A computational pipeline for data augmentation towards the improvement of disease classification and risk stratification models: A case study in two clinical domains

Pezoulas, Vasileios C.; Grigoriadis, Grigoris; Gkois, George; Tachos, Nikolaos S.; Smole, Tim; Bosnić, Zoran; Pičulin, Matej; Olivotto, Iacopo; Barlocco, Fausto; Robnik–Šikonja, Marko; Jakovljevic, Djordje G.; Goules, Andreas V.; Tzioufas, Athanasios G.; Fotiadis, Dimitrios I.

doi:10.1016/j.compbiomed.2021.104520

Cited by 12 publications

(3 citation statements)

References 20 publications

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“…Other mitigation strategies may include subject-wise cross-validation [ 32 ], resampling techniques [ 33 ], or data augmentation methods for medical images [ 34 ], and even exploring data imputation solutions for clinical data as suggested by Pezoulas et al [ 35 ]. In some cases, the required sample size is not achievable even after applying sample reduction strategies.…”

Section: Discussionmentioning

confidence: 99%

A practical solution to estimate the sample size required for clinical prediction models generated from observational research on data

Baeza-Delgado¹,

Alberich²,

Carot-Sierra

et al. 2022

Eur Radiol Exp

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Background Estimating the required sample size is crucial when developing and validating clinical prediction models. However, there is no consensus about how to determine the sample size in such a setting. Here, the goal was to compare available methods to define a practical solution to sample size estimation for clinical predictive models, as applied to Horizon 2020 PRIMAGE as a case study. Methods Three different methods (Riley’s; “rule of thumb” with 10 and 5 events per predictor) were employed to calculate the sample size required to develop predictive models to analyse the variation in sample size as a function of different parameters. Subsequently, the sample size for model validation was also estimated. Results To develop reliable predictive models, 1397 neuroblastoma patients are required, 1060 high-risk neuroblastoma patients and 1345 diffuse intrinsic pontine glioma (DIPG) patients. This sample size can be lowered by reducing the number of variables included in the model, by including direct measures of the outcome to be predicted and/or by increasing the follow-up period. For model validation, the estimated sample size resulted to be 326 patients for neuroblastoma, 246 for high-risk neuroblastoma, and 592 for DIPG. Conclusions Given the variability of the different sample sizes obtained, we recommend using methods based on epidemiological data and the nature of the results, as the results are tailored to the specific clinical problem. In addition, sample size can be reduced by lowering the number of parameter predictors, by including direct measures of the outcome of interest.

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Section: Discussionmentioning

confidence: 99%

A practical solution to estimate the sample size required for clinical prediction models generated from observational research on data

Baeza-Delgado¹,

Alberich²,

Carot-Sierra

et al. 2022

Eur Radiol Exp

View full text Add to dashboard Cite

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“…GANs have been successfully applied to various medical applications, including the study of electroencephalograms [27], brain tumor detection [28], and analysis of dermatological lesions [29]. Moreover, diversified GAN architectures have been utilized in [30] for risk stratification and disease classification in rare clinical cases, namely, primary Sjögren's syndrome and hypertrophic cardiomyopathy. Meanwhile, traditional algebraic transformation methods continue to find applications in the medical field, such as in [31], where five different direct image manipulation methods are analyzed for prostate cancer detection.…”

Section: Data Augmentationmentioning

confidence: 99%

“…The method of GANs has been applied to the study of electroencephalograms [27], the detection of brain tumors [28], and dermatological lesions [29]. GANs diverse in network structure are employed by the authors in [30] for risk stratification and disease classification in two rare clinical cases, namely, primary Sjögren's syndrome and hypertrophic cardiomyopathy.…”

Section: Data Augmentationmentioning

confidence: 99%

CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence

et al. 2023

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Data-driven algorithms have proven to be effective for a variety of medical tasks, including disease categorization and prediction, personalized medicine design, and imaging diagnostics. Although their performance is frequently on par with that of clinicians, their widespread use is constrained by a number of obstacles, including the requirement for high-quality data that are typical of the population, the difficulty of explaining how they operate, and ethical and regulatory concerns. The use of data augmentation and synthetic data generation methodologies, such as federated learning and explainable artificial intelligence ones, could provide a viable solution to the current issues, facilitating the widespread application of artificial intelligence algorithms in the clinical application domain and reducing the time needed for prevention, diagnosis, and prognosis by up to 70%. To this end, a novel AI-based functional framework is conceived and presented in this paper.

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Future Trends

2023

Multiscale Modelling in Biomedical Engineering

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A computational pipeline for data augmentation towards the improvement of disease classification and risk stratification models: A case study in two clinical domains

Cited by 12 publications

References 20 publications

A practical solution to estimate the sample size required for clinical prediction models generated from observational research on data

A practical solution to estimate the sample size required for clinical prediction models generated from observational research on data

CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence

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