care Unit faculty of Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan. Financial Support: This study was supported by the National Science and Technology Council, Taiwan, under grant MOST111-2221-E-346-002-MY3; the joint projects between the National Taipei University of Technology and the Chang Gung Memorial Hospital under Grant NTUT-CGMH-110-01 and NTUT-CGMH-109-01; Chang Gung Memorial Hospital Research Grants (CORPG3L0131, CMRPG3M0131 ~ 2, and CMRPG3L0151 ~ 3); and the Ministry of Science and Technology Research Grant (MOST 109-2314-B-182A-019-MY3). The sponsors had no role in the design or conduct of the study.
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Y-PH, SV, E-YK, and W-CW: conceptualization, investigation, and writing—review and editing. Y-PH and SV: methodology, formal analysis, and writing—original draft preparation. SV: software. Y-PH, E-YK, and W-CW: validation and supervision. Y-PH and W-CW: resources, project administration, and funding acquisition. E-YK, W-CW, YF, RT: data curation. E-YK, and W-CW: image annotation. All authors have read and agreed to the published version of the manuscript.
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All authors declared no conflict of interest regarding this study. This study was approved by the Institutional Review Board (IRB) of Chang Gung Memorial Hospital, Linkou, Taiwan.
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Huang, YP., Vadloori, S., Kang, E.YC. et al. Computer-aided detection of retinopathy of prematurity severity assessment via vessel tortuosity measurement in preterm infants’ fundus images. Eye 38, 3309–3317 (2024). https://doi.org/10.1038/s41433-024-03285-w
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DOI: https://doi.org/10.1038/s41433-024-03285-w
