Evaluation of Inspection Efficiency of Diatom Artificial Intelligence Search System Based on Scanning Electron Microscope

Abstract

ObjectiveTo explore the application values of diatom artificial intelligence （AI） search system in the diagnosis of drowning.MethodsThe liver and kidney tissues of 12 drowned corpses were taken and were performed with the diatom test, the view images were obtained by scanning electron microscopy （SEM）. Diatom detection and forensic expert manual identification were carried out under the thresholds of 0.5, 0.7 and 0.9 of the diatom AI search system, respectively. Diatom recall rate， precision rate and image exclusion rate were used to detect and compare the efficiency of diatom AI search system.ResultsThere was no statistical difference between the number of diatoms detected in the target marked by the diatom AI search system and the number of diatoms identified manually （P>0.05）; the recall rates of the diatom AI search system were statistically different under different thresholds （P<0.05）; the precision rates of the diatom AI system were statistically different under different thresholds（P<0.05）， and the highest precision rate was 53.15%; the image exclusion rates of the diatom AI search system were statistically different under different thresholds （P<0.05）， and the highest image exclusion rate was 99.72%. For the same sample， the time taken by the diatom AI search system to identify diatoms was only 1/7 of that of manual identification.ConclusionDiatom AI search system has a good application prospect in drowning cases. Its automatic diatom search ability is equal to that of experienced forensic experts, and it can greatly reduce the workload of manual observation of images.

Keywords

• forensic pathology
• drowning
• diatom test
• artificial intelligence
• automatic searching
• scanning electron microscope
• manual identification