A deep learning algorithm to identify cervical ossification of posterior longitudinal ligaments on radiography

Tamai, Katsuto; Terai, Hidetomi; Hoshino, Minoru; Yabu, Akito; Tabuchi, Hitoshi; Sasaki, Ryo; Nakamura, Hiroaki

doi:10.1038/s41598-022-06140-8

Cited by 9 publications

(13 citation statements)

References 21 publications

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“…The map function is implemented by a Mapper interface where a map () method is declared, and it is reconstructed. The following core code implements the implementation [14,15] of the map function in the process of designing and constructing the big data analysis of intrusion detection based on Hadoop: (1) the Mapper interface for the highest feature data sample inspired by Hadoop and related notes, using Java as a tool [16,17].…”

Section: Big Data Storage For Intrusion Detection Based On Hadoopmentioning

confidence: 99%

“…In the process of designing and building a Hadoop-based intrusion detection big data analysis, the output record [20] is written only after the feature data is displayed, and its quality code represents the correct feature data store ID. The reduced function is also defined when using Reducer, as defined in the deep learning-based intrusion detection big data store designed and built in this paper as follows: definition of Reducer intrusion detection feature big data access interface of the highest feature data sample [16,17].…”

Section: Big Data Storage For Intrusion Detection Based On Hadoopmentioning

confidence: 99%

“…During the detection process, the state can be judged and detected. Based on the above analysis, a network protocol model for anomaly detection is proposed [16].…”

Section: Design Of Anomaly Model For Network Protocol Abusementioning

confidence: 99%

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Intrusion Detection Method Based on Deep Learning

Tian

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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In today’s information age, the scale of the Internet is growing, the information capacity is also expanding explosively, and network security is becoming more and more important. Intrusion detection is regarded as a traditional security protection technology and is a key means to ensure the security of the network environment. Among them, the deep belief network performs well, and it can automatically learn abstract features for classification. In order to further improve the detection rate and reduce the false positive rate, it is necessary to improve the detection rate of small sample data. This paper builds an intelligent deep learning model and analysis model for intrusion detection data based on TensorFlow. By learning to identify network intrusion characteristic data, the characteristic data and model are stored in the big data storage system built by Hadoop. This algorithm has achieved good experiment result. Build a model knowledge base and an intrusion feature behavior library, use the decision tree model to automatically match the security control strategy, realize a highly intelligent security control model with self-learning ability, and solve the rapid identification of unknown intrusion behaviors. Experiments show that the algorithm can effectively improve the detection rate.

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Section: Big Data Storage For Intrusion Detection Based On Hadoopmentioning

confidence: 99%

Section: Big Data Storage For Intrusion Detection Based On Hadoopmentioning

confidence: 99%

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Intrusion Detection Method Based on Deep Learning

Tian

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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“…Koyama et al 18 combined a unique motion sensor with a deep-learning algorithm to detect hand clumsiness due to cervical myelopathy. We also developed a deep-learning algorithm to identify cervical OPLL on radiography, which had a significantly higher diagnostic accuracy than that of experienced spine physicians 19. These studies could be considered as aspects of radiomics, a method for extracting a large number of features from radiographic images using data-characterization algorithms 20.…”

Section: Discussionmentioning

confidence: 99%

“…We also developed a deep-learning algorithm to identify cervical OPLL on radiography, which had a significantly higher diagnostic accuracy than that of experienced spine physicians. 19 These studies could be considered as aspects of radiomics, a method for extracting a large number of features from radiographic images using data-characterization algorithms. 20 Radiomics significantly aids physicians in improving the efficiency and accuracy of their diagnoses and has even been used to prognosticate outcomes by measuring and analyzing features of medical images.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Algorithm for Identifying Cervical Cord Compression Due to Degenerative Canal Stenosis on Radiography

Tamai

Terai

Hoshino

et al. 2023

Spine

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Study design. Cross-sectional study. Objective. Validate the diagnostic accuracy of a deep-learning algorithm for cervical cord compression due to degenerative canal stenosis on radiography. Summary of Background Data. The diagnosis of degenerative cervical myelopathy is often delayed, resulting in improper management. Screening tools for suspected degenerative cervical myelopathy would help identify patients who require detailed physical evaluation. Materials and Methods. Data from 240 patients (120 with cervical stenosis on magnetic resonance imaging and 120 age and sexmatched controls) were randomly divided into training (n = 198) and test (n = 42) data sets. The deep-learning algorithm, designed to identify the suspected stenosis level on radiography, was constructed using a convolutional neural network model called Effi-cientNetB2, and radiography and magnetic resonance imaging data from the training data set. The accuracy and area under the curve of the receiver operating characteristic curve were calculated for the independent test data set. Finally, the number of correct diagnoses was compared between the algorithm and 10 physicians using the test cohort. Results. The diagnostic accuracy and area under the curve of the deep-learning algorithm were 0.81 and 0.81, respectively, in the independent test data set. The rate of correct responses in the test data set was significantly higher for the algorithm than for the physician's consensus (81.0% vs. 66.2%; P = 0.034). Furthermore, the accuracy of the algorithm was greater than that of each individual physician. Conclusions. We developed a deep-learning algorithm capable of suggesting the presence of cervical spinal cord compression on cervical radiography and highlighting the suspected levels on radiographic imaging when cord compression is identified. The diagnostic accuracy of the algorithm was greater than that of spine physicians.

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Multimodal Deep Learning-based Radiomics Approach for Predicting Surgical Outcomes in Patients with Cervical Ossification of the Posterior Longitudinal Ligament

Maki,

Furuya,

Katsumi

et al. 2024

Spine

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Study Design. A retrospective analysis. Objective. This research sought to develop a predictive model for surgical outcomes in patients with cervical ossification of the posterior longitudinal ligament (OPLL) using deep learning and machine learning (ML) techniques. Summary of Background Data. Determining surgical outcomes assists surgeons in communicating prognosis to patients and setting their expectations. Deep learning and ML are computational models that identify patterns from large datasets and make predictions. Methods. Of the 482 patients, 288 patients were included in the analysis. A minimal clinically important difference (MCID) was defined as gain in Japanese Orthopaedic Association (JOA) score of 2.5 points or more. The predictive model for MCID achievement at 1 year post-surgery was constructed using patient background, clinical symptoms, and preoperative imaging features (x-ray, CT, MRI) analyzed via LightGBM and deep learning with RadImagenet. Results. The median preoperative JOA score was 11.0 (IQR: 9.0-12.0), which significantly improved to 14.0 (IQR: 12.0-15.0) at 1 year after surgery (P < 0.001, Wilcoxon signed-rank test). The average improvement rate of the JOA score was 44.7%, and 60.1% of patients achieved the MCID. Our model exhibited an area under the receiver operating characteristic curve of 0.81 and the accuracy of 71.9% in predicting MCID at 1 year. Preoperative JOA score and certain preoperative imaging features were identified as the most significant factors in the predictive models. Conclusion. A predictive ML and deep learning model for surgical outcomes in OPLL patients is feasible, suggesting promising applications in spinal surgery. Level of evidence. 4

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A deep learning algorithm to identify cervical ossification of posterior longitudinal ligaments on radiography

Cited by 9 publications

References 21 publications

Intrusion Detection Method Based on Deep Learning

Intrusion Detection Method Based on Deep Learning

Deep Learning Algorithm for Identifying Cervical Cord Compression Due to Degenerative Canal Stenosis on Radiography

Multimodal Deep Learning-based Radiomics Approach for Predicting Surgical Outcomes in Patients with Cervical Ossification of the Posterior Longitudinal Ligament

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