This May, the 2024 Annual Meeting was held in Seattle, and after taking some time to absorb the whirlwind of activity and insights, three elements stand out:

1. ARVO is back!

The pandemic changed the face of many conferences and meetings, and ARVO was no exception. This year, the annual meeting returned to pre-pandemic levels of attendance and participation, and the energy and enthusiasm were palpable. The number of abstract presentations and scientific research presentations rebounded, and I was very proud of the large showing by my colleagues and fellow vision scientists from Doheny Eye Institute and UCLA; Doheny contributed 67 papers and posters among a total of 155 from UCLA.

In addition to the many new learning opportunities, there were a number of opportunities for forging collaborations and networking. Growing connections within the scientific community is essential to expanding our thinking and considering new methods of achieving outcomes to benefit all.

2. Tech innovations are driving progress.

Artificial intelligence has been a hot topic in almost every industry in recent years. In vision science, we’re seeing many developments in AI that have the potential to enhance assessments and diagnoses, as well as to quantify disease features precisely, something previously thought not possible.

Additionally, incredible gains are being made in genetic science and gene-based therapies. The recent groundbreaking FDA approval for a gene-editing approach to addressing sickle cell anemia, which can affect the eye, opens the door to other patient trials and new gene-based therapeutics.

In parallel with these advances in genetic and molecular therapies, significant progress is happening in high-resolution diagnostic technologies. For example, my colleagues at Doheny and UCLA demonstrated, for the first time, visualization of metabolic processes in individual retinal cells in the living eye.

Exciting innovation in the tech space inspires the ARVO 2025 Annual Meeting, which has the theme of “i3: Imagining Innovation and Intelligence in Vision Science.”

3. We need to communicate.

The closing keynote speech focused on a topic that some don’t readily associate with the scientific community: communication. The vision science community is producing incredible work and results, and while we are adept at sharing technical information with vision science colleagues, we cannot necessarily take the same approach with other audiences.

Communication needs to be tailored to reach various stakeholder groups, from the general public to policymakers to government and philanthropic organizations that hold the keys to funding. Advocacy is one of the pillars of ARVO’s five-year strategic plan, developed in 2023. We can only grow advocacy for our cause if we can clearly communicate its impact and importance.

Start planning for 2025.

ARVO 2025 Annual Meeting will be held in Salt Lake City, Utah, May 4-8, and as the newly appointed president of ARVO, I am already deep into the planning stages. If the 2024 meeting is anything to go by, our 2025 gathering will offer compelling content and unparalleled opportunities for collaboration and engagement. I invite all members of the vision science community to join me!

The post Key Takeaways from the ARVO 2024 Annual Meeting – the Vision Science Community Gathers appeared first on Medika Life.

AI builds on earlier computer vision efforts

Earlier efforts of applying computer vision techniques to analyze images involved creating a hard-coded algorithm containing rules that machines should follow in their search. While this method has worked well in many cases, where it fails is in cases that are outliers or are presented in a manner inconsistent with what models have been told to seek. To capture those types of instances, it would be necessary to revise the algorithm by writing new sets of rules as scenarios arise — an arduous and time-consuming process.

With AI-based approaches, researchers can feed thousands upon thousands of images to a system that will review and sort all the data it comes across, allowing it to detect patterns and to generalize. It is not foolproof, as encountering a not-yet-seen image will require some intervention to confirm whether that image should be added to a database, but this adjustment is relatively simple and allows AI to refine its learning quickly and serve as a more valuable, timely resource with compelling potential for patient care.

Medical AI algorithm offers clarity

Already, one of the first FDA-cleared medical AI algorithms is making a life-changing impact for patients around the world. A collaboration between Doheny Eye Institute and Eyenuk, leveraging NIH-sponsored grants, has resulted in a new screening system to detect diabetic retinopathy, a condition that can result in vision loss and blindness in people with diabetes. Screening is vitally important because 90% of diabetic blindness is preventable with timely detection and treatment.

For the AI screening tool, experts in image reading participated in algorithm development and training by furnishing annotated images of cases ranging from mild, non-proliferative retinopathy to the most severe stage of proliferative retinopathy. With the data input, the algorithm was trained to identify whether patients exhibited retinopathy that warranted a visit to an ophthalmologist to avoid future vision loss.

In the case of diabetic retinopathy, early stages of the condition may go unnoticed, or if the symptoms appear manageable, patients may postpone a visit to their physician. An AI screening tool offers the possibility of garnering greater compliance with recommendations for regular assessment. Capturing an image of the eye at a primary care physician’s office or even on a smartphone at home and sharing it with AI in the cloud for an immediate diagnosis could promote early detection and, hopefully, prevent blindness.

Similar work is being conducted in the area of age-related macular degeneration (AMD). Algorithms are being developed to detect features in patients with AMD to help physicians identify those who may be at high risk of progression to more advanced stages. The information can assist doctors in closer monitoring of at-risk individuals and in developing a treatment course.

Adding another tool to the toolbox

The advent of AI has ushered in countless conversations about the technology’s impact — good and bad — and the medical field has certainly not been immune to the debates. For one, AI is not perfect, and it can and will make mistakes. It’s necessary to consider the output of large language models that have generated hearsay or misinformation, and human experts must approach any output with a healthy dose of skepticism and be unafraid to challenge results.

Additionally, the application of any new technology, like AI, may result in unintended consequences. Should medical AI models become widely available, there is a risk that those without proper credentials may try to use them to make decisions, or to question decisions, which are best left to licensed medical professionals. AI models can provide actionable knowledge, but they will lack the broader context of a patient’s circumstances, values and conditions that their physicians should understand and consider in a holistic treatment plan.

If harnessed with care, AI has the ability to provide medical professionals with another tool to elevate patient care. Each case is unique and deserves a personalized approach, and AI may help doctors optimize their selection of treatment options. Physicians could leverage information furnished by AI, such as from clinical trials, and compare it alongside an individual’s characteristics to arrive at a care approach with the best chance for success.

AI is poised to be instrumental in the decision-making process for physicians, but ultimately, it cannot replace the human element. Medicine is much more than determining a diagnosis, and tough discussions and difficult decisions require empathy that only another human being can provide. With AI as an assistive tool, it opens the door for physicians to access more information and context to aid in making treatment recommendations and ultimately help improve their patients’ outcomes.

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