Automatic detection of ophthalmic artery stenosis using the adaptive neuro-fuzzy inference system

In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of ophthalmic artery stenosis. The ANFIS was used to detect ophthalmic artery stenosis when two features, resistivity and pulsatility indices, defining changes of ophthalmic arterial Doppler waveforms were used as inputs. The ophthalmic arterial Doppler signals were recorded from 115 subjects, of whom 52 suffered from ophthalmic artery stenosis and the rest were healthy. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of ophthalmic artery stenosis were obtained through analysis of the ANFIS. The performances of the classifiers were evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the ophthalmic artery stenosis.