Detection Of Cavities Research Articles

Positioning exercises in improving the quality of magnetic-controlled capsule endoscopy

BackgroundGood gastric preparation is indispensable for Magnetic-controlled Capsule Endoscopy (MCE) examination, but there is no consensus yet. We aim to explore the clinical application value of positioning exercises in improving the quality of MCE examination.MethodsClinical data of 326 patients who underwent MCE examination from January 2020 to December 2023 were collected. The included patients were divided into two groups: the conventional medication preparation group (CMP group, accepted mucosal cleansing medication only) and the positioning exercises group (PE group, accepted mucosal cleansing medication plus positioning exercises). A comparison was made between the two groups in terms of gastric cavity cleanliness score, visibility score, and detection rate of positive lesions.ResultsThe examination time was (21.29 ± 5.82) minutes in the PE group and (30.54 ± 6.37) minutes in the CMP group, showing a significant difference between the two groups (P < 0.001). The total cleanliness score and visibility score in the CMP group were 15.89 ± 2.82 and 10.93 ± 2.12, respectively. In contrast, the total cleanliness score and visibility score in the PE group were 19.52 ± 2.26 and 15.09 ± 2.31, respectively. The PE group showed significantly better cleanliness scores and visibility scores in all six anatomical regions compared to the CMP group (All P < 0.001). However, there was no significant difference in the detection rate of positive lesions between the two groups (All P > 0.05).ConclusionPositioning exercises before MCE examination can improve the quality of gastric mucosal images and reduce the duration of the examination for patients.

Empowering Portable Optoelectronics With Computer Vision for Intraoral Cavity Detection

Empowering Portable Optoelectronics With Computer Vision for Intraoral Cavity Detection

Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization.

Accurately detecting atmospheric carbon dioxide is a vital part of responding to the global greenhouse effect. Conventional off-axis integral cavity detection systems are computationally intensive and susceptible to environmental factors. This study deploys an Extreme Learning Machine model incorporating a cascaded integrator comb (CIC) filter into the off-axis integrating cavity. It is shown that appropriate parameters can effectively improve the performance of the instrument in terms of lower detection limit, accuracy, and root mean square deviation. The proposed method is incorporated successfully into a monitoring station situated near an industrial area for detecting atmospheric carbon dioxide (CO2) concentration daily.

Cavity detection of gravitational waves: Where do we stand?

Cavity detection of gravitational waves: Where do we stand?

An alternative approach for detecting cavities in reinforced concrete structures using GPR A-scan data

This paper introduces an alternative approach for detecting cavities in reinforced concrete walls using Ground Penetrating Radar (GPR) A-scan data. GPR, leveraging electromagnetic waves, is extensively applied for cavity detection within structures. The nature of electromagnetic waves, significantly influenced by reflective media and attenuating through them, requires specialized analysis methods for data interpretation. Traditional methods often involve identifying and eliminating overlapping reflection patterns or adjusting signal magnitude at specific depths to isolate peak signals from the target object's surface, which can be subjective and complex. To overcome these challenges, this study proposes quantitatively assessing the presence of cavities by analyzing the integral area of A-scan data within suspected ranges. Observations indicate a substantial difference in reflection patterns between areas with and without cavities, showcasing the potential of this approach for quantitative cavity detection. This approach offers a more objective and quantitative basis for identifying cavities in reinforced concrete structures.

In-silico and in-vitro studies revealed alpha-amyrin as a potent pnhibitor of TLR2 for the therapeutics of bacterial infection and sepsis.

This study employed a multifaceted approach to investigate the inhibitory potential of alpha-amyrin against TLR2, a key player in bacterial infection and sepsis. A high-resolution TLR2 model was constructed using Swiss-MODEL, exhibiting excellent quality with 100% sequence identity and coverage. Cavity detection revealed five significant cavities on TLR2. Molecular docking identifies alpha-amyrin as a potent inhibitor, displaying a strong binding affinity of -8.6 kcal/mol. Comprehensive analyses, including ADMET predictions, PASS analysis, and SwissTargetPrediction, affirm alpha-amyrin's drug-like properties and diverse biological activities. Cytotoxicity assays on HEK-293 cells confirm its safety, and fluorescence-based inhibition assays provide empirical evidence of its inhibitory potency on TLR2 enzymatic activity. Further validations in HUVECs show a significant decrease in TLR2 mRNA expression (p<0.01) and activity (p<0.05) upon alpha-amyrin treatment. In conclusion, this integrative study positions alpha-amyrin as a promising therapeutic candidate for TLR2 inhibition, emphasizing its potential in combating bacterial infections with safety and efficacy.

Pelatihan Cara Deteksi Dini Gigi Berlubang, Gigi Berjejal, dan Kelainan dalam Mulut pada Komunitas Orang Tua dan Guru Playgoup dan TK Al-Alaq di Jatibening

The first 5 years of a child's development stage is a golden period in a child's growth and development period. Children's dental and oral health is very important, because the mouth is the first gate in the human digestive system. Parents' knowledge regarding dental and oral hygiene and health is the basis for forming children's behavior in maintaining children's oral and dental hygiene. Parents need to be provided with adequate knowledge on how to do an early detection of cavities, crowding of teeth, and abnormalities in the mouth as a form of prevention against tooth decay in children in the home environment. Community Service (PkM) was carried out online via Zoom Meeting on Friday 1 July 2022, and was attended by 37 people including teachers, staff and parents of students from Playgoup School and Kindergarten Al-Alaq Jatibening. The material provided is in the form of early detection of tooth decay, how to check oral health at home, how to detect crowding of teeth early, soft tissue disorders in the form of ppt, e-flyer and video. Measure success by completing a pre-test and post-test consisting of 17 knowledge questions related to the topic presented. It was found that participants' knowledge increased by 31%. The results obtained from this activity are very good with partners who are willing to carry out PkM activities on an ongoing basis.

Critical-edge based tabu search algorithm for solving large-scale multi-vehicle Chinese postman problem

The min–max multi-vehicle Chinese postman problem is an NP-hard problem, which is widely used in path planning problems based on road network graphs, such as urban road structure probing planning, urban road underground cavity detection planning, high-voltage line inspection planning, and so on. With the rapid increase in the number of nodes and connections of road network graph, the solution time and path equilibrium constraints pose new challenges to the problem solving. In this paper, we propose a critical-edge tabu search algorithm, CTA-kroutes, for solving the min–max multi-vehicle postman problem for large-scale road networks. First, the initial solution with balanced path lengths is obtained by segmenting the Eulerian paths; second, the critical edges are moved in the initial solution to construct the neighborhood solution, and the tabu search algorithm is used to find the optimal solution iteratively; and lastly, the solution optimization algorithm is used at the end of each iteration to de-duplicate and optimally reconstruct the current search result. Experiments show that the CTA-kroutes algorithm can effectively improve the equalization of multi-vehicle paths and its applicability to large-scale road networks.

Detection and characterisation of icy cavities on the nucleus of comet 67P/Churyumov-Gerasimenko

Abstract We report on the detection of three icy cavities on the nucleus of comet 67P/Churyumov-Gerasimenko. They were identified on high-resolution anaglyphs built from images acquired by the OSIRIS instrument aboard the Rosetta spacecraft on 2016 April 9-10. Visually, they appear as bright patches of typically 15 to 30 m across whose large reflectances and spectral slopes in the visible substantiate the presence of sub-surface water ice. Using a new high-resolution photogrammetric shape model, we determined the three-dimensional shape of these cavities whose depth ranges from 20 to 47 m. Spectral slopes were interpreted with models combining water ice and refractory dark material and the water ice abundances in the cavities were found to amount to a few per cent. The determination of the lifetime of the icy cavities was strongly biased by the availability of appropriate and favourable observations, but we found evidences of values of up to two years. The icy cavities were found to be connected to jets well documented in past studies. A thermal model allowed us to track their solar insolation over a large part of the orbit of the comet and a transitory bright jet on 2015 July 18 was unambiguously linked to the brief illumination of the icy bottom of one of the cavities. These cavities are likely to be the first potential subsurface access points detected on a cometary nucleus and their lifetimes suggest that they reveal pristine sub-surface icy layers or pockets rather than recently recondensed water vapor.

Exploration of fruit parameters for non-destructive identification of calyx cavity in ‘Fuyu’ persimmon (Diospyros kaki L.)

ABSTRACT Calyx cavity is a common physiological disorder in many persimmon cultivars. It is characterised by a separation between the calyx and the surrounding flesh. The presence of calyx cavity in export fruit can lead to phytosanitary risks and reduced storage potential due to more rapid softening and increased chilling injury. Manual detection of calyx cavity at packing is very time-consuming and uneconomic. This work investigated the ability of non-destructive evaluation, combined with modelling, to identify calyx cavity in ‘Fuyu’ persimmons. Fruit were evaluated at harvest, followed by a period of nine-week storage in modified atmosphere packaging at 0°C. The presence and severity of calyx cavity was related with higher red colouration and fresh weight before storage. Using colour index data and weight data, binary classification of calyx cavity by linear discriminant analysis (LDA) resulted 74.1% correct segregation. The model correctly identified 70.5% of calyx cavity fruit (29.5% false negatives) and 77.4% of no cavity fruit (22.6% false positives). The use of non-destructive calyx cavity classification based on the evaluation of quality parameters is a starting point in providing the ability to segregate healthy fruit before packaging and storage. Further work is necessary to improve model performance before implementation is viable.

Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks' ensemble

BackgroundDental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in the detection of cavities in dental X-rays with 99.42% accuracy. MethodThe first model of CariSeg, trained using the U-Net architecture, segments the area of interest, the teeth, and crops the radiograph around it. The next component segments the carious lesions and it is an ensemble composed of three architectures: U-Net, Feature Pyramid Network, and DeeplabV3. For tooth identification two merged datasets were used: The Tufts Dental Database consisting of 1000 panoramic radiography images and another dataset of 116 anonymized panoramic X-rays, taken at Noor Medical Imaging Center, Qom. For carious lesion segmentation, a dataset consisting of 150 panoramic X-ray images was acquired from the Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca. ResultsThe experiments demonstrate that our approach results in 99.42% accuracy and a mean 68.2% Dice coefficient. ConclusionsAI helps in detecting carious lesions by analyzing dental X-rays and identifying cavities that might be missed by human observers, leading to earlier detection and treatment of cavities and resulting in better oral health outcomes.

Sensitivity analysis on GPR A-scan data for cavity detection based on area estimation approach

This study aims to determine the optimal range of A-scan data that enables the detection of cavities in reinforced concrete structures using ground-penetrating radar (GPR). GPR is commonly used as a nondestructive testing method for detecting cavities. However, the identification of cavities using B-scan data is challenging when the cavities are obscured by highly reflective objects such as reinforcements. To address this issue, the B-scan data can be substituted with the A-scan data by adopting an area estimation approach. However, this approach requires the determination of the location and range of the A-scan data. In this study, we analyzed GPR A-scan data obtained from a large-scale concrete block with cavities adjacent to a complex reinforcement layout. The location and range were varied to investigate their effects on the accuracy of object detection using the area estimation approach, and an appropriate range was suggested.

Integrating molecular and radiological screening tools during community-based active case-finding for tuberculosis and COVID-19 in southern Africa

ObjectivesTo evaluate diagnostic yield and feasibility of integrating testing for TB and COVID-19 using molecular and radiological screening tools during community-based active case-finding (ACF). MethodsCommunity-based participants with presumed TB and/or COVID-19 were recruited using a mobile clinic. Participants underwent simultaneous point-of-care (POC) testing for TB (sputum; Xpert Ultra) and COVID-19 (nasopharyngeal swabs; Xpert SARS-CoV-2). Sputum culture and SARS-CoV-2 RT-PCR served as reference standards. Participants underwent ultra-portable POC chest radiography with computer-aided detection (CAD). TB infectiousness was evaluated using smear microscopy, cough aerosol sampling studies (CASS), and chest radiographic cavity detection. Feasibility of POC testing was evaluated via user-appraisals. ResultsSix hundred and one participants were enrolled, with 144/601 (24.0%) reporting symptoms suggestive of TB and/or COVID-19. 16/144 (11.1%) participants tested positive for TB, while 10/144 (6.9%) tested positive for COVID-19 (2/144 [1.4%] had concurrent TB/COVID-19). Seven (7/16 [43.8%]) individuals with TB were probably infectious. Test-specific sensitivity and specificity (95% CI) were: Xpert Ultra 75.0% (42.8-94.5) and 96.9% (92.4-99.2); Xpert SARS-CoV-2 66.7% (22.3-95.7) and 97.1% (92.7-99.2). Area under the curve (AUC) for CAD4TB was 0.90 (0.82-0.97). User appraisals indicated POC Xpert to have ‘good’ user-friendliness. ConclusionsIntegrating TB/COVID-19 screening during community-based ACF using POC molecular and radiological tools is feasible, has a high diagnostic yield, and can identity probably infectious persons.

Digitization of Dental Services

Technology has taken over so many aspects of the world, especially in medicine. Dentistry is no exception. The transition to digital dentistry has grown more and more and a number of digital devices used in dental offices already go unnoticed but incorporate very advanced digital technology. The most common and frequently used form of dental dentistry is digital x-rays. The benefit of this technology is not only a much smaller volume of X-rays compared to the classic technology, but they allow a better and early detection of cavities. The delivery of contemporary oral healthcare should be guided by cutting-edge technology focused on achieving patient-centred outcomes. The integration of digitalization into dentistry will enhance oral healthcare to its highest potential. The emphasis in forthcoming decades should lie on research endeavours pertaining to digitization within the healthcare domain, particularly within dentistry. The primary objective should be the enhancement of data acquisition methodologies and the management of large datasets, while concurrently addressing the safety and security concerns associated with "Big Data".

Detection of air- and water-filled cavities beneath concrete plate using electromagnetic and acoustic waves

Underground excavation and tunneling may result in cavities filled with air or water. This study aims to detect cavities beneath underground concrete walls or tunnel linings and assess their risks by estimating the cavity thickness and width using electromagnetic and acoustic waves. In this study, air- and water-filled cavities are simulated, and nondestructive testing methods such as ground penetrating radar (GPR), time domain reflectometry (TDR), and microphones are used to investigate the thickness and width of a cavity beneath a reinforced concrete plate. The results show that GPR signals converted from temporal to spatial scales, combined with TDR measurements, can be used to accurately examine the cavity thickness. However, the examination of the cavity width based on GPR signals remains challenging because of the strong parabolic patterns caused by steel bars. Therefore, this study employs flexibility, which is the ratio of the particle velocity of the structure to the load signal according to the frequency. The flexibility of the concrete plate calculated using microphone signals increases in the cavity section for both air- and water-filled cavities because the flexural vibration mode prevails at the cavity–concrete plate interface. The characteristics of the microphone signals analyzed in the context of flexibility enabled the prediction of the cavity width beneath the concrete plate in both air- and water-filled cavities. GPR and TDR signatures combined with microphone signals can be used for the detection and risk assessment of cavities beneath underground structures when subjected to variations in the groundwater level.

Early Cavity Detection Using Image Processing Approach

Early Cavity Detection Using Image Processing Approach

Acoustic tunnel lining cavity detection using cepstral coefficients with optimized filter bank

Tunnels are an essential component of modern transportation infrastructure, and their structural health is critical to traffic safety, which can be seriously affected by tunnel lining cavities. In this paper, an acoustic-based detection approach for assessing the integrity of tunnel linings is studied. By tapping the tunnel lining surface, acoustic signals are sampled and analyzed using a novel feature parameter extraction algorithm-the energy-frequency cepstral coefficient, which uses wavelet packet decomposition to obtain energy distribution statistics in the frequency domain of the signal, and constructs a signal-dependent filter bank to achieve the cepstral coefficient extraction. Compared with the traditional Mel filter bank, this method can adaptively adjust the resolution of the filter bank according to the frequency characteristics of the classified samples. This allows for higher frequency resolution in regions where the energy distribution is concentrated. As a result, the extracted feature parameters achieve both dimensional compression and superior information retention. Experimental results show that the proposed energy-frequency cepstral coefficient feature outperforms the traditional Mel-frequency cepstral coefficient feature, resulting in a higher accuracy of tunnel lining detection. The convolutional neural network model achieves an accuracy of 99.2%, with a 78.9% reduction in error rate compared with the traditional Mel-frequency cepstral coefficient feature parameters. Additionally, a particle swarm optimization support vector machine model is trained to achieve an accuracy rate of 99.6% and an error rate reduction of 76.5%.

A Clutter Removal Method Based on the F-K Domain for Ground-Penetrating Radar in Complex Scenarios

Ground-penetrating radar (GPR) is a classic geophysical exploration method that utilizes the emission and reception of electromagnetic waves to non-destructively detect target objects in the target medium. It has been widely applied in various fields such as pipeline detection, cavity detection, and rebar detection. However, GPR systems are susceptible to environmental clutter interference, which poses challenges for data interpretation and subsequent processing. In this paper, the separability of clutter and target signal in the frequency-wavenumber (F-K) domain is validated through modeling, leading to the proposal of a comprehensive clutter removal method based on the F-K domain for complex scenarios. The direct coupling wave is initially eliminated by applying a peak matching mean subtraction filter, which avoids the artifacts. Subsequently, the F-K domain transformation is performed and surface clutter undulations are effectively removed using a method based on singular value decomposition and k-means clustering. Finally, an angle filter with Gaussian tapering at the edges is designed based on physical models to efficiently eliminate linear interference without undesired ringing interference. The commonly used clutter removal algorithms, including mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and traditional F-K filtering methods, are compared with the proposed algorithm on both the numerical simulated data and actual GPR data. The results from visual and quantitative analysis confirm that our proposed method is more effective than current commonly used clutter suppression algorithms. We have successfully enhanced the Signal-to-Clutter Ratio (SCR) of the GPR data, resulting in an Improvement Factor (IF) of 30.63 dB, 23.59 dB, and 30.60 dB for simulated data, experimental data, and TU1208 public data, respectively. The detection capability of buried targets is enhanced, thereby establishing a solid foundation for subsequent data interpretation and target identification.

Automated Dental Cavity Detection Using Machine Learning

Abstract: The prevalence of dental cavities and the importance of early detection and treatment have long been recognized in the field of dentistry. To address this issue, we present a project review of an automated dental cavity detection system that leverages the power of machine learning techniques. The objective of this study was to develop an accurate system capable of identifying dental cavities in radiographic images with high precision. In this project, we studied diverse dataset of dental X-ray images and implemented a deep learning model to perform cavity detection. It was taught the subtle patterns and characteristics suggestive of dental cavities using a sizable collection of annotated dental photos. Our project focused on three key components: data preprocessing, model training, and evaluation. The results of our study demonstrate the effectiveness of the automated dental cavity detection system. This project evaluation explores the real-world applications of our approach and how it could transform dental healthcare by facilitating early diagnosis and prompt treatment. Furthermore, we are integrating this model with android application for its usefulness to both patients and doctors. The system's ability to aid dentists and radiologists in their clinical decision-making process can significantly reduce the burden of dental diseases and improve patient outcomes. In addition to this, the system suggests further steps to patients for their treatment by helping in appointments and slot bookings. In conclusion, our automated dental cavity detection system, developed through the integration of machine learning techniques, exhibits great promise in enhancing the field of dentistry. The project highlights the accomplishments and future prospects of this technology, and its potential to improve preventive dental care and contribute to overall oral health.

Fiber-cavity ring-down temperature sensor using a transmissive configuration combined with the heterodyne detection technique.

A fiber Bragg grating temperature sensor based on the cavity ring-down technique is reported. A quasi-linear fiber cavity is proposed to reduce optical loss in the cavity instead of a conventional fiber-loop configuration. The heterodyne detection technique is applied to sensitively detect weak pulses from the cavity. A combination of the quasi-linear fiber cavity and heterodyne detection allows the sensitive detection of the decaying pulse trains and high-resolution measurement of the wavelength shift of the Bragg grating induced by the temperature change. A temperature resolution of about 0.01°C is achieved.