Development and Validation of a Deep Learning Model Using Convolutional Neural Networks to Identify Scaphoid Fractures in Radiographs

Yoon, Alfred P.; Lee, Yi Lun; Kane, Robert L; Kuo, Chien-Lin; Lin, Chi‐Hung; Chung, Kevin C.

doi:10.1001/jamanetworkopen.2021.6096

Cited by 47 publications

(46 citation statements)

References 35 publications

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“…e model-free approach works in the real environment for learning. Instead, the model-based algorithm reduces the interaction with the real environment during the learning phase [17][18][19]. e goal is to build models based on these interactions with the environment and then use that model to simulate other events.…”

Section: Related Workmentioning

confidence: 99%

Financial Market Sentiment Prediction Technology and Application Based on Deep Learning Model

Guo

2022

Computational Intelligence and Neuroscience

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In the real world, there are a variety of situations that require strategy control, that is reinforcement learning, as a method for studying the decision-making and behavioral strategies of intelligence. It has received a lot of research and empirical evidence on its functions and roles and is also a method recognized by scholars. Among them, combining reinforcement learning with sentiment analysis is an important theoretical research direction, but so far there is still relatively little research work about it, and it still has the problems of poor application effect and low accuracy rate. Therefore, in this study, we use the features related to sentiment analysis and deep reinforcement learning and use various algorithms for optimization to deal with the above problems. In this study, a sentiment analysis method incorporating knowledge graphs is designed using the characteristics of the stock trading market. A deep reinforcement learning investment trading strategy algorithm for sentiment analysis combined with knowledge graphs from this study is used in the subsequent experiments. The deep reinforcement learning system combining sentiment analysis and knowledge graph implemented in this study not only analyzes the algorithm from the theoretical aspect but also simulates data from the stock exchange market for experimental comparison and analysis. The experimental results illustrate that the deep reinforcement learning algorithm combining sentiment analysis and knowledge graphs used in this study can achieve better gains than the existing traditional reinforcement learning algorithms and has better practical application value.

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Section: Related Workmentioning

confidence: 99%

Financial Market Sentiment Prediction Technology and Application Based on Deep Learning Model

Guo

2022

Computational Intelligence and Neuroscience

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“…However, Yoon et al later established two model, namely the Apparent Fracture Model and the Occult Fracture Model, which excelled in identifying obvious and potential scaphoid fractures, respectively. Their research used more datasets and escalated deep learning algorithms, and both models showed higher sensitivity and specificity than that of Langerhuizen et al ( 29 ).…”

Section: Discussionmentioning

confidence: 93%

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Feng

Zhou

Wang

et al. 2022

Front. Public Health

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BackgroundAs a research hotspot, deep learning has been continuously combined with various research fields in medicine. Recently, there is a growing amount of deep learning-based researches in orthopedics. This bibliometric analysis aimed to identify the hotspots of deep learning applications in orthopedics in recent years and infer future research trends.MethodsWe screened global publication on deep learning applications in orthopedics by accessing the Web of Science Core Collection. The articles and reviews were collected without language and time restrictions. Citespace was applied to conduct the bibliometric analysis of the publications.ResultsA total of 822 articles and reviews were finally retrieved. The analysis showed that the application of deep learning in orthopedics has great prospects for development based on the annual publications. The most prolific country is the USA, followed by China. University of California San Francisco, and Skeletal Radiology are the most prolific institution and journal, respectively. LeCun Y is the most frequently cited author, and Nature has the highest impact factor in the cited journals. The current hot keywords are convolutional neural network, classification, segmentation, diagnosis, image, fracture, and osteoarthritis. The burst keywords are risk factor, identification, localization, and surgery. The timeline viewer showed two recent research directions for bone tumors and osteoporosis.ConclusionPublications on deep learning applications in orthopedics have increased in recent years, with the USA being the most prolific. The current research mainly focused on classifying, diagnosing and risk predicting in osteoarthritis and fractures from medical images. Future research directions may put emphasis on reducing intraoperative risk, predicting the occurrence of postoperative complications, screening for osteoporosis, and identification and classification of bone tumors from conventional imaging.

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“…format using MRICroGL and pruned to 512×512 pixels, with an average thickness of 3 mm. We selected 2364 MRI water images (1020 of patients and 1344 of control) with recognizable pancreas imaging and manually labeled them using LabelMe [ 38 ] for building up DCNN [ 39 ]. We used 315 participants’ images for training and 79 data sets for testing (approximately 80:20 ratio).…”

Section: Methodsmentioning

confidence: 99%

An Adapted Deep Convolutional Neural Network for Automatic Measurement of Pancreatic Fat and Pancreatic Volume in Clinical Multi-Protocol Magnetic Resonance Images: A Retrospective Study with Multi-Ethnic External Validation

et al. 2022

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Pancreatic volume and fat fraction are critical prognoses for metabolic diseases like type 2 diabetes (T2D). Magnetic Resonance Imaging (MRI) is a required non-invasive quantification method for the pancreatic fat fraction. The dramatic development of deep learning has enabled the automatic measurement of MR images. Therefore, based on MRI, we intend to develop a deep convolutional neural network (DCNN) that can accurately segment and measure pancreatic volume and fat fraction. This retrospective study involved abdominal MR images from 148 diabetic patients and 246 healthy normoglycemic participants. We randomly separated them into training and testing sets according to the proportion of 80:20. There were 2364 recognizable pancreas images labeled and pre-treated by an upgraded superpixel algorithm for a discernible pancreatic boundary. We then applied them to the novel DCNN model, mimicking the most accurate and latest manual pancreatic segmentation process. Fat phantom and erosion algorithms were employed to increase the accuracy. The results were evaluated by dice similarity coefficient (DSC). External validation datasets included 240 MR images from 10 additional patients. We assessed the pancreas and pancreatic fat volume using the DCNN and compared them with those of specialists. This DCNN employed the cutting-edge idea of manual pancreas segmentation and achieved the highest DSC (91.2%) compared with any reported models. It is the first framework to measure intra-pancreatic fat volume and fat deposition. Performance validation reflected by regression R2 value between manual operation and trained DCNN segmentation on the pancreas and pancreatic fat volume were 0.9764 and 0.9675, respectively. The performance of the novel DCNN enables accurate pancreas segmentation, pancreatic fat volume, fraction measurement, and calculation. It achieves the same segmentation level of experts. With further training, it may well surpass any expert and provide accurate measurements, which may have significant clinical relevance.

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Development and Validation of a Deep Learning Model Using Convolutional Neural Networks to Identify Scaphoid Fractures in Radiographs

Cited by 47 publications

References 35 publications

Financial Market Sentiment Prediction Technology and Application Based on Deep Learning Model

Financial Market Sentiment Prediction Technology and Application Based on Deep Learning Model

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

An Adapted Deep Convolutional Neural Network for Automatic Measurement of Pancreatic Fat and Pancreatic Volume in Clinical Multi-Protocol Magnetic Resonance Images: A Retrospective Study with Multi-Ethnic External Validation

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