
- Ophthalmology Times: January 2021
- Volume 46
- Issue 1
The value of new diagnostics and personalized medicine
Emerging technology may facilitate individualized care, disease management.
This article was reviewed by Justis P. Ehlers, MD
There is a great need for precision medicine in
Moreover, Ehlers said it also can enable an individualized therapeutic approach with targeted clinical trial enrollment as more therapeutics become available, the ability to understand treatment failures, and improve economic models.
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Ehlers is the Norman C. and Donna L. Harbert Endowed Chair for Ophthalmic Research at the Cleveland Clinic’s Cole Eye Institute, and director of the Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research at the Cleveland Clinic in Cleveland, Ohio.
While considering these needs, artificial intelligence (AI) often is considered as a solution. While AI could be a great asset to help in decision-making processes, caution must be exercised in the “black box” nature of some the algorithms.
As Ehlers pointed out, AI has the potential to be used nearly everywhere in patient care, “but more specifically, there is great opportunity for use of AI in automated detection of disease; automated disease characterization, such as in severity grading; big data assimilation for risk stratification or diagnostic accuracy; enabling next-generation disease interrogation, creating opportunities not possible previously; enhancing current analysis platforms to improve efficiency and accuracy of workflow; and opportunities in pattern recognition and phenotype classification using pharmacogenomics or imaging genomics to understand underlying diseases.”
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AI needs
In order to train neural networks, he explained, datasets of sufficient sizes and quality are needed composed of either annotated or raw images.
“In understanding AI technology, I think that understanding the ‘thought process’ is important, that is, how is the computer actually evaluating a feature of interest, how is it interpreting a finding, and what happens when things go wrong with AI systems?” Ehlers said.
Al use in
The role of AI in image analysis is being expanded by many research groups. The IDx Platform (Digital Diagnostics) is a companion piece to a standard fundus camera that uses software to detect
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The technology has 87% sensitivity and 90% specificity of identifying more than mild DR and was able to identify all patients with DR of level 43 or higher and therefore at higher risk.
The systemic features that AI can detect based on a very large dataset is the gender of the patient based on the fundus photo. The software may be picking up some information around the fovea to make that determination.
The neural network also can look at the retinal vessels to predict the systolic blood pressure, which was more accurate than the predictions of the diastolic blood pressures.
Practical application
Ehlers described how his laboratory is using multiple technologies in image assessment and higher order image characterization, including AI and radiomics-based feature extraction.
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Ultra-widefield angiography is critical for retinal vascular disease to understand the disease burden.
However, he explained, there is not currently an objective measure for treatment decision-making with antivascular endothelial growth factor therapy for DR that has been established as an optimal biomarker for treatment response.
In his laboratory, the research team have been developing platforms to quantify multiple parameters, including microaneurysm counts, ischemic index, and leakage index in DR.
His group has demonstrated that the leakage seen in ultra-widefield angiography is strongly correlated with the degree of DR, ranging from mild to moderate nonproliferative, severe, and proliferative DR.
When considering the next-generation analysis platforms, one advantage of integrating machine-learning technologies is the integration of multiple models into a single platform that provides enhanced performance or efficiency of assessment.
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In ultra-widefield angiography, examples of multiple model integration for platform development include region of interest detection, blood vessel segmentation, fovea/optic nerve identifier, image feature (eg, ischemia) segmentation, and artifact removal tools (eg, eyelid detector).
Next-generation OCT assessment
Higher-order
One example is hydroxychloroquine (Plaquenil, Sanofi) toxicity, where detecting early subtle damage can be challenging.
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“Utilizing targeted mapping of the ellipsoid zone, this system has been able to generate visualization maps to detect signaling of retinopathy earlier in the disease stage that might have implications for future toxicity screening,” he commented.
Looking ahead
These exudative metrics may provide greater understanding of therapeutic responses, the tempo of fluid resolution, and important features that provide disease insight.
“Computational image analysis, imaging biomarkers, and AI-based platforms are likely to play a major role in eye care in the future,” Ehlers concluded. “Additional research is needed to better understand how to integrate these into practice. Emerging technology related to this field may provide a major inflection point in practice to facilitate individualized care and overall disease management.”
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Justis P. Ehlers, MD
e:[email protected]
Ehlers serves as a consultant for Novartis, Allegro, Allergan, Genentech, Regeneron, Aerpio, Roche, Alcon, Leica, and Zeiss. Ehlers receives research support from Novartis, Allergan, Regeneron, Aerpio, Boehringer-Ingelheim, and Genentech. Ehlers has intellectual property licensed to Leica and multiple patents in the image analysis space.
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