Combined Techniques for Bilateral Maxillary and Nasal Reconstruction After Ablative Surgery for Adenoid Cystic Carcinoma

Indolent tumor growth up to large tumor masses and broad infiltration of surrounding tissue are the most typical characteristics of malignant tumors of the nasal cavity and paranasal sinuses. If surgery is a therapeutic option, extended resections and complex reconstruction modalities have to be taken into account. We present a combination of different reconstruction techniques to restore midface integrity after bilateral maxillectomy, including parts of the nasal skeleton, for adenoid cystic carcinoma. After obtaining tumor-free margins, reconstruction was performed using a microvascular double-flap technique to achieve a neo-maxilla and soft tissue lining of the oral cavity, dental implantology with prosthetic restoration and the insertion of a patient-specific implant for nasal re-shaping and stability. In cases of extended maxillary resection, a combination of different techniques can achieve sufficient functional and aesthetic rehabilitation, and restore quality of life. Further studies are warranted to evaluate the long-term stability of such complex reconstructions. However, local tumor control remains the highest priority and will be essential for years.

Secondary Treatment of Facial Asymmetry in Hemifacial Microsomia

Hemifacial microsomia is most often diagnosed at birth and comprises varying degrees of malformations of one side of the face. Depending on the malformations involved, multiple procedures are required as primary treatment approaches, often embedded in an interdisciplinary concept from birth to adolescence. However, with regard to the symmetry of the face, soft tissue and bony discrepancies between the normal and the affected side often remain recognizable or even persist after surgery, resulting in lasting disturbed facial harmony. Such patients may have a high burden of disease. In our case report, we present the clinical course of a 39-year-old female with hemifacial microsomia, who was suffering persistent facial asymmetry after primary treatment comprising surgery on the mandible and soft tissue augmentation with the use of a free muscle flap. By means of virtual planning tools and patient-specific implants for genioplasty and bony augmentation in a first step followed by soft tissue augmentation with autologous fat cells in a second step, a very satisfactory result was achieved for both patient and medical staff. Hence, for secondary treatment of facial asymmetry in adulthood, a combined and step-by-step therapy addressing both soft and hard tissue seems to be the key to success.

Brain Tumor Detection from MR Images using Image Process Techniques and Tools in Matlab Software

In the medical era the Brain tumor is one of the most important research areas in the field of medical sciences. Researcher are trying to find the reliable and cost effective medical equipment’s for the cancer and its type for the diagnosed, especially tumor has deferent kinds but the major two type are discussed in this research paper. Which are the benign and Pre-Malignant, this research work is proposed for these factors such as the accuracy of the MRI image for the tumor identification and actual placing were taken into consideration. In this study, an algorithm is proposed to detect the brain tumor from magnetic resonance image (MRI) data simple. As enhance the image quality for the easiness the tumor treatments and diagnosed for the patients. The proposed algorithm enhances the MR image quality and detects the Brain tumor which helps the Physician to diagnose the tumor easily. As well this algorithm automatically calculates the area of tumor, size and location of the tumor where it is present for diagnostic the Patient.

An IoT Based Health Monitoring System to Tackle COVID-19 in a Contagious Ward of Hospital

l: l: Email:l: Email:l: Regarding the highly contiguous novel coronavirus disease (COVID-19), it is unsafe for health professionals, being involved in a hospital isolation ward. The healthcare workers, stationed in a contagious ward provide relentless monitoring of specific health parameters of those patients. The rate of contagion proportionately depends on the time spent by the health workers in an isolation ward. This challenging task is fairly manageable by employing promising Internet-of-Things (IoT) based autonomous robots (i.e. smart bots) thus reducing the hazards of contamination to health workers. In this research paper, we introduce a smart bot that can periodically measure the health parameters of COVID-19 patients, for instance, body temperature, oxygen saturation levels, blood pressure, respiration rate, heart rate, blood glucose level, etc. The proposed smart bot will transfer from one patient to another in a structured pattern and autonomously collect, forward, store the data of the patients for further analysis. The goal of the work is to involve a significantly inferior number of health workers in contagious wards to control contagion, thus creating stress-free environs for health workers. Moreover, smart bots will offer health professionals to pay attention to non-COVID-19 patients and make things easier for regular health check-ups of individuals in need.

COVID-19 Data Analysis using Chest X-ray

The COVID-19 pandemic has caused large-scale outbreaks in more than 150 countries worldwide, causing massive damage to the livelihood of many people. The capacity to identify contaminated patients early and get unique treatment is quite possibly the primary stride in the battle against COVID-19. One of the quickest ways to diagnose patients is to use radiography and radiology images to detect the disease. Early studies have shown that chest X-rays of patients infected with COVID-19 have unique abnormalities. To identify COVID-19 patients from chest X-ray images, we used various deep learning models based on previous studies. We first compiled a data set of 2,815 chest radiographs from public sources. The model produces reliable and stable results with an accuracy of 91.6%, a Positive Predictive Value of 80%, a Negative Predictive Value of 100%, specificity of 87.50%, and Sensitivity of 100%. It is observed that the CNN-based architecture can diagnose COVID19 disease. The parameters’ outcomes can be further improved by increasing the dataset size and by developing the CNN-based architecture for training the model.

An IoT Based Health Monitoring System to Tackle COVID-19 in a Contagious Ward of Hospital

Regarding the highly contiguous novel coronavirus disease (COVID-19), it is unsafe for health professionals, being involved in a hospital isolation ward. The healthcare workers, stationed in a contagious ward provide relentless monitoring of specific health parameters of those patients. The rate of contagion proportionately depends on the time spent by the health workers in an isolation ward. This challenging task is fairly manageable by employing promising Internet-of-Things (IoT) based autonomous robots (i.e. smart bots) thus reducing the hazards of contamination to health workers. In this research paper, we introduce a smart bot that can periodically measure the health parameters of COVID-19 patients, for instance, body temperature, oxygen saturation levels, blood pressure, respiration rate, heart rate, blood glucose level, etc. The proposed smart bot will transfer from one patient to another in a structured pattern and autonomously collect, forward, store the data of the patients for further analysis. The goal of the work is to involve a significantly inferior number of health workers in contagious wards to control contagion, thus creating stress-free environs for health workers. Moreover, smart bots will offer health professionals to pay attention to non-COVID-19 patients and make things easier for regular health check-ups of individuals in need.

COVID-19 Data Analysis using Chest X-Ray

The COVID-19 pandemic has caused large-scale outbreaks in more than 150 countries worldwide, causing massive damage to the livelihood of many people. The capacity to identify contaminated patients early and get unique treatment is quite possibly the primary stride in the battle against COVID-19. One of the quickest ways to diagnose patients is to use radiography and radiology images to detect the disease. Early studies have shown that chest X-rays of patients infected with COVID-19 have unique abnormalities. To identify COVID-19 patients from chest X-ray images, we used various deep learning models based on previous studies. We first compiled a data set of 2,815 chest radiographs from public sources. The model produces reliable and stable results with an accuracy of 91.6%, a Positive Predictive Value of 80%, a Negative Predictive Value of 100%, specificity of 87.50%, and Sensitivity of 100%. It is observed that the CNN-based architecture can diagnose COVID-19 disease. The parameters’ outcomes can be further improved by increasing the dataset size and by developing the CNN-based architecture for training the model.

Brain Tumor Detection from MR Images using Image Process Techniques and Tools in Matlab Software

In the medical era the Brain tumor is one of the most important research areas in the field of medical sciences. Researcher are trying to find the reliable and cost effective medical equipment’s for the cancer and its type for the diagnosed, especially tumor has deferent kinds but the major two type are discussed in this research paper. Which are the benign and Pre-Malignant, this research work is proposed for these factors such as the accuracy of the MRI image for the tumor identification and actual placing were taken into consideration. In this study, an algorithm is proposed to detect the brain tumor from magnetic resonance image (MRI) data simple. As enhance the image quality for the easiness the tumor treatments and diagnosed for the patients. The proposed algorithm enhances the MR image quality and detects the Brain tumor which helps the Physician to diagnose the tumor easily. As well this algorithm automatically calculates the area of tumor, size and location of the tumor where it is present for diagnostic the Patient.

Morphological Characteristics of the Cysts of the Maxillary Sinuses

Today, there are several diagnostic methods that allow you to determine the exact size and nature of periapical formations. In addition, there are studies that describe the thickening of the sinus mucosa in patients with periapical lesions and show a causal relationship. Objectives: This study was to study the morphological changes in the cysts of the maxillary sinus. Methods: 50 patients with maxillary sinus cysts were evaluated. A total of 50 maxillary sinuses (13 men and 12 women) were analyzed taking into account changes in density in the sinus cavity. The thickening of the sinus mucosa and periapical lesions was measured in the caudal-cephalic direction. The axial and sagittal axes were taken as a basis on the sagittal and coronal sections. The presence of opacities was not directly related to periapical lesions. Conclusions. Lesions of the maxillary sinus floor have been associated with chronic periapical lesions larger than 4 mm. Clouding or thickening of the sinus mucosa was not associated with periapical lesions.