Abstract: Cytologic Scoring of Equine Exercise-induced Pulmonary Hemorrhage - Performance of Human Experts and a Deep Learning

Christof A. Bertram, Christian Marzahl, Alexander Bartel, Jason Stayt, Federico Bonsembiante, Janet Beeler-Marfisi, Ann K. Barton, Ginevra Brocca, Maria E. Gelain, Agnes C. Gläsel, Kelly du Preez, Kristina Weiler, Christiane Weissenbacher-Lang, Katharina Breininger, Marc Aubreville, Andreas K. Maier, Robert Klopfleisch, Jenny Hill

Abstract: Exercise-induced pulmonary hemorrhage (EIPH) is a common respiratory condition in race horses with negative implications on performance. The gold standard diagnostic method is cytology of bronchoalveolar lavage fluid using the time-consuming total hemosiderin score (THS). For the routine THS, 300 alveolar macrophages are classified into 5 grades based on the amount of intracellular hemosiderin pigment (degradation product of heme iron of red blood cells). Besides the high time investment, there is notable inter-rater variability in assigning hemosiderin grades. Thus automated image analysis is of high interest to improve this diagnostic test. In this study [1] we validated a deep learning-based algorithm (RetinaNet) in 52 whole slide images (WSI) against the performance of 10 experts (each graded 300 cells per case) and a ground truth with labels for all macrophage in theWSI (range: 596 - 8954 macrophages). Compared to the ground truth reference, the algorithm had a diagnostic accuracy of 92.3%, while the 10 experts had an accuracy of 75.5% (range: 63.4 - 92.3%). Automated analysis of a single WSI took on average 1:37 minutes, while experts required an average of 14 minutes for 300 cells. In conclusion, the deep learning-based algorithm has a high diagnostic accuracy and is, therefore, a promising tool to reduce expert labor and to facilitate the routine use of the THS.