Ossifying fibroma of the maxilla: A case report with literature review.

Ossifying fibroma is a benign fibro-osseous lesion arising from the periodontal ligament cells. The lesion may progressively enlarge with the mass affecting the mandible or maxilla, resulting in facial deformities and tooth displacement despite its benign nature. Here, we presented a case of an 18-year-old female with ossifying fibroma in the maxilla extending to the maxillary sinus, infraorbital area, and skull base, resulting in considerable facial asymmetry. Since the primary treatment of ossifying fibroma is surgical resection, it is essential to determine the areas where the lesion has expanded, where a 3-dimensional computed tomography scan could play a critical role in providing such information. A complete surgical excision and histopathologic examination in treating this patient are crucial, made possible by a meticulous preoperative radio imaging technique.

Chest X-ray dataset and ground truth for lung segmentation.

Chest X-ray images are a valuable tool for accurately and efficiently diagnosing Covid-19 with the assistance of computer technology. These images enable the detection of diseases in internal organs, particularly the lungs, by providing crucial information about the pathological state of the lungs and other internal organs and tissues. Segmentation plays an essential role in the earliest stages of disease detection through computer-assisted analysis of medical images. This method enables the extraction of significant elements from the image, facilitating the identification of relevant areas. In the subsequent stage, healthcare professionals might acquire more precise diagnosis outcomes. Deep learning plays a significant role in developing models to achieve exact and efficient diagnostic results in picture segmentation and image classification procedures. However, using deep learning models in the image segmentation process necessitates the availability of image datasets and ground truth that radiologists have validated to facilitate the training process. The dataset provided in this article comprises 292 chest X-ray images obtained from Airlangga University Hospital in Indonesia. These images are accompanied with ground truth data that has been meticulously verified by radiologists. The offered X-ray images encompass those of patients diagnosed with Covid-19, pneumonia and those representing normal conditions. The provided dataset exhibits potential utility in advancing artificial intelligence techniques for segmentation and classification procedures.

Structural magnetic resonance imaging in Parkinson disease with freezing of gait: A systematic review of literature.

Objective: This review aims to the existing structural neuroimaging literature in Parkinson disease presenting with freezing of gait. The summary of this article provides an opportunity for a better understanding of the structural findings of freezing of gait in Parkinson disease based on MRI. Methods: This systematic review of literature follows the procedures as described by the guideline of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results: Initial searches yielded 545 documents. After exclusions, 11 articles were included into our study. Current findings of structural MRI on freezing of gait in Parkinson disease are associated with structural damage between sensorimotor-related cortical grey matter structures and thalamus, but not cerebellum and smaller systems, as well as extensive injuries on white matter connecting between those structures. Conclusion: Current findings of structural MRI on freezing of gait in Parkinson disease are associated with structural damage between sensorimotor-related cortical grey matter structures and thalamus, but not cerebellum and smaller systems, as well as extensive injuries on white matter connecting between those structures.

Deep-Stacked Convolutional Neural Networks for Brain Abnormality Classification Based on MRI Images.

An automated diagnosis system is crucial for helping radiologists identify brain abnormalities efficiently. The convolutional neural network (CNN) algorithm of deep learning has the advantage of automated feature extraction beneficial for an automated diagnosis system. However, several challenges in the CNN-based classifiers of medical images, such as a lack of labeled data and class imbalance problems, can significantly hinder the performance. Meanwhile, the expertise of multiple clinicians may be required to achieve accurate diagnoses, which can be reflected in the use of multiple algorithms. In this paper, we present Deep-Stacked CNN, a deep heterogeneous model based on stacked generalization to harness the advantages of different CNN-based classifiers. The model aims to improve robustness in the task of multi-class brain disease classification when we have no opportunity to train single CNNs on sufficient data. We propose two levels of learning processes to obtain the desired model. At the first level, different pre-trained CNNs fine-tuned via transfer learning will be selected as the base classifiers through several procedures. Each base classifier has a unique expert-like character, which provides diversity to the diagnosis outcomes. At the second level, the base classifiers are stacked together through neural network, representing the meta-learner that best combines their outputs and generates the final prediction. The proposed Deep-Stacked CNN obtained an accuracy of 99.14% when evaluated on the untouched dataset. This model shows its superiority over existing methods in the same domain. It also requires fewer parameters and computations while maintaining outstanding performance.

Pancoast tumor mimicking lung tuberculosis, a case report.

It is well-recognized that tuberculosis (TB) can mimic several clinical diseases, particularly cancer. On several occasions, lung TB can be misdiagnosed as cancer, particularly in developed countries with a rare case of TB and high incidence of lung cancer, and vice versa- in which Indonesia, with a high incidence of TB, lung cancer may be mistakenly identified as TB, delaying the initiation of definitive therapy and causing unnecessary diagnostic and treatment procedures. We reported a 59-year-old male who complained of right upper chest pain, accompanied by chronic cough and weight loss, with a history of 6-month treatment with a TB regimen without resolution of his symptoms. Core biopsy CT guiding pathology anatomy revealed atypical adenocarcinoma. All patients seeking medical attention must be treated carefully, avoiding diagnostic procedures that can result in a delay in definitive therapy.

Radiological findings of partial expression pentalogy of Cantrell and other multiple congenital anomalies: A rare case report.

Pentalogy of Cantrell is a rare syndrome of anomalous malformation. In the present case, the syndrome was initially diagnosed as a complete pentad, including a supra-umbilical abdominal wall defect, a sternal defect, pericardial defects, an anterior diaphragmatic defect, and heart malformation. Diagnosis required several imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). In this case report, we present an 8-month-old female patient with a thoracic wall defect with ectopia cordis and a bilateral cleft lip and palate. In addition, a head CT scan showed craniosynostosis, hypogenesis of the corpus callosum, and tonsillar cerebellar herniation. Thoracoabdominal CT revealed herniation of the transverse colon up to the subcutaneous layer, diaphragmatic hernia, atrial septal defects (ASD), ventral septal defects (VSD), and a persistent left superior vena cava (PLSVC). A multidisciplinary approach is required for the optimal management of this syndrome. We describe a female infant who presented with pentalogy of Cantrell syndrome and include the findings from postnatal CT imaging.

VIM Line Technique for Determining the Ventral Intermediate Location.

AIM: To prove that VIM line technique created by using a mathematical model, can be used to identify the location of the ventral intermediate nucleus of the thalamus (VIM) MATERIAL and METHODS: Eleven patients with Parkinson?s disease (PD) were assessed. To determine the VIM location, 3-T magnetic resonance imaging and stereotactic protocol 128-slice computed tomography were used. The VIM line technique was performed by drawing a line from the end-point of the right external globus pallidus to that of the left external globus pallidus in the intercommissural plane. PD severity was measured using the Unified Parkinson?s Disease Rating Scale (UPDRS). RESULTS: A mathematical model was constructed to describe the VIM line technique for determining the VIM location. UPDRS scores before and after thalamotomy showed a significant decreasing trend (p=0.003). CONCLUSION: The VIM line technique using the mathematical model can be considered a referential method to determine the VIM location. Its effectiveness was demonstrated by decreased UPDRS scores in patients after VIM thalamotomy.

Age, neutrophil lymphocyte ratio, and radiographic assessment of the quantity of lung edema (RALE) score to predict in-hospital mortality in COVID-19 patients: a retrospective study.

Background: Available data suggest that case fatality rate of COVID-19 patients in Surabaya is higher than global cases. Thus, it is important to identify risk factors to prevent the mortality. This study aimed to assess the factors associated with hospital mortality of COVID-19 patients, and develop a prediction score based on these findings. Methods: We analyzed 111 patients, who were diagnosed with COVID-19 based on reverse-transcriptase polymerase chain reaction. The following patient characteristics were obtained from records: age, gender, type of symptoms, onset of symptoms, neutrophil lymphocyte ratio (NLR), absolute lymphocyte count, chest x-ray abnormalities, lung involvement, type of lesion, radiographic assessment of the quantity of lung edema (RALE) score, and mortality. Data were analyzed using SPSS 25.0. Results Multivariate analysis showed that age >50 years ( p=0.043), NLR score >5.8 ( p=0.016) and RALE score >2 ( p=0.002) can predict the mortality of COVID-19 patients in the hospital. ROC curve analysis of the score ability to predict mortality showed an area under the curve of 0.794. The cut-off point is 4.5, with a sensitivity of 96.7% and specificity of 49.4% to predict the mortality of COVID-19 patient in the hospital. Conclusions Age, NLR score and RALE score were associated with mortality of COVID-19 patients in the hospital and might be used as a predictor for mortality of COVID-19 patients in health care centre where radiologists are available. The prediction score may be useful for frontline physicians to effectively manage patients with a higher score to prevent mortality.

Vim line technique thalamotomy for Parkinson tremor: Case series.

INTRODUCTION: The ventral intermediate (Vim) nucleus of the thalamus is difficult to identify even with 3 T magnetic resonance imaging. Stereotactic Vim thalamotomy is a usual procedure to control Parkinson tremor. Successful relieving of the tremor depends on the accuracy of defining the Vim location. PRESENTATION OF CASES: Three patients with Parkinson tremor were subjected to stereotactic thalamotomy using the Vim line technique (VLT) so as to precisely determine the Vim location. All patients showed good results, with improved tremors, as indicated by the UPDRS score, without any complications. DISCUSSION: The precise targeting of the Vim nucleus is crucial importance for the successful Vim thalamotomy. Various method has been developed to determine Vim location. Atlas based and Guiot's technique routinely used by neurosurgeon. VLT is a new technique that has been developed to determine the Vim location on MRI. CONCLUSION: VLT is useful for the determination of the Vim location. However, further research is warranted to prove its effectiveness.