Knowledge-based model for medical decision support in choosing biometric formulas and intraocular lenses for cataract
Intraocular lens; Refractive error; Biometric formulas; Cataract; Cataract surgery; Artificial intelligence.
Cataract is the leading cause of blindness in the world, despite being reversible with simple surgical treatment. In recent years, efforts have been made so that cataract surgery, in addition to treating blindness, would also correct pre-existing refractive defects through the choice of intraocular lenses with adequate convergence power. The choice of the power of the new lens is made through biometric formulas that have been developed over the last 50 years. Despite showing good results, there is room for improvement, especially in patients with atypical eyes. In this study, multiple computational models were developed in order to assist the ophthalmologist in preoperative decision-making, through predictive algorithms that assess whether the patient will likely obtain a good result with a given lens and biometric formula.