Quantitative image analytics for stratified pulmonary medicine

Recently we proposed spatio-pathological stratification of lungs from multiple subjects. This enabled a pulmonary disease landscape to objectively diagnose pathology, track progression and assess pharmacologic response within and across patients. Even though the approach based on unsupervised affinity propagation clustering of a symmetric pairwise dissimilarity metric showed strong statistical and clinical correlation, it did not address the possibility of candidates being potential outliers within a cluster and consequently being triaged to suboptimal personalized care. In this paper, we address this limitation through the use of an asymmetric dissimilarity metric and a density-based outlier detection technique to identify the natural outliers within the individual clusters. In a database of 370 datasets, 28 outliers were detected among 20 clinically correlated clusters. The proposed quantitative analytics could facilitate an optimized landscape wherein every patient is triaged through the most appropriate individualized pulmonary care.