scholarly journals Expert System for Mandibular Condyle Detection and Osteoarthritis Classification in Panoramic Imaging Using R-CNN and CNN

2020 ◽  
Vol 10 (21) ◽  
pp. 7464
Author(s):  
Donghyun Kim ◽  
Eunhye Choi ◽  
Ho Gul Jeong ◽  
Joonho Chang ◽  
Sekyoung Youm
Keyword(s):  
Mandibular Condyle ◽  
Tissue Response ◽  
X Ray ◽  
Panoramic Imaging ◽  
Internal Surfaces ◽  
Pain Specialist ◽  
Temporomandibular Joint Osteoarthritis ◽  
Panoramic Images ◽  
Sensitivity Specificity ◽  
Visual Geometry

Temporomandibular joint osteoarthritis (TMJ OA) is a degenerative condition of the TMJ led by a pathological tissue response of the joint under mechanical loading. It is characterized by the progressive destruction of the internal surfaces of the joint, which can result in debilitating pain and joint noise. Panoramic imaging can be used as a basic screening tool with thorough clinical examination in diagnosing TMJ OA. This paper proposes an algorithm that can extract the condylar region and determine its abnormality by using convolutional neural networks (CNNs) and Faster region-based CNNs (R-CNNs). Panoramic images are collected retrospectively and 1000 images are classified into three categories—normal, abnormal, and unreadable—by a dentist or orofacial pain specialist. Labels indicating whether the condyle is detected and its location enabled more clearly recognizable panoramic images. The uneven proportion of normal to abnormal data is adjusted by duplicating and rotating the images. An R-CNN model and a Visual Geometry Group-16 (VGG16) model are used for learning and condyle discrimination, respectively. To prevent overfitting, the images are rotated ±10° and shifted by 10%. The average precision of condyle detection using an R-CNN at intersection over union (IoU) >0.5 is 99.4% (right side) and 100% (left side). The sensitivity, specificity, and accuracy of the TMJ OA classification algorithm using a CNN are 0.54, 0.94, and 0.84, respectively. The findings demonstrate that classifying panoramic images through CNNs is possible. It is expected that artificial intelligence will be more actively applied to analyze panoramic X-ray images in the future.

X-ray microtomography

1988 ◽  
Vol 46 ◽  
pp. 998-999
Author(s):  
H.W. Deckman ◽  
B.F. Flannery ◽  
J.H. Dunsmuir ◽  
K.D' Amico
Keyword(s):  
Computed Tomography ◽  
Internal Structure ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Tissue Structure ◽  
Individual Element ◽  
X Ray ◽  
Non Invasive ◽  
Panoramic Imaging ◽  
Three Dimensional Images

We have developed a new X-ray microscope which produces complete three dimensional images of samples. The microscope operates by performing X-ray tomography with unprecedented resolution. Tomography is a non-invasive imaging technique that creates maps of the internal structure of samples from measurement of the attenuation of penetrating radiation. As conventionally practiced in medical Computed Tomography (CT), radiologists produce maps of bone and tissue structure in several planar sections that reveal features with 1mm resolution and 1% contrast. Microtomography extends the capability of CT in several ways. First, the resolution which approaches one micron, is one thousand times higher than that of the medical CT. Second, our approach acquires and analyses the data in a panoramic imaging format that directly produces three-dimensional maps in a series of contiguous stacked planes. Typical maps available today consist of three hundred planar sections each containing 512x512 pixels. Finally, and perhaps of most import scientifically, microtomography using a synchrotron X-ray source, allows us to generate maps of individual element.

scholarly journals The diagnostic value of chest X-ray in coronavirus disease 2019: A comparative study of X-ray and CT

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110162
Author(s):  
Fengxia Zeng ◽  
Yong Cai ◽  
Yi Guo ◽  
Weiguo Chen ◽  
Min Lin ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Diagnostic Value ◽  
Rapid Identification ◽  
Chest Ct ◽  
Ct Scans ◽  
Ground Glass ◽  
X Ray ◽  
Chest X Ray ◽  
Laboratory Examinations ◽  
Sensitivity Specificity

As the coronavirus disease 2019 (COVID-19) epidemic spreads around the world, the demand for imaging examinations increases accordingly. The value of conventional chest radiography (CCR) remains unclear. In this study, we aimed to investigate the diagnostic value of CCR in the detection of COVID-19 through a comparative analysis of CCR and CT. This study included 49 patients with 52 CT images and chest radiographs of pathogen-confirmed COVID-19 cases and COVID-19-suspected cases that were found to be negative (non-COVID-19). The performance of CCR in detecting COVID-19 was compared to CT imaging. The major signatures that allowed for differentiation between COVID-19 and non-COVID-19 cases were also evaluated. Approximately 75% (39/52) of images had positive findings on the chest x-ray examinations, while 80.7% (42/52) had positive chest CT scans. The COVID-19 group accounted for 88.4% (23/26) of positive chest X-ray examinations and 96.1% (25/26) of positive chest CT scans. The sensitivity, specificity, and accuracy of CCR for abnormal shadows were 88%, 80%, and 87%, respectively, for all patients. For the COVID-19 group, the accuracy of CCR was 92%. The primary signature on CCR was flocculent shadows in both groups. The shadows were primarily in the bi-pulmonary, which was significantly different from non-COVID-19 patients ( p = 0.008). The major CT finding of COVID-19 patients was ground-glass opacities in both lungs, while in non-COVID-19 patients, consolidations combined with ground-glass opacities were more common in one lung than both lungs ( p = 0.0001). CCR showed excellent performance in detecting abnormal shadows in patients with confirmed COVID-19. However, it has limited value in differentiating COVID-19 patients from non-COVID-19 patients. Through the typical epidemiological history, laboratory examinations, and clinical symptoms, combined with the distributive characteristics of shadows, CCR may be useful to identify patients with possible COVID-19. This will allow for the rapid identification and quarantine of patients.

scholarly journals The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2206
Author(s):  
Dana Li ◽  
Lea Marie Pehrson ◽  
Carsten Ammitzbøl Lauridsen ◽  
Lea Tøttrup ◽  
Marco Fraccaro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Assessment Tool ◽  
Cochrane Library ◽  
Original Research ◽  
Quality Assessment Tool ◽  
Bias Assessment ◽  
X Ray ◽  
Chest X Ray ◽  
Sensitivity Specificity

Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.

scholarly journals 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S424-S425
Author(s):  
Dan Ding ◽  
Anna Stachel ◽  
Eduardo Iturrate ◽  
Michael Phillips
Keyword(s):  
Language Processing ◽  
Cost Effective ◽  
The United States ◽  
Surveillance Data ◽  
X Rays ◽  
Healthcare Facilities ◽  
X Ray ◽  
Chest X Ray ◽  
Sensitivity Specificity ◽  
Time Critical

Abstract Background Pneumonia (PNU) is the second most common nosocomial infection in the United States and is associated with substantial morbidity and mortality. While definitions from CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety - the algorithm is still laborious. We propose an implementation of a refined algorithm script which combines two CDC definitions with the use of natural language processing (NLP), a tool which relies on pattern matching to determine whether a condition of interest is reported as present or absent in a report, to automate PNU surveillance. Methods Using SAS v9.4 to write a query, we used a combination of National Healthcare Safety Network’s (NHSN) PNU and ventilator-associated event (VAE) definitions that use discrete fields found in electronic medical records (EMR) and trained an NLP tool to determine whether chest x-ray report was indicative of PNU (Fig1). To validate, we assessed sensitivity/specificity of NLP tool results compared with clinicians’ interpretations. Results The NLP tool was highly accurate in classifying the presence of PNU in chest x-rays. After training the NLP tool, there were only 4% discrepancies between NLP tool and clinicians interpretations of 223 x-ray reports - sensitivity 92.2% (81.1–97.8), specificity 97.1% (93.4–99.1), PPV 90.4% (79.0–96.8), NPV 97.7% (94.1–99.4). Combining the automated use of discrete EMR fields with NLP tool significantly reduces the time spent manually reviewing EMRs. A manual review for PNU without automation requires approximately 10 minutes each day per admission. With a monthly average of 2,350 adult admissions at our hospital and 16,170 patient-days for admissions with at least 2 days, the algorithm saves approximately 2,695 review hours. Conclusion The use of discrete EMR fields with an NLP tool proves to be a timelier, cost-effective yet accurate alternative to manual PNU surveillance review. By allowing an automated algorithm to review PNU, timely reports can be sent to units about individual cases. Compared with traditional CDC surveillance definitions, an automated tool allows real-time critical review for infection and prevention activities. Disclosures All authors: No reported disclosures.

scholarly journals Evaluation of Regenerative Processes in a Rat Model of Mandibular Condyle Defect using in vivo Micro X-Ray Computed Tomography

2012 ◽  
Vol 21 (4) ◽  
pp. 407-412 ◽  
Author(s):  
Tadahito Saito ◽  
Takayuki Mashimo ◽  
Hiroshi Shiratsuchi ◽  
Shunsuke Namaki ◽  
Kunihito Matsumoto ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Rat Model ◽  
Mandibular Condyle ◽  
Regenerative Processes ◽  
X Ray ◽  
X Ray Computed

Silica in Woody Stems

1974 ◽  
Vol 22 (2) ◽  
pp. 211 ◽  
Author(s):  
G Scurfield ◽  
CA Anderson ◽  
ER Segnit
Keyword(s):  
The Other ◽  
X Ray Diffraction ◽  
Xylem Parenchyma ◽  
X Ray ◽  
Woody Perennial ◽  
Internal Surfaces ◽  
Parenchyma Cells ◽  
Ray Parenchyma ◽  
Ray Parenchyma Cells ◽  
Scanning Electron

Scanning electron microscopy has been used to examine silica isolated by chemical means from the wood of 32 species of woody perennial. The silica consists of aggregate grains lying free in the lumina or in ray and xylem parenchyma cells in 24 of the species. It occurs as dense silica in the other species, filling the lumina or lining the internal surfaces of vessels (and fibres) in all cases except Gynotroches axillaris where it is deposited in ray parenchyma cells. Infrared spectra and X-ray diffraction diagrams, obtained for specimens of both sorts of silica, are indistinguishable from those for amorphous silica. Aggregate grain and dense silicas are also alike in that their differential thermal analysis curves show a rather broad endothermic peak between 175° and 205°C. The results are discussed in relation to possible modes of deposition of the two sorts of silica and the tendency for silica in ray parenchyma cells to be associated with polyphenols.

scholarly journals Structure and Stability of Microvoids in a-Si:H

1993 ◽  
Vol 297 ◽  
Author(s):  
R. Biswas ◽  
I. Kwon
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Structural Properties ◽  
Small Angle ◽  
Limited Range ◽  
Dangling Bonds ◽  
X Ray ◽  
X Ray Scattering ◽  
Internal Surfaces ◽  
Dynamics Simulations

Microvoids have been observed in a-Si:H as demonstrated by small angle X-ray scattering. We have studied the structural properties of these microvoids with molecular dynamics simulations. Using molecular dynamics simulations with classical potentials, we have created microvoids by removing Si and H atoms from a computer generated a-Si:H network. The internal surfaces of the microvoids were passivated with additional H atoms and the microvoids were fully relaxed. Microvoids over a limited range of sizes (5-90 missing atoms) were examined. We obtained a relaxed microvoid structure with no dangling bonds for a microvoid with 17 missing atoms, whereas other sizes examined produced less relaxed models with short H-H distances at the microvoid surface. The strains near the microvoid surface are described. The microvoid model was stable to local excitations on weak bonds in the vicinity of the microvoid.

scholarly journals A New Deep Learning Pipeline to Detect Covid-19 on Chest X-Ray Images using Local Binary Pattern, Dual Tree Complex Wavelet Transform and Convolutional Neural Networks

2020 ◽  
Author(s):  
Huseyin Yaşar ◽  
Murat Ceylan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Test Data ◽  
Local Binary Pattern ◽  
Complex Wavelet Transform ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray ◽  
Sensitivity Specificity

Abstract At the end of 2019, a new type of virus, belonging to the coronaviridae family has emerged and it is considered that the virus in question is of zootonic origin. The virus that emerged in China first affected this country and then spread worldwide. Pneumonia develops due to Covid-19 virus in patients having severe disease symptoms. Many literature studies have been carried out in the process where the effects of the disease-induced pneumonia in lungs have been demonstrated with the help of chest X-ray imaging. In this study, which aims at early diagnosis of Covid-19 disease by using X-Ray images, the deep-learning approach, which is a state-of-the-art artificial intelligence method, was used and automatic classification of images was performed using Convolutional Neural Networks (CNN). In the first training-test data set used in the study, there were a total of 230 abnormal and 80 normal X-Ray images, while in the second training-test data set there were 476 X-Ray images, of which 150 abnormal and 326 normal. Thus, classification results have been provided for two data sets, containing predominantly abnormal images and predominantly normal images respectively. In the study, a 23-layer CNN architecture was developed. Within the scope of the study, results were obtained by using chest X-Ray images directly in training-test procedures and the sub-band images obtained by applying Dual Tree Complex Wavelet Transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying Local Binary Pattern (LBP) to the chest X-Ray images. Within the scope of the study, a new result generation algorithm having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in the study, the trainings were carried out using the k-fold cross validation method. Here the k value was chosen 23. Considering the highest results of the tests performed in the study, values of sensitivity, specificity, accuracy and AUC for the first training-test data set were calculated to be 1, 1, 0,9913 and 0,9996; while for the second data set of training-test, they were 1, 0,9969, 0,9958 and 0,9996 respectively. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy and AUC for the first training-test data set were 0,9933, 0,9725, 0,9843 and 0,9988; while for the second training-test data set, they were 0,9813, 0,9908, 0,9857 and 0,9983 respectively.

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