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Insightful Vision: How Artificial Intelligence Transforms Age-Related Macular Degeneration Care

by | Jun 1, 2024 | Manuscripts | 0 comments

Reshma S Suresh, B.Optom

M.Optom Student, The Sankara Nethralaya Academy, Chennai, India

 

Keywords: Artificial intelligence, Age related macular degeneration, Screening

Introduction

Age-related Macular Degeneration (AMD) is a progressive eye condition affecting millions worldwide, particularly the elderly. While Artificial Intelligence (AI) is a broad discipline, machine learning is a specific subfield that utilises computer algorithms capable of “learning” by stimulating human intelligence. Machine learning algorithms have been employed in the medical field since the 1970s and have demonstrably proven their worth in computer-aided diagnosis, screening, and disease prognosis. AI techniques have already been applied to screen and diagnose a range of conditions, including Diabetic retinopathy, AMD, Macular oedema (ME), Glaucoma, Keratoconus, Post-LASIK corneal ectasia, Retinopathy of prematurity (ROP), and cataracts. AI can also be used to predict the prognosis of various ocular conditions. (1)

1. Early detection and diagnosis

Early detection and diagnosis is crucial in managing AMD effectively. AI-powered imaging techniques, such as Optical coherence tomography and fundus photography, offer a promising avenue for early diagnosis. It can detect the subtle changes in the retina indicative of AMD at its nascent stages. By swiftly identifying high-risk individuals, AI facilitates timely interventions, potentially preventing irreversible vision loss. (2)

2. Monitoring progression

AMD is characterised by the gradual deterioration of central vision, making regular monitoring imperative. AI streamlines this process through pattern recognition and deep learning algorithms that allows to track disease progression with precision, altering healthcare providers to deviations from the norm promptly. (3)

3. Personalised treatments

The treatment landscape for AMD includes Anti-vascular endothelial growth factor (Anti-VEGF) injections and photodynamic therapy along with various other modalities. AI driven predicting modelling offers a solution by analysing diverse datasets, including genetic profiles, clinical histories, and treatment responses. (4)

4. Enhancing research efforts

AI not only facilitates clinical practice but also accelerates research endeavours aimed at unravelling the complexities of AMD. By shifting through vast repositories of medical literature and genomic data, AI identifies novel biomarkers and therapeutic targets, driving innovation in AMD research. (5)

Conclusion

The convergence of AI and AMD represents a beacon of hope for millions afflicted by this debilitating condition. AI advancements have the ability to lower healthcare costs while also greatly enhancing patient access to clinical screening and diagnosis of AMD. By harnessing the power of AI, healthcare providers can offer innovative solutions in early detection, personalised treatment planning, remote monitoring, predictive analytics, and patient education. As technology continues to evolve, the future holds even greater promise for advancing the field of AMD management through interdisciplinary collaboration and innovation.

 

References

  1. Armstrong, G. W., & Lorch, A. C. (2020). A(eye): A Review of Current Applications of Artificial Intelligence and Machine Learning in Ophthalmology. International ophthalmology clinics, 60(1), 57–71.
  2. Udayaraju, P., & Jeyanthi, P. (2022). Early diagnosis of age-related macular degeneration (ARMD) using deep learning. In Intelligent Systems and Sustainable Computing: Proceedings of ICISSC 2021(pp. 657-663). Singapore: Springer Nature Singapore.
  3. Dong, L., Yang, Q., Zhang, R. H., & Wei, W. B. (2021). Artificial intelligence for the detection of age-related macular degeneration in color fundus photographs: A systematic review and meta-analysis. EClinicalMedicine, 35, 100875.
  4. Tan, T. E., Wong, T. Y., & Ting, D. S. W. (2021). Artificial intelligence for prediction of anti–VEGF treatment burden in retinal diseases: towards precision medicine. Ophthalmology Retina, 5(7), 601-603.
  5. Bhuiyan, A., Wong, T. Y., Ting, D. S. W., Govindaiah, A., Souied, E. H., & Smith, R. T. (2020). Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD. Translational vision science & technology, 9(2), 25.

 

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