Interpretable Machine Learning-Based Risk Score for Predicting 10-Year Corneal Graft Survival After Penetrating Keratoplasty and Deep Anterior Lamellar Keratoplasty in Asian Eyes.

Journal: Cornea

PMID: 39046776

Abstract

PURPOSE: To predict 10-year graft survival after deep anterior lamellar keratoplasty (DALK) and penetrating keratoplasty (PK) using a machine learning (ML)-based interpretable risk score.

Authors

Clarissa Ng Yin Ling

Singapore National Eye Centre, Singapore.
Feng He

Department of Ophthalmology, Peking Union Medical College Hospital, Dongcheng District, Beijing, China.
Stephanie Lang

Department of Radiation Oncology, University Hospital Zurich, Switzerland.
Charumathi Sabanayagam

Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore.
Ching-Yu Cheng

Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore.
Anshu Arundhati

Singapore National Eye Centre, Singapore.
Jodhbir S Mehta

Singapore National Eye Centre, Singapore, Singapore.
Marcus Ang

Singapore National Eye Centre, Singapore, Singapore.

Keywords

Adult Aged Asian People Corneal Diseases Corneal Transplantation Female Follow-Up Studies Graft Survival Humans Keratoplasty, Penetrating Machine Learning Male Middle Aged Registries Retrospective Studies Risk Assessment Risk Factors Singapore Visual Acuity

External Resources

View on PubMed Access via DOI PubMed (39046776)

Interpretable Machine Learning-Based Risk Score for Predicting 10-Year Corneal Graft Survival After Penetrating Keratoplasty and Deep Anterior Lamellar Keratoplasty in Asian Eyes.

Abstract

Authors

Keywords

External Resources

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