Journal
INTERNATIONAL JOURNAL OF LEGAL MEDICINE
Volume -, Issue -, Pages -Publisher
SPRINGER
DOI: 10.1007/s00414-023-03136-5
Keywords
Computational pathology; Pulmonary fat embolism; Deep learning; Digital pathology; Convolutional neural network
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This study utilized computational pathology to precisely quantify fat emboli in whole slide images, achieving a high accuracy rate. The results demonstrate the potential of computational pathology as an affordable and rapid method for fatal PFE diagnosis in forensic practice.
Pulmonary fat embolism (PFE) as a cause of death often occurs in trauma cases such as fractures and soft tissue contusions. Traditional PFE diagnosis relies on subjective methods and special stains like oil red O. This study utilizes computational pathology, combining digital pathology and deep learning algorithms, to precisely quantify fat emboli in whole slide images using conventional hematoxylin-eosin (H&E) staining. The results demonstrate deep learning's ability to identify fat droplet morphology in lung microvessels, achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.98. The AI-quantified fat globules generally matched the Falzi scoring system with oil red O staining. The relative quantity of fat emboli against lung area was calculated by the algorithm, determining a diagnostic threshold of 8.275% for fatal PFE. A diagnostic strategy based on this threshold achieved a high AUC of 0.984, similar to manual identification with special stains but surpassing H&E staining. This demonstrates computational pathology's potential as an affordable, rapid, and precise method for fatal PFE diagnosis in forensic practice.
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