scholarly journals Deep Neural Network Based Artificial Intelligence Assisted Diagnosis of Bone Scintigraphy for Cancer Bone Metastasis

2020 ◽  
Author(s):  
Zhen Zhao ◽  
Yong Pi ◽  
Lisha Jiang ◽  
Yongzhao Xiang ◽  
Jianan Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Bone Metastasis ◽  
Bone Metastases ◽  
Bone Scintigraphy ◽  
Diagnostic Performance ◽  
Deep Neural Network ◽  
Diagnostic Techniques ◽  
Comparable Performance ◽  
Assisted Diagnosis

Abstract Background: Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detection of cancer bone metastasis, and it occupies great workload for nuclear physicians. So we aim to architecture an automatic image interpreting system to assist physicians for diagnosis.Methods: We developed an artificial intelligence (AI) model based on a deep neural network with 12222 cases of 99mTc-MDP bone scintigraphy, and evaluated its diagnostic performance of bone metastases.Results: This AI model demonstrated diagnostic performance by areas under the curve (AUC) of receiver operating characteristic (ROC) was 0.988 for breast cancer, 0.955 for prostate cancer, 0.957 for lung cancer, and 0.971 for other cancers. Applying this AI model to a new dataset of 400 BS cases, it represented comparable performance to that of human physicians in individually classifying bone metastasis. Further AI-consulting interpretation also improved human diagnostic sensitivity and accuracy.Conclusion: In total, this AI model performed valuable benefit for nuclear physicians in timely and accurate evaluation of cancer bone metastases.

scholarly journals Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhen Zhao ◽  
Yong Pi ◽  
Lisha Jiang ◽  
Yongzhao Xiang ◽  
Jianan Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Nuclear Medicine ◽  
Bone Metastasis ◽  
Bone Scintigraphy ◽  
Diagnostic Performance ◽  
Deep Neural Network ◽  
Diagnostic Techniques ◽  
Comparable Performance ◽  
Assisted Diagnosis

Abstract Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detecting cancer bone metastasis, and it occupies an enormous workload for nuclear medicine physicians. So, we aimed to architecture an automatic image interpreting system to assist physicians for diagnosis. We developed an artificial intelligence (AI) model based on a deep neural network with 12,222 cases of 99mTc-MDP bone scintigraphy and evaluated its diagnostic performance of bone metastasis. This AI model demonstrated considerable diagnostic performance, the areas under the curve (AUC) of receiver operating characteristic (ROC) was 0.988 for breast cancer, 0.955 for prostate cancer, 0.957 for lung cancer, and 0.971 for other cancers. Applying this AI model to a new dataset of 400 BS cases, it represented comparable performance to that of human physicians individually classifying bone metastasis. Further AI-consulted interpretation also improved human diagnostic sensitivity and accuracy. In total, this AI model performed a valuable benefit for nuclear medicine physicians in timely and accurate evaluation of cancer bone metastasis.

scholarly journals Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning

Metals ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 451
Author(s):  
Martin A. Kesse ◽  
Eric Buah ◽  
Heikki Handroos ◽  
Godwin K. Ayetor
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Neural Network ◽  
Predictive Accuracy ◽  
Welding Process ◽  
Tig Welding ◽  
Bead Geometry ◽  
Bead Width ◽  
Modeling Tools ◽  
Welding Processes

Recent developments in artificial intelligence (AI) modeling tools allows for envisaging that AI will remove elements of human mechanical effort from welding operations. This paper contributes to this development by proposing an AI tungsten inert gas (TIG) welding algorithm that can assist human welders to select desirable end factors to achieve good weld quality in the welding process. To demonstrate its feasibility, the proposed model has been tested with data from 27 experiments using current, arc length and welding speed as control parameters to predict weld bead width. A fuzzy deep neural network, which is a combination of fuzzy logic and deep neural network approaches, is applied in the algorithm. Simulations were carried out on an experimental test dataset with the AI TIG welding algorithm. The results showed 92.59% predictive accuracy (25 out of 27 correct answers) as compared to the results from the experiment. The performance of the algorithm at this nascent stage demonstrates the feasibility of the proposed method. This performance shows that in future work, if its predictive accuracy is improved with human input and more data, it could achieve the level of accuracy that could support the human welder in the field to enhance efficiency in the welding process. The findings are useful for industries that are in the welding trade and serve as an educational tool.

Evaluation of bone metastasis of castration-resistant prostate cancer after enzalutamide and abiraterone using Bone Scan Index on bone scintigraphy.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. e596-e596
Author(s):  
Suguru Kadomoto ◽  
Kouji Izumi ◽  
Takahiro Nohara ◽  
Konaka Hiroyuki ◽  
Yoshifumi Kadono ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Bone Metastasis ◽  
Bone Metastases ◽  
Bone Scintigraphy ◽  
Bone Scan ◽  
Average Rate ◽  
Castration Resistant Prostate Cancer ◽  
Bone Scan Index ◽  
Castration Resistant ◽  
Better Than

e596 Background: It was ambiguous till now to evaluate the change of bone metastasis by various treatments. To quantify the change of bone metastases by enzalutamide, abiraterone, and docetaxel for the castration-resistant prostate cancer (CRPC) with bone metastases (bmCRPC), we employed Bone Scan Index (BSI) on bone scintigraphy. Methods: We retrospectively evaluated the change of PSA and bone metastases of CRPC patients who were treated with enzalutamide (Enz), abiraterone (Abi) and/or docetaxel (DOC) in our hospital. All patients underwent Tc-99m MDP bone scintigraphy. The degree of bone metastases was analyzed using BSI, which was calculated by BONENAVI (FUJIFILM RI Pharma, Japan; EXINIbone, EXINI Diagnostics, Sweden). 19 patients were treated with enzalutamide (8 cases: pre-docetaxel, 11 cases: post-docetaxel). The median PSA of patients treated with Enz was 12.64 ng/ml (1.63-199 ng/ml). And 11 patients were treated with abiraterone (5 cases: pre-docetaxel, 6 cases: post-docetaxel). The median PSA of patients treated with Abi was 26.37 ng/ml (2.29-199 ng/ml). Results: We observed decline of PSA in 18/30 cases (9 cases: pre-DOC, 9 cases: post-DOC). Decline of PSA to 50% or more was observed in 14 cases. In contrast, decline of BSI was observed in 53.3% (16/30) cases and decline of PSA to 25% or more was observed in only 6 cases. BSI decreased in 84.6% (11/13) of pre-DOC setting and in 29.4% (5/17) of post-DOC setting indicating that change of BSI was poor in post-DOC setting. However, DOC had already decreased BSI in 91.7% (11/12) before Abi or Enz treatment. Moreover, the average rate of BSI decline in the patients that BSI decreased by DOC was better than the patients that BSI decreased by Abi/Enz (-48.46% vs -28.56%). Finally, although the rate of BSI change by Enz was weakly correlated with the rate of PSA decline (y = 0.3906x + 25.35, R2 = 0.3423), BSI continued to increase in four cases in spite of PSA decline. Conclusions: BSI using BONENAVI on bone scintigraphy was helpful for evaluating the effectiveness of treatment and following-up of bmCRPC.

scholarly journals Artificial Intelligence Segmented Dynamic Video Images for Continuity Analysis in the Detection of Severe Cardiovascular Disease

2021 ◽  
Vol 14 ◽  
Author(s):  
Xi Zhu ◽  
Wei Xia ◽  
Zhuqing Bao ◽  
Yaohui Zhong ◽  
Yu Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Cardiovascular Diseases ◽  
High Risk ◽  
Sudden Cardiac Death ◽  
Sudden Death ◽  
Cardiac Death ◽  
Deep Neural Network ◽  
Video Image ◽  
Bundle Branch Block

In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four layers stack structure is designed to automatically extract the deep features of the segmented dynamic video image shot, and six categories of normal, atrial premature, ventricular premature, right bundle branch block, left bundle branch block, and pacing are achieved through hierarchical training and optimization. Accurate recognition of heartbeats with an average accuracy of 99.5%. It provides technical assistance for the intelligent prediction of high-risk cardiovascular diseases like ventricular fibrillation. An intelligent prediction algorithm for sudden cardiac death based on the echolocation network was proposed. By designing an echolocation network with a multilayer serial structure, an intelligent distinction between sudden cardiac death signal and non-sudden death signal was realized, and the signal was predicted 5 min before sudden death occurred, with an average prediction accuracy of 94.32%. Using the self-learning capability of stack sparse auto-coding network, a large amount of label-free data is designed to train the stack sparse auto-coding deep neural network to automatically extract deep representations of plaque features. A small amount of labeled data then introduced to micro-train the entire network. Through the automatic analysis of the fiber cap thickness in the plaques, the automatic identification of thin fiber cap-like vulnerable plaques was achieved, and the average overlap of vulnerable regions reached 87%. The overall time for the automatic plaque and vulnerable plaque recognition algorithm was 0.54 s. It provides theoretical support for accurate diagnosis and endogenous analysis of high-risk cardiovascular diseases.

scholarly journals Image Classification Using Reinforcement Learning

2020 ◽  
Vol 23 (6) ◽  
pp. 1172-1191
Author(s):  
Artem Aleksandrovich Elizarov ◽  
Evgenii Viktorovich Razinkov
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Reinforcement Learning ◽  
Image Classification ◽  
Deep Neural Network ◽  
Learning Algorithms ◽  
Further Development

Recently, such a direction of machine learning as reinforcement learning has been actively developing. As a consequence, attempts are being made to use reinforcement learning for solving computer vision problems, in particular for solving the problem of image classification. The tasks of computer vision are currently one of the most urgent tasks of artificial intelligence. The article proposes a method for image classification in the form of a deep neural network using reinforcement learning. The idea of ​​the developed method comes down to solving the problem of a contextual multi-armed bandit using various strategies for achieving a compromise between exploitation and research and reinforcement learning algorithms. Strategies such as -greedy, -softmax, -decay-softmax, and the UCB1 method, and reinforcement learning algorithms such as DQN, REINFORCE, and A2C are considered. The analysis of the influence of various parameters on the efficiency of the method is carried out, and options for further development of the method are proposed.

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