May 26, 2023 - Diabetic Retinopathy
Let’s say your family members have glaucoma, macular degeneration, diabetic retinopathy, dementia, or cardiovascular disease.
Using the eyes into the body’s window can detect very early changes in our vascular system, brain, and eye health, which could determine how soon your doctor recommends preventative health practices.
There is a new technology called Deep Learning which measures the biological clock (your cellular age) and can measure the health status of biological age. Doctors could offer proactive treatments at an earlier stage of the disease.
Deep learning is a form of artificial intelligence that analyzes retinal images and recognizes patterns using specifically designed to process visual data and has shown remarkable success in pattern recognition tasks, including diagnosing retinal diseases like glaucoma, macular degeneration, diabetic retinopathy, dementia, and cardiovascular disease.
Diagnosing retinal diseases using biological age estimation through deep learning is an emerging area of research. Biological age refers to an individual’s physiological age, which may differ from their chronological age. Deep learning models can analyze retinal images and extract features indicative of an individual’s biological age, providing insights into their overall health and potential risk for retinal diseases.
Several studies have explored the use of deep learning for estimating biological age from retinal images and its association with retinal disease diagnosis. Here are some key research findings in this area:
1. Biological Age Estimation: Deep learning models have estimated an individual’s biological age based on retinal images. These models learn patterns and features from the pictures associated with aging-related changes in the retina. By comparing the estimated biological age with the chronological age, researchers can identify individuals who exhibit accelerated or delayed aging in the retina.
2. Predicting Retinal Disease Risk: Researchers have investigated the relationship between estimated biological age and the risk of retinal diseases. Studies have shown that individuals with higher biological age, indicating accelerated aging in the retina, are more likely to develop retinal diseases such as age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma.
3. Early Detection and Diagnosis: Deep learning models can also be trained to detect specific retinal diseases based on biological age estimation. By analyzing retinal features associated with disease progression, these models can provide early detection and diagnosis of retinal diseases. This early identification allows timely intervention and management, potentially preventing or delaying vision loss.
4. Risk Stratification and Personalized Treatment: Biological age estimation using deep learning can contribute to risk stratification and personalized treatment plans. By assessing an individual’s biological age and disease risk, healthcare professionals can tailor interventions and therapies to target patients at higher risk or with specific disease characteristics.
Here is how deep learning works:
1. Data collection: A large dataset of retinal photographs has been collected by Google Health, typically consisting of images labeled with the corresponding diagnoses or disease stages. The dataset represents a diverse range of eye conditions with age to train the AI deep learning model.
2. Preprocessing: The retinal photographs are preprocessed to enhance image quality and remove artifacts. Preprocessing may include image resizing, normalization, contrast adjustment, and noise reduction.
3. Model training: The AI model is trained using preprocessed retinal photographs. The model is trained to learn and extract relevant features from the images, associating them with specific eye conditions. Training involves feeding the labeled images to the model, which adjusts its internal parameters through backpropagation.
4. Validation and fine-tuning: The trained model is evaluated using a separate validation dataset to measure its performance and make necessary adjustments. Hyperparameter tuning and optimization techniques may be applied to improve the model’s accuracy and generalization capabilities.
5. Testing and deployment: Once the model achieves satisfactory performance on the validation dataset, it can be tested on new, unseen retinal photographs. The model predicts the presence or severity of eye conditions based on the features it has learned. These predictions can then assist healthcare professionals in diagnosis and treatment decisions.
So far, researchers deploying deep learning models in a clinical setting require rigorous validation, verification, and regulatory compliance.
It’s important to note that training a deep learning model for retinal disease diagnosis requires large, labeled datasets with diverse cases. The models must be validated and tested on independent datasets to ensure their accuracy, generalization, and reliability in clinical settings. Collaboration between deep learning experts and domain experts, such as ophthalmologists and optometrists is crucial to ensure the models’ clinical applicability and interpretability.
Deep learning could someday predict the outcome of eye disease, brain, and cardiovascular health and be used as another biomarker to help practitioners care for their patients better. You might be able to snap a picture of your eye, send it to a database, find out the state of your eye-brain-vascular health, and start a proactive protocol that day! Stay tuned.