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

doi:10.12116/j.issn.1004-5619.2021.410719

Abstract

Abstract: Objective

To explore the application values of diatom artificial intelligence （AI） search system in the diagnosis of drowning.

Methods

The 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.

Results

There 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.

Conclusion

Diatom 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.

Key words: forensic pathology, drowning, diatom test, artificial intelligence, automatic searching, scanning electron microscope, manual identification

CLC Number:

DF795.1

Dan-yuan YU, Jing-jian LIU, Chao LIU, Yu-kun DU, Ping HUANG, Ji ZHANG, Wei-min YU, Ying-chao HU, Jian ZHAO, Jian-ding CHENG. Evaluation of Inspection Efficiency of Diatom Artificial Intelligence Search System Based on Scanning Electron Microscope[J]. Journal of Forensic Medicine, 2022, 38(1): 40-45.

Figures/Tables 6

Fig. 1 The diatom AI automatic searching system

Tab. 1 Diatom detection result of the diatom AI search system

阈值	N-M/（ $x ˉ$ ±s，张）	TP+FP/（ $x ˉ$ ±s，个）	TP/［M（P₂₅~P₇₅），个］
0.5	1 268.67±323.21	3 365.25±2 190.30	28.00（13.00~361.00）
0.7	967.75±383.31^1）	1 865.33±1 278.96^1）	26.00（11.25~343.75）
0.9	306.50±260.36^1）2）	373.83±358.89^1）2）	15.50（9.25~239.50）

Tab. 1 Diatom detection result of the diatom AI search system

阈值	N-M/（ $x ˉ$ ±s，张）	TP+FP/（ $x ˉ$ ±s，个）	TP/［M（P₂₅~P₇₅），个］
0.5	1 268.67±323.21	3 365.25±2 190.30	28.00（13.00~361.00）
0.7	967.75±383.31^1）	1 865.33±1 278.96^1）	26.00（11.25~343.75）
0.9	306.50±260.36^1）2）	373.83±358.89^1）2）	15.50（9.25~239.50）

Tab. 2 RR of the diatom AI search system

阈值	RR/（ $x ˉ$ ±s）	最小值	最大值
0.5	91.12±6.16	78.57	100.00
0.7	84.00±10.48^1）	64.29	100.00
0.9	56.95±9.00^1）2）	45.71	69.23

Tab. 2 RR of the diatom AI search system

阈值	RR/（ $x ˉ$ ±s）	最小值	最大值
0.5	91.12±6.16	78.57	100.00
0.7	84.00±10.48^1）	64.29	100.00
0.9	56.95±9.00^1）2）	45.71	69.23

Tab. 3 PR of the diatom AI search system

阈值	PR/[M（P₂₅~P₇₅）]	最小值	最大值
0.5	1.02（0.68~9.11）	0.32	16.83
0.7	1.92（1.19~15.58）^1）	0.62	24.64
0.9	10.20（5.47~41.23）^1）2）	3.42	53.15

Fig. 2 Diatoms identification under different thresholds

Tab. 4 ER of the diatom AI automatic searching system

阈值	ER	最小值	最大值
0.5	95.76±1.09	93.98	97.17
0.7	96.77±1.30^1）	94.58	98.45
0.9	98.97±0.88^1）2）	97.37	99.72

References 20

1	PIETTE M H A， DE LETTER E A. Drowning： Still a difficult autopsy diagnosis[J]. Forensic Sci Int，2006，163（1/2）：1-9. doi：10.1016/j.forsciint.2004. 10.027 .
2	ZHAO J， LIU C， HU S， et al. Microwave digestion - vacuum filtration - automated scanning electron microscopy as a sensitive method for forensic diatom test[J]. Int J Legal Med，2013，127（2）：459-463. doi：10.1007/s00414-012-0756-9 .
3	赵建，袁自闯，张彦吉，等. 两种硅藻检验方法的比较[J].中国法医学杂志，2015，30（1）：62-65. doi：10.13618/j.issn.1001-5728.2015.01.018 .
	ZHAO J， YUAN Z C， ZHANG Y J， et al. The comparative study of two diatom test methods[J]. Zhongguo Fayixue Zazhi，2015，30（1）：62-65.
4	ZHAO J， LIU C， BARDEESI A S A， et al. The diagnostic value of quantitative assessment of diatom test for drowning： An analysis of 128 water-related death cases using microwave digestion-vacuum filtration-automated scanning electron microscopy[J]. J Forensic Sci，2017，62（6）：1638-1642. doi：10.1111/1556-4029.13455 .
5	ZHANG P， KANG X， ZHANG S， et al. The length and width of diatoms in drowning cases as the evidence of diatoms penetrating the alveoli-capillary barrier[J]. Int J Legal Med，2020，134（3）：1037-1042. doi：10.1007/s00414-019-02164-4 .
6	ZHAO J， ZHANG P P， XUE Y， et al. Correlation analysis of diatom content in the organs and drowning mediums for the drowning death cases[J]. Aust J Forensic Sci，2021，53（2）：191-198. doi：10.1080/00450618.2019.1682667 .
7	GIRI B S， TRIPATHI C B， CHOWDARY Y B. Characterization of drowning by diatom test[J]. Indian J Med Res，1993，98：40-43.
8	HÜRLIMANN J， FEER P， ELBER F， et al. Diatom detection in the diagnosis of death by drowning[J]. Int J Legal Med，2000，114（1/2）：6-14. doi：10.1007/s004149900122 .
9	ZHOU Y Y， ZHANG J， HUANG J， et al. Digital whole-slide image analysis for automated diatom test in forensic cases of drowning using a convolutional neural network algorithm[J]. Forensic Sci Int，2019，302：109922. doi：10.1016/j.forsciint.2019.109922 .
10	BUENO G， DENIZ O， PEDRAZA A， et al. Automated diatom classification （Part A）： Handcrafted feature approaches[J]. Appl Sci，2017，7（8）：753. doi：10.3390/app7080753 .
11	MITCHELL F. The use of artificial intelligence in digital forensics： An introduction[J]. Digit Evid Electron Signature Law Rev，2014，7：35-41. doi：10.14296/deeslr.v7i0.1922 .
12	骆巧琦，李雪松，梁君荣，等. 基于形状特征的硅藻显微图像自动识别[J].厦门大学学报（自然科学版），2011，50（4）：690-696.
	LUO Q Q， LI X S， LIANG J R， et al. Automatic identification of diatom microscopic images based on contour features[J]. Xiamen Daxue Xuebao （Natural sciences），2011，50（4）：690-696.
13	周圆圆，曹永杰，杨越，等. 基于人工智能硅藻自动化识别系统的实际案例应用[J].法医学杂志，2020，36（2）：239-242. doi：10.12116/j.issn.1004-5619.2020.02.017 .
	ZHOU Y Y， CAO Y J， YANG Y， et al. Application of artificial intelligence automatic diatom identification system in practical cases[J]. Fayixue Zazhi，2020，36（2）：239-242.
14	LOWE D G. Distinctive image features from scale-invariant keypoints[J]. Int J Comput Vis，2004，60（2）：91-110. doi：10.1023/b:visi.0000029664.99615.94 .
15	FELZENSZWALB P F， GIRSHICK R B， MCALLESTER D， et al. Object detection with discriminatively trained part-based models[J]. IEEE Trans Pattern Anal Mach Intell，2010，32（9）：1627-1645. doi：10.1109/TPAMI.2009.167 .
16	YU W M， XUE Y， KNOOPS R， et al. Automated diatom searching in the digital scanning electron microscopy images of drowning cases using the deep neural networks[J]. Int J Leg Med，2021，135（2）：497-508. doi：10.1007/s00414-020-02392-z .
17	LIN T Y， GOYAL P， GIRSHICK R， et al. Focal loss for dense object detection[J]. 2017 IEEE Int Conf Comput Vis ICCV，2017：2999-3007. doi：10.1109/ICCV.2017.324 .
18	BEDELI M， GERADTS Z， EIJK E VAN. Clothing identification via deep learning： Forensic applications[J]. Forensic Sci Res，2018，3（3）：219-229. doi：10.1080/20961790.2018.1526251 .
19	K A， GRZONKOWSKI S， LEKHAC N A. Enabling trust in deep learning models： A digital forensics case study[C]//2018 17th IEEE International Conference on Trust， Security and Privacy In Computing and Communications/ 12th IEEE International Conference on Big Data Science and Engineering （TrustCom/BigDataSE）. New York：IEEE， 2018：1250-1255. doi：10.1109/TrustCom/BigDataSE.2018. 00172 .
20	REN S Q， HE K M， GIRSHICK R， et al. Faster R-CNN： Towards real-time object detection with region proposal networks[J]. IEEE Trans Pattern Anal Mach Intell，2017，39（6）：1137-1149. doi：10.1109/TPAMI.2016.2577031 .

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