Scholarlink

Background/objectives <p>To evaluate the effectiveness of deep learning (DL) algorithms in diagnosing Retinopathy of Prematurity (ROP) cases that requires treatment using fundus images submitted to the ROP clinic as part of a telemedicine consultation system.</p> Subjects/methods <p>This retrospective cross-sectional study analysed 1700 RetCam fundus images from 141 preterm infants screened for ROP at Khatam-Al-Anbia Eye Hospital. The images underwent preprocessing using Contrast Limited Adaptive Histogram Equalisation (CLAHE), Automated Multiscale Retinex (AMSR), and a machine learning-based optimisation approach (ML). Various convolutional neural network (CNN) models such as MobileNet, ResNet-18, ResNet-50, and DenseNet-121, were evaluated for their diagnostic performance utilising accuracy, sensitivity, specificity, and F1-score metrics.</p> Results <p>Among the models tested, MobileNet with CLAHE preprocessing achieved the highest accuracy (91.39%) and sensitivity (94.90%), establishing it as the most effective model for ROP detection. DenseNet-121 with CLAHE preprocessing showcased high sensitivity (94.26%) but slightly lower accuracy (90.98%). Additionally, ResNet-50 with AMSR preprocessing also demonstrated high accuracy (90.58%) and sensitivity (91.44%). These findings underscore the feasibility of DL models for real-time ROP screening in telemedicine environments.</p> Conclusion <p>MobileNet with CLAHE preprocessing exhibited the highest diagnostic performance in identifying treatment-requiring ROP, positioning it as a promising tool for AI-assisted screening. Further validation in varied clinical settings is necessary to confirm its real-world applicability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning algorithms for timely diagnosis of retinopathy of prematurity requiring treatment

  • 期刊论文

  • 发表日期：

    2025年11月3日

查看全文
  • Nasser Shoeibi,
  • Nafise Ameri,
  • Mohammad Reza Hoseinkhani,
  • Mohammad Reza Ansari Astaneh,
  • Mehrdad Motamed Shariati,
  • Seyedeh Maryam Hosseini,
  • Mojtaba Abrishami,
  • Majid Abrishami,
  • Ghodsieh Zamani,
  • Hamid Reza Heidarzadeh

摘要

Background/objectives

To evaluate the effectiveness of deep learning (DL) algorithms in diagnosing Retinopathy of Prematurity (ROP) cases that requires treatment using fundus images submitted to the ROP clinic as part of a telemedicine consultation system.

Subjects/methods

This retrospective cross-sectional study analysed 1700 RetCam fundus images from 141 preterm infants screened for ROP at Khatam-Al-Anbia Eye Hospital. The images underwent preprocessing using Contrast Limited Adaptive Histogram Equalisation (CLAHE), Automated Multiscale Retinex (AMSR), and a machine learning-based optimisation approach (ML). Various convolutional neural network (CNN) models such as MobileNet, ResNet-18, ResNet-50, and DenseNet-121, were evaluated for their diagnostic performance utilising accuracy, sensitivity, specificity, and F1-score metrics.

Results

Among the models tested, MobileNet with CLAHE preprocessing achieved the highest accuracy (91.39%) and sensitivity (94.90%), establishing it as the most effective model for ROP detection. DenseNet-121 with CLAHE preprocessing showcased high sensitivity (94.26%) but slightly lower accuracy (90.98%). Additionally, ResNet-50 with AMSR preprocessing also demonstrated high accuracy (90.58%) and sensitivity (91.44%). These findings underscore the feasibility of DL models for real-time ROP screening in telemedicine environments.

Conclusion

MobileNet with CLAHE preprocessing exhibited the highest diagnostic performance in identifying treatment-requiring ROP, positioning it as a promising tool for AI-assisted screening. Further validation in varied clinical settings is necessary to confirm its real-world applicability.