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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...
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Deceptive Tricks in Artificial Intelligence: Adversarial Attacks in Ophthalmology.

Agnieszka M Zbrzezny1,2, Andrzej E Grzybowski3

  • 1Faculty of Mathematics and Computer Science, University of Warmia and Mazury, 10-710 Olsztyn, Poland.

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Artificial intelligence (AI) enhances ophthalmic disease diagnosis but faces adversarial attacks. Developing robust AI requires addressing these security threats to ensure trustworthy medical applications and prevent misdiagnoses.

Keywords:
adversarial attacksartificial intelligenceophthalmology

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Area of Science:

  • Ophthalmic diagnostics
  • Artificial intelligence in medicine
  • Cybersecurity in healthcare

Background:

  • AI systems show high effectiveness in diagnosing various ophthalmic diseases, matching ophthalmologist performance.
  • The safety and trustworthiness of AI in medical applications, particularly in identifying eye diseases, are critical concerns.
  • Adversarial attacks pose an emerging threat to AI diagnostic systems, necessitating focused research.

Purpose of the Study:

  • To review the landscape of adversarial attacks on AI systems used for ophthalmic disease diagnosis.
  • To highlight the need for developing specific defense mechanisms against these attacks in medical imaging.
  • To emphasize the importance of creating algorithms for validating AI computations and explaining their findings.

Main Methods:

  • A literature review was conducted, searching open-access research papers on PubMed and Google.
  • The study references "Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems" by Ma et al. as a foundational resource.
  • The review focused on identifying unique attack strategies and the current lack of specialized algorithms for ophthalmic image types.

Main Results:

  • AI algorithms are effective for diagnosing conditions like cataracts, diabetic retinopathy, and glaucoma.
  • Existing research discusses adversarial attacks, but unique algorithms for ophthalmic image attacks are underdeveloped.
  • Adversarial attacks can lead to inaccurate AI findings and potentially severe consequences in healthcare.

Conclusions:

  • There is a critical need to develop specialized algorithms to defend AI ophthalmic diagnostic systems against adversarial attacks.
  • Building trust in AI for healthcare requires robust validation and explainability of AI model computations.
  • Mitigating adversarial threats is essential for ensuring the safe and reliable deployment of AI in ophthalmology.