Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury

This study aimed to explore the application value of magnetic resonance imaging (MRI) images based on deep learning algorithms in the diagnosis of tibial plateau fractures combined with meniscus injuries. The original MRI image was input into the deep learning convolutional neural network (CNN), and the knee joint undersampled and fully sampled MRI image data were used for training to obtain a neural network model that can effectively remove the noise and blur of the undersampled image. Then, the image was reconstructed by the Regridding model to obtain an image with less noise and clearer structure. At the same time, all subjects underwent knee MRI examinations, and algorithms were used to analyze the sensitivity, specificity, and accuracy of their images. It was found that of 160 menisci from 80 cases of tibial plateau fractures, 64 were normal meniscus and 88 were injured menisci. The sensitivity, specificity, and accuracy of optimized MRI in diagnosing fracture of tibial plateau combined with meniscus injury were 96.9%, 93.2%, and 95.3%, respectively. In conclusion, the restored MRI images have high sensitivity in the diagnosis of meniscus injury and high consistency with the intraoperative results. It suggests that the optimized MRI image is effective in the diagnosis of meniscus injury.

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A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2019-0004-oa ◽

2019 ◽

Vol 144 (3) ◽

pp. 370-378 ◽

Cited By ~ 7

Author(s):

David R. Martin ◽

Joshua A. Hanson ◽

Rama R. Gullapalli ◽

Fred A. Schultz ◽

Aisha Sethi ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Gold Standard ◽

Phase 1 ◽

Phase 2 ◽

Gastric Biopsies ◽

H Pylori ◽

Sensitivity Specificity ◽

Receiver Operating

Context.— Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched. Objective.— To investigate the use of DL for nonneoplastic gastric biopsies. Design.— Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion. Results.— For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] = 99.7%) and H pylori (AUC = 100%), and 40% for reactive gastropathy (AUC = 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%). Conclusions.— A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis.

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Deep learning for improved prediction of late-onset cardiomyopathy among childhood cancer survivors: A report from the St. Jude Lifetime Cohort (SJLIFE).

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.10545 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 10545-10545

Author(s):

Fatma Gunturkun ◽

Robert L Davis ◽

Gregory T. Armstrong ◽

John L. Jefferies ◽

Kirsten K. Ness ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

High Risk ◽

Cancer Survivors ◽

Childhood Cancer ◽

Childhood Cancer Survivors ◽

Learning Approaches ◽

Baseline Evaluation ◽

Sensitivity Specificity ◽

Ecg Data

10545 Background: Early identification of survivors at high risk for treatment-induced cardiomyopathy may allow for prevention and/or early intervention. We utilized deep learning methods using COG guideline-recommended baseline electrocardiography (ECG) to improve prediction of future cardiomyopathy. Methods: SJLIFE is a cohort of 5-year clinically assessed childhood cancer survivors including baseline ECG measurements. Development of cardiomyopathy was identified from clinical and echocardiographic measurement using CTCAE criteria (grade 3-4). We applied deep learning approaches to ECG, treatment exposure and demographic data obtained at baseline SJLIFE assessment. We trained a cascaded model combining a 12-layer 1D convolutional neural network to extract features from waveform ECG signals with a 2-layer dense neural network to embed features from other phenotypic data in tabular format to determine if use of deep learning with ECG data could improve prediction of cardiomyopathy. Results: Among 1,218 subjects (median age 31.7 years, range 18.4-66.4) without cardiomyopathy at baseline evaluation, 616 (51%) were male, 1,041 (85%) white, 157 (13%) African American and 792 (65%) were survivors of lymphoma/leukemia. Follow-up averaged 5 (0.5 to 9) years from baseline examination. Mean chest radiation dose was 1350 cGy (range 0 to 6,200 cGy) and mean cumulative anthracycline dose was 191 mg/m2 (range o to 734 mg/m2). A total of 114 (9.4%) survivors developed cardiomyopathy after baseline. A cascaded deep learning model built on a training set (N = 974 participants) classified cardiomyopathy in the test set (N = 244 participants) using both clinical and ECG data with a sensitivity of 70%, specificity of 73%, and AUC of 0.74 (95% CI 0.63-0.85), compared to a model using clinical data alone (sensitivity 61%, specificity 62%, and AUC 0.67, 95% CI 0.56-0.79). In subgroup analyses, models predicting cardiomyopathy within 0-4 years following baseline had a sensitivity, specificity, and AUC of 77%, 78%, and 0.78 (0.65-0.91), respectively. When predicting cardiomyopathy 5-9 years following baseline, model performance dropped to a sensitivity, specificity, and AUC of 70%, 70%, and 0.68 (0.50-0.87), respectively. Conclusions: Deep learning using ECG at baseline evaluation significantly improved prediction of cardiomyopathy in childhood cancer survivors at high risk for cardiomyopathy. Future directions will incorporate deep learning approaches to echocardiography to further improve prediction.

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Correlation Between CT Images of Lateral Plateau and Lateral Meniscus Injuries in Patients With Schatzker II Tibial Plateau Fractures：A Retrospective Study

10.21203/rs.3.rs-543373/v1 ◽

2021 ◽

Author(s):

Pu Ying ◽

Lei Zhu ◽

Wenge Ding ◽

Yue Xu ◽

Xiaowei Jiang ◽

...

Keyword(s):

Tibial Plateau ◽

Lateral Meniscus ◽

Ct Images ◽

Tibial Plateau Fractures ◽

Posterior Horn ◽

Operating Point ◽

Optimal Operating ◽

Meniscus Injury ◽

Lateral Plateau ◽

Meniscus Injuries

Abstract Background: There is a great deal of controversy on whether routine MRI examination is needed for fresh fractures while the vast majority of patients with tibial plateau fractures receive preoperative X-ray and CT examinations. The purpose of the study was to analyze the exact correlation between CT images of lateral plateau and lateral meniscus injuries in Schatzker II tibial plateau fractures. Methods: Two hundred and ninety-six Schatzker II tibial plateau fracture patients from August 2012 to January 2021 in two trauma centers were enrolled for the analysis. According to the actual situation during open reduction internal fixation (ORIF) and knee arthroscopic surgery, patients were divided into meniscus injury (including rupture, incarceration, etc.) and non-meniscus injury groups. By measuring the value of both lateral plateau depression (LPD) and lateral plateau widening (LPW) of lateral tibial plateau on the coronary CT images, the correlation of which and lateral meniscus injury was analyzed. Meanwhile, the relevant receiver operating characteristic (ROC) curve was drawn to evaluate the optimal operating point of these two indicators which could predict meniscus injury. Results: Meniscus injury group mainly showed injuries involving the mid-body and posterior horn of the meniscus (98.1%, 157/160). The average LPD was 13.2 ± 3.2 mm, while the average value of the group without meniscus injury was 9.4 ± 3.2 mm. The difference was statistically significant (P < 0.05). The average LPW was 8.0 ± 1.4 mm and 6.8 ± 1.6 mm in two groups with a significant difference (P < 0.05). The optimal operating point of LPD and LPW was 7.9 mm (sensitivity-95.0%, specificity-58.8%, area under the curve (AUC-0.818) and 7.5 mm (sensitivity-70.0%, specificity-70.6%, AUC-0.724), respectively. Conclusions: The mid-body and posterior horn of lateral meniscus injury is more likely to occur in patients who had Schatzker II tibial plateau fractures when LPD > 7.9 mm and/or LPW > 7.5 mm on CT manifestations and these findings will definitely provide guidance for orthopedic surgeons in treating such injuries. During the operation, more attention should be paid to the treatment of the meniscus and full consideration is needed be taken to situations such as meniscus rupture, incarceration and other possible fracture reduction difficulties, poor vertical line, etc., in order to achieve better surgical results.

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Deep Learning-Based Image Feature with Arthroscopy-Aided Early Diagnosis and Treatment of Meniscus Injury of Knee Joint

Journal of Healthcare Engineering ◽

10.1155/2021/2254594 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Zijian Li ◽

Shiyou Ren ◽

Xintao Zhang ◽

Lu Bai ◽

Changqing Jiang ◽

...

Keyword(s):

Deep Learning ◽

Knee Joint ◽

Knee Function ◽

Partial Resection ◽

Knee Meniscus ◽

Meniscus Injury ◽

Secondary Resection ◽

Network Algorithm ◽

Meniscus Suture ◽

Mri Image

The aim of this study is to explore the clinical effect of deep learning-based MRI-assisted arthroscopy in the early treatment of knee meniscus sports injury. Based on convolutional neural network algorithm, Adam algorithm was introduced to optimize it, and the magnetic resonance imaging (MRI) image super-resolution reconstruction model (SRCNN) was established. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were compared between SRCNN and other algorithms. Sixty patients with meniscus injury of knee joint were studied. Arthroscopic surgery was performed according to the patients’ actual type of injury, and knee scores were evaluated for all patients. Then, postoperative scores and MRI results were analyzed. The results showed that the PSNR and SSIM values of the SRCNN algorithm were (42.19 ± 4.37) dB and 0.9951, respectively, which were significantly higher than those of other algorithms ( P < 0.05). Among patients with meniscus injury, 17 cases (28.33%) were treated with meniscus suture, 39 cases (65.00%) underwent secondary resection, 3 cases (5.00%) underwent partial resection, and 1 case (1.67%) underwent full resection. After meniscus suture, secondary resection, partial resection, and total resection, the knee function scores of patients after treatment were (83.17 ± 8.63), (80.06 ± 7.96), (84.34 ± 7.74), and (85.52 ± 5.97), respectively. There was no great difference in knee function scores after different methods of treatment ( P > 0.05), and there were considerable differences compared with those before treatment ( P < 0.01). Compared with the results of arthroscopy, there was no significant difference in the grading of meniscus injury by MRI ( P > 0.05). To sum up, the SRCNN algorithm based on the deep convolutional network algorithm improved the MRI image quality and the diagnosis of knee meniscus injuries. Arthroscopic knee surgery had good results and had great clinical application and promotion value.

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A Deep Recurrent Neural Network with Gated Momentum Unit for CT Image Reconstruction

10.36227/techrxiv.15066138 ◽

2021 ◽

Author(s):

Masaki Ikuta

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Reconstruction ◽

Image Data ◽

Observation Data ◽

Ct Image ◽

Image Study ◽

Data Set ◽

Image Domain ◽

Ct Image Reconstruction

<div><div><div><p>Many algorithms and methods have been proposed for Computed Tomography (CT) image reconstruction, partic- ularly with the recent surge of interest in machine learning and deep learning methods. The majority of recently proposed methods are, however, limited to the image domain processing where deep learning is used to learn the mapping from a noisy image data set to a true image data set. While deep learning-based methods can produce higher quality images than conventional model-based post-processing algorithms, these methods have lim- itations. Deep learning-based methods used in the image domain are not sufficient for compensating for lost information during a forward and a backward projection in CT image reconstruction especially with a presence of high noise. In this paper, we propose a new Recurrent Neural Network (RNN) architecture for CT image reconstruction. We propose the Gated Momentum Unit (GMU) that has been extended from the Gated Recurrent Unit (GRU) but it is specifically designed for image processing inverse problems. This new RNN cell performs an iterative optimization with an accelerated convergence. The GMU has a few gates to regulate information flow where the gates decide to keep important long-term information and discard insignificant short- term detail. Besides, the GMU has a likelihood term and a prior term analogous to the Iterative Reconstruction (IR). This helps ensure estimated images are consistent with observation data while the prior term makes sure the likelihood term does not overfit each individual observation data. We conducted a synthetic image study along with a real CT image study to demonstrate this proposed method achieved the highest level of Peak Signal to Noise Ratio (PSNR) and Structure Similarity (SSIM). Also, we showed this algorithm converged faster than other well-known methods.</p></div></div></div>

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Spatial Based Deep Learning Autonomous Wheel Robot Using CNN

Lontar Komputer Jurnal Ilmiah Teknologi Informasi ◽

10.24843/lkjiti.2020.v11.i03.p05 ◽

2020 ◽

Vol 11 (3) ◽

pp. 167

Author(s):

Eko Wahyu Prasetyo ◽

Nambo Hidetaka ◽

Dwi Arman Prasetya ◽

Wahyu Dirgantara ◽

Hari Fitria Windi

Keyword(s):

Neural Network ◽

Deep Learning ◽

Autonomous Vehicle ◽

Image Data ◽

Steering Control ◽

Accuracy Rate ◽

Artificial Intelligent ◽

The Third ◽

Network Method ◽

Wheeled Vehicles

The development of technology is growing rapidly; one of the most popular among the scientist is robotics technology. Recently, the robot was created to resemble the function of the human brain. Robots can make decisions without being helped by humans, known as AI (Artificial Intelligent). Now, this technology is being developed so that it can be used in wheeled vehicles, where these vehicles can run without any obstacles. Furthermore, of research, Nvidia introduced an autonomous vehicle named Nvidia Dave-2, which became popular. It showed an accuracy rate of 90%. The CNN (Convolutional Neural Network) method is used in the track recognition process with input in the form of a trajectory that has been taken from several angles. The data is trained using Jupiter's notebook, and then the training results can be used to automate the movement of the robot on the track where the data has been retrieved. The results obtained are then used by the robot to determine the path it will take. Many images that are taken as data, precise the results will be, but the time to train the image data will also be longer. From the data that has been obtained, the highest train loss on the first epoch is 1.829455, and the highest test loss on the third epoch is 30.90127. This indicates better steering control, which means better stability.

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Batik Classification using Deep Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9039.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 11416-11421

Keyword(s):

Neural Network ◽

Deep Learning ◽

Data Analysis ◽

Convolutional Neural Network ◽

Image Data ◽

High Accuracy ◽

Learning Method ◽

Philosophy Of Life ◽

Proposed Model

Batik is one of the Indonesian cultural heritages that has been recognized by the global community. Indonesian batik has a vast diversity in motifs that illustrate the philosophy of life, the ancestral heritage and also reflects the origin of batik itself. Because of the manybatik motifs, problems arise in determining the type of batik itself. Therefore, we need a classification method that can classify various batik motifs automatically based on the batik images. The technique of image classification that is used widely now is deep learning method. This technique has been proven of its capacity in identifying images in high accuracy. Architecture that is widely used for the image data analysis is Convolutional Neural Network (CNN) because this architecture is able to detect and recognize objects in an image. This workproposes to use the method of CNN and VGG architecture that have been modified to overcome the problems of classification of the batik motifs. Experiments of using 2.448 batik images from 5 classes of batik motifs showed that the proposed model has successfully achieved an accuracy of 96.30%.

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An Enhanced Deep Recurrent Neural Network for Autism Spectrum Disorder Diagnosis

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3893 ◽

2021 ◽

Vol 11 (12) ◽

pp. 3028-3037

Author(s):

D. Pavithra ◽

A. N. Jayanthi

Keyword(s):

Neural Network ◽

Autism Spectrum Disorder ◽

Deep Learning ◽

Recurrent Neural Network ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Domain Identification ◽

Investigation Area ◽

Deep Recurrent Neural Network ◽

Sensitivity Specificity

Autism Spectrum Disorder is one of the major investigation area in current era. There are many research works introduced earlier for handling the Autism Spectrum Disorders. However those research works doesn’t achieve the expected accuracy level. The accuracy and prediction efficiency can be increased by building a better classification system using Deep Learning. This paper focuses on the deep learning technique for Autism Diagnosis and the domain identification. In the proposed work, an Enhanced Deep Recurrent Neural Network has been developed for the detection of ASD at all ages. It attempts to predict the autism spectrum in the children along with prediction of areas which can predict the autism in the prior level. The main advantage of EDRNN is to provide higher accuracy in classification and domain identification. Here Artificial Algal Algorithm is used for identifying the most relevant features from the existing feature set. This model was evaluated for the data that followed Indian Scale for Assessment of Autism. The results obtained for the proposed EDRNN has better accuracy, sensitivity, specificity, recall and precision.

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CBIR-CNN: Content Based Image Retrieval on celebrity data using Deep Convolution Neural Network

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666200129111928 ◽

2020 ◽

Vol 13 ◽

Author(s):

Pushpendra Singh ◽

P.N. Hrisheekesha ◽

Vinai Kumar Singh

Keyword(s):

Neural Network ◽

Computer Vision ◽

Deep Learning ◽

Image Retrieval ◽

Image Data ◽

Content Based Image Retrieval ◽

Facial Image ◽

Huge Amount ◽

Proposed Model ◽

Deep Convolution Neural Network

Background: Finding region of interest in an image and content-based image analysis has been a challenging task for last two decades. With the advancement in image processing, computer vision field and huge amount of image data generation, to manage this huge amount of data Content-Based Image Retrieval System (CBIR) has attracted several researchers as a common technique to manage this huge amount of data. It is an approach of searching user interest, based on visual information present in an image. The requirement of high computation power and huge memory limits deployment of CBIR technique in real-time scenarios. Objective: In this paper an advanced deep learning model is applied for CBIR on facial image data. We design a deep convolution neural network architecture where activation of convolution layer is used for feature representation and include max pooling as feature reduction technique. Furthermore, our model uses partial feature mapping as image descriptor to incorporate the property that facial image contains repeated information. Method: Existing CBIR approaches primarily consider colour, texture and low-level features for mapping and localizing image segments. While deep learning has shown high performance in numerous fields of research, its application in CBIR is still very limited. Human face contains significant information to be used in a content driven task and applicable to various applications of computer vision and multimedia systems. In this research work, a deep learning-based model has been discussed for content-based image retrieval (CBIR). In CBIR, there are two important things 1) classification and 2) retrieval of image based on similarity. For the classification purpose a four-convolution layer model has been proposed. For the calculation of the similarity Euclidian distance measure has been used between the images. Results: Proposed model is completely unsupervised, and it is fast and accurate in comparison to other deep learning models applied for CBIR over facial dataset. The proposed method provided satisfactory results from the experiment. It outperforms other CNN-based models and other unsupervised techniques used for CBIR. The proposed method provided satisfactory results from the experiment and it outperforms other CNN-based models such as VGG16, Inception V3, ResNet50 and MobileNet. Moreover, the performance of proposed model has been compared with pre-trained models in terms of accuracy, storage space and inference time.

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Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense

Journal of Sensors ◽

10.1155/2019/7630926 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 18

Author(s):

Yunong Tian ◽

Guodong Yang ◽

Zhe Wang ◽

En Li ◽

Zize Liang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Optical Sensors ◽

Data Augmentation ◽

Image Data ◽

Disease Diagnosis ◽

Lesion Detection ◽

Training Data ◽

Yield Reduction ◽

Learning Technology

Plant disease is one of the primary causes of crop yield reduction. With the development of computer vision and deep learning technology, autonomous detection of plant surface lesion images collected by optical sensors has become an important research direction for timely crop disease diagnosis. In this paper, an anthracnose lesion detection method based on deep learning is proposed. Firstly, for the problem of insufficient image data caused by the random occurrence of apple diseases, in addition to traditional image augmentation techniques, Cycle-Consistent Adversarial Network (CycleGAN) deep learning model is used in this paper to accomplish data augmentation. These methods effectively enrich the diversity of training data and provide a solid foundation for training the detection model. In this paper, on the basis of image data augmentation, densely connected neural network (DenseNet) is utilized to optimize feature layers of the YOLO-V3 model which have lower resolution. DenseNet greatly improves the utilization of features in the neural network and enhances the detection result of the YOLO-V3 model. It is verified in experiments that the improved model exceeds Faster R-CNN with VGG16 NET, the original YOLO-V3 model, and other three state-of-the-art networks in detection performance, and it can realize real-time detection. The proposed method can be well applied to the detection of anthracnose lesions on apple surfaces in orchards.

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