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Innovative artificial intelligence tools in ophthalmology

 

Seattle, Wash. — Four recent studies presented this week at the Association for Research in Vision and Ophthalmology’s (ARVO) 2024 Annual Meeting in Seattle, Wash. demonstrated the benefits of involving artificial intelligence (AI) in vision research and global health. They showed the advantages of having AI systems to support scientists, clinicians and patients.

Promising new anterior eye imaging tool

A team in France developed a new imaging tool for anterior eye imaging, optical transmission tomography (OTT). OTT was inspired by two scientific approaches, phase-contrast microscopy and asymmetric retroillumination microscopy. Most imaging methods rely on reflection, whereas OTT uses back illumination from the eye’s fundus and a special interference technique to highlight the front part of the eye with great clarity.

This approach provides a new perspective on the cellular organization of the eye. When it was used to study two young and healthy individuals, OTT’s viewing area was 25 times larger than that of the specular and confocal microscopies and three times larger than that of an advanced research system such as curved-field optical coherence tomography (OCT).

Viacheslav Mazlin, PhD, the lead scientist, said “Micro-resolution across the macro-scale in non-contact OTT provides a new look into the cellular changes involved in anterior eye conditions. OTT holds the potential to improve our understanding of dry eye disease and improve outcomes of refractive/cataract surgeries undertaken by millions of people each year.”

  • Abstract title: Optical transmission tomography: Technology and capabilities of a novel anterior eye imaging tool
  • Presentation start/end time: Tuesday, May 7, 8:45am – 9 am PT
  • Location: Room 2, Tahoma Level 2, Seattle Convention Center – Arch Building
  • Presentation number: 2829

Enhancing inherited retinal disease datasets

To understand and diagnose inherited retinal diseases (IRDs), phenotype-genotype recognition is used. This requires looking at both visible signs in the retina, phenotype, and the genetic makeup, genotype, to understand these diseases. Images of the retina are analyzed manually using a variety of techniques to identify these features. This can be a strenuous and time-consuming subjective process, usually only executed by trained specialists in IRDs.

William Woof, PhD, the lead data scientist of the Eye2Gene team from various institutions in the United Kingdom, said, “We developed a set of AI algorithms to automatically identify features within retinal scans of IRD patients with a known gene diagnosis and used them to create a large IRD dataset for research.” A subset of manually segmented data from optical coherence tomography (OCT) and fundus autofluorescence (FAF) scans were used to train the AI models. Then, these models were used to analyze the remaining scans in the entire dataset, providing them with a large database.

They found that using AI was effective in analyzing big datasets of retinal images. Woof noted, “This dataset will help deepen our understanding of the phenotype-genotype patterns in various IRDs and, along with our AI models, lower the barrier to entry for quantifying the potential impact of new treatments." 

  • Abstract title: Creating the world’s largest dataset of segmented Inherited Retinal Disease features by bootstrapping manual annotations with AI
  • Presentation start/end time: Tuesday, May 7, 1:15 – 3pm PT
  • Location: Exhibit/Poster Hall, Seattle Convention Center - Arch Building
  • Posterboard number: B0397

Ensuring equity in AI’s role in diabetic retinopathy detection 

With the increasing prevalence of diabetes, the expenses and workload related to screening for diabetic eye diseases follow suit. Prior studies have demonstrated that automated technology for detecting diabetic eye diseases from eye images can identify images with DR as accurately as human graders. This would offer the potential for substantial workload reduction.

A United Kingdom team led by Alicja Rudnicka, PhD examined eight automated retinal image analysis systems (ARIAS) in 200,000 screening visits, approximately 1.2 million images. They discovered that they performed consistently well for individuals with diabetes in identifying moderate-to-severe diabetic eye disease. This finding suggests that ARIAS could serve as an effective tool for triaging individuals into high-risk groups for human grading and low-risk groups not needing human grading.

Rudnicka expressed that “AI algorithms accurately detect diabetic eye disease, potentially solving a global problem. Medical devices need to have equitable outcomes across sociodemographic groups, particularly by sex, age, level of deprivation and ethnicity. Our independent study measures and describes the algorithmic fairness of multiple commercially available algorithms on real-world screening data…the largest and most diverse study of its kind — thus contributing to public and clinical trust and, we hope, widespread implementation of this transformative technology." This study was funded by the National Institute for Health and Care Research (NIHCR).

  • Abstract title: Evaluation of equity in performance of Artificial Intelligence for diabetic retinopathy (DR) detection
  • Presentation start/end time: Wednesday, May 8, 2:30 – 2:45pm PT
  • Location: Room 4, Skagit Lower Level, Seattle Convention Center – Arch Building
  • Presentation number: 4922

 

Evaluating a novel AI system in managing and screening diabetic retinopathy

The management and regular screening of diabetic retinopathy (DR) performed by primary care physicians (PCPs) continues to be a global health challenge, especially in low-resource areas. Thus, Tien Y. Wong, MD, PhD, FARVO, along with an international team created a new deep learning-large language model system (DeepDR-LLM) to assist PCPs in improving DR screening and diabetes care.

The AI system utilized fundus images and integrated deep learning for image analysis with the capabilities of large language models (LLMs). The LLM module was developed using 371,763 management suggestions from 267,730 patients. A prospective study was performed on 769 patients to determine its impact. Then they examined the “adherence to management recommendations” among patients who received care from PCPs without assistance and those who received care from PCPs supported by DeepDR-LLM. The study showed that newly diagnosed patients under the care of PCPs assisted by DeepDR-LLM expressed better self-management tactics, were more likely to follow DR referral, and showed improvement in both the quality and the level of empathy in management recommendations.  

“We have developed a novel AI system for diabetic retinopathy (DR) screening that combines an automatic assessment of DR status from the fundus image, with a LLM-guided recommendation to individual patients,” said Wong, “removing the need for specialized ophthalmology services for DR screening, particularly in low-income countries.”

  • Abstract title: An Integrated Image-based Deep Learning and Language Models for Diabetic Retinopathy: A Multi-Stage Development, Testing and Prospective Comparative Study
  • Presentation start/end time: Wednesday, May 8, 3:15 – 3:30pm PT
  • Location: Room 4, Skagit Lower Level, Seattle Convention Center – Arch Building
  • Presentation number: 4925

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The Association for Research in Vision and Ophthalmology (ARVO) is the largest eye and vision research organization in the world. Members include approximately 10,000 eye and vision researchers from over 75 countries. ARVO advances research worldwide into understanding the visual system and preventing, treating and curing its disorders. Learn more at ARVO.org.

Media contact:
Jenniffer Scherhaufer, MMC, CAE
1.240.221.2923
media@arvo.org

 

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