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BMC Medical Informatics and Decision Making

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Open Access 01-12-2023 | Computed Tomography | Research

Anomaly prediction of CT equipment based on IoMT data

Authors: Changxi Wang, Qilin Liu, Haopeng Zhou, Tong Wu, Haowen Liu, Jin Huang, Yixuan Zhuo, Zhenlin Li, Kang Li

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

Abstract

Background

Large-scale medical equipment, which is extensively implemented in medical services, is of vital importance for diagnosis but vulnerable to various anomalies and failures. Most hospitals that conduct regular maintenance have been suffering from medical equipment-related incidents for years. Currently, the Internet of Medical Things (IoMT) has emerged as a crucial tool in monitoring the real-time status of the medical equipment. In this paper, we develop an IoMT system of Computed Tomography (CT) equipment in the West China Hospital, Sichuan University and collected the system status time-series data. Novel multivariate time-series classification models and frameworks are proposed to predict the anomalies of CT equipment. The important features that are closely related to the equipment anomalies are identified with the model.

Methods

We extracted the real-time CT equipment status time-series data of 11 equipment between May 19, 2020 and May 19, 2021 from the IoMT, which includes the equipment oil temperature, anode voltage, etc. The arcs are identified as labels of anomalies due to their relationship with decreased imaging quality and CT equipment failures. To improve prediction accuracy, the statistics and transformations of the raw historical time-series data segment in the sliding time window are used to construct new features. Due to the particularity of time-series data, two frameworks are proposed for splitting the training and test sets. Then the Decision Tree, Support Vector Machine, Logistic Regression, Naive Bayesian, and K-Nearest Neighbor classification models are used to classify the system status. We also compare our model to state-of-the-art models.

Results

The results show that the anomaly prediction accuracy and recall of our method are 79% and 77%, respectively. The oil temperature and anode voltage are identified as the decisive features that may lead to anomalies. The proposed model outperforms the others when predicting the anomalies of the CT equipment based on our dataset.

Conclusions

The proposed method could predict the state of CT equipment and be used as a reference for practical maintenance, where unexpected anomalies of medical equipment could be reduced. It also brings new insights into how to handle non-uniform and imbalanced time series data in practical cases.

B. Wang. Medical equipment maintenance: management and oversight. In: Synthesis Lectures on Biomedical Engineering. Springer; 2012. p.1–85. https://doi.org/10.2200/S00450ED1V01Y201209BME045.

McClead RE, Brady M. Sentinel events/patient safety events. Pediatr Rev. 2016;37(10):448–50. https://doi.org/10.1542/pir.2015-0163.CrossRefPubMed

Wang B, et al. Evidence-based maintenance: part I: measuring maintenance effectiveness with failure codes. J Clin Eng. 2010;35(3):132–44. https://doi.org/10.1097/JCE.0b013e3181e6231e.CrossRef

Yu T, Zhu C, Chang Q, Wang J. Imperfect corrective maintenance scheduling for energy efficient manufacturing systems through online task allocation method. J Manuf Syst. 2019;53:282–90. https://doi.org/10.1016/j.jmsy.2019.11.002.CrossRef

Yang SK. A condition-based failure-prediction and processing-scheme for preventive maintenance. IEEE Trans Reliab. 2003;52(3):373–83. https://doi.org/10.1109/TR.2003.816402.CrossRef

Tsai Y-T, Wang K-S, Teng H-Y. Optimizing preventive maintenance for mechanical components using genetic algorithms. Reliab Eng Syst Saf. 2001;74(1):89–97. https://doi.org/10.1016/S0951-8320(01)00065-5.CrossRef

Lin D, Zuo MJ, Yam RCM. General sequential imperfect preventive maintenance models. Int J Reliab Qual Saf Eng. 2000;7(3):253–66. https://doi.org/10.1142/S0218539300000213.CrossRef

You M-Y, Li H, Meng G. Control-limit preventive maintenance policies for components subject to imperfect preventive maintenance and variable operational conditions. Reliab Eng Syst Saf. 2011;96(5):590–8. https://doi.org/10.1016/j.ress.2010.12.015.CrossRef

Jim J-H, Park DH. Optimal periodic preventive maintenance schedules with improvement factors depending on number of preventive maintenances. Asia-Pacific J Oper Res. 2007;24(1):111–24. https://doi.org/10.1142/S0217595907001139.CrossRef

10.

Ljungberg O. Measurement of overall equipment effectiveness as a basis for TPM activities. Int J Oper Prod Manag. 1998;18(5):495–507. https://doi.org/10.1108/01443579810206334.CrossRef

11.

Grall A, Dieulle L, Bérenguer C, Roussignol M. Continuous-time predictive-maintenance scheduling for a deteriorating system. IEEE Trans Reliab. 2002;51(2):141–50. https://doi.org/10.1109/TR.2002.1011518.CrossRef

12.

Garcia MC, Sanz-Bobi MA, Pico JD. SIMAP: intelligent system for predictive maintenance: application to the health condition monitoring of a windturbine gearbox. Comput Ind. 2006;57(6):552–68. https://doi.org/10.1016/j.compind.2006.02.011.CrossRef

13.

Abdennadher K, Venet P, Rojat G, Rétif J-M, Rosset C. A real-time predictive-maintenance system of aluminum electrolytic capacitors used in uninterrupted power supplies. IEEE Trans Ind Appl. 2010;46(4):1644–52. https://doi.org/10.1109/TIA.2010.2049972.CrossRef

14.

Rezvanizaniani SM, Dempsey J, Lee J. An effective predictive maintenance approach based on historical maintenance data using a probabilistic risk assessment: PHM14 data challenge. Int J Progno Health Manag. 2020;5(2):1–13. https://doi.org/10.36001/ijphm.2014.v5i2.2237.CrossRef

15.

Lee J. Strategy and challenges on remote diagnostics and maintenance for manufacturing equipment. In: Annual Reliability and Maintainability Symposium. IEEE; 1997. p.368–370. https://doi.org/10.1109/RAMS.1997.571735.

16.

Penrose HW. Simple time-to-failure estimation techniques for reliability and maintenance of equipment. IEEE Electr Insul Mag. 2009;25(4):14–8. https://doi.org/10.1109/MEI.2009.5191412.CrossRef

17.

Hashemian HM. Wireless sensors for predictive maintenance of rotating equipment in research reactors. Ann Nucl Energy. 2011;38(2–3):665–80. https://doi.org/10.1016/j.anucene.2010.09.012.CrossRef

18.

Cassady CR, Murdock WP Jr, Pohl EA. Selective maintenance for support equipment involving multiple maintenance actions. Eur J Oper Res. 2001;129(2):252–8. https://doi.org/10.1016/S0377-2217(00)00222-8.CrossRef

19.

Panagiotidou S, Tagaras G. Evaluation of maintenance policies for equipment subject to quality shifts and failures. Int J Prod Res. 2008;46(20):5761–79. https://doi.org/10.1080/00207540601182260.CrossRef

20.

Khalaf AB, Hamam Y, Alayli Y, Djouani K. The effect of maintenance on the survival of medical equipment. J Eng Des Technol. 2013;11(2):142–57. https://doi.org/10.1108/JEDT-06-2011-0033.CrossRef

21.

Maktoubian J, Ansari K. An IoT architecture for preventive maintenance of medical devices in healthcare organizations. Heal Technol. 2019;9:233–43. https://doi.org/10.1007/s12553-018-00286-0.CrossRef

22.

Li S, Xu LD, Zhao S. The internet of things: a survey. Inf Syst Front. 2015;17(2):243–59. https://doi.org/10.1007/s10796-014-9492-7.CrossRef

23.

Syed L, Jabeen S, Manimala S, Alsaeedi A. Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques. Future Gener Comput Syst. 2019;101:136–51. https://doi.org/10.1016/j.future.2019.06.004.CrossRef

24.

Gupta S, Kambli R, Wagh S, Kazi F. Support-vector-machine-based proactive cascade prediction in smart grid using probabilistic framework. IEEE Trans Industr Electron. 2015;62(4):2478–86. https://doi.org/10.1109/TIE.2014.2361493.CrossRef

25.

Mohammadpour R, Shaharuddin S, Chang CK, Zakaria NA, Ghani AA, Chan NW. Prediction of water quality index in constructed wetlands using support vector machine. Environ Sci Pollut Res. 2015;22(8):6208–19. https://doi.org/10.1007/s11356-014-3806-7.CrossRef

26.

Garmaroodi MSS, Farivar F, Haghighi MS, Shoorehdeli MA, Jolfaei A. Detection of anomalies in industrial IoT systems by data mining: study of CHRIST osmotron water purification system. IEEE Internet Things J. 2021;8(13):10280–7. https://doi.org/10.1109/jiot.2020.3034311.CrossRef

27.

Canizo M, Onieva E, Conde A, Charramendieta S, Trujillo S. Real-time predictive maintenance for wind turbines using big data frameworks. In: IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE; 2017. https://doi.org/10.1109/ICPHM.2017.7998308.

28.

Jiang Y, Dai P, Fang P, Zhong RY, Cao X. Electrical-STGCN: an electrical spatio-temporal graph convolutional network for intelligent predictive maintenance. IEEE Trans Industr Inf. 2022;18(12):8509–18. https://doi.org/10.1109/tii.2022.3143148.CrossRef

29.

Jiang Y, Dai P, Fang P, Zhong RY, Zhao X, Cao X. A2-LSTM for predictive maintenance of industrial equipment based on machine learning. Comput Ind Eng. 2022;172:108560. https://doi.org/10.1016/j.cie.2022.108560.CrossRef

30.

Bashir MA, Sanhory MH, Alrasheed FJ, Abdelrahman A, Abdullah AA. Abdullah. X-ray tube arc preventation by stabilization of voltage in a dual energy CT scanner: a review study. In: 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). 2019. https://doi.org/10.1109/iccceee46830.2019.9071025.

31.

Bottaro M, Moralles M, Viana V, Donatiello GL, Silva EP. A practical method to determine the heating and cooling curves of x-ray tube assemblies. Med Phys. 2007;34(10):3982–6. https://doi.org/10.1118/1.2776672.CrossRefPubMed

32.

Tabakov S. X-ray tube arcing: manifestation and detection under quality control. MEDICAL PHYSICS international. 2018; 6(1):157–161. http://mpijournal.org/MPI-v06i01.aspx.

33.

Mithun S, et al. A rare cause of tube arcing artifact seen in computed tomography image of a positron emission tomography/computed tomography scanner. Indian J Radiol Imaging. 2016;26(1):153–5.CrossRefPubMedPubMedCentral

34.

Koch L, Lesche A, and Maring W. Fighting arcing and field emission in medical X-ray tubes. In: Proceedings of 17th International Symposium on Discharges and Electrical Insulation in Vacuum. IEEE; 1996. p.1077–1081. https://doi.org/10.1109/DEIV.1996.545531.

35.

Rajwade J, Miller L, Simon D. Partial-data interpolation method for arc handling in a computed tomography scanner. Comput Med Imaging Graph. 2012;36:387–95. https://doi.org/10.1016/j.compmedimag.2012.04.004.CrossRefPubMed

36.

Zink FE. X-ray tubes. Radiographics. 1997;17(5):1259–68. https://doi.org/10.1148/radiographics.17.5.9308113.CrossRefPubMed

37.

Schäfer P, and Leser U. Multivariate time series classification with WEASEL+MUSE. Journal. 2017. https://doi.org/10.48550/arXiv.1711.11343.

38.

He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009;21(9):1263–84. https://doi.org/10.1109/TKDE.2008.239.CrossRef

39.

Schäfer P. The BOSS is concerned with time series classification in the presence of noise. Data Mining Knowledge Discovery. 2015; 29(6):1505–1530. https://link.springer.com/article/https://doi.org/10.1007/s10618-014-0377-7.

40.

Middlehurst M, Large J and Bagnall A. The canonical interval forest (CIF) classifier for time series classification. In: 2020 IEEE International Conference on Big Data (Big Data). IEEE; 2020. p.188–195. https://doi.org/10.1109/BigData50022.2020.9378424.

41.

Middlehurst M, Large J, Flynn M, Lines J, Bostrom A, Bagnall A. HIVE-COTE 2.0: a new meta ensemble for time series classification. Machine Learn. 2021;110:3211–43. https://doi.org/10.1007/s10994-021-06057-9.CrossRef

42.

Middlehurst M, Large J, Cawley G and Bagnall A. The temporal dictionary ensemble (TDE) classifier for time series classification. In: ECML PKDD 2020: Machine Learning and Knowledge Discovery in Databases. Springer; 2021. p.660–676. https://doi.org/10.1007/978-3-030-67658-2_38.

43.

Shokoohi-Yekta M, Hu B, Jin H, Wang J, Keogh E. Generalizing DTW to the multi-dimensional case requires an adaptive approach. Data Min Knowl Disc. 2017;31(1):1–31. https://doi.org/10.1007/s10618-016-0455-0.CrossRef

Title: Anomaly prediction of CT equipment based on IoMT data
Authors: Changxi Wang
Qilin Liu
Haopeng Zhou
Tong Wu
Haowen Liu
Jin Huang
Yixuan Zhuo
Zhenlin Li
Kang Li
Publication date: 01-12-2023
Publisher: BioMed Central
Keywords: Computed Tomography
Computed Tomography
Published in: BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-023-02267-4

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Springer Medicine

Anomaly prediction of CT equipment based on IoMT data

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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