Thousands of sexual assault cases in the United States are backlogged. This has been a growing issue for years that has increased the difficulty of solving these cases and providing closure to the victims. The analysis process for each case includes the identification of body fluids, presumptive testing, confirmatory testing, and DNA extraction. The only confirmatory method for semen identification is a microscopic visualization of sperm cells. The time spent on microscopic analysis varies depending on the complexity of the samples and the skills of the analyst. While the identification of sperm cells is informative, it can be very time-consuming and labor intensive. Some forensic laboratories choose to skip this step and submit samples directly for DNA analysis. Conducting DNA analysis on unscreened samples can increase the cost of testing when negative samples are analyzed as well as the time it takes to process each case.
Automated microscopy has been available for decades and more recently has been paired with artificial intelligence to detect sperm cells on microscope slides. In this research, the MetaSystems automated microscope was used to analyze slides that mimic forensic sexual assault samples. Slides were also examined using traditional microscopy. The automated system quickly provided an accurate quantification of the number of sperm cells present in a sample, which can inform downstream DNA testing. The software was successful in identifying sperm cells treated with Christmas tree and hematoxylin and eosin stains, even among epithelial cells and various contaminants. Results demonstrated that an artificial intelligence-driven forensic sperm cell detection microscope can significantly reduce the time it takes to locate and identify sperm cells and estimate sperm cell quantity compared to a lengthier and more tedious manual search. Drawbacks to the system include the relatively high cost and reduced ability to accurately detect sperm cells amid contaminants that are of similar morphology.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/48088 |
| Date | 13 February 2024 |
| Creators | Alao, Itunu |
| Contributors | Brodeur, Amy N., Kosiorek, Kevin |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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