Dataset Information

AI-based differential diagnosis of dementia etiologies on multimodal data.

ABSTRACT: Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.

SUBMITTER: Xue C

PROVIDER: S-EPMC10996713 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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AI-based differential diagnosis of dementia etiologies on multimodal data.

Xue Chonghua C Kowshik Sahana S SS Lteif Diala D Puducheri Shreyas S Jasodanand Varuna H VH Zhou Olivia T OT Walia Anika S AS Guney Osman B OB Zhang J Diana JD Pham Serena T ST Kaliaev Artem A Andreu-Arasa V Carlota VC Dwyer Brigid C BC Farris Chad W CW Hao Honglin H Kedar Sachin S Mian Asim Z AZ Murman Daniel L DL O'Shea Sarah A SA Paul Aaron B AB Rohatgi Saurabh S Saint-Hilaire Marie-Helene MH Sartor Emmett A EA Setty Bindu N BN Small Juan E JE Swaminathan Arun A Taraschenko Olga O Yuan Jing J Zhou Yan Y Zhu Shuhan S Karjadi Cody C Ang Ting Fang Alvin TFA Bargal Sarah A SA Plummer Bryan A BA Poston Kathleen L KL Ahangaran Meysam M Au Rhoda R Kolachalama Vijaya B VB

medRxiv : the preprint server for health sciences 20240326

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, dra ...[more]

PMID: 38585870

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Project description:BackgroundAccording to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world's elderly people. Day by day the number of Alzheimer's patients is rising. Considering the increasing rate and the dangers, Alzheimer's disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer's diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data.ObjectiveTo solve these issues, this paper proposes a novel explainable Alzheimer's disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, MRI segmentation data, and psychological data. However, currently, there is very little understanding of multimodal five-class classification of Alzheimer's disease.MethodFor predicting five class classifications, 9 most popular Machine Learning models are used. These models are Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaB), Support Vector Machine (SVM), and Naive Bayes (NB). Among these models RF has scored the highest value. Besides for explainability, SHapley Additive exPlanation (SHAP) is used in this research work.Results and conclusionsThe performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer's disease, cognitively normal, non-Alzheimer's dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer's disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer's patient management architecture is also proposed in this work.

Project description:Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer's disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies. We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important. A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44% females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation. The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3%). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50% of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6%. Data-driven CDSSs can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different dementias and controls from each other. In addition to the predicted class, it also provides a confidence measure for the classification.

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Project description:The goal of this study is to investigate the value of tau phosphorylated at threonine 181 (P-tau181P) in the Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarker panel for differential dementia diagnosis in autopsy confirmed AD and non-AD patients. The study population consisted of 140 autopsy confirmed AD and 77 autopsy confirmed non-AD dementia patients. CSF concentrations of amyloid-? peptide of 42 amino acids (A?1-42), total tau protein (T-tau), and P-tau181P were determined with single analyte ELISA-kits (INNOTEST(®), Fujirebio, Ghent, Belgium). Diagnostic accuracy was assessed through receiver operating characteristic (ROC) curve analyses to obtain area under the curve (AUC) values and to define optimal cutoff values to discriminate AD from pooled and individual non-AD groups. ROC curve analyses were only performed on biomarkers and ratios that differed significantly between the groups. Pairwise comparison of AUC values was performed by means of DeLong tests. The A?1-42/P-tau181P ratio (AUC?=?0.770) performed significantly better than A?1-42 (AUC?=?0.677, P?=?0.004), T-tau (AUC?=?0.592, P?<?0.001), and A?1-42/T-tau (AUC?=?0.678, P?=?0.001), while P-tau181P (AUC?=?0.720) performed significantly better than T-tau (AUC?=?0.592, P?<?0.001) to discriminate between AD and the pooled non-AD group. When comparing AD and the individual non-AD diagnoses, A?1-42/P-tau181P (AUC?=?0.894) discriminated AD from frontotemporal dementia significantly better than A?1-42 (AUC?=?0.776, P?=?0.020) and T-tau (AUC?=?0.746, P?=?0.004), while P-tau181P/T-tau (AUC?=?0.958) significantly improved the differentiation between AD and Creutzfeldt-Jakob disease as compared to A?1-42 (AUC?=?0.688, P?=?0.004), T-tau (AUC?=?0.874, P?=?0.040), and A?1-42/P-tau181P (AUC?=?0.760, P?=?0.003). In conclusion, this study demonstrates P-tau181P is an essential component of the AD CSF biomarker panel, and combined assessment of A?1-42, T-tau, and P-tau181P renders, to present date, the highest diagnostic power to discriminate between AD and non-AD dementias.

Dataset Information

AI-based differential diagnosis of dementia etiologies on multimodal data.

Publications

AI-based differential diagnosis of dementia etiologies on multimodal data.

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