Artificial intelligence is increasingly integrated into medical imaging, particularly in the field of neurology, where it has shown potential in the diagnosis and monitoring of multiple sclerosis (MS). With MRI serving as the primary tool for assessing disease activity, AI-based software solutions are designed to assist radiologists in detecting and analysing MS-related lesions over time. These systems promise improved efficiency and accuracy by automating lesion detection and reducing the subjectivity of human interpretation. However, challenges remain, particularly concerning variability in imaging equipment and false-positive findings, which could impact clinical decision-making. Evaluating the strengths and limitations of AI-based lesion assessment is essential to understanding its role in clinical practice and its potential for improving patient outcomes.
Enhancing Detection and Efficiency
One of the most significant advantages of AI-assisted MRI analysis in MS is its ability to improve lesion detection while increasing workflow efficiency. In clinical practice, radiologists are required to compare follow-up MRI scans with baseline images to determine the presence of new or enlarging lesions, a process that is time-consuming and prone to variability among different observers. AI software has demonstrated high sensitivity in this task, meaning that when new or enlarging lesions are present, the AI is highly likely to detect them. Additionally, AI-based lesion segmentation tools can perform comparably to manual expert annotations, further validating their reliability.
Another notable strength of AI in this context is its high negative predictive value. This means that when AI reports the absence of new or enlarging lesions, it is highly reliable, reducing the need for exhaustive manual verification by radiologists. This capability can streamline the radiological workflow by allowing experts to focus on more complex cases that require detailed assessment. The automation of lesion tracking can also contribute to a more standardised approach to MS monitoring, potentially improving consistency in clinical reporting.
However, despite these benefits, the use of AI in MS lesion assessment is not without its challenges. While AI excels at detecting changes in lesion burden, its performance varies depending on the quality and consistency of the MRI data it processes. Differences in imaging protocols and equipment can introduce discrepancies in lesion detection, affecting the overall reliability of AI-generated reports.
Challenges with False Positives
Despite its strengths in lesion detection, AI-based MS imaging analysis faces a notable limitation: the occurrence of false positives. False-positive findings occur when AI incorrectly identifies stable lesions or normal anatomical structures as new or enlarging MS-related lesions. This issue is particularly pronounced when follow-up MRI scans are conducted on different scanners or with varying imaging parameters, leading to inconsistencies in signal intensity and image quality.
Studies have shown that when follow-up imaging is performed on the same MRI machine using identical protocols, AI specificity is relatively high, meaning fewer false-positive findings occur. However, when different scanners are used, specificity declines, and false-positive rates increase. A significant proportion of these incorrect detections result from subtle differences in lesion appearance, signal intensity variations or artefacts introduced by the imaging process. Some lesions may appear brighter or slightly larger on follow-up scans due to differences in MRI field strength or manufacturer-specific processing, leading AI algorithms to mistakenly classify them as new or enlarging.
False-positive findings can have direct clinical implications. An overestimation of disease activity may prompt unnecessary changes in treatment, exposing patients to potential side effects without clear evidence of worsening disease. This highlights the need for radiologists to carefully interpret AI-generated reports and verify lesion progression manually to ensure accurate clinical decision-making. While AI serves as a useful tool for initial lesion assessment, it cannot replace the expertise of a trained radiologist in distinguishing true pathology from imaging artefacts.
Impact of MRI Variability
The effectiveness of AI-based lesion assessment is heavily influenced by MRI consistency. Ideally, follow-up scans should be conducted on the same MRI machine using identical imaging protocols to minimise variability. However, in real-world clinical settings, this is not always feasible. Patients may undergo follow-up scans at different hospitals or imaging centres, where equipment and protocols may differ. Additionally, routine equipment upgrades and service availability may necessitate imaging on different MRI machines, introducing further variability in the data.
When follow-up MRIs are conducted on different scanners, AI accuracy declines, and the rate of false positives increases significantly. Differences in field strength, acquisition parameters and image processing techniques between machines contribute to inconsistencies in lesion appearance. This variability can lead AI to detect stable lesions as newly appearing, misclassify normal anatomical structures or overlook small but clinically significant changes.
While MRI standardisation is recommended, achieving complete uniformity across different healthcare settings is challenging. This underscores the importance of expert oversight in AI-assisted lesion assessment. Radiologists must remain actively involved in reviewing AI-generated findings to differentiate between true disease progression and artefactual changes resulting from scanner variability. By integrating AI into the diagnostic workflow while maintaining human validation, clinicians can leverage the benefits of AI without compromising diagnostic accuracy.
AI-powered MRI analysis in MS offers significant advantages in detecting new and enlarging lesions with high sensitivity. Its ability to streamline workflows and assist radiologists makes it a promising tool in clinical practice, particularly in cases where no new lesions are present. However, the risk of false positives, particularly when MRI scans are acquired on different machines, remains a major challenge. False-positive lesion detection can lead to unnecessary treatment modifications, highlighting the need for careful interpretation of AI-generated reports.
AI can support MS monitoring by enhancing efficiency and standardisation, but its effectiveness depends on imaging consistency and expert validation. While AI tools are valuable in clinical practice, they must be used as an adjunct to radiological expertise rather than a replacement. Addressing the challenges of scanner variability and ensuring human oversight will be key to maximising the benefits of AI in neurological imaging.
Source: European Journal of Radiology
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