Machine learning predicting myopic regression after corneal refractive surgery using preoperative data and fundus photography.
Juntae KimIk Hee RyuJin Kuk KimIn Sik LeeHong Kyu KimEoksoo HanTae Keun YooPublished in: Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie (2022)
Our machine learning algorithm provides an efficient strategy to identify high-risk patients with myopic regression without additional labor, cost, and time. Surgeons might benefit from preoperative risk assessment of myopic regression, patient counseling before surgery, and surgical option decisions.
Keyphrases
- machine learning
- minimally invasive
- coronary artery bypass
- risk assessment
- big data
- artificial intelligence
- patients undergoing
- deep learning
- surgical site infection
- case report
- heavy metals
- quality improvement
- electronic health record
- diabetic retinopathy
- cataract surgery
- optical coherence tomography
- human health
- coronary artery disease
- climate change
- hepatitis c virus
- hiv infected
- acute coronary syndrome
- thoracic surgery