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Bertram CA, Marzahl C, Bartel A, Stayt J, Bonsembiante F, Beeler-Marfisi J, Barton AK, Brocca G, Gelain ME, Gläsel A, du Preez K, Weiler K, Weissenbacher-Lang C, Breininger K, Aubreville M, Maier A, Klopfleisch R, Hill J. Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm. Vet Pathol 2023; 60:75-85. [PMID: 36384369 PMCID: PMC9827485 DOI: 10.1177/03009858221137582] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological specimens stained for iron were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotator's and algorithmic performance included a ground truth dataset, the mean annotators' THSs, and chemical iron measurements. Results of the study showed that annotators had marked interobserver variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared with the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or ≥ 75) of 75.7%, whereas, the algorithm had an accuracy of 92.3% with no relevant differences in correlation with chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis.
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Affiliation(s)
- Christof A. Bertram
- University of Veterinary Medicine
Vienna, Vienna, Austria
- Freie Universität Berlin, Berlin,
Germany
| | - Christian Marzahl
- Friedrich-Alexander-Universität
Erlangen-Nürnberg, Erlangen, Germany
- EUROIMMUN Medizinische Labordiagnostika
AG, Lübeck, Germany
| | - Alexander Bartel
- Freie Universität Berlin, Berlin,
Germany
- Alexander Bartel, Department of Veterinary
Medicine, Institute for Veterinary Epidemiology and Biostatistics, Freie
Universität Berlin, Koenigsweg 67, Berlin, 14163 Berlin, Germany.
| | - Jason Stayt
- Novavet Diagnostics, Bayswater, Western
Australia
| | | | | | | | | | | | - Agnes Gläsel
- Justus-Liebig-Universität Giessen,
Giessen, Germany
| | | | | | | | | | | | - Andreas Maier
- Friedrich-Alexander-Universität
Erlangen-Nürnberg, Erlangen, Germany
| | | | - Jenny Hill
- Novavet Diagnostics, Bayswater, Western
Australia
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Inter-species cell detection - datasets on pulmonary hemosiderophages in equine, human and feline specimens. Sci Data 2022; 9:269. [PMID: 35660753 PMCID: PMC9166691 DOI: 10.1038/s41597-022-01389-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 05/16/2022] [Indexed: 11/08/2022] Open
Abstract
Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset, which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologist. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered.
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