Terson syndrome with persistent vitreous hemorrhage following traumatic brain injury

Deep learning-based hemorrhage detection for diabetic retinopathy screening

Diabetic retinopathy is a retinal compilation that causes visual impairment. Hemorrhage is one of the pathological symptoms of diabetic retinopathy that emerges during disease development. Therefore, hemorrhage detection reveals the presence of diabetic retinopathy in the early phase. Diagnosing the disease in its initial stage is crucial to adopt proper treatment so the repercussions can be prevented. The automatic deep learning-based hemorrhage detection method is proposed that can be used as the second interpreter for ophthalmologists to reduce the time and complexity of conventional screening methods. The quality of the images was enhanced, and the prospective hemorrhage locations were estimated in the preprocessing stage. Modified gamma correction adaptively illuminates fundus images by using gradient information to address the nonuniform brightness levels of images. The algorithm estimated the locations of potential candidates by using a Gaussian match filter, entropy thresholding, and mathematical morphology. The required objects were segmented using the regional diversity at estimated locations. The novel hemorrhage network is propounded for hemorrhage classification and compared with the renowned deep models. Two datasets benchmarked the model's performance using sensitivity, specificity, precision, and accuracy metrics. Despite being the shallowest network, the proposed network marked competitive results than LeNet-5, AlexNet, ResNet50, and VGG-16. The hemorrhage network was assessed using training time and classification accuracy through synthetic experimentation. Results showed promising accuracy in the classification stage while significantly reducing training time. The research concluded that increasing deep network layers does not guarantee good results but rather increases training time. The suitable architecture of a deep model and its appropriate parameters are critical for obtaining excellent outcomes.

Terson's syndrome, the current concepts and management strategies: A review of literature

Terson's Syndrome describes intraocular hemorrhage secondary to an acutely raised intracranial pressure (ICP). Although Terson's Syndrome is common amongst patients with subarachnoid hemorrhage (SAH), it is underdiagnosed and often overlooked. This review discusses the current understanding of the etiopathogenesis, clinical features, and management of Terson's Syndrome and highlights the visual and prognostic implications to stress the importance of timely diagnosis and management. The origin of intraocular hemorrhage in Terson's Syndrome has been debated. A recognized theory suggests that an acutely raised ICP induces effusion of cerebrospinal fluid into the optic nerve sheath which dilates the retrobulbar aspect of the sheath in the orbit. Dilatation mechanically compresses the central retinal vein and retinochoroidal veins resulting in venous hypertension and rupture of thin retinal vessels. A commonly reported clinical feature is decreased visual acuity and blurred vision. These may be accompanied by symptoms of increased ICP including loss of consciousness and headache. Diagnosis is established using evidence from the clinical presentation, ophthalmoscopy, and, when required, imaging including B-mode ultrasound, CT, MRI, and fluorescein angiography. Terson's Syndrome is managed conservatively by observation for mild cases and with vitrectomy for bilateral cases and for patients whose hemorrhage has not spontaneously resolved after an observational period. Terson's Syndrome can be used as a prognostic indicator of morbidity and mortality in underlying pathology like SAH. Fundoscopy of patients with SAH, acutely raised ICP or visual disturbance with unknown etiology can help establish a timely Terson's Syndrome diagnosis. This may avoid the risk of permanent visual impairment.