Innovation in retinal image technologies
In recent years, the development of new retinal imaging techniques has led to significant improvements in the diagnosis and treatment of eye disorders. However, such imaging modalities unfortunately are often not sensitive enough to detect even the early stages of complex diseases of the retina. This work investigates the new perspectives in these aspects, associated with the development of retinal imaging technologies. Machine Learning techniques, and advanced optics, will overcome these limitations and provide a more complete understanding of retinal diseases.
Objective:
The goal of this study is to compare the new retinal imaging technologies and approaches with traditional ones for the detection of early retinal diseases such as diabetic retinopathy and age-related macular degeneration. Similarly, the objectives of the study include determining the effect of enhanced image resolution and depth improvement on the accuracy level of diagnostic tests.
Results:
Retinal image analysis of 200 patients demonstrated that greater accuracy of the diagnostic imaging studies was achieved with the new retinal imaging technologies. Additionally, early retinal disease detection improved by 35% using new imaging modalities compared to standard techniques (p < 0.01). Improvement in the resolution of microvasculature and deeper retinal layers was achieved – the false negative rate significantly decreased (p < 0.05). Meanwhile, due to the usage of machine learning integrated into the internal screening program, 40% of the time was saved for the diagnosis of working patients.
Discussion & Conclusion:
The remarkable advances in the imaging of the retina suggest that new approaches must be sought regarding the recognition and treatment of the diseases of the retina at the early stages. The improved resolution, as well as depth in visualization, facilitate better quality images of the retinal structures, hence grounds for a great impact in clinical practice. These developments can change the paradigms of ocular imaging where accuracy and efficiency enhancement will be the focus. As the trend in AI continues toward being more personalized rather than generic, there is a need for further work on improving AI mode analysis.
Keywords: Retinal imaging, precision diagnostics, machine learning,
