Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review.
Sana NiaziMarta Jiménez-GarcíaOliver FindlZisis GatzioufasFarideh DoroodgarMohammad Hasan ShahriariMohammad Ali JavadiPublished in: Diagnostics (Basel, Switzerland) (2023)
The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus.
Keyphrases
- artificial intelligence
- big data
- machine learning
- deep learning
- optical coherence tomography
- neural network
- high resolution
- end stage renal disease
- clinical practice
- wound healing
- chronic kidney disease
- ejection fraction
- newly diagnosed
- cataract surgery
- prognostic factors
- electronic health record
- decision making
- peritoneal dialysis
- photodynamic therapy
- quantum dots
- label free
- fluorescence imaging