Neural network computer program to determine photorefractive keratectomy nomograms.

PURPOSE: To evaluate a commercially available neural network program for calculation of photorefractive keratectomy treatment nomograms.
SETTING: University referral refractive surgery clinic.
METHODS: PRK/LASIK Brain, a commercial neural network computer program, was trained using the demographics, preoperative clinical data, surgical parameters, and 1 year postoperative clinical data of 44 patients treated with a Summit Technology excimer laser using a 5.0 mm optical zone. The neural-network derived nomogram was compared with the standard treatment nomogram for each patient. The relative contribution of age, sex, keratometry, and intraocular pressure (IOP) to the predicted nomograms was also assessed.
RESULTS: Nomograms produced by the neural network were qualitatively similar to the standard nomogram. The sequence of data entry during training affected the network's predictions. Entry ordered by outcome (as opposed to entry by chronological order) yielded a nomogram that was more consistent with the standard nomogram. However, both outcome- and chronologically ordered network-derived nomograms diverged from the standard nomogram in individual patients, including a subset for whom use of the standard nomogram yielded desired refractive results (within 0.25 diopter of emmetropia). Further analysis of the neural-network-derived nomograms revealed marked sensitivity to sex, age, keratometry readings, and IOP.
CONCLUSIONS: Neural networks offer a potential means of individualizing treatment nomograms, to account for patient demographics, preoperative examination, surgeon style, and equipment bias. However, a data set of 44 patients was not sufficient to train the PRK/LASIK Brain network to accurately predict treatment parameters in individual cases in the training set. A larger training set or a different learning algorithm may be required to improve the neural network's performance.