Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics.
Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology research to deal with large and complex imaging data sets. Nowadays, ML tools have become easily accessible to anyone. Such a low threshold to accessibility might lead to inappropriate usage and misinterpretati...
| Publicado en: | Diagnostic & Interventional Radiology Vol. 28; no. 5; pp. 450 - 463 |
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| Autor principal: | |
| Formato: | diagnostic images research tables/charts Journal Article |
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Galenos Yayinevi Tic. LTD. STI
Sep2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159741424&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159741424 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13053825 39NM jtl: Diagnostic & Interventional Radiology issn: 13053825 maglogo: N pubinfo: dt: Sep2022 vid: 28 iid: 5 pid: 28155 pub: Galenos Yayinevi Tic. LTD. STI artinfo: ui: 159741424 159741424 NLM36218149 159741424 10.5152/dir.2022.211297 NLM36218149 159741424 ppf: 450 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics. aug: au: Koçak, Burak affil: Department of Radiology, Basaksehir Cam and Sakura City Hospital, Basaksehir, İstanbul, Turkey sug: subj: Specialties, Medical Methods Artificial Intelligence Human Reproducibility of Results ab: Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology research to deal with large and complex imaging data sets. Nowadays, ML tools have become easily accessible to anyone. Such a low threshold to accessibility might lead to inappropriate usage and misinterpretation, without a clear intention. Therefore, ensuring methodological rigor is of paramount importance. Getting closer to the real-world clinical implementation of AI, a basic understanding of the main concepts should be a must for every radiology professional. In this respect, simplified explanations of the key concepts along with pitfalls and recommendations would be helpful for general radiology community to develop and improve their AI mindset. In this work, twenty-two key issues are reviewed within three categories: pre-modeling, modeling, and post-modeling. Firstly, the concept is shortly defined for each issue. Then, related common pitfalls and best practices are provided. Specifically, the issues included in this paper were validity of scientific question, unrepresentative samples, sample size, missing data, quality of reference standard, batch effect, reliability of features, feature scaling, multi-collinearity, class imbalance, data and target leakage, high-dimensional data, optimization, overfitting, generalization, performance metrics, clinical utility, comparison with conventional statistical and clinical methods, interpretability and explainability, randomness, transparent reporting, and sharing data. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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