ArticleInternational journal of legal medicine2021
An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm.
Article in International journal of legal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Effects of routine laboratory parameter variations on diatom preservation: Implications for the standardization of forensic diatom testing.International journal of legal medicine · 2026Article
- A simple optical microscopy-based diatom classification approach for forensic drowning site inference: a pilot study.International journal of legal medicine · 2025Article
- A diagnostic strategy for pulmonary fat embolism based on routine H&E staining using computational pathology.International journal of legal medicine · 2024Article
- "UDE DIATOMS in the Wild 2024": a new image dataset of freshwater diatoms for training deep learning models.GigaScience · 2024Article
- Artificial intelligence-driven microbiome data analysis for estimation of postmortem interval and crime location.Frontiers in microbiology · 2024Review
- Validation and optimization of the diatom L/D ratio as a diagnostic marker for drowning.International journal of legal medicine · 2023Article
- Inference of drowning sites using bacterial composition and random forest algorithm.Frontiers in microbiology · 2023Article
- With or without human interference for precise age estimation based on machine learning?International journal of legal medicine · 2022Article
- An improved automated diatom detection method based on YOLOv5 framework and its preliminary study for taxonomy recognition in the forensic diatom test.Frontiers in microbiology · 2022Article
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Authors and funding
12 authors at 6 institutions in 2 countries.
Funding
Abstract
Seasonal or monthly databases of the diatom populations in specific bodies of water are needed to infer the drowning site of a drowned body. However, existing diatom testing methods are laborious, time-consuming, and costly and usually require specific expertise. In this study, we developed an artificial intelligence (AI)-based system as a substitute for manual morphological examination capable of identifying and classifying diatoms at the species level. Within two days, the system collected information on diatom profiles in the Huangpu and Suzhou Rivers of Shanghai, China. In an animal experiment, the similarities of diatom profiles between lung tissues and water samples were evaluated through a modified Jensen-Shannon (JS) divergence measure for drowning site inference, reaching a prediction accuracy of 92.31%. Considering its high efficiency and simplicity, our proposed method is believed to be more applicable than existing methods for seasonal or monthly water monitoring of diatom populations from sections of interconnected rivers, which would help police narrow the investigation scope to confirm the identity of an immersed body.
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Registered trials
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