Description

This episode explores the intersection of eye health, technology and artificial intelligence with Professor Andrew Turpin, the Chair of Ophthalmic Data at the Lions Eye Institute jointly appointed at Curtin University. Andrew is a computer scientist with niche skills in eye health, whose research adds a unique perspective to eye care and the advancement of eye care technologies.

Together, we delve into the fusion of eye health and computer science, with an emphasis on AI-driven healthcare solutions.

Key topics include:

• Evolution of Visual Acuity Testing: Challenges and innovations in clinical practice.
• Advancements in Testing Technologies: Potential of virtual reality headsets and handheld OCT devices.
• Perimetry Testing: Improvements needed for efficiency and reducing unnecessary tests, with a focus on real-time eye tracking and head-mounted perimeters.
• Objective Testing Methods: Role of fundus imaging and AI analysis, highlighting deep learning applications and the importance of data quality.
• AI in Clinical Practice: Augmenting clinicians and automating tasks like fundus photography and OCT imaging. We highlight collaborations such as Google DeepMind and Lions Eye Institute's work on detecting diabetic retinopathy in remote communities.
• Data Privacy and Integration: Challenges in data protection, managing personal information, and legal responsibilities of AI technologies.
• Emerging Technologies: The future of AI and retinal imaging, including the use of VR headsets and advancements in both hardware and software.

Here are some of Andrew’s recommendations for interesting insights into AI:

Artificial Intelligence and Human Life: Five Lessons for Radiology from the 737 MAX Disasters: https://pubs.rsna.org/doi/10.1148/ryai.2020190111

Lessons learned from translating AI from development to deployment in healthcare: https://www.nature.com/articles/s41591-023-02293-9

And an interesting diabetic retinopathy project: https://docs.aireadi.org/ because they have created a new license for data use that restricts sharing the data but also includes text about identifying individuals and sharing the data inadvertently. Anyone who is thinking of using/releasing data should look at it.

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