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Coronary artery disease (CAD) is one of the most common cardiovascular disorders affecting millions of individuals globally. It is the leading cause of mortality in both the wealthy and impoverished nations. CAD patients exhibit a wide range of symptoms, some of which are not evident until a major incident occurs. The development of techniques for early detection and precise diagnosis is heavily dependent on research. The proposed system introduces a novel approach, Generative Adversarial Networks Augmented Naïve Bayes (GAN-ANB), to classify high-risk CAD patients using Coronary Computed Tomography Angiography (CCTA) imaging data. The database included images from Coronary Computed Tomography Angiography (CCTA) records of 5,000 individuals. The developed GAN framework consists of a generator to generate synthetic patient profiles, and a discriminator to distinguish between genuine and synthetic profiles to improve the identification of high-risk CAD patients. Adding synthetic data to the training process allowed the discriminator to be utilized further to improve predictive modeling. The performance of the GAN-enhanced prediction model was assessed using accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve (ROC). The model exhibited an outstanding Dice Similarity Coefficient (0.91), Mean Intersection Over Union (0.90), recall (0.96), and precision (0.98) in differentiating between high-risk and low-risk individuals. The identification of high-risk patients with CAD is greatly enhanced by the integration of GANs with clinical and imaging data. ROC of 0.99 was achieved by the GAN-ANB model, which outperformed conventional machine learning models, was achieved using the GAN-ANB model. High cholesterol level, diabetes, and some CCTA-derived imaging characteristics, including plaque load and luminal stenosis, were among the major predictors. This method offers a powerful tool for early diagnosis and intervention, potentially leading to improved patient outcomes and lower healthcare expenditure. [ABSTRACT FROM AUTHOR]

Summary: Objective: To evaluate the diagnostic efficacy of a deep learning (DL) model based on PET/CT images for distinguishing and predicting various pathological subtypes of invasive lung adenocarcinoma. Methods: A total of 250 patients diagnosed with invasive lung cancer were included in this retrospective study. The pathological subtypes of the cancer were recorded. PET/CT images were analyzed, including measurements and recordings of the short and long diameters on the maximum cross-sectional plane of the CT image, the density of the lesion, and the associated imaging signs. The SUVmax, SUVmean, and the lesion's long and short diameters on the PET image were also measured. A manual diagnostic model was constructed to analyze its diagnostic performance across different pathological subtypes. The acquired images were first denoised, followed by data augmentation to expand the dataset. The U-Net network architecture was then employed for feature extraction and network segmentation. The classification network was based on the ResNet residual network to address the issue of gradient vanishing in deep networks. Batch normalization was applied to ensure the feature matrix followed a distribution with a mean of 0 and a variance of 1. The images were divided into training, validation, and test sets in a ratio of 6:2:2 to train the model. The deep learning model was then constructed to analyze its diagnostic performance across different pathological subtypes. Results: Statistically significant differences (P < 0.05) were observed among the four different subtypes in PET/CT imaging performance. The AUC and diagnostic accuracy of the manual diagnostic model for different pathological subtypes were as follows: APA: 0.647, 0.664; SPA: 0.737, 0.772; PPA: 0.698, 0.780; LPA: 0.849, 0.904. Chi-square tests indicated significant statistical differences among these subtypes (P < 0.05). The AUC and diagnostic accuracy of the deep learning model for the different pathological subtypes were as follows: APA: 0.854, 0.864; SPA: 0.930, 0.936; PPA: 0.878, 0.888; LPA: 0.900, 0.920. Chi-square tests also indicated significant statistical differences among these subtypes (P < 0.05). The Delong test showed that the diagnostic performance of the deep learning model was superior to that of the manual diagnostic model (P < 0.05). Conclusions: The deep learning model based on PET/CT images exhibits high diagnostic efficacy in distinguishing and diagnosing various pathological subtypes of invasive lung adenocarcinoma, demonstrating the significant potential of deep learning techniques in accurately identifying and predicting disease subgroups. [ABSTRACT FROM AUTHOR]

Inflammatory myofibroblastic tumor (IMT) is a tumor composed of differentiated myofibroblastic spindle-shaped cells. It occurs in the soft tissues of the abdomen and lungs, and is very rare in the sphenoid sinus. The diagnosis depends on histopathology and immunohistochemistry, and is easily misdiagnosed. Although metastasis is rare, the recurrence rate is high. Surgical resection is the treatment of choice, and where complete resection is difficult, radiation therapy, hormonal therapy, or molecular targeted drug therapy can be administered. We report a rare case of IMT with a primary origin in the sphenoid sinus, which was entirely resected by nasal endoscopy and confirmed by histological examination. [ABSTRACT FROM AUTHOR]

Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article. [ABSTRACT FROM AUTHOR]

Coeliac artery compression stenosis caused by the median arcuate ligament can lead to aneurysm formation in the pancreatico-duodenal arteries that can eventually result in a spontaneous rupture leading to retroperitoneal haemorrhage. In this case series, we describe the cases of three patients, all presenting with sudden epigastric pain, diagnosed as spontaneous haematoma, complicating a median arcuate ligament syndrome. [ABSTRACT FROM AUTHOR]

Bone metaplasia or heterotopic ossification of the appendix is an extremely rare condition, which is usually associated with mucinous appendiceal neoplasms. This case describes a young male patient, who presented to the surgical emergency department with clinical and computed tomography signs of acute appendicitis. Laparoscopic appendectomy was performed. The final histopathological examination revealed a bone metaplasia of the distal appendix, without any other associated appendiceal pathology. According to our knowledge and literature review, this is the first such case described in the English literature yet. [ABSTRACT FROM AUTHOR]

Phleboliths within the parotid gland are exceedingly rare. We report a case of a venous malformation with multiple phleboliths that involved the left parotid gland and extended into the extraparotid tissue in a 43-year-old woman. We also review 13 similar cases that have been reported since 1948, and we highlight the significance of distinguishing phleboliths from sialoliths because management of the two can be entirely different. Phleboliths in and around the salivary glands primarily affect the parotid and submandibularglands in adult women. Patients can present with recurrent, intermittent swelling that may be associated with food intake and hyposalivation. Any sialolithiasis located outside of its most common location in the submandibular glands must be investigated thoroughly before surgery. [ABSTRACT FROM AUTHOR]