Artificial Intelligence and Infectious Keratitis: Where Are We Now?

Area of Science:

• Ophthalmology
• Medical Diagnostics
• Artificial Intelligence

Background:

• Infectious keratitis (IK) is a major cause of corneal blindness globally, presenting significant diagnostic challenges.
• Current diagnostic methods for IK are often experience-dependent, invasive, costly, and time-consuming.
• Accurate and timely diagnosis is crucial for preventing vision loss in IK cases.

Purpose of the Study:

• To provide a comprehensive review of artificial intelligence (AI) applications in the diagnosis and etiology detection of infectious keratitis.
• To highlight the potential of AI in overcoming limitations of traditional diagnostic methods for IK.
• To discuss the utility of AI in improving IK diagnosis, particularly in underserved regions.

Main Methods:

• Review of recent studies utilizing AI for the diagnosis of anterior segment diseases, specifically infectious keratitis.
• Analysis of AI algorithm performance in accuracy, speed, and etiological detection of IK.
• Evaluation of AI's potential to address challenges in IK diagnosis in resource-limited settings.

Main Results:

• AI algorithms demonstrate high accuracy and speed in diagnosing infectious keratitis and identifying causative pathogens.
• AI can overcome limitations associated with traditional diagnostic techniques like slit-lamp examination and corneal cultures.
• AI-powered diagnostics show promise for improving patient outcomes, especially in remote or low-resource areas.

Conclusions:

• Artificial intelligence presents a significant advancement in the diagnosis and etiological detection of infectious keratitis.
• AI tools can enhance diagnostic capabilities, offering rapid and accurate results for IK.
• The integration of AI in ophthalmology holds substantial potential for managing corneal blindness globally.