Neural hypernetwork approach for pulmonary embolism diagnosis

Rucco, Matteo; Sousa-Rodrigues, David; Merelli, Emanuela; Johnson, Jeffrey; Falsetti, Lorenzo; Nitti, Cinzia; Salvi, Aldo

doi:10.1186/s13104-015-1554-5

“…The cause of the reduction of neurons in the hidden layer can be well-conducted risk factor selection and efficient training of the neural network (18). Rucco et al used a combined ANN, more advanced methods, and a larger sample size in their study and achieved an accuracy index of 94 percent in prediction (19). Whereas, in this study two different types of neural networks and a higher number of risk factors were utilized.…”

Section: Discussionmentioning

confidence: 91%

“…The results of a number of studies indicate that the application of ANN method can help physicians with the clinical diagnosis and prevention of PE and, additionally, has decreased the demand for CT scan and perfusion scan diagnostic methods (16)(17)(18)(19)(20). The existence and application of smart systems to improve medical cares and reducing the number of DVT and PE cases seem to be essential considering the importance of the case and the issue of medical and diagnostic errors.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network

Agharezaei¹,

Agharezaei²,

Nemati³

et al. 2016

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Background: Venous thromboembolism is a common cause of mortality among hospitalized patients and yet it is preventable through detecting the precipitating factors and a prompt diagnosis by specialists. The present study has been carried out in order to assist specialists in the diagnosis and prediction of the risk level of pulmonary embolism in patients, by means of artificial neural network. high risk patients diagnosed through perfusion scan diagnostic method were also diagnosed correctly through the method proposed in the present study. Conclusions: The results of this study can be a good resource for physicians, medical assistants, and healthcare staff to diagnose high risk patients more precisely and prevent the mortalities. Additionally, expenses and other unnecessary diagnostic methods such as perfusion scans can be efficiently reduced. Method

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“…The cause of the reduction of neurons in the hidden layer can be well-conducted risk factor selection and efficient training of the neural network (18). Rucco et al used a combined ANN, more advanced methods, and a larger sample size in their study and achieved an accuracy index of 94 percent in prediction (19). Whereas, in this study two different types of neural networks and a higher number of risk factors were utilized.…”

Section: Discussionmentioning

confidence: 99%

The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network

Agharezaei

¹

,

Agharezaei

²

,

Nemati

³

et al. 2016

View full text Add to dashboard Cite

Background:Venous thromboembolism is a common cause of mortality among hospitalized patients and yet it is preventable through detecting the precipitating factors and a prompt diagnosis by specialists. The present study has been carried out in order to assist specialists in the diagnosis and prediction of the risk level of pulmonary embolism in patients, by means of artificial neural network.Method:A number of 31 risk factors have been used in this study in order to evaluate the conditions of 294 patients hospitalized in 3 educational hospitals affiliated with Kerman University of Medical Sciences. Two types of artificial neural networks, namely Feed-Forward Back Propagation and Elman Back Propagation, were compared in this study.Results:Through an optimized artificial neural network model, an accuracy and risk level index of 93.23 percent was achieved and, subsequently, the results have been compared with those obtained from the perfusion scan of the patients. 86.61 percent of high risk patients diagnosed through perfusion scan diagnostic method were also diagnosed correctly through the method proposed in the present study.Conclusions:The results of this study can be a good resource for physicians, medical assistants, and healthcare staff to diagnose high risk patients more precisely and prevent the mortalities. Additionally, expenses and other unnecessary diagnostic methods such as perfusion scans can be efficiently reduced.

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“…Matteo Rucco introduced an integrative approach based on Q-analysis with machine learning [95]. The new approach, called Neural Hypernetwork, has been applied in a case study of PE diagnosis, involving data from 28 diagnostic features of 1427 people considered to be at risk of PE and obtained a satisfying recognition rate of 94%.…”

Section: Pulmonary Embolism (Pe)mentioning

confidence: 99%

Survey on deep learning for pulmonary medical imaging

Ma

¹

,

Song

²

,

Tian

³

et al. 2019

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As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

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Neural hypernetwork approach for pulmonary embolism diagnosis

Cited by 33 publications

References 33 publications

The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network

The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network

The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network

Survey on deep learning for pulmonary medical imaging

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