Artificial intelligence and radiomics in nuclear medicine: potentials and challenges

Aktolun, Cumali

doi:10.1007/s00259-019-04593-0

Cited by 28 publications

(15 citation statements)

References 23 publications

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“…There are many data modelling techniques and also many open source tools that can be used to facilitate the design of a data model. The choice of the optimal algorithm depends on a variety of factors that include, but are not limited to, data type/learning approach (supervised or unsupervised learning), the importance of accuracy in the chosen model, the need for speed in data analysis, the data analysed, the size of the data set, the need for hierarchical output, or the need for categorical variables [30].…”

Section: Discussionmentioning

confidence: 99%

Leveraging Data Science for a Personalized Haemodialysis

Hueso¹,

Haro²,

Calabia³

et al. 2020

Kidney Dis

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Background: The 2019 Science for Dialysis Meeting at Bellvitge University Hospital was devoted to the challenges and opportunities posed by the use of data science to facilitate precision and personalized medicine in nephrology, and to describe new approaches and technologies. The meeting included separate sections for issues in data collection and data analysis. As part of data collection, we presented the institutional ARGOS e-health project, which provides a common model for the standardization of clinical practice. We also pay specific attention to the way in which randomized controlled trials offer data that may be critical to decision-making in the real world. The opportunities of open source software (OSS) for data science in clinical practice were also discussed. Summary: Precision medicine aims to provide the right treatment for the right patients at the right time and is deeply connected to data science. Dialysis patients are highly dependent on technology to live, and their treatment generates a huge volume of data that has to be analysed. Data science has emerged as a tool to provide an integrated approach to data collection, storage, cleaning, processing, analysis, and interpretation from potentially large volumes of information. This is meant to be a perspective article about data science based on the experience of the experts invited to the Science for Dialysis Meeting and provides an up-to-date perspective of the potential of data science in kidney disease and dialysis. Key messages: Healthcare is quickly becoming data-dependent, and data science is a discipline that holds the promise of contributing to the development of personalized medicine, although nephrology still lags behind in this process. The key idea is to ensure that data will guide medical decisions based on individual patient characteristics rather than on averages over a whole population usually based on randomized controlled trials that excluded kidney disease patients. Furthermore, there is increasing interest in obtaining data about the effectiveness of available treatments in current patient care based on pragmatic clinical trials. The use of data science in this context is becoming increasingly feasible in part thanks to the swift developments in OSS.

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Section: Discussionmentioning

confidence: 99%

Leveraging Data Science for a Personalized Haemodialysis

Hueso¹,

Haro²,

Calabia³

et al. 2020

Kidney Dis

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“…It is widely known that individual differences in tumors, as a result of tumor heterogeneity, are the key to the lack of effective treatment. 6,7 Clinically, there is a great heterogeneity of tumors, and clinicians need clear decision-making information to support decisions about the treatment. 8 Tumor heterogeneity is mainly manifested in morphology, gene expression, metabolism, proliferation, metastasis, and response to treatment.…”

Section: Why Is Radiomics a Good Choice To Personalize Patient Treatmmentioning

confidence: 99%

Radiomics in radiotherapy: Applications and future challenges

Qiu

Duan

Yin

2020

Precision Radiation Oncology

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Radiomics has the potential to personalize patient treatment by using medical images that are already being acquired in clinical practice. Recently, with the development of computational and imaging technology, radiotherapy has brought unlimited opportunities driven by radiomics in individual cancer treatment and precision medicine care. This article reviews the advances in the application of radiomics in lung cancer, head and neck cancer, and other cancer sites. Additionally, we comment on the future challenges of radiomic research.

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“…Machine learning is emergent in nuclear medicine and offers great potentials and challenges (Uribe et al., 2019). It offers the opportunity to save time for clinicians by performing repetitive tasks, generating preliminary reports and detecting what is not visible to the human eye (Aktolun, 2019). The main challenges of machine learning in nuclear medicine, outlined by Aktolun, are the insufficient amount of data to train generated model and the labelling of the data set by experts that takes a lot of time (Aktolun, 2019).…”

Section: Review On Machine Learning Applications For Predictive Data mentioning

confidence: 99%

“…It offers the opportunity to save time for clinicians by performing repetitive tasks, generating preliminary reports and detecting what is not visible to the human eye (Aktolun, 2019). The main challenges of machine learning in nuclear medicine, outlined by Aktolun, are the insufficient amount of data to train generated model and the labelling of the data set by experts that takes a lot of time (Aktolun, 2019). The most frequently encountered data are radiomics that include high‐throughput computation of quantitative medical images features (Ibrahim et al., 2019).…”

Section: Review On Machine Learning Applications For Predictive Data mentioning

confidence: 99%

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

Jamin

Ábrahám

Humeau-Heurtier

2020

Clin Physio Funct Imaging

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With the evidence-based medicine, doctors have settings and data from which they can evaluate the risk-benefit ratio of a treatment. They can also analyse different information to make risk-based choices. With the emergence of new technologies in medicine, a large amount of data is recorded offering interesting perspectives with machine learning for predictive data analytics. However, the collection, storage and use of data raise ethical and security issues. Internet of things (IoT) is a field that groups all the systems that can be identified, perform measurements and do tasks while being open on the Internet. It is an emerging field (Stojkoska & Trivodaliev, 2017; Whitmore et al., 2015) that leads to various IoT applications in healthcare (Islam et al., 2015; Yin et al., 2016). Pavel et al. (2013) proposed to divide the future of IoT health care into four parts: patient-based applications, physician-based applications, treatment and prevention

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Artificial intelligence and radiomics in nuclear medicine: potentials and challenges

Cited by 28 publications

References 23 publications

Leveraging Data Science for a Personalized Haemodialysis

Leveraging Data Science for a Personalized Haemodialysis

Radiomics in radiotherapy: Applications and future challenges

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

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