COVID-19 radiograph prognosis using a deep CResNeXt network.

COVID-19 has caused an epidemic in the entire world and it is caused by the novel virus SARS-COV-2. In severe conditions, this virus can cause a critical lung infection or viral pneumonia. To administer the correct treatment to patients, COVID-19 testing is important for diagnosing and determining patients who are infected with COVID-19, as opposed to those infected with other bacterial or viral infections. In this paper, a CResNeXt chest radiograph COVID-19 prediction model is proposed using residual network architecture. The advantage of the proposed model is that it requires lesser free hyper-parameters as compared to other residual networks. In addition, the training time per epochs of the model is very less compared to VGG19, ResNet-50, ResNeXt. The proposed CResNeXt model's binary classification (COVID-19 versus No-Finding) accuracy is observed to be 98.63% and 99.99% and multi-class classification (COVID-19, Pneumonia, and No-Finding) accuracy is observed to be 97.42% and 99.27% on the original and augmented datasets, respectively.

Paper-based dots and smartphone for detecting counterfeit country eggs.

Herein, we present for the first time, the employment of paper-based devices for rapidly differentiating original country eggs from the plain broiler eggs that have been coated with tea to appear as the former. The devices leverage two types of phenomena involving the phenols present in tea in precisely 5 min, namely precipitation, which produces a well-defined dark bluish precipitate on the surface of the counterfeit country eggs or tea-coated broiler eggs and de-coloration, wherein the dried layer of tea coating present on the surface of the dummy country eggs get dissolved, thereby revealing the white colour of the plain broiler egg shell. To reduce the subjectivity, a smartphone application 'Eggo' has been developed which is capable of detecting the spots produced by both the methods using mobile's camera. Fourier Transform Infrared Spectroscopy (FTIR) analysis was performed to study the changes occurring on the shell surface. Such sophisticated yet simple technologies will revolutionize food fraud analysis.

Surface tension measurement of normal human blood samples by pendant drop method.

The surface tension of blood plays an important role not only in the birth and decompression sickness but also in other functionality of the organism. It also provides capillary action during blood flow process. In this article, a simple and low-cost device is designed and fabricated for measuring the surface tension of blood by pendant drop method. In this device, a droplet of blood is formed in a closed chamber on tip of an 18-gauge blunt needle and it is photographed by a camera in very humid conditions (RH = 99%) to minimise the evaporation. A wetted wick is provided at the bottom of the chamber for maintaining constant relative humidity in chamber. Surface tension of blood is inferred using drop shape factor method and image analysis technique at various experimental conditions. This device is validated and calibrated with surface tension measurements of water and silicone oil. Its measurements are in good agreement against data reported in literature. Post-validation, surface tensions of blood samples with and without anticoagulant of healthy persons at various temperatures (range from 20 to 40 °C) was measured. It was found that the surface tension of normal blood samples strongly correlates with blood temperature. The surface tension of female blood was remarkably different from same of male blood. However, the effect of age (21-60 year) on the surface tension was negligible for all practical purposes. Increased percentage of anticoagulant in blood increases its surface tension. This research specifies a baseline for surface tension of normal blood samples at various conditions which in turn provides new insights to pathologists in identifying various disease conditions.

Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images.

Burn is one of the serious public health problems. Usually, burn diagnoses are based on expert medical and clinical experience and it is necessary to have a medical or clinical expert to conduct an examination in restorative clinics or at emergency rooms in hospitals. But sometimes a patient may have a burn where there is no specialized facility available, and in such a case a computerized automatic burn assessment tool may aid diagnosis. Burn area, depth, and location are the critical factors in determining the severity of burns. In this paper, a classification model to diagnose burns is presented using automated machine learning. The objective of the research is to develop the feature extraction model to classify the burn. The proposed method based on support vector machine (SVM) is evaluated on a standard data set of burns-BIP_US database. Training is performed by classifying images into two classes, i.e., those that need grafts and those that are non-graft. The 74 images of test data set are tested with the proposed SVM based method and according to the ground truth, the accuracy of 82.43% was achieved for the SVM based model, which was higher than the 79.73% achieved in past work using the multidimensional scaling analysis (MDS) approach.

Development of a Stand-Alone Independent Graphical User Interface for Neurological Disease Prediction with Automated Extraction and Segmentation of Gray and White Matter in Brain MRI Images.

This research presents an independent stand-alone graphical computational tool which functions as a neurological disease prediction framework for diagnosis of neurological disorders to assist neurologists or researchers in the field to perform automatic segmentation of gray and white matter regions in brain MRI images. The tool was built in collaboration with neurologists and neurosurgeons and many of the features are based on their feedback. This tool provides the user automatized functionality to perform automatic segmentation and extract the gray and white matter regions of patient brain image data using an algorithm called adapted fuzzy c-means (FCM) membership-based clustering with preprocessing using the elliptical Hough transform and postprocessing using connected region analysis. Dice coefficients for several patient brain MRI images were calculated to measure the similarity between the manual tracings by experts and automatic segmentations obtained in this research. The average Dice coefficients are 0.86 for gray matter, 0.88 for white matter, and 0.87 for total cortical matter. Dice coefficients of the proposed algorithm were also the highest when compared with previously published standard state-of-the-art brain MRI segmentation algorithms in terms of accuracy in segmenting the gray matter, white matter, and total cortical matter.

Image-based clustering and connected component labeling for rapid automated left and right ventricular endocardial volume extraction and segmentation in full cardiac cycle multi-frame MRI images of cardiac patients.

A rapid method for left and right ventricular endocardial volume segmentation and clinical cardiac parameter calculation from MRI images of cardiac patients is presented. The clinical motivation is providing cardiologists a tool for assessing the cardiac function in a patient through the left ventricular endocardial volume's ejection fraction. A new method combining adapted fuzzy membership-based c-means pixel clustering and connected regions component labeling is used for automatic segmentation of the left and right ventricular endocardial volumes. This proposed pixel clustering with labeling approach avoids manual initialization or user intervention and does not require specifying the region of interest. This method fully automatically extracts the left and right ventricular endocardial volumes and avoids manual tracing on all MRI image frames in the complete cardiac cycle from systole to diastole. The average computational processing time per frame is 0.6 s, making it much more efficient than deformable methods, which need several iterations for the evolution of the snake or contour. Accuracy of the automated method presented herein was validated against manual tracing-based extraction, performed with the guidance of cardiac experts, on several MRI frames. Dice coefficients between the proposed automatic versus manual traced ventricular endocardial volume segmentations were observed to be 0.9781 ± 0.0070 (for left ventricular endocardial volume) and 0.9819 ± 0.0058 (for right ventricular endocardial volume), and the Pearson correlation coefficients were observed to be 0.9655 ± 0.0206 (for left ventricular endocardial volume) and 0.9870 ± 0.0131 (for right ventricular endocardial volume). Graphical abstract The left ventricular endocardial volume segmentation methodology illustrated as a series of algorithms.

Diastema Closure in Anterior Teeth Using a Posterior Matrix.

Presence of diastema between anterior teeth is often considered an onerous esthetic problem. Various treatment modalities are available for diastema closure. However, not all diastemas can be treated the same in terms of modality or timing. The extent and the etiology of the diastema must be properly evaluated. Proper case selection is of paramount importance for a successful treatment. In this case report, diastema closure was performed with direct composite restorations. One bottle etch-and-rinse adhesive was used and a single shade was used to close the diastemas. Contoured sectional posterior matrix was used to achieve anatomic contouring of the proximal surfaces of the teeth. This was followed by finishing and polishing using polishing discs. Patient was kept on recall every 6 months. Conclusion. Diastema closure with correct anatomic contouring is easy to perform using the contoured sectional matrices. At 14-month recall, no clinical signs of failure like discoloration or fracture were evident. Also, patient did not complain of any sensitivity. Thus, direct composite restorations serve as durable and highly esthetic restorations leading to complete patient satisfaction.

Four cuspal maxillary second premolar with single root and three root canals: Case report.

Traditional configuration of maxillary second premolars has been described to have two cusps, one root and one or two root canals. The endodontic literature reports considerable anatomic aberrations in the root canal morphology of maxillary second premolar but the literature available on the variation in cuspal anatomy and its relationship to the root canal anatomy is sparse. The purpose of this clinical report was to describe the root and root canal configuration of a maxillary second premolar with four cusps.

Automatic disease screening method using image processing for dried blood microfluidic drop stain pattern recognition.

This paper examines programmed automatic recognition of infection from samples of dried stains of micro-scale drops of patient blood. This technique has the upside of being low-cost and less-intrusive and not requiring puncturing the patient with a needle for drawing blood, which is especially critical for infants and the matured. It also does not require expensive pathological blood test laboratory equipment. The method is shown in this work to be successful for ailment identification in patients suffering from tuberculosis and anaemia. Illness affects the physical properties of blood, which thus influence the samples of dried micro-scale blood drop stains. For instance, if a patient has a severe drop in platelet count, which is often the case of dengue or malaria patients, the blood's physical property of viscosity drops substantially, i.e. the blood is thinner. Thus, the blood micro-scale drop stain samples can be utilised for diagnosing maladies. This paper presents programmed automatic examination of the dried micro-scale drop blood stain designs utilising an algorithm based on pattern recognition. The samples of micro-scale blood drop stains of ordinary non-infected people are clearly recognisable as well as the samples of micro-scale blood drop stains of sick people, due to key distinguishing features. As a contextual analysis, the micro-scale blood drop stains of patients infected with tuberculosis have been contrasted with the micro-scale blood drop stains of typical normal healthy people. The paper dives into the fundamental flow mechanics behind how the samples of the dried micro-scale blood drop stain is shaped. What has been found is a thick ring like feature in the dried micro-scale blood drop stains of non-ailing people and thin shape like lines in the dried micro-scale blood drop stains of patients with anaemia or tuberculosis disease. The ring like feature at the periphery is caused by an outward stream conveying suspended particles to the edge. Concentric rings (brought on by internal Marangoni flow) and deposition in the centre of the stain are patterns that were found in the dried micro-scale drop blood stain samples of ordinary healthy people.

Absorbable Suture as an Apical Matrix in Single Visit Apexification with Mineral Trioxide Aggregate.

Several procedures have been recommended to induce the root end barrier formation in teeth with open apices. Conventional treatment for such cases will require many appointments with an average duration of 12.9 months. During this period, the root canal is susceptible to reinfection from around the provisional restoration, which may promote apical periodontitis and arrest of apical repair. Mineral trioxide aggregate (MTA) has been successfully used for one visit apexification wherein the root canal can be obturated within 24 hours after placement of MTA. Using a matrix prior to the placement of MTA avoids its extrusion, reduces leakage in the sealing material, and allows favorable response of the periapical tissues. This report presents a case of apexification where an absorbable suture was used as an apical matrix. Use of an absorbable suture circumvents all the problems associated with other conventional materials. Conclusion. Placement of the matrix made from the suture material is predictable and is easily positioned at the apex and the length can be adjusted as required. 10-month follow-up of the case shows resorbed matrix and bone healing in the periapical region. The patient was asymptomatic during the whole follow-up period and tooth exhibited mobility within physiologic limits and was functioning normally.

Multi-scale parameterisation of a myocardial perfusion model using whole-organ arterial networks.

A method to extract myocardial coronary permeabilities appropriate to parameterise a continuum porous perfusion model using the underlying anatomical vascular network is developed. Canine and porcine whole-heart discrete arterial models were extracted from high-resolution cryomicrotome vessel image stacks. Five parameterisation methods were considered that are primarily distinguished by the level of anatomical data used in the definition of the permeability and pressure-coupling fields. Continuum multi-compartment porous perfusion model pressure results derived using these parameterisation methods were compared quantitatively via a root-mean-square metric to the Poiseuille pressure solved on the discrete arterial vasculature. The use of anatomical detail to parameterise the porous medium significantly improved the continuum pressure results. The majority of this improvement was attributed to the use of anatomically-derived pressure-coupling fields. It was found that the best results were most reliably obtained by using porosity-scaled isotropic permeabilities and anatomically-derived pressure-coupling fields. This paper presents the first continuum perfusion model where all parameters were derived from the underlying anatomical vascular network.

Model-based vasculature extraction from optical fluorescence cryomicrotome images.

The aim of this study was to develop a novel method to reconstruct 3-D coronary vasculature from cryomicrotome images, comprised of two distinct sets of data-fluorescent microsphere beads and coronary vasculature. Fluorescent beads and cast injected into the vasculature were separately imaged with different filter settings to obtain the microsphere and vascular data, respectively. To extract the vascular anatomy, light scattering in the tissue was modelled using a point spread function (PSF). The PSF was parametrized by optical tissue excitation and emission attenuation coefficients, which were estimated by fitting simulated images of microspheres convolved with the PSF model to the experimental microsphere images. These parameters were then applied within a new model-based method for vessel radius estimation. Current state-of-the-art radii estimation methods and the proposed model-based method were applied on vessel phantoms. In this validation study, the full-width half-maximum method of radii estimation, when performed on the raw data without correcting for the optical blurring, resulted in 42.9% error on average for the 170 µm vessel. In comparison, the model-based method resulted in 0.6% error on average for the same phantom. Whole-organ porcine coronary vasculature was automatically reconstructed with the new model-based vascular extraction method.

Intramural spatial variation of optical tissue properties measured with fluorescence microsphere images of porcine cardiac tissue.

This proceeding studies the optical fluorescence images of a porcine heart filled with microspheres of two colors, carmine and red. A significant difference in the total optical tissue attenuation coefficient was observed between excitation and emission for both carmine (excitation - 13+/-4(1/mm) and emission - 9.4+/-3(1/mm)) and red (excitation -29+/-5(1/mm) and emission - 25+/-5(1/mm)), indicating that optical tissue properties can change significantly for a small change in light wavelength. The above-mentioned large ranges of variation observed in the tissue attenuation coefficient for excitation and emission (both for carmine and red) suggest significant intramural variation of optical properties across the entire organ. Patterns of global spatial variation in optical attenuation properties in tissue across the entire organ were observed. A novel method using fluorescence microsphere images is presented for measurement of the tissue attenuation's intramural variation across an entire organ.