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Computed tomography and ultrasonography: a comparative study in the diagnosis of analgesic nephropathy

Segasothy M, Abdul Samad S, Zulfiqar A, Shaariah W, Morad Z, Prasad Menon S

Nephron, 1994;66(1):62-6.
PMID: 8107955

Computed tomography (CT) and ultrasonography (US) were performed in 40 patients who had consumed excessive quantities of analgesics (> 1 kg) to compare their value in the diagnosis of analgesic nephropathy (AN). The computed tomography and sonographic features were renal papillary calcifications. Renal papillary necrosis (RPN) was documented in 20 of 40 patients by US and in 14 of 40 patients by CT. In 11 patients, both US and CT were positive. In 9 patients, US was positive whilst CT was negative. In 3 patients, CT was positive whilst US was negative. Prevalence of RPN was 50% using US and 35% using CT. Using US as a gold standard, sensitivity of CT was 55%, specificity 85%, positive predictive value 78.6% and negative predictive value 34.6%. Percent agreement with CT and US was 70%. Cohen's kappa statistic adjusting for chance agreement was 40%. Based on these results, it is found that US yielded a higher percentage of positive cases of RPN.

Matched MeSH terms: Tomography, X-Ray Computed/statistics & numerical data
Allergic phenotype of chronic rhinosinusitis based on radiologic pattern of disease

Hamizan AW, Loftus PA, Alvarado R, Ho J, Kalish L, Sacks R, et al.

Laryngoscope, 2018 09;128(9):2015-2021.
PMID: 29602169 DOI: 10.1002/lary.27180

OBJECTIVES/HYPOTHESIS: Polypoid edema of the middle turbinate is a marker of inhalant allergy. Extensive edematous changes may result in limited central nasal and sinus disease, which has been called central compartment atopic disease (CCAD). Radiologically, this is seen as soft tissue thickening in the central portion of the sinonasal cavity with or without paranasal sinus involvement. When the sinuses are involved, the soft tissue thickening spares the sinus roof or lateral wall (centrally limited). This centrally limited radiological pattern was assessed among chronic rhinosinusitis (CRS) patients and compared to allergy status.
STUDY DESIGN: Diagnostic cross-sectional study.
METHODS: This study included consecutive CRS patients without prior sinus surgery. Computed tomography (CT) scans of the paranasal sinuses were blindly assessed and allergy status was confirmed by serum or skin testing. Individual sinus cavities were defined as either centrally limited or diffuse disease. The radiological pattern that may predict allergy was determined, and its diagnostic accuracy was calculated.
RESULTS: One hundred twelve patients diagnosed to have CRS, representing 224 sides, were assessed (age 46.31 ± 13.57 years, 38.39% female, 41.07% asthma, Lund-Mackay CT score 15.88 ± 4.35, 56.25% atopic). The radiological pattern defined by centrally limited changes in all of the paranasal sinuses was associated with allergy status (73.53% vs. 53.16%, P = .03). This predicted atopy with 90.82% specificity, 73.53% positive predictive value, likelihood positive ratios of 2.16, and diagnostic odds ratio of 4.59.
CONCLUSIONS: A central radiological pattern of mucosal disease is associated with inhalant allergen sensitization. This group may represent a CCAD subgroup of patients with mainly allergic etiology.
LEVEL OF EVIDENCE: 3b Laryngoscope, 128:2015-2021, 2018.

Matched MeSH terms: Tomography, X-Ray Computed/statistics & numerical data*
Fulltext An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19

Serena Low WC, Chuah JH, Tee CATH, Anis S, Shoaib MA, Faisal A, et al.

Comput Math Methods Med, 2021;2021:5528144.
PMID: 34194535 DOI: 10.1155/2021/5528144

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.

Matched MeSH terms: Tomography, X-Ray Computed/statistics & numerical data
Fulltext An Analysis Review of Detection Coronavirus Disease 2019 (COVID-19) Based on Biosensor Application

Taha BA, Al Mashhadany Y, Hafiz Mokhtar MH, Dzulkefly Bin Zan MS, Arsad N

Sensors (Basel), 2020 Nov 26;20(23).
PMID: 33256085 DOI: 10.3390/s20236764

Timely detection and diagnosis are essentially needed to guide outbreak measures and infection control. It is vital to improve healthcare quality in public places, markets, schools and airports and provide useful insights into the technological environment and help researchers acknowledge the choices and gaps available in this field. In this narrative review, the detection of coronavirus disease 2019 (COVID-19) technologies is summarized and discussed with a comparison between them from several aspects to arrive at an accurate decision on the feasibility of applying the best of these techniques in the biosensors that operate using laser detection technology. The collection of data in this analysis was done by using six reliable academic databases, namely, Science Direct, IEEE Xplore, Scopus, Web of Science, Google Scholar and PubMed. This review includes an analysis review of three highlights: evaluating the hazard of pandemic COVID-19 transmission styles and comparing them with Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) to identify the main causes of the virus spreading, a critical analysis to diagnose coronavirus disease 2019 (COVID-19) based on artificial intelligence using CT scans and CXR images and types of biosensors. Finally, we select the best methods that can potentially stop the propagation of the coronavirus pandemic.

Matched MeSH terms: Tomography, X-Ray Computed/statistics & numerical data
Fulltext Standardization of Malaysian adult female nasal cavity

Lee CF, Abdullah MZ, Ahmad KA, Lutfi Shuaib I

Comput Math Methods Med, 2013;2013:519071.
PMID: 23840279 DOI: 10.1155/2013/519071

This research focuses on creating a standardized nasal cavity model of adult Malaysian females. The methodology implemented in this research is a new approach compared to other methods used by previous researchers. This study involves 26 females who represent the test subjects for this preliminary study. Computational fluid dynamic (CFD) analysis was carried out to better understand the characteristics of the standardized model and to compare it to the available standardized Caucasian model. This comparison includes cross-sectional areas for both half-models as well as velocity contours along the nasal cavities. The Malaysian female standardized model is larger in cross-sectional area compared to the standardized Caucasian model thus leading to lower average velocity magnitudes. The standardized model was further evaluated with four more Malaysian female test subjects based on its cross-sectional areas and average velocity magnitudes along the nasal cavities. This evaluation shows that the generated model represents an averaged and standardized model of adult Malaysian females.

Matched MeSH terms: Tomography, X-Ray Computed/statistics & numerical data
Radiation dose in coronary CT angiography associated with prospective ECG-triggering technique: comparisons with different CT generations

Sabarudin A, Sun Z, Ng KH

Radiat Prot Dosimetry, 2013;154(3):301-7.
PMID: 22972797 DOI: 10.1093/rpd/ncs243

A retrospective analysis was performed in patients undergoing prospective ECG-triggered coronary computed tomography (CT) angiography (CCTA) with the single-source 64-slice CT (SSCT), dual-source 64-slice CT (DSCT), dual-source 128-slice CT and 320-slice CT with the aim of comparing the radiation dose associated with different CT generations. A total of 164 patients undergoing prospective ECG-triggered CCTA with different types of CT scanners were studied with the mean effective doses estimated at 6.8 ± 3.2, 4.2 ± 1.9, 4.1±0.6 and 3.8 ± 1.4 mSv corresponding to the 128-slice DSCT, 64-slice DSCT, 64-slice SSCT and 320-slice CT scanners. In this study a positive relationship was found between the effective dose and the body mass index (BMI). A low radiation dose is achieved in prospective ECG-triggered CCTA, regardless of the CT scanner generation. BMI is identified as the major factor that has a direct impact on the effective dose associated with prospective ECG-triggered CCTA.

Matched MeSH terms: Tomography, X-Ray Computed/statistics & numerical data*