Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review.
Howard MaileJi-Peng Olivia LiDaniel GoreMarcello LeucciPádraig J MulhollandScott C HauAnita SzaboIsmail MoghulKonstantinos BalaskasKaoru FujinamiPirro G HysiAlice E DavidsonPetra LiskovaAlison J HardcastleStephen J TuftNikolas PontikosPublished in: JMIR medical informatics (2021)
Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression.
Keyphrases
- machine learning
- systematic review
- artificial intelligence
- big data
- end stage renal disease
- deep learning
- ejection fraction
- chronic kidney disease
- healthcare
- newly diagnosed
- primary care
- clinical practice
- prognostic factors
- randomized controlled trial
- quality improvement
- patient reported outcomes
- loop mediated isothermal amplification
- combination therapy
- label free
- smoking cessation
- real time pcr