Missed detection of intracranial hemorrhage in Head CT scans has significantly impacted patient morbidity and mortality. Early detection of intracranial hemorrhage enables patients to receive appropriate treatment which resulted in a better outcome. Some doctors have limited experience in interpreting the CT scan hence increasing the probability to miss the hemorrhage. The main objective of this study is to develop an algorithm model capable of detecting intracranial hemorrhage in a head CT scan. We are using deep learning from a convolutional neural network (CNN) to produce this algorithm module. This was a cross-sectional study using secondary data, in which 200 data were collected from public datasets. All of the samples have been anonymized into secondary data. The algorithm model is trained using deep learning via a Jupyter Notebook platform. To analyze the algorithm model performance, a confusion matrix was used to measure the accuracy, sensitivity, specificity, precision, and F1 score. This study showed that from 200 training data, 95 samples were true positive, 95 samples were true negative, 7 samples were false positive, and 3 samples were false negative. This algorithm model shows high sensitivity (0.9694), high specificity (0.9314), high precision (0.9314), and high accuracy (0.9500) with an F1 score of 0.9500. This study has proven that deep learning by using CNN enables us to create an accurate classifier that can differentiate between head CT scan with intracranial hemorrhage and without hemorrhage. The main objective of this study is to develop an algorithm model capable of detecting intracranial hemorrhage in a head CT scan. We are using deep learning from a convolutional neural network (CNN) to produce this algorithm module. This was a cross-sectional study using secondary data, in which 200 data were collected from public datasets. This dataset is owned by Abdul Kader Helwan, an academic staff at Al-Manar University of Tripoli, Lebanon. Permission to use the dataset for this research was officially obtained from the owner. All of samples have been anonymized into secondary data. The data is divided into train, validation, and test samples. The algorithm model is trained using deep learning via a Jupyter Notebook platform. To analyze the algorithm model performance, confusion matrix was used to measure the accuracy, sensitivity, specificity, precision, and F1 score. This study showed that from 200 training data, 95 samples were true positive, 95 samples were true negative, 7 samples were false positive, and 3 samples were false negative. This algorithm model shows high sensitivity (0.9694), high specificity (0.9314), high precision (0.9314), and high accuracy (0.9500) with F1 score of 0.9500. Hence, this study has proven that deep learning by using CNN enables us to create an accurate classifier that can differentiate between head CT scan with intracranial hemorrhage and without hemorrhage.
Pulmonary tuberculosis (PTB) is common in tropical country like Malaysia. Prolonged PTB infection may lead to mycotic pulmonary artery pseudoaneurysm (PAP). We report a case of childhood non-tuberculous pulmonary infection causing mycotic PAP which resolved spontaneously after antibiotics therapy. A 1 year 6 months old girl underlying Down syndrome presented with prolonged fever for two weeks , cough and breathlessness. Her leucocytes count were elevated and she developed several hypotensive episodes secondary to septicaemia. Chest radiograph showed loculated right sided pleural effusion. Ultrasound revealed complex pleural collection and initial aspiration revealed a thick stale blood. Thinking of possible vascular cause, ultrasound able to locate a well-defined rounded structure with high flow velocity seen on Doppler ultrasound consistent with pseudoaneurysm and CT thorax confirmed the findings. Pulmonary artery angiogram prior to embolization revealed no evidence of abnormal vasculature or contrast blush at the region of interest. Complimentary ultrasound showed evidence of spontaneous thrombosis within the pseudoaneurysm.Non-tuberculous PAP is a rare but possible life-threatening sequela of pneumonia. Pleural drainage in a haemothorax with concomitant mycotic thoracic pseudoaneurysm may cause loss of pressure tamponade and will end up with devastating consequences. Careful ultrasound image acquisition must be made by the attending radiologist prior to pleural drainage.
The aim of this case report is to highlight an anatomical variant of the right hepatic artery during angiography. The course of surgery or endovascular treatment is very dependent on this information. A 67-year-old male patient with hypertension and diabetes mellitus was admitted to our institution due to episodes of upper gastrointestinal bleeding. The digital subtraction angiography scan showed the right hepatic artery (RHA) is originated from the superior mesenteric artery (SMA) rather than the usual RHA that is coming from the coeliac trunk.
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