Imaging Manifestations and Evaluation of Postoperative Complications of Bone and Joint Infections under Deep Learning

To explore and evaluate the imaging manifestations of postoperative complications of bone and joint infections based on deep learning, a retrospective study was performed on 40 patients with bone and joint infections in the Department of Orthopedics of Orthopedics Hospital of Henan Province of Luoyang City. Sensitivity and Dice similarity coefficient (DSC) were used to evaluate the image results by convolutional neural network (CNN) algorithm. Imaging features of postoperative complications in 40 patients were analyzed. Then, three imaging methods were used to diagnose the features. Sensitivity and specificity were used to evaluate the diagnostic performance of three imaging methods for imaging features. Compared with professional doctors and biomarker algorithms, the sensitivity of CNN algorithm proposed was 90.6%, and DSC was 84.1%. Compared with traditional methods, the CNN algorithm has higher image resolution and wider and more accurate lesion area recognition and division. The three manifestations of soft tissue abscess, periosteum swelling, and bone damage were postoperative imaging features of bone and joint infections. In addition, compared with X-ray, CT examination and MRI examination were better for the examination of imaging characteristics. CT and MRI had higher sensitivity and specificity than X-ray. The experimental results show that CNN algorithm can effectively identify and divide pathological images and assist doctors to diagnose the images more efficiently in clinic.

Download Full-text

Computed Tomography Angiography under Deep Learning in the Treatment of Atherosclerosis with Rapamycin

Journal of Healthcare Engineering ◽

10.1155/2021/4543702 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Fuguang Ji ◽

Shuai Zhou ◽

Zhangshuan Bi

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Computed Tomography Angiography ◽

Atherosclerotic Lesion ◽

Region Of Interest ◽

Left Ventricular ◽

Dice Similarity Coefficient ◽

Imaging Features ◽

Lesion Area ◽

Imaging Characteristics

The clinical characteristics and vascular computed tomography (CT) imaging characteristics of patients were explored so as to assist clinicians in diagnosing patients with atherosclerosis. 316 patients with atherosclerosis who were hospitalized for emergency treatment were treated with rapamycin (RAPA) in the hospital. A group of manually delineated left ventricular myocardia (LVM) on the patient’s coronary computed tomography angiography (CCTA) were selected as the region of interest for imaging features extracted. The CCTA images of 80% of patients were randomly selected for training, and those of 20% of patients were used for verification. The correlation matrix method was used to remove redundant image omics features under different correlation thresholds. In the validation set, CCTA diagnostic parameters were about 40 times higher than the manually segmented data. The average dice similarity coefficient was 91.6%. The proposed method also produced a very small centroid distance (mean 1.058 mm, standard deviation 1.245 mm) and volume difference (mean 1.640), with a segmentation time of about 1.45 ± 0.51 s, compared to about 744.8 ± 117.49 s for physician manual segmentation. Therefore, the deep learning model effectively segmented the atherosclerotic lesion area, measured and assisted the diagnosis of future atherosclerosis clinical cases, improved medical efficiency, and accurately identified the patient’s lesion area. It had great application potential in helping diagnosis and curative effect analysis of atherosclerosis.

Download Full-text

Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging

Applied Sciences ◽

10.3390/app11020782 ◽

2021 ◽

Vol 11 (2) ◽

pp. 782 ◽

Cited By ~ 1

Author(s):

Albert Comelli ◽

Navdeep Dahiya ◽

Alessandro Stefano ◽

Federica Vernuccio ◽

Marzia Portoghese ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Deep Learning ◽

Magnetic Resonance ◽

Prostate Gland ◽

Dice Similarity Coefficient ◽

Processing Unit ◽

Imaging Features ◽

Resonance Imaging ◽

Central Processing ◽

Prostate Segmentation

Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization.

Download Full-text

Machine learning techniques as an eﬃcient alternative diagnostic tool for COVID-19 cases

10.22514/sv.2021.110 ◽

2021 ◽

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sensitivity And Specificity ◽

Predictive Power ◽

Characteristic Curve ◽

False Negative ◽

Machine Learning Techniques ◽

Support Vector ◽

X Ray ◽

Learning Techniques

Background: The SARS-CoV-2 virus has demonstrated the weakness of many health systems worldwide, creating a saturation and lack of access to treatments. A bottleneck to fight this pandemic relates to the lack of diagnostic infrastructure for early detection of positive cases, particularly in rural and impoverished areas of developing countries. In this context, less costly and fast machine learning (ML) diagnosis-based systems are helpful. However, most of the research has focused on deep-learning techniques for diagnosis, which are computationally and technologically expensive. ML models have been mainly used as a benchmark and are not entirely explored in the existing literature on the topic of this paper. Objective: To analyze the capabilities of ML techniques (compared to deep learning) to diagnose COVID-19 cases based on X-ray images, assessing the performance of these techniques and using their predictive power for such a diagnosis. Methods: A factorial experiment was designed to establish this power with X-ray chest images of healthy, pneumonia, and COVID-19 infected patients. This design considers data-balancing methods, feature extraction approaches, different algorithms, and hyper-parameter optimization. The ML techniques were evaluated based on classification metrics, including accuracy, the area under the receiver operating characteristic curve (AUROC), F1-score, sensitivity, and specificity. Results: The design of experiment provided the mean and its confidence intervals for the predictive capability of different ML techniques, which reached AUROC values as high as 90% with suitable sensitivity and specificity. Among the learning algorithms, support vector machines and random forest performed best. The down-sampling method for unbalanced data improved the predictive power significantly for the images used in this study. Conclusions: Our investigation demonstrated that ML techniques are able to identify COVID-19 infected patients. The results provided suitable values of sensitivity and specificity, minimizing the false-positive or false-negative rates. The models were trained with significantly low computational resources, which helps to provide access and deployment in rural and impoverished areas.

Download Full-text

DEEP LEARNING (CNN) MODEL FOR COVID-19 DETECTION FROM CHEST XRAY IMAGES

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v06i04.010 ◽

2021 ◽

Vol 6 (4) ◽

Author(s):

Ahmed Mohamed ◽

Ahmed Abdelhady

Keyword(s):

Neural Network ◽

Deep Learning ◽

Early Stage ◽

Open Data ◽

Disease Outbreak ◽

Imaging Features ◽

X Ray ◽

X Ray Imaging ◽

Chest X Ray ◽

Data Source

The Coronavirus disease outbreak result in many people to have severe respira- tory problems and it was recognized as a global health threat. Since the virus is targeting the lungs in the human body initially, chest x-ray imaging features were considered to be useful for the detection of the infection in the early stage. In this study, the chest x-ray data of 130 infected patients from an open data source that referenced Cohen J. Morrison P. Dao L., 2020 was used to build a CNN( Convolutional Neural-Network) model for the early detection of the disease. The model was trained with both infected and not-infected peoples’ chest x-ray images with 100 epochs which led to 0.98 accuracy finally. In order to use this model as a professional diagnosis element, it is highly recommended it be improved with more images and the model can be restructured to get a better accuracy.

Download Full-text

Radiologic Mimics of Osteomyelitis and Septic Arthritis: A Pictorial Essay

Radiology Research and Practice ◽

10.1155/2021/9912257 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Wanyin Lim ◽

Christen D. Barras ◽

Steven Zadow

Keyword(s):

Septic Arthritis ◽

Imaging Techniques ◽

Routine Practice ◽

Imaging Features ◽

Cross Sectional ◽

Musculoskeletal Infection ◽

Neoplastic Processes ◽

Joint Infections ◽

Bone And Joint Infections ◽

Pictorial Review

Various imaging techniques may be employed in the investigation of suspected bone and joint infections. These include ultrasound, radiography, functional imaging such as positron emission tomography (PET) and nuclear scintigraphy, and cross-sectional imaging, including computed tomography (CT) and magnetic resonance imaging (MRI). The cross-sectional modalities represent the imaging workhorse in routine practice. The role of imaging also extends to include assessment of the anatomical extent of infection, potentially associated complications, and treatment response. The imaging appearances of bone and joint infections are heterogeneous and depend on the duration of infection, an individual patient’s immune status, and virulence of culprit organisms. To add to the complexity of radiodiagnosis, one of the pitfalls of imaging musculoskeletal infection is the presence of other conditions that can share overlapping imaging features. This includes osteoarthritis, vasculopathy, inflammatory, and even neoplastic processes. Different pathologies may also coexist, for example, diabetic neuropathy and osteomyelitis. This pictorial review aims to highlight potential mimics of osteomyelitis and septic arthritis that are regularly encountered, with emphasis on specific imaging features that may aid the radiologist and clinician in distinguishing an infective from a noninfective aetiology.

Download Full-text

Diagnostic accuracy of X-ray versus CT in COVID-19: a propensity-matched database study

BMJ Open ◽

10.1136/bmjopen-2020-042946 ◽

2020 ◽

Vol 10 (11) ◽

pp. e042946

Author(s):

Aditya Borakati ◽

Adrian Perera ◽

James Johnson ◽

Tara Sood

Keyword(s):

Diagnostic Accuracy ◽

Sensitivity And Specificity ◽

Vital Signs ◽

Health Science ◽

British Society ◽

Initial Assessment ◽

Imaging Features ◽

Rt Pcr ◽

Thoracic Imaging ◽

X Ray

ObjectivesTo identify the diagnostic accuracy of common imaging modalities, chest X-ray (CXR) and CT, for diagnosis of COVID-19 in the general emergency population in the UK and to find the association between imaging features and outcomes in these patients.DesignRetrospective analysis of electronic patient records.SettingTertiary academic health science centre and designated centre for high consequence infectious diseases in London, UK.Participants1198 patients who attended the emergency department with paired reverse transcriptase PCR (RT-PCR) swabs for SARS-CoV-2 and CXR between 16 March and 16 April 2020.Main outcome measuresSensitivity and specificity of CXR and CT for diagnosis of COVID-19 using the British Society of Thoracic Imaging reporting templates. Reference standard was any RT-PCR positive naso-oropharyngeal swab within 30 days of attendance. ORs of CXR in association with vital signs, laboratory values and 30-day outcomes were calculated.ResultsSensitivity and specificity of CXR for COVID-19 diagnosis were 0.56 (95% CI 0.51 to 0.60) and 0.60 (95% CI 0.54 to 0.65), respectively. For CT scans, these were 0.85 (95% CI 0.79 to 0.90) and 0.50 (95% CI 0.41 to 0.60), respectively. This gave a statistically significant mean increase in sensitivity with CT of 29% (95% CI 19% to 38%, p<0.0001) compared with CXR. Specificity was not significantly different between the two modalities.CXR findings were not statistically significantly or clinically meaningfully associated with vital signs, laboratory parameters or 30-day outcomes.ConclusionsCT has substantially improved diagnostic performance over CXR in COVID-19. CT should be strongly considered in the initial assessment for suspected COVID-19. This gives potential for increased sensitivity and considerably faster turnaround time, where capacity allows and balanced against excess radiation exposure risk.

Download Full-text

Chest X-Ray Has Poor Diagnostic Accuracy and Prognostic Significance in COVID-19: A Propensity Matched Database Study

10.1101/2020.07.07.20147934 ◽

2020 ◽

Author(s):

Aditya Borakati ◽

Adrian Perera ◽

James Johnson ◽

Tara Sood

Keyword(s):

Computed Tomography ◽

Diagnostic Accuracy ◽

Sensitivity And Specificity ◽

Vital Signs ◽

Health Science ◽

British Society ◽

Imaging Features ◽

Rt Pcr ◽

X Ray ◽

Chest X Ray

Objectives: To identify the diagnostic accuracy of common imaging modalities, chest X-ray (CXR) and computed tomography (CT) for diagnosis of COVID-19 in the general emergency population in the UK and to find the association between imaging features and outcomes in these patients. Design: Retrospective analysis of electronic patient records Setting: Tertiary academic health science centre and designated centre for high consequence infectious diseases in London, UK. Participants: 1,198 patients who attended the emergency department with paired RT-PCR swabs for SARS-CoV 2 and CXR between 16th March and 16th April 2020 Main outcome measures: Sensitivity and specificity of CXR and CT for diagnosis of COVID-19 using the British Society of Thoracic Imaging reporting templates. Reference standard was any reverse transcriptase polymerase chain reaction (RT-PCR) positive naso-oropharyngeal swab within 30 days of attendance. Odds ratios of CXR in association with vital signs, laboratory values and 30-day outcomes were calculated. Results: Sensitivity and specificity of CXR for COVID-19 diagnosis were 0.56 (95% CI 0.51-0.60) and 0.60 (95% CI 0.54-0.65), respectively. For CT scans these were 0.85 (95% CI 0.79-0.90) and 0.50 (95% CI 0.41-0.60), respectively. This gave a statistically significant mean increase in sensitivity with CT compared with CXR, of 29% (95% CI 19%-38%, p<0.0001). Specificity was not significantly different between the two modalities. Chest X-ray findings were not statistically significantly or clinical meaningfully associated with vital signs, laboratory parameters or 30-day outcomes. Conclusions: Computed tomography has substantially improved diagnostic performance over CXR in COVID-19. CT should be strongly considered in the initial assessment for suspected COVID-19. This gives potential for increased sensitivity and considerably faster turnaround time, where capacity allows and balanced against excess radiation exposure risk.

Download Full-text

Repeatability and Reproducibility of MRI-based Radiomic Features in Rectal Cancer

10.21203/rs.3.rs-1105008/v1 ◽

2021 ◽

Author(s):

Robba Rai ◽

Michael B. Barton ◽

Phillip Chlap ◽

Gary P. Liney ◽

Carsten Brink ◽

...

Keyword(s):

Rectal Cancer ◽

Intraclass Correlation ◽

Magnetic Resonance Images ◽

Image Resolution ◽

Imaging Biomarkers ◽

Dice Similarity Coefficient ◽

Imaging Features ◽

Imaging Protocol ◽

Repeatability And Reproducibility ◽

The Impact

Abstract Radiomics of magnetic resonance images (MRI) in rectal cancer can non-invasively characterise tumour heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable; the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer. An MRI radiomics phantom was used to measure the longitudinal repeatability of radiomic features and the impact of post-processing changes related to image resolution and noise. Repeatability measurements in rectal cancers were also quantified in a cohort of ten patients with test-retest imaging amongst two observers. We found that many radiomic features; particularly from texture classes, were highly sensitive to changes in image resolution and noise. 49% of features had coefficient of variations ≤ 10% in longitudinal phantom measurements. 75% of radiomic features in in vivo test-retest measurements had an intraclass correlation coefficient of ≥ 0.8. We saw excellent interobserver agreement with mean dice similarity coefficient of 0.95 ± 0.04 for test and retest scans. The results of this study show that even when using a consistent imaging protocol many radiomic features were unstable. Therefore, caution must be taken when selecting features for potential imaging biomarkers.

Download Full-text