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Cytologic scoring of equine exercise-induced pulmonary hemorrhage

  • 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 reducedExercise-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.show moreshow less

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Author:Christof BertramORCiD, Christian MarzahlORCiD, Alexander BartelORCiD, Jason Stayt, Federico BonsembianteORCiD, Janet Beeler-MarfisiORCiD, Ann K. Barton, Ginevra BroccaORCiD, Maria Elena GelainORCiD, Agnes C. Gläsel, Kelly du Preez, Kristina Weiler, Christiane Weissenbacher-LangORCiD, Katharina BreiningerORCiD, Marc AubrevilleORCiD, Andreas MaierORCiD, Robert KlopfleischORCiD, Jenny Hill
Subtitle (English):Performance of human experts and a deep learning-based algorithm
Language:English
Document Type:Article
Year of first Publication:2022
published in (English):Veterinary Pathology
Publisher:Sage
Place of publication:London
ISSN:1544-2217
Volume:60
Issue:1
First Page:75
Last Page:85
Review:peer-review
Open Access:ja
Version:published
Tag:artificial intelligence; automated image analysis; bronchoalveolar lavage fluid; computational pathology; digital pathology; equine; pulmonary hemorrhage; respiratory disease; total hemosiderin score
URN:urn:nbn:de:bvb:573-35196
Related Identifier:https://doi.org/10.1177/03009858221137582
Supplement:https://journals.sagepub.com/doi/suppl/10.1177/03009858221137582/suppl_file/sj-pdf-1-vet-10.1177_03009858221137582.pdf
Faculties / Institutes / Organizations:Fakultät Informatik
AImotion Bavaria
Licence (German):License Logo Creative Commons BY 4.0
Release Date:2023/04/14