Analysis and Diagnosis of Hemolytic Specimens by AU5800 Biochemical Analyzer Combined with AI Technology

Jili Qian; Tianbo Song; Quan Zhang; Guoqing Cai; Meiqing Cai

doi:10.54097/qoseeQ5N

Authors

Jili Qian
Tianbo Song
Quan Zhang
Guoqing Cai
Meiqing Cai

DOI:

https://doi.org/10.54097/qoseeQ5N

Keywords:

Automatic Biochemical Analyzer, Intelligent Algorithm, Hemolytic Specimen, Biochemical Test

Abstract

There is a close correlation between biochemical analysis instruments and artificial intelligence, in biomedical engineering, biochemical analysis instruments produce a large number of complex data, including spectral data, mass spectrometry data, electrochemical data, etc. Artificial intelligence algorithms can be used to process and interpret this data, extract useful information from it, identify features and trends, and help researchers better understand biochemical processes. Moreover, artificial intelligence can be used to realize the data pattern recognition and classification of biochemical analysis instruments. Through machine learning techniques, algorithms can be trained to automatically identify differences between different samples or analysis results, helping to identify different biomolecules or compounds. It can also be used for fault diagnosis and maintenance of analytical instruments. By monitoring instrument performance data, algorithms can detect potential problems and provide repair recommendations, reduce instrument downtime, and between multiple biochemical analysis instruments, as well as with other laboratory equipment and databases, AI can be used for data consolidation and comprehensive analysis to help researchers obtain more comprehensive information. In this paper, the influence of Beckman 5800 automatic biochemical analyzer on the results of hemolysis test was discussed through the example of automatic biological differentiation instrument combined with artificial intelligence.

Downloads

Download data is not yet available.

References

ICS H . Recommendations for reference method for hemoglobinometry in human blood (ICS H standard 1995 ) andspecifications for international hemoglobincyamide standard (4th edition) [J]·J Clin Pathol, 1996,49(4) 271-274.

Jasmin G.Methods and achievements inexperimental pathology. Vol 11 Basel: SKarger 1984:74-96.

Baak JPA. A manul of morphometry indiagnostic pathology. Berlin; Springer,1983:1-180Weinstein RS. Prospects for telepathology.Hum Patho1 1986;17(5):433.

Herman CJ, et al, Recentt progress irclinical quantitative cytology. Arch PatholLab Med 1987; 111(6):502.

Chang Che, Bo Liu, Shulin Li, Jiaxin Huang, and Hao Hu. Deep learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10):36–41, 2023.DOI: 10.1021/acs.jctc.3c00031.

Hao Hu, Shulin Li, Jiaxin Huang, Bo Liu, and Change Che. Casting product image data for quality inspection with xception and data augmentation. Journal of Theory and Practice of Engineering Science, 3(10):42–46, 2023. https://doi. org/10. 53469/ jtpes.2023.03(10).06.

Chang Che, Qunwei Lin, Xinyu Zhao, Jiaxin Huang, and Liqiang Yu. 2023. Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation. In Proceedings of the 2023 6th International Conference on Big Data Technologies (ICBDT '23). Association for Computing Machinery, New York, NY, USA, 414–418. https://doi.org/ 10. 1145/ 3627377.3627442.

Y. Wang, K. Yang, W. Wan, Y. Zhang and Q. Liu, "Energy-Efficient Data and Energy Integrated Management Strategy for IoT Devices Based on RF Energy Harvesting," in IEEE Internet of Things Journal, vol. 8, no. 17, pp. 13640-13651, 1 Sept.1, 2021, doi: 10.1109/JIOT.2021.3068040.

Y. Wang, K. Yang, W. Wan, Y. Zhang and Q. Liu, "Energy-Efficient Data and Energy Integrated Management Strategy for IoT Devices Based on RF Energy Harvesting," in IEEE Internet of Things Journal, vol. 8, no. 17, pp. 13640-13651, 1 Sept.1, 2021, DOI: 10.1109/JIOT.2021.3068040.

Wang, Y, Yang, K, Wan, W, Mei, H. Adaptive energy saving algorithms for Internet of Things devices integrating end and edge strategies. Trans Emerging Tel Tech. 2021; 32: e4122. DOI: https://doi.org/10.1002/ett.4122.

Xu, J., Pan, L., Zeng, Q., Sun, W., & Wan, W. Based on TPUGRAPHS Predicting Model Runtimes Using Graph Neural Networks. https://api.semanticscholar.org/Corpus.

Yao, J., Zou, Y., Du, S., Wu, H., & Yuan, B. Progress in the Application of Artificial Intelligence in Ultrasound Diagnosis of Breast Cancer. DOI:https://api.semanticscholar.org/Corpus.

Zhou Y, Chen S, Wu Y, Li L, Lou Q, Chen Y, Xu S. Multi-clinical index classifier combined with AI algorithm model to predict the prognosis of gallbladder cancer. Front Oncol. 2023 May 10;13:1171837. DOI: 10.3389/fonc.2023.1171837. PMID: 37234992; PMCID: PMC10206143.

Li L, Xu C, Wu W, et al. Zero-resource knowledge-grounded dialogue generation[J]. Advances in Neural Information Processing Systems, 2020, 33: 8475-8485. DOI: https:// doi. org/ 10.48550/arXiv.2008.12918.

Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281–285.DOI: 10.1111/jgs.18617.

Che, C., Hu, H., Zhao, X., Li, S., & Lin, Q. (2023). Advancing Cancer Document Classification with R andom Forest. Academic Journal of Science and Technology, 8(1), 278–280. https://doi.org/10.54097/ajst.v8i1.14333.

S. Tianbo, H. Weijun, C. Jiangfeng, L. Weijia, Y. Quan and H. Kun, "Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition," 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China, 2023, pp. 834-837, doi: 10.1109/ICCECE58074.2023.10135464.