PATIENTS AND METHODS: Fourteen patients with normal ejection fraction and 16 patients with reduced ejection fraction were compared with 20 healthy individuals. Phase-contrast MRI was used to assess intraventricular flow variables and speckle-tracking echocardiography to assess myocardial strain and left ventricular (LV) dyssynchrony. Infarct size was acquired using delayed-enhancement MRI.
RESULTS: The results obtained showed no significant differences in intraventricular flow variables between the healthy group and the patients with normal ejection fraction group, whereas considerable reductions in kinetic energy (KE) fluctuation index, E' (P<0.001) and vortex KE (P=0.003) were found in the patients with reduced ejection fraction group. In multivariate analysis, only vortex KE and infarct size were significantly related to LV ejection fraction (P<0.001); furthermore, vortex KE was correlated negatively with energy dissipation, energy dissipation index (r=-0.44, P=0.021).
CONCLUSION: This study highlights that flow energetic indices have limited applicability as early predictors of LV progressive dysfunction, whereas vortex KE could be an alternative to LV performance.
MATERIALS AND METHODS: The AGA is a new measured angle formed between the line from midglenoid to lateral end of the acromion with the line parallel to the glenoid surface. The AGA was measured in a group of 85 shoulders with RCT, 49 with GHOA and 103 non-RCT/GHOA control shoulders. The AGA was compared with other radiological parameters, such as, the critical shoulder angle (CSA), the acromion index (AI) and the acromiohumeral interval (AHI). Correlational and regression analysis were performed using SPSS 20.
RESULTS: The mean AGA was 50.9° (45.2-56.5°) in the control group, 53.3° (47.6-59.1°) in RCT group and 45.5° (37.7-53.2°) in OA group. Among patients with AGA > 51.5°, 61% were in the RCT group and among patients with AGA < 44.5°, 56% were in OA group. Pearson correlation analysis had shown significant correlation between AGA and CSA ( r = 0.925, p < 0.001). It was also significant of AHI in RCT group with mean 6.6 mm (4.7-8.5 mm) and significant AI in OA group with mean 0.68 (0.57-0.78) with p value < 0.001 respectively.
CONCLUSION: The AGA method of measurement is an excellent predictive parameter for diagnosing RCT and GHOA.
METHODS: Eight scientific databases are selected as an appropriate database and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed as the basis method for conducting this systematic and meta-analysis review. Regarding the main objective of this research, some inclusion and exclusion criteria were considered to limit our investigation. To achieve a structured meta-analysis, all eligible articles were classified based on authors, publication year, journals or conferences, applied fuzzy methods, main objectives of the research, problems and research gaps, tools utilized to model the fuzzy system, medical disciplines, sample sizes, the inputs and outputs of the system, findings, results and finally the impact of applied fuzzy methods to improve diagnosis. Then, we analyzed the results obtained from these classifications to indicate the effect of fuzzy methods in decreasing the complexity of diagnosis.
RESULTS: Consequently, the result of this study approved the effectiveness of applying different fuzzy methods in diseases diagnosis process, presenting new insights for researchers about what kind of diseases which have been more focused. This will help to determine the diagnostic aspects of medical disciplines that are being neglected.
CONCLUSIONS: Overall, this systematic review provides an appropriate platform for further research by identifying the research needs in the domain of disease diagnosis.
Method: Twenty-seven patients were included in this study conducted from 1st January to 31st December 2013. All patients were skeletally mature and scheduled to undergo primary anterior cruciate ligament reconstruction using 4S-STG autograft. Ultrasonographic examination of semitendinosus and gracilis tendons to measure the cross sectional area was conducted and anthropometric data (weight, height, leg length and thigh circumference) was measured one day prior to surgery. True autograft diameters were measured intraoperatively using closed-hole sizing block in 0.5 mm incremental size.
Results: There is a statistically significant correlation between the measured combined cross sectional area (semitendinosus and gracilis tendons) and 4S-STG autograft diameter (p = 0.023). An adequate autograft size (at least 7 mm) can be obtained when the combined cross sectional area is at least 15 mm2. There was no correlation with the anthropometric data except for thigh circumference (p = 0.037). Autograft size of at least 7 mm can be obtained when the thigh circumference is at least 41 mm.
Conclusions: Both combined cross sectional area (semitendinosus and gracilis tendons) and thigh circumference can be used to predict an adequate 4S-STG autograft size.
METHODS: 120 primary pterygium participants were selected from patients who visited an ophthalmology clinic. We adopted image analysis software in calculating the size of invading pterygium to the cornea. The marking of the calculated area was done manually, and the total area size was measured in pixel. The computed area is defined as the area from the apex of pterygium to the limbal-corneal border. Then, from the pixel, it was transformed into a percentage (%), which represents the CPTA relative to the entire corneal surface area. Intra- and inter-observer reliability testing were performed by repeating the tracing process twice with a different sequence of images at least one (1) month apart. Intraclass correlation (ICC) and scatter plot were used to describe the reliability of measurement.
RESULTS: The overall mean (N=120) of CPTA was 45.26±13.51% (CI: 42.38-48.36). Reliability for region of interest (ROI) demarcation of CPTA were excellent with intra and inter-agreement of 0.995 (95% CI, 0.994-0.998; P<0.001) and 0.994 (95% CI, 0.992-0.997; P<0.001) respectively. The new method was positively associated with corneal astigmatism (P<0.01). This method was able to predict 37% of the variance in CA compared to 21% using standard method.
CONCLUSIONS: Image analysis method is useful, reliable and practical in the clinical setting to objectively quantify actual pterygium size, shapes and its effects on the anterior corneal curvature.
DISCUSSION: It is a set of various methodologies which are used to capture internal or external images of the human body and organs for clinical and diagnosis needs to examine human form for various kind of ailments. Computationally intelligent machine learning techniques and their application in medical imaging can play a significant role in expediting the diagnosis process and making it more precise.
CONCLUSION: This review presents an up-to-date coverage about research topics which include recent literature in the areas of MRI imaging, comparison with other modalities, noise in MRI and machine learning techniques to remove the noise.
METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases.
RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier.
CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis.
KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.