Currently, various medical equipment has been extensively implemented in all aspects of medical services, including disease diagnosis, patient condition monitoring and rehabilitation. Particularly, the large-scale digital radiology equipment such as Computed Tomography (CT), allowing for clear cross-sectional images of internal organs through X-rays, is of vital importance for medical facilities to treat patients. However, the CT equipment, embedding sophisticated operating systems, is vulnerable to various types of damages during the operation. Anomalies such as abnormal parameters, unstable current, damages of components and system outage, which occur unexpectedly during the equipment operation, have long plagued the hospitals as a problem. The equipment anomalies could result in unexpected delays in patient care, costly maintenance service and even serious patient incidents. In fact, the survey conducted by The Joint Commission (TJC)  shows that the “sentinel events” (i.e., safety accidents such as premature deaths, preventable diseases, severe injuries and disability accidents, the occurrence of which are not primarily related to patients’ illness ) associated with the failure of medical equipment is usually among the top ten reasons of medical accidents each year. It is reported that there were a total of 176 medical equipment-related incidents in the US, accounting for 2.9% of the total number of 6093 activities collected from 8 hospitals during the period 2004–2011 . Therefore, medical facilities including hospitals and healthcare organizations must ensure high-level reliability of medical equipment to guarantee patients’ safety and meet the required operation standards.
To date, the maintenance strategies including Corrective Maintenance (CM) , Preventive Maintenance (PM) [5–10] and Predictive Maintenance (PdM) [11–14], etc. have been successfully applied to various fields such as mechanical engineering [15, 16], nuclear engineering , management science [18, 19] etc., which greatly improved the management level of those systems. However, the applications of maintenance models have not been thoroughly addressed on the medical equipment. Generally, most medical facilities perform their equipment maintenance by merely following the manufacturer’s recommendations. The manufacturer establishes maintenance plans about when and how long maintenance will take place, and provides maintenance guidance for each equipment and components. This type of routine maintenance scheduling does improve the reliability and reduce the failure risks of medical equipment to some extent. However, the regular inspection and replacement are inconsistent in most cases and are inefficient to cope with all types of failures, especially for those that are frequent and random .
As various monitoring technologies and tools have been developed during the last few decades, it was announced that the combination of preventive maintenance with monitoring data along with data analysis techniques would be the appropriate approach to predict equipment anomalies . The Internet of Things (IoT), which integrates information of machine components through Internet by using modern information technologies, has emerged as a crucial technology to monitor the real-time status of targeted equipment and provide warnings in advance . Particularly, the Internet of Medical Things (IoMT), where health data obtained from wearable devices and sensors to monitor real-time physical conditions , has received extensive attention. Currently, the development of IoMT is still at its early stage and most of the existing IoMT systems are focusing on improving the level of diagnosis that related to human body, rather than the medical equipment . Likewise, it requires data-driven approaches to effectively store, preprocess and analyze the massive amount of log data that generated by IoT [25–27].
In this paper, we use machine-learning methods to predict CT anomalies based on the real-time condition data of CT equipment provided by the IoMT in West China Hospital of Sichuan University. In the CT system, parameters related to the condition of CT, such as oil temperature, anode voltage, daily arcing time and daily scan time, etc. are continuously monitored. By comparing different machine-learning models, we finally choose the one with the best prediction performance and improve the accuracy above 70% by adjusting parameters. Our research can significantly minimize the stagnation and losses, and improve the maintenance management. To the best knowledge of the authors, this is the first time that a sophisticated IoMT on large-scale medical equipment is developed and meanwhile, to be applied to investigate the anomaly of the medical equipment. Meanwhile, it is fairly new to combine the state-of-art machine learning models with the advanced monitoring tools in the medical field.
The rest of this paper is organized as follows: section 2 describes a typical CT system and the dataset we obtained from IoMT in the West China Hospital; section 3 demonstrates our research procedure; section 4 shows the results; conclusions are given in Section 5, with some perspectives and discussions on the future development.