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Gunzer, F; Jantscher, M; Hassler, EM; Kau, T; Reishofer, G.
Reproducibility of artificial intelligence models in computed tomography of the head: a quantitative analysis.
Insights Imaging. 2022; 13(1):173 Doi: 10.1186/s13244-022-01311-7 [OPEN ACCESS]
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Leading authors Med Uni Graz: Gunzer Felix; Reishofer Gernot
Co-authors Med Uni Graz: Hassler Eva Maria; Jantscher Michael; Kau Thomas
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Abstract:: When developing artificial intelligence (AI) software for applications in radiology, the underlying research must be transferable to other real-world problems. To verify to what degree this is true, we reviewed research on AI algorithms for computed tomography of the head. A systematic review was conducted according to the preferred reporting items for systematic reviews and meta-analyses. We identified 83 articles and analyzed them in terms of transparency of data and code, pre-processing, type of algorithm, architecture, hyperparameter, performance measure, and balancing of dataset in relation to epidemiology. We also classified all articles by their main functionality (classification, detection, segmentation, prediction, triage, image reconstruction, image registration, fusion of imaging modalities). We found that only a minority of authors provided open source code (10.15%, n 0 7), making the replication of results difficult. Convolutional neural networks were predominantly used (32.61%, n = 15), whereas hyperparameters were less frequently reported (32.61%, n = 15). Data sets were mostly from single center sources (84.05%, n = 58), increasing the susceptibility of the models to bias, which increases the error rate of the models. The prevalence of brain lesions in the training (0.49 ± 0.30) and testing (0.45 ± 0.29) datasets differed from real-world epidemiology (0.21 ± 0.28), which may overestimate performances. This review highlights the need for open source code, external validation, and consideration of disease prevalence.
Find related publications in this database (Keywords): Artificial intelligence; Head CT; Reproducibility; Epidemiology; Machine learning