Cerebral aneurysms present a unique medical challenge–otherwise known as intracranial or brain aneurysms–since these dilations, which appear at the weak points of the brain’s vessels and usually carry no indications, get bigger with time and can therefore cause a rupture.
The aneurysm may rupture when the expansion becomes too much and the wall of the vessel is too thin, leading to a potentially life-threatening event called subarachnoid hemorrhage (SAH). This requires immediate medical assistance, according to professionals at Johns Hopkins Medicine.
Current measurement approaches have certain limitations, hindering the ability to determine and monitor aneurysms for a potential rupture – vital for optimized patient outcomes. However, taking steps to monitor aneurysm growth and detecting it prior can help mitigate these risks significantly.
Researchers investigated if AI could help measure intracranial aneurysms—the subject of a study published in the Journal of NeuroInterventional Surgery last year. Their experiment suggested that AI might lead to improving this measure.
The team utilized an AI-based tool – Rapid Aneurysm – to assess anomalous aneurysms found to have ruptured during conservative management. By studying them through this volumetric measurement device, the team found that it could reliably detect enlargement beyond what is serviced by traditional techniques.
According to the researchers, the tool’s higher sensitivity in detecting aneurysmal enlargements has important implications for clinical treatment. Their findings suggest that this increased awareness could be instrumental in changing medical processes and ultimately contributing to better patient outcomes.
Dr. David Fiorella, director of the Stony Brook Cerebrovascular Center and co-director of the Stony Brook Cerebrovascular and Comprehensive Stroke Center, and Dr. David Dodick, professor emeritus at Mayo Clinic and chief science officer chair for Atria Academy of Science.
In email interviews with HealthITAnalytics, the potential for augmenting clinical decision-making with the tool was discussed.
Understanding The Challenges of Stratifying Aneurysm Rupture Risk
Tracking aneurysms can be difficult, and traditional methods may not be effective enough. It is not always straightforward to identify, measure, and evaluate this type of health complication using the usual approaches.
Fiorella says:
“The greatest challenge associated with tracking aneurysms lies in the conventional tools and approaches used to assess their precise shape and size,”
Aneurysms present a difficult challenge regarding how they can be detected, measured, and monitored due to various shapes, sizes, and placements.
Dodick says:
“It’s a manual and, oftentimes, time-consuming process for over-burdened care teams.”
“Variability between physicians is also a problem when measuring aneurysms, making it difficult to evaluate aneurysm growth accurately and consistently. Not properly identifying growth can have a significant influence on treatment decisions, with potentially serious consequences.”
Clinical strategies to address rupture risk and reduce the associated challenges often utilize imaging, over time, of aneurysm changes to inform if further intervention is needed. This imaging allows physicians to stratify risk and potentially react sooner with treatment.
Fiorella went on to say:
“Currently, we assess rupture risk by manually measuring the largest diameter of the aneurysm on [computed tomography angiography] or [magnetic resonance angiography] 2D source images, visualizing aneurysm shape, and comparing it over time in consecutive scans.”
Dodick added that aneurysms are typically monitored over specific time intervals — which are adjusted based on aneurysm stability — using these methods until there is a strong indicator of rupture risk. Then, the care team will consider possible interventions and their associated risks.
Fiorella noted that this approach could challenge clinicians in measuring an aneurysm’s maximum diameter and shape when reliably detecting growth and predicting rupture risk. Consequently, making accurate and reproducible assessments would be difficult.
Fiorella continues to say:
“Usually, the clinician looks at the scan to the best of their ability and compares to [prior scans].”
“Oftentimes, this is limited, as the scans have been performed at different facilities, and sometimes the provided reconstructed imaging planes are different. Direct measurements are performed, but depending on these reconstructions, it can be difficult to measure in the exact same area on each comparison scan, and subtle differences in shape can be difficult or even impossible to detect. In addition, these comparisons are time and labor-intensive for the busy clinician.”
Amid a backdrop of limited resources, some health systems harness digital tools’ power to assist in rupture risk stratification when seeing patients in clinics. Such efforts highlight the need for novel strategies to help evaluate and predict an individual’s precariousness.
Artificial intelligence (AI) for aneurysm rupture risk monitoring represents a significant advancement in neurosurgery. By leveraging machine learning algorithms and predictive analytics, healthcare providers can better identify patients at higher risk of aneurysm rupture, leading to more targeted and effective treatment.
Additionally, AI can help clinicians to make more informed decisions about the timing and type of intervention, potentially reducing the risk of complications and improving patient outcomes. However, challenges still need to be addressed, including more extensive and diverse datasets to train AI models and the importance of maintaining human oversight and control.
Source: health analytics
