Artificial Intelligence in Nuclear Medicine

Nensa, Felix; Demircioğlu, Aydın; Rischpler, Christoph

doi:10.2967/jnumed.118.220590

Cited by 133 publications

(77 citation statements)

References 67 publications

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“…The use of data mining in the analysis of medical data is currently becoming a popular issue. Data mining methods are applied in order to use various data obtained from medical examinations in the diagnosis of diseases [21]. In this respect, data mining can be defined as the work of extracting implicit information of value among large-scale data [31].…”

Section: Discussionmentioning

confidence: 99%

“…In order to classify diseases using diagnostic data, it is necessary to create a model that reveals hidden patterns in these data sets. Entropy-based methods, logistic regression models, Bayesian classifiers, and artificial neural networks are the commonly used classification methods [21][22][23]31,33]. The classification rules that are formed by applying these methods on training data are then applied to test data in order to predict the class of each subject.…”

Section: Discussionmentioning

confidence: 99%

“…In this respect, data mining methods may be applied to classify PET images as rightor left-sided TLE and the outputs of the classification algorithm may support the human interpreter with classification and differential diagnosis [21]. The application of data mining is based on the evaluation of quantitative data obtained from images by statistical methods, such as logistic regression, model trees, and naive Bayes classifiers [21][22][23]. In TLE patients with a favorable postsurgical outcome, the definite lateralization results may be used in the supervised learning of data mining methods.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of brain FDG PET images in temporal lobe epilepsy for lateralization of epileptogenic focus using data mining methods

Akdemir¹,

Capraz²,

Ateş³

et al. 2020

Turk J Med Sci

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Background/aim: In temporal lobe epilepsy (TLE), brain positron emission tomography (PET) performed with F-18 fluorodeoxyglucose (FDG) is commonly used for lateralization of the epileptogenic temporal lobe. In this study, we aimed to evaluate the success of quantitative analysis of brain FDG PET images using data mining methods in the lateralization of the epileptogenic temporal lobe. Materials and methods: Presurgical interictal brain FDG PET images of 49 adult mesial TLE patients with a minimum of 2 years of postsurgical follow-up and Engel I outcomes were retrospectively analyzed. Asymmetry indices were calculated from PET images from the mesial temporal lobe and its contiguous structures. The J48 and the logistic model tree (LMT) data mining algorithms were used to find classification rules for the lateralization of the epileptogenic temporal lobe. The classification results obtained by these rules were compared with the physicians' visual readings and the findings of single-patient statistical parametric mapping (SPM) analyses in a test set of 18 patients. An additional 5-fold cross-validation was applied to the data to overcome the limitation of a relatively small sample size. Results: In the lateralization of 18 patients in the test set, J48 and LMT methods were successful in 16 (89%) and 17 (94%) patients, respectively. The visual consensus readings were correct in all patients and SPM results were correct in 16 patients. The 5-fold crossvalidation method resulted in a mean correct lateralization ratio of 96% (47/49) for the LMT algorithm. This ratio was 88% (43 / 49) for the J48 algorithm. Conclusion: Lateralization of the epileptogenic temporal lobe with data mining methods using regional metabolic asymmetry values obtained from interictal brain FDG PET images in mesial TLE patients is highly accurate. The application of data mining can contribute to the reader in the process of visual evaluation of FDG PET images of the brain.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of brain FDG PET images in temporal lobe epilepsy for lateralization of epileptogenic focus using data mining methods

Akdemir¹,

Capraz²,

Ateş³

et al. 2020

Turk J Med Sci

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“…AI and ML may aid medical imaging-based diagnosis, not only in terms of the detection of disease, but also in management, reporting, and prognostication. [1] ANN is a computational model of ML based on the human brain. It has been found that ANN are powerful tools for pattern recognition, signal processing, image or speech data compression, and learning expert systems.…”

Section: Introductionmentioning

confidence: 99%

Usefulness of an artificial neural network for a beginner to achieve similar interpretations to an expert when examining myocardial perfusion images

Chiba

Kudo

Ideguchi

et al. 2020

Preprint

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Background: This study examined whether using an artificial neural network (ANN) helps beginners in diagnostic cardiac imaging to achieve similar results to experts when interpreting stress myocardial perfusion imaging (MPI).Methods: 138 patients underwent stress MPI with Tc-labeled agents. An expert and a beginner interpreted stress/rest MPI with or without the ANN and the results were compared. The myocardium was divided into 5 regions (the apex; septum; anterior; lateral, and inferior regions), and the degree of myocardial blood flow was evaluated from 0 to 4, and SSS, SRS, and SDS were calculated. The ANN effect, defined as the difference in each of these scores between with and without the ANN, was calculated to investigate the influence of ANN on the interpreters' performance. Results: The ANN effect was smaller for the expert than for the beginner. Conclusions: When interpreting MPI, beginners may achieve similar results to experts by using an ANN. Thus, interpreting MPI with ANN may be useful for beginners.

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“…Because of the rapid progress of machine learning in the interpretation of medical images, dire predictions have been made about the future of radiology as a clinical discipline (7-9). Nensa et al focus on the use case ''nuclear medicine'' for artificial intelligence, highlighting associated challenges but also opportunities (10). As a consequence of the advances in algorithms, deep learning, computing power, and the introduction of digital PET allowing for fast dynamic image acquisition trigger the hope of finding clinical evidence and relevance for radiomics.…”

mentioning

confidence: 99%