Detection and Classification of Myocardial Delayed Enhancement                     Patterns on MR Images with Deep Neural Networks: A Feasibility                     Study

Ohta, Yoshiji; Yunaga, Hiroto; Kitao, Shinichiro; Fukuda, Tetsuya; Ogawa, Toshihide

doi:10.1148/ryai.2019180061

Cited by 9 publications

(10 citation statements)

References 21 publications

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“…Herein, we used ResNet50 because it exhibited the best diagnostic performance in a certain task. 26 , 27 Fifth, the time interval between the DXA and CT did not appear to match between the test data set of 87 (14–185) days and the training and validation data set of 49 (9–121) days. Although this difference was not statistically significant, we could not rule out the possibility of affecting the performance of the prediction of BMD and TBS with deep learning in this study.…”

Section: Discussionmentioning

confidence: 96%

“…AlexNet, GoogLeNet, and ResNet are often used as CNN models for diagnostic imaging analysis. Herein, we used ResNet50 because it exhibited the best diagnostic performance in a certain task 26,27 . Fifth, the time interval between the DXA and CT did not appear to match between the test data set of 87 (14–185) days and the training and validation data set of 49 (9–121) days.…”

Section: Discussionmentioning

confidence: 99%

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Feasibility of Bone Mineral Density and Bone Microarchitecture Assessment Using Deep Learning With a Convolutional Neural Network

Yoshida,

Tanabe,

Nishiyama

et al. 2023

J Comput Assist Tomogr

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Objectives: We evaluated the feasibility of using deep learning with a convolutional neural network for predicting bone mineral density (BMD) and bone microarchitecture from conventional computed tomography (CT) images acquired by multivendor scanners. Methods:We enrolled 402 patients who underwent noncontrast CT examinations, including L1-L4 vertebrae, and dual-energy x-ray absorptiometry (DXA) examination. Among these, 280 patients (3360 sagittal vertebral images), 70 patients (280 sagittal vertebral images), and 52 patients (208 sagittal vertebral images) were assigned to the training data set for deep learning model development, the validation, and the test data set, respectively. Bone mineral density and the trabecular bone score (TBS), an index of bone microarchitecture, were assessed by DXA. BMD DL and TBS DL were predicted by deep learning with a convolutional neural network (ResNet50). Pearson correlation tests assessed the correlation between BMD DL and BMD, and TBS DL and TBS. The diagnostic performance of BMD DL for osteopenia/osteoporosis and that of TBS DL for bone microarchitecture impairment were evaluated using receiver operating characteristic curve analysis.Results: BMD DL and BMD correlated strongly (r = 0.81, P < 0.01), whereas TBS DL and TBS correlated moderately (r = 0.54, P < 0.01). The sensitivity and specificity of BMD DL for identifying osteopenia or osteoporosis were 93% and 90%, and 100% and 94%, respectively. The sensitivity and specificity of TBS DL for identifying patients with bone microarchitecture impairment were 73% for all values. Conclusions:The BMD DL and TBS DL derived from conventional CT images could identify patients who should undergo DXA, which could be a gatekeeper tool for detecting latent osteoporosis/osteopenia or bone microarchitecture impairment.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Feasibility of Bone Mineral Density and Bone Microarchitecture Assessment Using Deep Learning With a Convolutional Neural Network

Yoshida,

Tanabe,

Nishiyama

et al. 2023

J Comput Assist Tomogr

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“…This feature can help distinguish between ischemia and non-ischemic cardiomyopathy and reveal myocardial dysfunction. Researchers investigated a group of 200 patients and found that their accuracy ranged from 78.9% to 82.1 percent [160].…”

Section: Application Of Machine Learning In Cardiology Through Imagin...mentioning

confidence: 99%

Artificial intelligence and machine learning in precision and genomic medicine

Quazi

2022

Med Oncol

152

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The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

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Section: Genomicsmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Precision and Genomic Medicine

Quazi¹

2021

Preprint

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The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision-making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of data sets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

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Detection and Classification of Myocardial Delayed Enhancement Patterns on MR Images with Deep Neural Networks: A Feasibility Study

Cited by 9 publications

References 21 publications

Feasibility of Bone Mineral Density and Bone Microarchitecture Assessment Using Deep Learning With a Convolutional Neural Network

Feasibility of Bone Mineral Density and Bone Microarchitecture Assessment Using Deep Learning With a Convolutional Neural Network

Artificial intelligence and machine learning in precision and genomic medicine

Artificial Intelligence and Machine Learning in Precision and Genomic Medicine

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