Deep learning-based interpretation of basal/acetazolamide brain perfusion SPECT leveraging unstructured reading reports

Ryoo, Hyun Gee; Choi, Hongyoon; Lee, Dong Hoon

doi:10.1007/s00259-019-04670-4

Cited by 9 publications

(10 citation statements)

References 25 publications

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“…Machine learning was used to distinguish patients with MMD presenting atherosclerotic disease or normal controls based on MRI imaging [41], to predict the bleeding risk based on digital subtraction angiography of the internal carotid artery [42], to identify cerebrovascular injury based on hemodynamic imaging [43], to study the abnormal cortical development of patients with MMD based on MRI imaging [44], to identify patients with MMD from controls based on X‐ray images of the skull [32], and to study hemodynamic abnormalities based on arterial spin labeling MRI [45, 46]. Convolution neural networks have also been used to evaluate and diagnose MMD based on digital subtraction angiography [47] and single photon emission computed tomography [48]. The studies described above provided extensive analyses of patients with MMD with high accuracy based on a large imaging system.…”

Section: Discussionmentioning

confidence: 99%

“…Accuracy, precision, recall and F1‐score were used in this study to evaluate the performance of the model [48], whose definitions were as follows:

Accuracy goodbreak= \frac{TP + TN}{TP + TN + FP + FN}

Precision goodbreak= \frac{TP}{TP + FP}

Recall goodbreak= \frac{TP}{TP + FN}

F 1 goodbreak= \frac{2 * Precision * Recall}{Precision + Recall} goodbreak= \frac{2 TP}{2 TP + FP + FN}

TP, TN, FP and FN represent the number of correctly classified positive samples, correctly classified negative samples, incorrectly classified negative samples and incorrectly classified positive samples, respectively.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Identification of moyamoya disease based on cerebral oxygen saturation signals using machine learning methods

Gao

Zou

et al. 2022

Journal of Biophotonics

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Section: Discussionmentioning

confidence: 99%

“…Accuracy, precision, recall and F1‐score were used in this study to evaluate the performance of the model [48], whose definitions were as follows:

Accuracy goodbreak= \frac{TP + TN}{TP + TN + FP + FN}

Precision goodbreak= \frac{TP}{TP + FP}

Recall goodbreak= \frac{TP}{TP + FN}

F 1 goodbreak= \frac{2 * Precision * Recall}{Precision + Recall} goodbreak= \frac{2 TP}{2 TP + FP + FN}

Section: Methodsmentioning

confidence: 99%

Identification of moyamoya disease based on cerebral oxygen saturation signals using machine learning methods

Gao

Zou

et al. 2022

Journal of Biophotonics

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“…Advances in the theory of stochastic gradient (in an analogy of MLEM and OSEM) [97] made it possible to train ML with big data by faster convergence, which enabled us to train DL with a more complex structure more efficiently. [24,107,108] and Parkinson's disease [109], and brain perfusion reserve decreases [110]). These methods [115,116], increasing spatial resolution of PET [117], and generating an MR-like mask from amyloid PET [118]).…”

Section: ) Rise Of Ml/dl Algorithmsmentioning

confidence: 99%

Nuclear Medicine Physics: Review of Advanced Technology

2020

Progress in Medical Physics

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This review aims to provide a brief, comprehensive overview of advanced technologies of nuclear medicine physics, with a focus on recent developments from both hardware and software perspectives. Developments in image acquisition/reconstruction, especially the time-of-flight and point spread function, have potential advantages in the image signal-to-noise ratio and spatial resolution. Modern detector materials and devices (including lutetium oxyorthosilicate, cadmium zinc tellurium, and silicon photomultiplier) as well as modern nuclear medicine imaging systems (including positron emission tomography [PET]/computerized tomography [CT], whole-body PET, PET/magnetic resonance [MR], and digital PET) enable not only high-quality digital image acquisition, but also subsequent image processing, including image reconstruction and postreconstruction methods. Moreover, theranostics in nuclear medicine extend the usefulness of nuclear medicine physics far more than quantitative image-based diagnosis, playing a key role in personalized/precision medicine by raising the importance of internal radiation dosimetry in nuclear medicine. Now that deep-learning-based image processing can be incorporated in nuclear medicine image acquisition/processing, the aforementioned fields of nuclear medicine physics face the new era of Industry 4.0. Ongoing technological developments in nuclear medicine physics are leading to enhanced image quality and decreased radiation exposure as well as quantitative and personalized healthcare.

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“…RNN is typically used when time series or historical clinical data are essential (164). Meanwhile, LSTM is useful when the input data is present within the time sequence (165). Finally, RCNN and Mask R-CNN are used for segmentation (166).…”

Section: Deep Learning Strategies Using Mri Ct and The Usmentioning

confidence: 99%

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Saba¹,

Skandha²,

Gupta³

et al. 2021

Ann Transl Med

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Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type

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Deep learning-based interpretation of basal/acetazolamide brain perfusion SPECT leveraging unstructured reading reports

Cited by 9 publications

References 25 publications

Identification of moyamoya disease based on cerebral oxygen saturation signals using machine learning methods

Identification of moyamoya disease based on cerebral oxygen saturation signals using machine learning methods

Nuclear Medicine Physics: Review of Advanced Technology

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

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