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Idiopathic normal pressure hydrocephalus (iNPH), a reversible form of dementia accounting for an estimated 1.6–5.4% of dementia cases, is frequently underdiagnosed and misdiagnosed due to its symptomatic overlap with conditions like Alzheimer’s (AD) and Parkinson’s (PD). [1] Recent advances in medical imaging and artificial intelligence (AI), however, offer promising solutions, including a study by Lee et al. that uses AI-driven 3D T1-weighted MRI volumetric analysis to identify key brain features associated with iNPH and automate measuring biomarkers. [2] Their model demonstrated robust diagnostic performance, achieving an area under the curve (AUC) of 0.956 for high-convexity tightness and 0.830 for Sylvian fissure enlargement. Additionally, cross-validation and unseen test set yielded AUCs of 0.983 and 0.936, respectively. Such performance metrics underscore how AI-driven tools can enhance diagnostic accuracy and streamline workflows in clinical settings by enabling rapid, automated analysis of neuroimaging data, addressing the need for large-scale screenings for targeted care and effective intervention.
However, despite its advancements, Lee et al.’s study has several limitations that require careful consideration. First, the preselected cohort of iNPH, PD, AD, and healthy controls (HC) may not reflect the real-world prevalence of these diseases, introducing selection bias and raising concerns about the model’s generalizability. For instance, the cohort’s iNPH prevalence (24.6% of 452 patients) far exceeds real-world estimates. Second, the reliance on a single machine learning classifier, XGBoost, without comparison to alternative AI approaches, such as deep learning or vision-language models, limits insights into whether new methodologies could enhance performance, especially with AI’s rapid evolution in medical imaging. Third, the lack of external validation across diverse scan parameters and population characteristics questions the model’s adaptability to real-world clinical environments, where variability in equipment and patient demographics is common. These methodological constraints highlight the challenges of translating AI innovations into practice.
Beyond technical limitations, broader hurdles persist in integrating AI into healthcare. Algorithm interpretability remains a critical barrier, as clinicians require transparent decision-making processes to trust AI outputs. Data generalizability is another concern—models must perform consistently across heterogeneous datasets to avoid biases. Also, AI should complement—not replace—clinical judgment, rather serving as a screening tool to flag potential cases for specialist review. While Lee et al.’s model shows high sensitivity, validating its utility against diagnoses by experienced radiologists and epidemiological benchmarks ensures reliability.
Overall, Lee et al. represents a promising step toward advancing iNPH diagnosis. Al’s ability to streamline workflows and reduce delays could address underdiagnosis and misdiagnosis. Future efforts must prioritize multi-center trials, diverse AI comparisons, and rigorous validation. As AI evolves, clinician-researcher collaboration must guide development to align tools with real-world needs, enabling earlier interventions and improved patient outcomes.
References
1. Bendella Z, Purrer V, Haase R, et al. Brain and ventricle volume alterations in idiopathic normal pressure hydrocephalus determined by artificial intelligence-based MRI volumetry. Diagnostics (Basel) 2024;14(13):1422. DOI: https://doi.org/10.3390/diagnostics14131422
2. Lee J, Kim D, Suh CH, et al. Automated idiopathic normal pressure hydrocephalus diagnosis via artificial intelligence-based 3D T1 MRI volumetric analysis. AJNR Am J Neuroradiol 2025;46(1):33-40. DOI: https://doi.org/10.3174/ajnr.A8489