Hybrid Integent Staging of Age-Related Macular Degeneration for Decision-Making on Patient Management Tactics
Source abstract
Abstract
Treatment efficacy for age-related macular degeneration relies on early diagnosis and precise determination of the disease stage. This involves analyzing biomarkers in retinal images, which can be challenging when handling a large flow of patients and can compromise the quality of healthcare services. Clinical decision support systems offer a solution to this issue by employing intelligent algorithms to recognize biomarkers and specify the age-related macular degeneration stage through the analysis of retinal images. However, different stages of age-related macular degeneration may exhibit similar biomarkers, complicating the application of intelligent algorithms. This article presents a hybrid and hierarchical classification method for solving these problems. By leveraging the hybrid structure of the classifier, we can effectively manage issues commonly encountered with medical datasets, such as class imbalance and strong correlations between variables. The modifications to the intelligent algorithm proposed in this work for staging age-related macular degeneration resulted in an increase in average accuracy, sensitivity, and specificity of 20% compared to initial values. The Cohen’s Kappa coefficient, used for consistency estimation between the regression model and expert assessments of the intermediate class severity, was 0.708, indicating a high level of agreement.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Retinal Imaging and Analysis · Retinal Diseases and Treatments · Retinal and Optic Conditions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Photonics Engineering and Research Advances (SPhERA), Ufa University of Science and Technology, 32 Z. Validi Street, 450076 Ufa, Russia
Department of Ophthalmology, Bashkir State Medical University, 3 Lenin Street, 450008 Ufa, Russia
Funding
Ministry of Science and Higher Education of the Russian Federation within the state assignment for UUST · research laboratory “Sensor systems based on integrated photonics devices” of the Eurasian Scientific and Educational Center
References (67)
Show source-provided references
1. The Diagnosis and Treatment of Age-Related Macular Degeneration
2. Antiangiogenic Approaches to Age-Related Macular Degeneration Today10.1016/j.ophtha.2009.06.048 ↗
3. Intravitreal anti-VEGF injections for treating wet age-related macular degeneration: A systematic review and meta-analysis10.2147/DDDT.S86269 ↗
4. Lee, Y., Ahn, E.J., and Hussain, A. (2022). Saponin-Mediated Rejuvenation of Bruch’s Membrane: A New Strategy for Intervention in Dry Age-Related Macular Degeneration (AMD). Recent Adv. New Perspect. Manag. Macular Degener.10.5772/intechopen.96818 ↗
5. Towards the Development of Longer and More Efficacious Therapies for Wet and Dry Age-related Macular Degeneration
6. Current Concepts and Modalities for Monitoring the Fellow Eye in Neovascular Age-Related Macular Degeneration: An Expert Panel Consensus10.1097/IAE.0000000000002768 ↗
7. Clinical Classification of Age-related Macular Degeneration10.1016/j.ophtha.2012.10.036 ↗
8. Treatments for Dry Age-Related Macular Degeneration and Stargardt Disease: A Systematic Review10.3310/hta22270 ↗
9. Early and intermediate age-related macular degeneration: Update and clinical review10.2147/CIA.S142685 ↗
10. Age-related macular degeneration and recent developments: New hope for old eyes?10.1136/pgmj.2006.052944 ↗
11. The role of retinal fluid location in atrophy and fibrosis evolution of patients with neovascular agerelated macular degeneration longterm treated in real world
12. Considerations for the Identification and Management of Geographic Atrophy: Recommendations from an Expert Panel10.2147/OPTH.S445755 ↗
13. Subretinal fibrosis secondary to neovascular age-related macular degeneration: Mechanisms and potential therapeutic targets10.4103/NRR.NRR-D-23-01642 ↗
14. Fabre, M., Mateo, L., Lamaa, D., Baillif, S., Pagès, G., Demange, L., Ronco, C., and Benhida, R. (2022). Recent Advances in Age-Related Macular Degeneration Therapies. Molecules, 27.10.3390/molecules27165089 ↗
15. Lombardo, M., Serrao, S., and Lombardo, G. (2022). Challenges in Age-Related Macular Degeneration: From Risk Factors to Novel Diagnostics and Prevention Strategies. Front. Med., 9.10.3389/fmed.2022.887104 ↗
16. Systematic review of prognostic factors associated with progression to late age-related macular degeneration: Pinnacle study report 210.1016/j.survophthal.2023.10.010 ↗
17. C5 inhibitor avacincaptad pegol treatment for geographic atrophy: A comprehensive review10.1080/1750743X.2024.2368342 ↗
18. Silencing of YAP attenuates pericytemyofibroblast transition and subretinal fibrosis in experimental model of choroidal neovascularization10.1002/cbin.11809 ↗
19. Baseline Visual Acuity at Wet AMD Diagnosis Predicts Long-Term Vision Outcomes: An Analysis of the IRIS Registry10.3928/23258160-20201104-05 ↗
20. Pharmacologic Treatment of Wet Type Age-related Macular Degeneration; Current and Evolving Therapies
21. Flores, R., Carneiro, Â., Tenreiro, S., and Seabra, M.C. (2021). Retinal Progression Biomarkers of Early and Intermediate Age-Related Macular Degeneration. Life, 12.10.3390/life12010036 ↗
22. Biomarkers for the Progression of Intermediate Age-Related Macular Degeneration10.1007/s40123-023-00807-9 ↗
23. Recent Updates on the Diagnosis and Management of Age-Related Macular Degeneration10.1016/j.mayocpiqo.2024.05.003 ↗
24. Sheeba, T.M., Raj, S.A.A., and Anand, M. (2023, January 17). Computational Intelligent Techniques and Analysis on Automated Retinal Layer Segmentation and Detection of Retinal Diseases. Proceedings of the 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.10.1109/ICACCS57279.2023.10112832 ↗
25. Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review10.1007/s40123-023-00775-0 ↗
26. Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks10.1001/jamaophthalmol.2017.3782 ↗
27. Leingang, O., Riedl, S., Mai, J., Reiter, G.S., Faustmann, G., Fuchs, P., Scholl, H.P.N., Sivaprasad, S., Rueckert, D., and Lotery, A. (2023). Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5). Sci. Rep., 13.10.1038/s41598-023-46626-7 ↗
28. Age-related Macular Degeneration detection using deep convolutional neural network10.1016/j.future.2018.05.001 ↗
29. Govindaiah, A., Hussain, M.A., Smith, R.T., and Bhuiyan, A. (2018, January 4–7). Deep convolutional neural network based screening and assessment of age-related macular degeneration from fundus images. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.10.1109/ISBI.2018.8363863 ↗
30. Abd El-Khalek, A.A., Balaha, H.M., Alghamdi, N.S., Ghazal, M., Khalil, A.T., Abo-Elsoud, M.E.A., and El-Baz, A. (2024). A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images. Sci. Rep., 14.10.1038/s41598-024-52131-2 ↗
31. Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging10.1167/iovs.17-21789 ↗
32. Yellapragada, B., Hornhauer, S., Snyder, K., Yu, S., and Yiu, G. (2020). Unsupervised Deep Learning for Grading of Age-Related Macular Degeneration Using Retinal Fundus Images. arXiv.
33. Predicting conversion to wet age-related macular degeneration using deep learning10.1038/s41591-020-0867-7 ↗
34. Quantification of Key Retinal Features in Early and Late Age-Related Macular Degeneration Using Deep Learning10.1016/j.ajo.2020.12.034 ↗
35. Borrelli, E., Serafino, S., Ricardi, F., Coletto, A., Neri, G., Olivieri, C., Ulla, L., Foti, C., Marolo, P., and Toro, M.D. (2024). Deep Learning in Neovascular Age-Related Macular Degeneration. Medicina, 60.10.3390/medicina60060990 ↗
36. Deep Learning–Based Clustering of OCT Images for Biomarker Discovery in Age-Related Macular Degeneration (PINNACLE Study Report 4)10.1016/j.xops.2024.100543 ↗
37. U-Net-Based Segmentation of Current Imaging Biomarkers in OCT-Scans of Patients with Age Related Macular Degeneration
38. A Learning Framework for Medical Image-Based Intelligent Diagnosis from Imbalanced Datasets
39. Prudhomme, C., Schaffert, M., and Ponciano, J.J. (2023, January 26–29). Deep Learning Datasets Challenges for Semantic Segmentation—A Survey. Proceedings of the Lecture Notes in Informatics, Berlin, Germany.
40. Yang, S., Zhou, X., Wang, J., Xie, G., Lv, C., Gao, P., and Lv, B. (2020, January 3–7). Unsupervised Domain Adaptation for Cross-Device OCT Lesion Detection via Learning Adaptive Features. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA.10.1109/ISBI45749.2020.9098380 ↗
41. Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering10.1109/TIP.2020.3011269 ↗
42. Brunekreef, J., Marcus, E., Sheombarsing, R., Sonke, J.J., and Teuwen, J. (2023). Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms. arXiv.10.1109/CVPR52733.2024.00396 ↗
43. Methods of medical image segmentation analysis to improve the effectiveness of diagnosing lung diseases10.13166/jms/191311 ↗
44. A Hybrid Clustering Approach for link prediction in Heterogeneous Information Networks10.1007/s10115-023-01914-6 ↗
45. Mavaie, P., Holder, L., and Skinner, M.K. (2023). Hybrid deep learning approach to improve classification of low-volume high-dimensional data. BMC Bioinform., 24.10.1186/s12859-023-05557-w ↗
46. A hierarchical multiple classifier learning algorithm10.1007/s10044-002-0187-1 ↗
47. A survey of hierarchical classification across different application domains10.1007/s10618-010-0175-9 ↗
48. Hierarchical Feature Selection Based on Label Distribution Learning
49. Rezende, P.M., Xavier, J.S., Ascher, D.B., Fernandes, G.R., and Pires, D.E.V. (2022). Evaluating hierarchical machine learning approaches to classify biological databases. Briefings Bioinform., 23.10.1093/bib/bbac216 ↗
50. Ojo, M.O., and Zahid, A. (2023). Improving Deep Learning Classifiers Performance via Preprocessing and Class Imbalance Approaches in a Plant Disease Detection Pipeline. Agronomy, 13.10.3390/agronomy13030887 ↗
51. Long, Q.Y., Wang, F.Y., Hu, Y., Gao, B., Zhang, C., Ban, B.H., and Tian, X.B. (2024). Development of the Interpretable Typing Prediction Model for Osteosarcoma and Chondrosarcoma Based on Machine Learning and Radiomics: A Multicenter Retrospective Study. Front. Med., 11.10.3389/fmed.2024.1497309 ↗
52. Hierarchical-Instance Contrastive Learning for Minority Detection on Imbalanced Medical Datasets10.1109/TMI.2023.3310716 ↗
53. Zhao, F. (2022, January 20). A review of traditional image segmentation methods. Proceedings of the 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), Chongqing, China.
54. Lindeberg, T. (2009). Scale-Space. Wiley Encyclopedia of Computer Science and Engineering, John Wiley & Sons.10.1002/9780470050118.ecse609 ↗
55. Deep learning models for digital image processing: A review10.1007/s10462-023-10631-z ↗
56. Zeng, S., Zhao, Y., and Li, S. (2022, January 26). Comparison Between the Traditional and Deep Learning Algorithms on Image Matching. Proceedings of the 2022 IEEE/ACIS 22nd International Conference on Computer and Information Science (ICIS), Zhuhai, China.10.1109/ICIS54925.2022.9882428 ↗
57. Bria, A., Marrocco, C., and Tortorella, F. (2020). Addressing class imbalance in deep learning for small lesion detection on medical images. Comput. Biol. Med., 120.10.1016/j.compbiomed.2020.103735 ↗
58. Image Classification With Small Datasets: Overview and Benchmark10.1109/ACCESS.2022.3172939 ↗
59. Diab, M.S., and Rodriguez-Villegas, E. (2022, January 11–15). Performance Evaluation of Embedded Image Classification Models Using Edge Impulse for Application on Medical Images. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Scotland, UK.10.1109/EMBC48229.2022.9871108 ↗
60. Jude Chukwura Obi (2023). A comparative study of several classification metrics and their performances on data. World J. Adv. Eng. Technol. Sci., 8, 308–314.10.30574/wjaets.2023.8.1.0054 ↗
61. Westfall, P.H., Troendle, J.F., and Pennello, G. (2010). Multiple McNemar Tests. Biometrics, 66.10.1111/j.1541-0420.2010.01408.x ↗
62. Alternatives to P Value: Confidence Interval and Effect Size10.4097/kjae.2016.69.6.555 ↗
63. Abdin, A.D., Devenijn, M., Fulga, R., Langenbucher, A., Seitz, B., and Kaymak, H. (2023). Prevalence of Geographic Atrophy in Advanced Age-Related Macular Degeneration (AMD) in Daily Practice. J. Clin. Med., 12.10.3390/jcm12144862 ↗
64. Cheng, A.M.S., Chalam, K.V., Brar, V.S., Yang, D.T.Y., Bhatt, J., Banoub, R.G., and Gupta, S.K. (2023). Recent Advances in Imaging Macular Atrophy for Late-Stage Age-Related Macular Degeneration. Diagnostics, 13.10.20944/preprints202310.0180.v1 ↗
65. Zhu, X., and Ghahramani, Z. (2002). Learning from Labeled and Unlabeled Data with Label Propagation, Carnegie Mellon University. Technical Report CMU-CALD-02-107.
66. Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not10.5194/gmd-15-5481-2022 ↗
67. Pacurari, A.C., Bhattarai, S., Muhammad, A., Avram, C., Mederle, A.O., Rosca, O., Bratosin, F., Bogdan, I., Fericean, R.M., and Biris, M. (2023). Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review. Diagnostics, 13.10.3390/diagnostics13132145 ↗