Multispectral Pattern Recognition Reveals a Diversity of Clinical Signs in Intermediate Age-Related Macular Degeneration.

Purpose To develop a proof-of-concept, computational method for the quantification and classification of fundus images in intermediate age-related macular degeneration (AMD). Methods Multispectral, unsupervised pattern recognition was applied to 184 fundus images from 10 normal and 36 intermediate AMD eyes. The imaging results of preprocessed, grayscale images from three modalities (infrared, green, and fundus autofluorescence scanning laser ophthalmoscopy) were automatically classified into various clusters sharing a common spectral signature, using a k-means clustering algorithm. Class separability was calculated by using transformed divergence (DT). The classification results for large drusen, pigmentary abnormalities, and areas unaffected by AMD were compared against three expert observers for concordance, and to calculate sensitivity and specificity. Results Multispectral, unsupervised pattern recognition successfully identified a finite number of AMD-specific, statistically separable signatures in eyes with intermediate AMD. By using a correct classification criterion of >83% for identical clusters and a total of 1693 expert annotations, the sensitivity and specificity of multispectral pattern recognition for the detection of AMD lesions was 74% and 98%, respectively. Large drusen and pigmentary abnormalities were correctly classified in 75% and 68% of instances, respectively. Conclusions We describe herein a novel approach for the classification of multispectral images in intermediate AMD. Automated classification of intermediate AMD, using multispectral pattern recognition, has moderate sensitivity and high specificity, when compared against clinical experts. The methods described may have a future role in AMD screening or monitoring.