Aresta Simona, Nemni Raffaello, Zanardo Moreno, Sirabian Graziella, Capelli Dario, Alì Marco, Vitali Paolo, Bertoldo Enrico Giuseppe, Fiolo Valentina, Bonanno Lilla, Maresca Giuseppa, Battista Petronilla, Sardanelli Francesco, Pizzini Francesca Benedetta, Castiglioni Isabella, Salvatore Christian
Department of Science, Technology and Society, University School for Advanced Studies IUSS Pavia, Pavia, Italy.
Centro Diagnostico Italiano S.p.A., Milan, Italy.
Front Neurol. 2025 May 19;16:1568086. doi: 10.3389/fneur.2025.1568086. eCollection 2025.
In 2024, 11 European scientific societies/organizations and one patient advocacy association have defined a patient-centered biomarker-based diagnostic workflow for memory clinics evaluating neurocognitive disorders.
We tested the performance of an artificial intelligence (AI) tool applied to neuropsychological and magnetic resonance imaging (MRI) assessment for staging and causal hypothesis, which are the two recommended workflow steps guiding the next one recommending optimal biomarkers to be used for a biological diagnosis of neurocognitive disorders, according to intersocietal recommendations. Moreover, we assessed the AI performance in predicting the progression to Alzheimer's disease (AD)-dementia.
For the three-class classification of staging (n patients = 426), the inter-rater AI-humans agreement was substantial for both healthy subjects/subjective cognitive impairment/worried-well vs. all the remaining groups (rest) (Cohen's = 0.81) and mild cognitive impairment/mild dementia vs. rest = 0.70) classification, almost perfect for moderate/severe dementia vs. rest =0.90) classification. For the three-class classification of causal hypotheses ( = 112), the AI performance vs. biomarker-based diagnosis was: positive predictive value 91% [95% CI: 84-96%]; negative predictive value 100%, and accuracy 91% [84-96%]. For the binary classification of progression or not progression to AD-dementia at 24-month, with clinical conversion as a reference standard ( = 341), the AI performance was: sensitivity 89% [84-94%], specificity 82% [77-87%]; accuracy 85% [81-89%]; and area under the receiver operating characteristic curve 83% [79-87%].
The AI tool showed high agreement with human assessment for staging, high accuracy with biomarkers for causal hypotheses of neurocognitive disorders and predicted progression to AD at 24-month with 89% sensitivity and 82% specificity.
2024年，11个欧洲科学学会/组织和1个患者权益倡导协会为评估神经认知障碍的记忆诊所定义了一种以患者为中心的基于生物标志物的诊断工作流程。
我们测试了一种人工智能（AI）工具在神经心理学和磁共振成像（MRI）评估中用于分期和因果假设的性能，根据跨学会建议，这是指导下一个步骤（推荐用于神经认知障碍生物学诊断的最佳生物标志物）的两个推荐工作流程步骤。此外，我们评估了AI在预测向阿尔茨海默病（AD）痴呆进展方面的性能。
对于分期的三级分类（n = 426例患者），人工智能与人类评级者之间在健康受试者/主观认知障碍/担忧健康者与所有其余组（其余）之间的一致性很高（Cohen's = 0.81），在轻度认知障碍/轻度痴呆与其余组之间的分类一致性为0.70，在中度/重度痴呆与其余组之间的分类几乎完美（= 0.90）。对于因果假设的三级分类（= 112），人工智能与基于生物标志物的诊断相比的性能为：阳性预测值91% [95% CI：84 - 96%]；阴性预测值1OO%，准确率91% [84 - 96%]。对于以临床转化为参考标准（= 341）的24个月时向AD痴呆进展或未进展的二元分类，人工智能的性能为：敏感性89% [84 - 94%]，特异性82% [77 - 87%]；准确率85% [81 - 89%]；以及受试者工作特征曲线下面积83% [79 - 87%]。
该人工智能工具在分期方面与人类评估显示出高度一致性，在神经认知障碍因果假设的生物标志物方面具有高准确率，并在24个月时预测向AD进展的敏感性为89%，特异性为82%。
