Combining genetic testing with AI and machine learning offers virtually unlimited opportunities in disease detection, management, and treatment – provided you have the right data…
Eric Bernabei | | Quick Read
sponsored by Avellino
There is a misconception in ophthalmology that AI and machine learning automatically make everything better – but that is not necessarily true. Though both are tremendously powerful tools, a company needs access to a significant amount of data for them to be truly effective. The reality is that, though many claim to use AI, few are able to harness the true power of AI and machine learning because of the limited data sets being analyzed. For the technology to reach its full potential, data sets need to grow and analyses must become more complex. As a global leader in precision medicine, gene therapy and molecular diagnostics, Avellino understands this truth all too well. Eric Bernabei, Chief Sales and Marketing Officer, explains how they are harnessing AI for good.
How is AI shaping the landscape of ophthalmology?
AI and machine learning are best applied in areas of massive data sets and associations – both of which require speed and comprehension faster than humans can manage. This makes genetics the most obvious application for AI, as the purpose of precision medicine is to create the most targeted solutions possible. Genetic testing and gene therapy have received a lot of attention of late – and for good reason. They open a myriad of opportunities for early detection, prevention, and treatment modalities. It is these opportunities that led us to the development and launch of AvaGen, the first genetic test to determine keratoconus risk factors and corneal dystrophies.
How is Avellino using these advanced tools to benefit ophthalmologists?
Avellino’s ultimate goal is to provide physicians with enough genetic information on their patients to allow treatment choices that address both symptoms and predispositions or potential changes based on the patient’s unique genetic makeup. To this end and based on an ever-growing data set, we are developing algorithms that search for genetic mutations associated with systemic eye diseases. The gene and variant analyses that AI algorithms can generate should lead to improved patient care, as physicians will be able to combine the results of their own testing with the previously “unseen” genetic makeup of their patients.
How do you see AI evolving in the near future?
What we’re doing right now is just the start. As we develop larger data sets, the more precise we will become in regards to risk scoring and further test development. AI and machine learning give us the ability to go deeper into development and discovery – by applying our proprietary AI-based algorithms to the data sets built via AvaGen, we will be able to bring a much richer understanding of the role that family history and genetics play in the development, and, eventually, progression of different types of keratoconus and other diseases of the eye and beyond. There is tremendous value in that research alone for physicians and patients alike. AI has allowed us to identify some of those other diseases associated with keratoconus, and will be the engine to drive the discovery of new diagnostic tests, as well as the identification of therapies targeting specific disease variants.
Are there any restrictions that must be obeyed for this to happen?
Of course, this work is taking place against a wider backdrop. We see data privacy and protection laws being enacted around the world – including restrictions on the transfer of data between countries. Geographic limitations on the use and transfer of data already exist, and all healthcare companies will have to demonstrate their ability to be careful stewards if they want to be entrusted with – and have access to – data across borders. That access, paired with AI, will be the catalyst to even greater healthcare breakthroughs in the future.