AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging

Yousefirizi, Fereshteh; Decazes, Pierre; Amyar, Amine; Ruan, Su; Saboury, Babak; Rahmim, Arman

doi:10.1016/j.cpet.2021.09.010

Cited by 45 publications

(25 citation statements)

References 142 publications

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“…Neural networks may provide more flexibility and accuracy compared to threshold approaches, for instance through incorporating user input on accuracy of the selection over time. It is expected that numerous AI solutions will emerge that aim to make routine organ and lesion delineation more readily available in the clinic in order to empower more advanced image analytics (58). As an example, routine quantification of metabolic tumor volume (MTV) from FDG PET/CT images, known to be superior to SUV quantification for a number of predictive/prognostic tasks for certain cancers, is expected to be routinely enabled by such methods (e.g.…”

Section: Part B: Opportunities For Ai Towards Improved Image Analysismentioning

confidence: 99%

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Saboury

Bradshaw

Boellaard³

et al. 2022

J Nucl Med

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Section: Part B: Opportunities For Ai Towards Improved Image Analysismentioning

confidence: 99%

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Saboury

Bradshaw

Boellaard³

et al. 2022

J Nucl Med

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“…Note that there is an extension of the CLAM solution in [Lu et al, 2021b], where information injection is limited to the concatenation of an intermediate data representation within a neural network with information about the patient's sex. Such an approach is common where an image and a small amount of tabular data are required to make a predic- tion, however, it does not provide context about the spatial relationships of the patches.…”

Section: Proposed Contributionmentioning

confidence: 99%

Information Injection to Deep Learning Solutions in Knowledge Transfer

Andersen

Chiarandini

2022

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

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We present ``Together about Dementia'', a mobile health app that aims to help persons with dementia when they get lost through the help of caregivers, relatives, and volunteering citizens. When the app detects that a person with dementia is disoriented, wandering, and getting lost, it triggers an alarm that activates a relative and possibly a volunteer in the proximity. A backend system based on a microservice and serverless architecture performs the detection of wandering and the subsequent coordination of users that are put on a mission to the rescue of the person with dementia. The backend system implements an AI technique for spatio-temporal anomaly detection based on location data recorded by the frontend system installed on the portable device of the person with dementia.

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“…Through the process of “training,” these algorithms improve in using and mapping the observed variables (“features” or “predictors”) to subdivide the data sample into sets of outcome variables (“labels” or “targets”). Based on “labels,” ML can be classified into three broad subsets: supervised, unsupervised, and reinforcement learning [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

Liberini¹,

Laudicella

Balma³

et al. 2022

Eur Radiol Exp

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In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients’ risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these “big data” in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.

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AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging

Cited by 45 publications

References 142 publications

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Information Injection to Deep Learning Solutions in Knowledge Transfer

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

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