This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.
A retrospective analysis was performed in patients undergoing prospective ECG-triggered coronary computed tomography (CT) angiography (CCTA) with the single-source 64-slice CT (SSCT), dual-source 64-slice CT (DSCT), dual-source 128-slice CT and 320-slice CT with the aim of comparing the radiation dose associated with different CT generations. A total of 164 patients undergoing prospective ECG-triggered CCTA with different types of CT scanners were studied with the mean effective doses estimated at 6.8 ± 3.2, 4.2 ± 1.9, 4.1±0.6 and 3.8 ± 1.4 mSv corresponding to the 128-slice DSCT, 64-slice DSCT, 64-slice SSCT and 320-slice CT scanners. In this study a positive relationship was found between the effective dose and the body mass index (BMI). A low radiation dose is achieved in prospective ECG-triggered CCTA, regardless of the CT scanner generation. BMI is identified as the major factor that has a direct impact on the effective dose associated with prospective ECG-triggered CCTA.