Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study.

The use of deep learning (DL) techniques for automated diagnosis of large vessel occlusion (LVO) and collateral scoring on computed tomography angiography (CTA) is gaining attention. In this study, a state-of-the-art self-configuring object detection network called nnDetection was used to detect LVO and assess collateralization on CTA scans using a multi-task 3D object detection approach. The model was trained on single-phase CTA scans of 2425 patients at five centers, and its performance was evaluated on an external test set of 345 patients from another center. Ground-truth labels for the presence of LVO and collateral scores were provided by three radiologists. The nnDetection model achieved a diagnostic accuracy of 98.26% (95% CI 96.25-99.36%) in identifying LVO, correctly classifying 339 out of 345 CTA scans in the external test set. The DL-based collateral scores had a kappa of 0.80, indicating good agreement with the consensus of the radiologists. These results demonstrate that the self-configuring 3D nnDetection model can accurately detect LVO on single-phase CTA scans and provide semi-quantitative collateral scores, offering a comprehensive approach for automated stroke diagnostics in patients with LVO.

A joint convolutional-recurrent neural network with an attention mechanism for detecting intracranial hemorrhage on noncontrast head CT.

To investigate the performance of a joint convolutional neural networks-recurrent neural networks (CNN-RNN) using an attention mechanism in identifying and classifying intracranial hemorrhage (ICH) on a large multi-center dataset; to test its performance in a prospective independent sample consisting of consecutive real-world patients. All consecutive patients who underwent emergency non-contrast-enhanced head CT in five different centers were retrospectively gathered. Five neuroradiologists created the ground-truth labels. The development dataset was divided into the training and validation set. After the development phase, we integrated the deep learning model into an independent center's PACS environment for over six months for assessing the performance in a real clinical setting. Three radiologists created the ground-truth labels of the testing set with a majority voting. A total of 55,179 head CT scans of 48,070 patients, 28,253 men (58.77%), with a mean age of 53.84 ± 17.64 years (range 18-89) were enrolled in the study. The validation sample comprised 5211 head CT scans, with 991 being annotated as ICH-positive. The model's binary accuracy, sensitivity, and specificity on the validation set were 99.41%, 99.70%, and 98.91, respectively. During the prospective implementation, the model yielded an accuracy of 96.02% on 452 head CT scans with an average prediction time of 45 ± 8 s. The joint CNN-RNN model with an attention mechanism yielded excellent diagnostic accuracy in assessing ICH and its subtypes on a large-scale sample. The model was seamlessly integrated into the radiology workflow. Though slightly decreased performance, it provided decisions on the sample of consecutive real-world patients within a minute.

Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study.

There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively included DWI data of patients with acute ischemic lesions from six centers. Dataset A (n = 2986) and B (n = 3951) included data from Siemens and GE MRI scanners, respectively. The datasets were split into the training (80%), validation (10%), and internal test (10%) sets, and six neuroradiologists created ground-truth masks. Models A and B were the proposed neural networks trained on datasets A and B. The models subsequently fine-tuned across the datasets using their validation data. Another radiologist performed the segmentation on the test sets for comparisons. The median Dice scores of models A and B were 0.858 and 0.857 for the internal tests, which were non-inferior to the radiologist's performance, but demonstrated lower performance than the radiologist on the external tests. Fine-tuned models A and B achieved median Dice scores of 0.832 and 0.846, which were non-inferior to the radiologist's performance on the external tests. The present work shows that the inter-vendor operability of deep learning for the segmentation of ischemic lesions on DWI might be enhanced via transfer learning; thereby, their clinical applicability and generalizability could be improved.

A case with angiographic demonstration of isolated anterior spinal artery occlusion.

Anterior spinal artery syndrome (ASAS) is a rare syndrome which occurs due to thrombosis of anterior spinal artery (ASA) which supplies anterior two thirds of the spinal cord. A 27-year-old female patient was admitted to emergency clinic with sudden onset neck pain, sensory loss and weakness in proximal upper extremities which occurred at rest. Thrombophilia assessment tests were negative. Echocardiography was normal. Serum viral markers were negative. In cerebrospinal fluid (CSF) examination, cell count and biochemistry was normal, oligoclonal band was negative, viral markers for herpes simplex virus (HSV) type-1 and type-2, Brucella, Borrellia, Treponema pallidum, Tuberculosis were negative. Diffusion restriction which reveals acute ischemia was detected in Diffusion weighted MRI. Digital subtraction angiography (DSA) was performed. Medical treatment was 300mg/day acetilsalycilic acid. Patient was discharged from neurology clinics to receive rehabilitation against spasticity.

A case with reversible neurotoxicity induced by metronidazole.

Background - Metronidazole is a synthetic antibiotic, which has been commonly used for protozoal and anaerobic infections. It rarely causes dose - and duration - unrelated reversible neurotoxicity. It can induce hyperintense T2/FLAIR MRI lesions in several areas of the brain. Although the clinical status is catastrophic, it is completely reversible after discontinuation of the medicine. Case report - 36-year-old female patient who had recent brain abscess history was under treatment of metronidazole for 40 days. She admitted to Emergency Department with newly onset myalgia, nausea, vomiting, blurred vision and cerebellar signs. She had nystagmus in all directions of gaze, ataxia and incompetence in tandem walk. Bilateral hyperintense lesions in splenium of corpus callosum, mesencephalon and dentate nuclei were detected in T2/FLAIR MRI. Although lumbar puncture analysis was normal, her lesions were thought to be related to activation of the brain abscess and metronidazole was started to be given by intravenous way instead of oral. As lesions got bigger and clinical status got worse, metronidazole was stopped. After discontinuation of metronidazole, we detected a dramatic improvement in patient's clinical status and MRI lesions reduced. Conclusion - Although metronidazole induced neurotoxicity is a very rare complication of the treatment, clinicians should be aware of this entity because its adverse effects are completely reversible after discontinuation of the treatment.