Every year, 15 million people worldwide suffer a stroke. The speed of stroke management is essential: 87% of them are ischemic, and the patient loses around 1.9 million neurons per minute without treatment. A stroke treated too late can have serious consequences: 20% of patients die in the year following the stroke, and 40% have significant sequelae.
Radiology plays a major role in the diagnosis of stroke. How can we optimize the management of emergencies in radiology to speed up the management of stroke patients and improve their care?
What tools can be used to diagnose a stroke in the hospital?
Several tools can be used to diagnose a stroke:
Computed tomography (CT): also known as a CT scan or simply a scan, tomography is one of the first tools to help diagnose stroke. It allows radiologists to analyze three-dimensional images. A brain scan can therefore quickly detect signs of hemorrhage or ischemia, which are indicators of a stroke in the patient.
Magnetic resonance imaging (MRI): more accurate than scans, this radiographic examination uses magnetic fields to provide detailed 3D images of organs, bones and soft tissues. MRI is particularly effective in identifying areas of brain damage, whether ischemic or hemorrhagic, and thus in assessing the damage caused by stroke.
Angiography: this radiological examination provides images of the veins and arteries of the brain. It is often performed in addition to CT or MRI scans to visualize blood vessels and detect the clots or stenoses that often cause strokes.
Post-processing software, facilitating stroke emergency management
Post-processing software involves digital solutions that supplement radiological examination by facilitating the analysis of images, whether they are obtained by tomography, MRI, etc.
They facilitate the radiologist’s work in interpreting stroke images and facilitate multi-level diagnosis. First, post-processing software improves image quality by allowing the adjustment of several parameters, such as brightness, contrast, etc. This makes it easier to detect areas of ischemia and hemorrhage.
It also has advanced features that can accurately measure lesions, such as the Alberta Stroke Program Early CT Score (ASPECT), which assesses the extent of brain damage after an ischemic stroke. It offers 3D reconstruction, allowing specialists to better visualize brain structures and areas affected by stroke.
In summary, post-processing software facilitates the understanding and analysis of medical images and provides valuable information about the nature of the stroke and its consequences for the patient. It reduces image analysis time and optimizes the management of stroke emergencies by accelerating patient care and promoting informed decision-making to treat them.
Staff training, a key issue in diagnosing stroke
In stroke management, every minute counts to minimize the impact of brain damage and reduce the patient’s risk of permanent sequelae.
In addition to radiologists, training all medical staff in the recognition and management of stroke symptoms is crucial. Training days should therefore be organized to raise awareness of the protocols to be adopted, but also to update knowledge of good practices to be followed, as these evolve over time.
The theory should be put into practice through simulations that reproduce stroke scenarios in controlled environments. These allow the hospital’s medical staff to test management protocols, validate their knowledge of symptoms and the different types of stroke, and hone their reflexes to increase their responsiveness in real-life cases.
Stroke management requires an interdisciplinary approach. Training radiologists and neurologists as well as emergency physicians and nurses in teamwork sessions improves communication and the efficiency of interventions, ensuring faster, coordinated patient care and rapid implementation of appropriate care protocol.
Stroke treatment: the emerging role of AI
Artificial intelligence has a role to play in early stroke detection. A French start-up has, for example, developed a mobile application for individuals that, thanks to a system based on a video analysis of the patient, makes it possible to detect a stroke, assess its extent and alert the emergency services.
In post-processing software, AI is used to automatically detect anomalies and compare them with different pathological models. This makes it possible to detect strokes earlier, accurately determine their nature and send alerts to specialists in order to reduce the time taken to manage them.
Artificial intelligence can also be used preventively. For example, it can identify dangerous deposits in arteries during angiography to anticipate and treat the risk of stroke even before it occurs.
Diagnosis and treatment of stroke require rapid and effective emergency management. Post-processing software and new innovations based on artificial intelligence reduce the time it takes to identify and manage cardiovascular events. It is therefore essential for hospitals to equip themselves with these technologies in order to reduce the mortality and sequelae associated with these events.
