A comparison of artificial neural network and linear statistical models for the detection of glaucoma based on measurements of the optic nerve head collected with a scanning laser ophthalmoscope

• Main Author: Parfitt, Craig Michael
• Language: English
• Published: 2009
• Online Access: http://hdl.handle.net/2429/5845

The scanning laser ophthalmoscope is a device used by ophthalmologists to obtain topographic images of patients' optic nerve heads (ONHs). Measurements are taken from these images that quantitatively describe the shape of the ONH. Glaucoma involves the loss of retinal nerve fibers, which in turn produces a change in the ONH shape. However, it is not known which shape parameters are most relevant for the diagnosis of glaucoma. To solve this problem, a forward stepping linear discriminant function analysis (DFA), and a non-linear feedforward artificial neural network (ANN) were used to build classification models for patient data. Patients were first independently classified using perimetry data (visual fields) into normal and abnormal (glaucomatous) groups. The DFA built a classification function based upon individual entry of variables into the model using the F statistic as the entry criterion at each step. The classification function was validated using a jackknife validation method which removes bias from the model. The ANN was trained with error back-propagation using the conjugate gradient method to update the synaptic weights. The entire data set (45 normals and 44 abnormals) was utilized for cross validation, one normal and one glaucoma each time. The error rate of the training set was required to be less than 20% to ensure valid network convergence. The DFA had a diagnostic precision of 88.8% {86.7% specificity and 90.9% sensitivity} with a jackknife validation of 87.6% {86.7% specificity and 88.6% sensitivity}. The ANN trained with an average training classification rate of 87.8% with a cross validated classification rate of 87.8% {86.7% specificity and 88.8% sensitivity}. Receiver operating characteristic curve analysis showed that both classification models were comparable; ROC area was 0.916 for DFA and 0.879 for ANN. Both the DFA and the ANN classification generalized well, which indicates that the ONH measurements are useful for the detection of glaucoma.