Exploring the Future of Neuro-Diagnostics:
Advanced Imaging and AI
Artist’s impression of artificial intelligence participating in the Neuro-diagnostic imaging
The diagnostic process of a neurological disease or condition is intricate and multidisciplinary. It requires skill, and intellectual rigor and is crucial for patient care. A detailed physical examination and patient history leads to provisional diagnosis. To confirm the diagnosis, several types of tests are prescribed. Such diagnostic tests for neurological conditions involves different kinds of tests such as pathological-serum testing, imaging tests, and often nerve conduction studies, lumbar puncture, electromyography, and electroencephalogram, etc.
Neuroimaging is central to neuro-diagnostics and prognosis owing to its disciplined process of relating lesion visualization to symptoms. Neuroimaging modalities/techniques have progressed in the past two decades. The techniques are quicker, less invasive and more comfortable. Tests now provide more information which leads to early detection. The advancement and sophistication in neurodiagnostic testing, leads to decreased morbidity and mortality which contributes to the goal of providing an increased quality of life for patients suffering from neurological disease. In recent years, the resolution of X-ray, computed tomography (CT) and magnetic resonance imaging (MRI) has improved which helps us identify structural abnormalities with reliability and accuracy. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) enables specialists to explore dynamic information on molecular causes of neurological diseases.
Neuroimaging techniques can noninvasively map the function and structure of the brain. They can either directly measure the currents and magnetic fields produced by neural activity, or by injecting radioisotope agents to outline diseased regions through emitted radiation or by measuring tissue-specific responses to external energy source viz. a magnetic field.
Identifying information about the structural and physiological brain activities is obtained from the signals received, which answers questions about structural integrity relevant in clinical applications and relating brain function to human behavior and cognition. In more than 50% of cases of severe head injury, significant focal lesions such as cerebral hemorrhages and contusions are identified.
Traumatic brain injuries (TBI) are often caused by a forceful bump, blow, or a jolt to the head or the body or it can also happen because of an object that pierces the skull and enters the brain. If it is a serious TBI, it can lead to permanent and severe disability or death. A neurological exam, which typically includes physical examination and a review of the patient’s medical history, tests the motor and sensory skills, speech, hearing, coordination and balance. Other abilities like changes in mood or behavior and mental status of the patient are also monitored. This helps healthcare providers to assess how well a TBI patient’s brain and body are working. Brain scans are used to evaluate the primary brain injury to determine the extent of damage to the brain. The medical providers then determine if surgery is required to repair the damage to the brain. The need for imaging is determined by the condition of the patient and their symptoms.
The most commonly used imaging technology that is used to assess people with suspected moderate to severe TBI is computed tomography (CT). Measurements of X-ray transmissions generate cross-sectional images from thin slices of patient tissues using a computer.
. Because of widespread availability of the test, capability of whole-body imaging in multiple injured patients, rapid imaging acquisition, low associated costs, superior bone detail, and compatibility with most medical devices, the testing of unstable patients is possible. Non-contrast CT scan is the neuroimaging diagnostic test of choice for TBI.
Magnetic resonance imaging (MRI) is used after the initial assessment and treatment to produce detailed images of body tissue. It picks up subtle changes in the brain that the CT scan might have missed as it is a more sensitive test. Even with all these improvements, available imaging technologies and other measures are inadequate to detect mild concussive injuries.
For acute neurological conditions a CT scan is preferred and an MRI for subacute or chronic cases. Stroke is another acute neurological condition that requires brain imaging, in addition to TBI. Initial imaging study for stroke patients is non-contrast CT scan. It identifies hyperdense hemorrhage and differentiates it from cerebral infarction. However, precise identification of the infarct area is usually impossible in a CT scan because early signs of infarction are subtle.
CT is also used to identify a midline shift (MLS). The entire brain is pushed off-center because the pressure exerted by the buildup of blood and swelling around the damaged brain tissues. But in unstable patients or frequent progressive monitoring of the bleeding, CT scan is not practical. The trauma caused to other structures of the brain as they are pushed and pulled out of their natural position can lead to serious complications and fatal risk.
In India, there is a common occurrence of delay in surgery for traumatic intracranial hematomas, exceeding the golden hour period. During the golden hour, if prompt treatment is given, the likelihood of preventing death is very high.
The development of computational methods has been getting increasing attention for the extraction of objective imaging features (biomarkers). Biomarkers are capable of correlating with clinical outcome, response to treatment, and/or disease phenotype. It is possible to build powerful predictive models that assist in the management of patients with a wide range of neurological disorders. This can be achieved with the use of biomarkers, and imaging data combined with artificial intelligence. This leads to personalized treatment and better clinical outcomes.
AI-based tools can enable faster and more accurate diagnosis, effective disease monitoring, personalized treatment plans, and new therapeutic approaches. Extensive datasets and patient information are processed by machine learning algorithms. AI-driven systems can identify unique and unbiased patterns in data faster and more efficiently than manual processes. An AI system currently in use for detecting stroke patterns in LVOs in acute strokes automatically sends out an alert to the stroke treatment team without any human intervention. The AI system saves on average 52 minutes before emergency stroke intervention is instituted.
Each person with a rare neurological disorder has a particular need for personalized treatment because they have a unique genetic profile and set of symptoms. The increasing availability of storing, sharing, and computing facilities and improving capabilities of imaging devices generate larger and larger amounts of data. Patterns in large datasets that are not immediately apparent can be identified easily using artificial intelligence helping better prognosis.
We also need a good closing paragraph. we can add 2-3 sentences about Neurshield products. Mostly instead of writing a lot about conventional techniques and who/where they are used, we need to add about what is AI revolution that is happening in healthcare, and how AI is impacting neurodiagnostics with direct evidence.
References:
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