# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
## 2 Related Work  
The utilisation of the PM in the medical equipment has been considerably slower than in other areas. A review by [PERSON], [PERSON] and [PERSON] [3] from 2023 was only able to identify nine different \"PdM applications on ME (medical equipment)\". Of those, only two were related to the same class of devices--an Infant incubator and seven others used different classes of medical equipment. The authors also try to explore and name possible reasons, such as regulatory (missing guidelines), complexity and fragmentation of devices, variety of device manufacturers, data privacy/security, and costs. They also try to propose possible solutions to overcome these challenges. Another reason for such a small number of studies could be that, to the best of our knowledge, no medical equipment-related PM dataset is currently released under a permissive licence. This fact leads to a situation where studies in this area are trying to create a PdM system for different machine types, with different input data and labelling, and through different means/with different models being utilised. When selecting the studies from the review by the type of used medical equipment, the most relevant study mentioned in the review would be that of [PERSON], [PERSON], and [PERSON] [7]. They analysed past failures in MRI (six years) and CT (3 years) and utilised ARMA and LPC methods to model availability, reliability, and performance efficiency. From the perspective of the used data, we identified three works in which a higher number of machine-generated features related to different aspects of the devices were considered. [PERSON] et al. [8] employed the k-NN algorithm to predict the failures of clinical chemistry analysers. Starting from 27 analyser-generated features, they cleaned the data and applied PCA to obtain 9 features. Their best-performing model was a 10-NN model, which was able to obtain an accuracy of 94.98%. Two other works related to classifying devices as fit or unfit for duty after the yearly \"safety and performance inspection\". The work of [PERSON] et al. [9] classified infant incubators into two classes--requiring maintenance and safe for usage. They gathered 140 samples with 30 features and applied 5 different classifiers--NB, DT, RF, k-NN, and SVM. The best-performing algorithm was DT, with an accuracy of 98.5%. The work of [PERSON] et al. [10] concerned classifying defibrillators as fit to be used or faulty. They utilised a dataset of 1221 samples with 38 samples each. Firstly, they tried to utilise four different feature selection techniques by using all features and extraction via the InfoGain algorithm, extraction via the genetic algorithm, and extraction via the wrapper algorithm. Second, they combined these feature selection approaches with DT, RF, k-NN, SVM, and NB algorithms. Their best performance was achieved by genetic algorithm feature selection combined with an RF classifier (100%). When RF was combined with other feature extraction algorithms, it was able to achieve an accuracy of 99.6%. As the proposed system should be used for the PdM of an X-ray tube, we have tried to find and analyse further works that concern CT/X-ray machines or the X-ray tube itself. [PERSON] et al. [11] used Markov chains to model one of the five health states of the CT machine--ranging from very good to unacceptable, to estimate the need for maintenance. They used the failure history of 8 CTs from different Spanish hospitals over 4 years to extract states for different machines. In the end, they proved that Markov chains could model the CT machine's lifetime accurately. [PERSON] and [PERSON] [12] applied a continuous-time Markov chain to model 3 different states of the CT--\"perfect\", \"deteriorated\", and \"damaged\" and optimise maintenance policies to maximise the profit/maintenance ratio. This work was more about when and how to assign technicians/repair machines and optimise the repair cycle than how to detect when to repair. [PERSON] et al.