Futuristic machine eye
This story is part of a series on the current progression in Regenerative Medicine. This piece is part of a series dedicated to the eye and improvements in restoring vision.
In 1999, I defined regenerative medicine as the collection of interventions that restore to normal function tissues and organs that have been damaged by disease, injured by trauma, or worn by time. I include a full spectrum of chemical, gene, and protein-based medicines, cell-based therapies, and biomechanical interventions that achieve that goal.
Glaucoma is a leading cause of irreversible blindness and disability worldwide. It is a group of eye conditions that damage the optic nerve, which connects the eye to the brain, leading to vision loss. According to a systematic review and meta-analysis of the global causes of blindness and distance vision impairment by Brien Holden Vision Institute, glaucoma is the second leading cause of blindness globally.
The global prevalence of glaucoma is expected to increase from 76 million people in 2020 to 111.8 million by 2040. This increase in the prevalence of glaucoma will have a significant economic impact on healthcare systems and individual patients. Traditional methods for diagnosing glaucoma and detecting its progression using standard technology are challenging in clinical practice. However, using artificial intelligence (AI) and deep learning (DL) algorithms in healthcare can improve the diagnosis and screening of glaucoma.
How is Artificial Intelligence Used For Diagnosis of Eye Problems?
In their recent article, a team from Nanjing Medical University in China explained and illustrated the process of using artificial intelligence and deep learning-based algorithms to diagnose eye-related disorders and diseases.
A flowchart depicting the process of constructing and evaluating a deep learning model.
Generally speaking, diagnosing glaucoma using AI involves carefully processing various data, like optic disc photos, visual fields, and intraocular pressure. The algorithm eliminates noise, artifacts, and irrelevant information to ensure accurate results while learning distinctive features and patterns associated with glaucoma through training. It is rigorously tested in the validation phase to confirm its effectiveness. If successful, it undergoes further evaluation in the testing phase to ensure reliable diagnoses.
When the algorithm is implemented in clinical practice, clinicians collect data such as optic disc photographs, visual fields, and intraocular pressure readings from patients and preprocess the data before applying the algorithm to diagnose glaucoma.
The Role of Deep Learning in Diagnosing Glaucoma
One of the essential roles of deep learning in glaucoma diagnosis is the ability to screen and differentiate normal eyes from those with early stages of glaucoma. Deep learning models trained with fundus photographs can identify characteristic fundus lesions in glaucoma, including retinal nerve fiber layer abnormalities. This would allow diagnosing glaucoma earlier and potentially decrease the risk of visual impairment.
Furthermore, deep learning algorithms trained with optical coherence tomography (OCT) data can detect microstructural damage due to glaucoma and its progression over time. According to research conducted by Wake Forest School of Medicine in North Carolina, deep learning algorithms are proving to be more accurate than manual or automated segmentation methods in identifying early signs of glaucoma.
Deep learning can also detect glaucoma from fundus image regions outside the optic nerve head (ONH), as seen in a study done by a team in Sydney, Australia. This is highly promising for the broader clinical applicability of deep learning in computer-aided glaucoma screening and follow-up. It can also provide a comprehensive assessment of the retina, allowing clinicians to detect early signs of glaucoma that may have gone unnoticed.
Advantages of Artificial Intelligence and Deep Learning For Diagnosis
Duke Eye Center of Duke University reviewed the advantages of using sophisticated deep learning algorithms for glaucoma diagnosis. They found that these algorithms can deliver diagnoses significantly faster than conventional methods, greatly enhancing efficiency and expediting treatment plans. Additionally, these algorithms' accuracy surpasses traditional methods, enabling early detection and intervention, which can effectively halt disease progression. This improves patient outcomes and reduces healthcare costs associated with late-stage treatments.
Deep learning algorithms have tremendous potential for improving medical care access, especially for underserved populations living in remote areas without access to ophthalmologists. These algorithms allow patients in these areas to receive timely and accurate diagnoses, leading to better healthcare outcomes and reducing healthcare disparities. These algorithms enable eye care services to be accessed equally, regardless of location.
Moreover, healthcare professionals can use deep learning algorithms to minimize diagnostic variability, leading to more reliable and precise assessments. This, in turn, instills greater confidence in the accuracy of medical diagnoses and propels improved patient care.
Challenges to Implementing Deep Learning In Clinical Practice
Despite the promising results, several challenges must be addressed when implementing deep learning algorithms for detecting glaucoma in clinical practice.
One of the primary challenges stems from the need for more standardization in the data sets used to train these algorithms. Since different research studies and healthcare institutions may use varying data collection techniques and formats, it becomes crucial to establish standardized data sets that can be used uniformly to train learning algorithms specifically for diagnosing glaucoma.
In addition to data standardization, another obstacle lies in the accessibility of these algorithms to all healthcare providers. While they have shown great potential in detecting glaucoma, their deployment and usage may not be feasible for all healthcare professionals, especially those serving underserved populations. Hence, there is a need to develop user-friendly interfaces and tools that enable healthcare providers from diverse backgrounds to utilize deep learning algorithms effectively in detecting glaucoma among underserved populations.
Setting Our Sights on the Future
Glaucoma is a significant cause of blindness and disability worldwide. Its prevalence will increase in the coming years, significantly impacting healthcare systems and individual patients. The advances in artificial intelligence and deep learning algorithms in healthcare have shown great potential in improving the accuracy of diagnosing glaucoma and detecting its progression. These algorithms can provide faster and more accurate diagnoses, potentially improving access to care for underserved populations and reducing diagnosis variability.
However, some challenges must be addressed before deep learning algorithms can be widely implemented in clinical practice for detecting glaucoma. These include focusing on data standardization and improving accessibility. By addressing these challenges, we can pave the way for widespread and accurate implementation of deep learning algorithms in clinical practice for the early detection and management of glaucoma.
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