How advances in deep learning algorithms for neurology imaging are helping improve outcomes and ease the burden on radiologists.
Neuroradiology has evolved significantly over the past several decades, largely driven by advances in imaging technologies like computed tomography (CT) and magnetic resonance imaging (MRI). Steady growth in the use of these imaging modalities in recent years is creating challenges for neuroradiologists, with increasing workloads and demands for faster interpretation leading to growing levels of burnout symptoms among practitioners.
The rise of artificial intelligence promises to help address the challenges in neuroradiology, spurred by the availability of large imaging datasets and significant computational power. AI also has the potential to improve patient outcomes, by helping accelerate the diagnosis and triage of critical conditions such as traumatic brain injury and stroke.
Advances in AI techniques have already led to an explosion of practical applications within neuroradiology, with algorithms demonstrating the ability to streamline workflows, enhance diagnostic and treatment precision, and bolster the capability of quantitative imaging. Deep learning technology has enabled the creation of AI tools that perform everything from disease detection and workflow optimization to lesion quantification and segmentation.
In this article, we explore how AI tools are delivering value in several key areas of neurology, including stroke care, brain volumetrics, acute brain injury, and neuro-oncology.
AI in stroke care
When it comes to stroke cases, time is everything – the faster a stroke patient receives treatment, the better the outcome. In this area of neurology, the role of AI is primarily focused on stroke care coordination – identifying strokes early and coordinating care to ensure patients receive mechanical thrombectomy within the critical time window.
By automating processes and prioritizing cases, AI tools aim to reduce the "door-to-needle time" and enable more patients to get the right treatment, in the right place, at the right time. AI applications are helping automate the calculation of the ASPECTS score, removing the subjectivity and potential for inconsistency that can occur when radiologists manually assess and score brain regions affected by ischemic stroke. AI tools can also automate the complex analysis of perfusion studies, which help differentiate between the ischemic core and potentially viable “penumbra” regions of the brain.
The information generated by these applications can play a key role in determining a patient's eligibility for mechanical thrombectomy treatment. Crucially, this can help make the process faster and more standardized, which is important in the time-sensitive context of acute stroke care.
In May 2024, the largest prospective evaluation of stroke AI imaging was presented at the European Stroke Organisation Conference (ESOC). The study assessed more than 83,000 patients over a three-year period and showed that using AI could enable a 50% increase in the number of patients receiving mechanical thrombectomy and reduce door-in-door-out time by nearly 50 minutes.
AI in acute care
Comparable to its role stroke care, in the context of trauma and acute brain injury, AI is primarily focused on improving patient outcomes by accelerating the identification and triage of critical conditions from non-contrast CT scans. Regulatory pathways, such as the FDA’s expedited clearance for AI-enabled triage devices, have facilitated the entry of these algorithms into clinical practice.
The main role of AI in acute care is to expedite the identification of critical findings, prioritize the most urgent cases, and provide an additional layer of detection to support radiologists in making timely and accurate diagnoses. This can lead to faster treatment and improved patient outcomes in acute trauma and brain injury scenarios.
Today, AI applications can analyze CT scans of the head and identify the presence of conditions such as intracranial hemorrhages, large vessel occlusions, and traumatic brain injuries. These applications can then flag the cases and prioritize them on the radiologist's worklist, ensuring that critical cases are reviewed first.
In addition, AI can function as a "safety net" by detecting findings that the radiologist may have missed, such as brain bleeds or skull fractures. This helps ensure that no critical abnormalities are overlooked, especially in time-sensitive acute care scenarios.
It should be noted that there are differences in the regulatory clearances and capabilities of AI applications between different regions. For example, in Europe, some AI applications can go beyond detecting abnormalities to localize and quantify them, whereas in the US applications tend to be mainly focused on triage and prioritization.
AI in brain volumetrics
Beyond triage, AI is proving instrumental in disease quantification, and algorithms are being applied to estimate the volume of anatomical structures, which can help in the diagnosis and assessment of a wide range of neurodegenerative conditions. Historically, manual measurement of brain structures can be prone to variability and inconsistency, especially across different time points and clinicians. Volumetric AI applications can provide a more standardized and objective approach, reducing measurement errors and ensuring consistency in interpretation.
Today, AI applications can segment and measure the different anatomical structures within the brain, such as the temporal lobe, frontal lobe, and occipital lobe. This allows for the quantification of brain volumes, which can then be compared to population norms. In addition, by measuring and tracking changes in brain volumes over time, AI can help monitor the progression of neurodegenerative diseases like Alzheimer's and multiple sclerosis, which can aid in the diagnosis and management of these conditions, as well as the evaluation of treatment effectiveness.
Ultimately, manually measuring and comparing brain volumes can be a tedious and time-consuming process for radiologists. AI can help streamline the process by automating these tasks, enable more accurate, consistent, and efficient measurement and tracking of brain structures. This can aid in the diagnosis, monitoring, and management of various neurological conditions, as well as saving time and allowing radiologists to focus on other aspects of patient care.
Finally, one area of current interest for AI application development is the approval of new drugs for neurodegenerative disease, such as lecanemab, which was recently approved for the treatment of Alzheimer’s disease. Lecanemab requires monitoring of a patient’s brain through MRI scans to look for specific abnormalities called amyloid-related imaging abnormalities (ARIA) and is a key factor in determining eligibility for the drug. This is expected to drive increased interest in the development and adoption of AI applications that can assist in this process.
AI in neuro-oncology
The role of AI in oncology applications for the brain is still an emerging area, however there are some key areas in which it is already delivering benefits, particularly in terms of tumor analysis and characterization. Some AI tools can provide additional information about the tumor characteristics that may not be readily apparent from standard imaging, including assessing factors like tissue oxygenation, tumor enhancement rates, and other biomarkers that can inform treatment decisions.
In addition, AI can be used to segment and measure brain tumors, allowing for comparison of the tumor size and characteristics before and after treatment. This can help assess the effectiveness of treatments, such as surgery or radiation therapy, and guide further decisions around patient care.
While oncology applications haven't gained as much traction as some other neurology use cases, this is an area expected to grow in interest going forward. As new treatments and monitoring requirements emerge, the role of AI in brain tumor analysis and management is likely to become more prominent. Indeed, some AI vendors are developing their own proprietary methods and biomarkers for analyzing brain tumors, which can provide additional insights beyond standard imaging protocols.
For example, traditional imaging methods often struggle to differentiate between tumor growth and effects like pseudo-progression or post-treatment radiation effect (PTRE), which can appear similar on scans. Distinguishing these conditions is crucial, as it helps determine if a patient should continue treatment, consider surgery, or qualify for clinical trials. Unfortunately, no standardized method exists to accurately distinguish between these conditions without prolonged imaging or invasive biopsies, making timely, non-invasive diagnosis challenging.
To address this challenge, a proprietary AI platform has been developed that can produce “fractional tumor burden” (FTB) maps, providing clinicians with objective, rapid insights into tumor progression and treatment effectiveness. These maps also assist in targeting biopsies and planning treatments such as surgery and radiation.
Build a neuroradiology AI ecosystem
AI has introduced a transformative approach in neuroradiology, promising to enhance diagnostic precision, streamline treatment planning, and improve workflow efficiency. By automating repetitive tasks and enabling more advanced analyses, AI is set to be a core driver of neuroradiology’s evolution, ultimately contributing to more timely, targeted, and data-informed patient care.
However, healthcare providers now face numerous choices in neuroradiology AI solutions, with a key challenge being to identify the option best suited to their specific needs. A strategic solution to this lies in partnering with a single AI platform provider, like Blackford, which allows organizations to trial AI applications with their own data before committing to purchases.
The swift expansion of AI in neuroradiology and other medical fields has resulted in a proliferation of seemingly similar algorithms. Blackford recognizes the complexities healthcare providers face when navigating these options, as factors like patient demographics, scanner type, and institutional workflows can greatly impact effectiveness. By providing multiple applications for critical use cases—such as fracture detection—the Blackford platform empowers providers to conduct retrospective analyses, helping them select the most effective solution for their needs.
Centralized access to AI solutions through a dedicated platform enables healthcare providers to efficiently evaluate, deploy, and manage a variety of imaging AI applications. This approach optimizes resource use, reduces deployment times, and lowers costs, while also consolidating training, monitoring, and support within a single system.
A platform-based strategy offers additional operational advantages by simplifying contract management, deployment processes, and support needs within a unified interface. Importantly, these platforms integrate smoothly with existing hospital infrastructure, including DICOM interfaces, to ensure optimal performance.
Furthermore, such platforms mitigate "analysis paralysis" by providing a curated selection of vetted AI applications across various specialties beyond neurology, including cardiology, pulmonology, MSK, trauma, and women’s health. Through a single contract, hospitals can access bundled AI applications, streamlining procurement and eliminating the complexities of purchasing individual algorithms.
Ultimately, by adopting a single-platform strategy, clinicians and institutions gain access to a diverse range of AI solutions that can cater to various neurology use cases, including trauma, stroke, and oncology, supported by enhanced reporting capabilities. This provides a more comprehensive solution for departments or enterprises, rather than having to adopt multiple, disparate AI applications. This flexibility and scalability can help organizations build a comprehensive neurology AI ecosystem tailored to their requirements.