AUTHOR=Yu Weimin , Xiang Qingqing , Hu Yingchao , Du Yukun , Kang Xiaodong , Zheng Dongyun , Shi He , Xu Quyi , Li Zhigang , Niu Yong , Liu Chao , Zhao Jian TITLE=An improved automated diatom detection method based on YOLOv5 framework and its preliminary study for taxonomy recognition in the forensic diatom test JOURNAL=Frontiers in Microbiology VOLUME=Volume 13 - 2022 YEAR=2022 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.963059 DOI=10.3389/fmicb.2022.963059 ISSN=1664-302X ABSTRACT=Diatom test is a forensic technique that can provide supportive evidence in the diagnosis of drowning but requires the laborious observation and counting of diatoms using a microscopy with too much effort, and therefore it is promising to introduce artificial intelligence (AI) to make the test process automatic. In this paper, we propose an artificial intelligence solution based on YOLOv5 framework for the automatic detection and recognition of the diatom genera. To evaluate the performance of this AI solution on different scenarios, we collected five lab-grown diatom genera and the samples of some organic tissues from drowning cases to investigate the potential upper/lower limits of the capability in detecting the diatoms and recognizing their genera. Based on the study of the paper, a recall score 0.95 together with the corresponding precision score 0.9 were achieved on the samples of the five lab-grown diatom genera via cross validation, and the accuracy of the evaluation on the cases of kidney and liver is above 0.85 based on the precision and recall scores, which demonstrate the effectiveness of the AI solution to be used in drowning forensic routine.