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 artificial intelligence (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 microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged 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 clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.

Objective: To develop consensus‐based recommendations for the management of adult and pediatric patients with new‐onset refractory status epilepticus (NORSE)/febrile infection‐related epilepsy syndrome (FIRES) based on best available evidence and expert opinion.Methods: The Delphi methodology was followed. A facilitator group of nine experts was established who defined the scope, users, and suggestions for recommendations. Following a review of the current literature, recommendation statements concerning diagnosis, treatment, and research directions were generated that were then voted on using a scale of 1 (strongly disagree) to 9 (strongly agree) by a panel of 48 experts in the field. Consensus that a statement was appropriate was reached if the median score was greater than or equal to 7, and inappropriate if the median score was less than or equal to 3.Results: Overall, 85 recommendation statements achieved consensus. The recommendations are divided into five sections: (1) disease characteristics; (2) diagnostic testing and sampling; (3) acute treatment; (4) treatment in the post‐acute phase; and (5) research, registries, and future directions in NORSE/FIRES. These are summarized in this article along with two practical clinical flowsheets: one for diagnosis and evaluation and one for acute treatment. A corresponding evidence‐based analysis of all 85 recommendations alongside responses by the Delphi panel is presented in a companion article.Significance: The recommendations generated by this consensus can be used as a guide for the diagnosis; evaluation; and management of patients with NORSE/FIRES; and for planning of future research.

Objective: This study was undertaken to develop consensus‐based recommendations for the management of adult and pediatric patients with new onset refractory status epilepticus (NORSE)/febrile infection‐related epilepsy syndrome (FIRES) based on best evidence and experience.Methods: The Delphi methodology was followed. A facilitator group of nine experts was established, who defined the scope, users, and suggestions for recommendations. Following a review of the current literature, recommendation statements concerning diagnosis, treatment, and research directions were generated, which were then rated on a scale of 1 (strongly disagree) to 9 (strongly agree) by a panel of 48 experts in the field. Consensus that a statement was appropriate was reached if the median score was ≥7 and inappropriate if the median score was ≤3. The analysis of evidence was mapped to the results of each statement included in the Delphi survey.Results: Overall, 85 recommendation statements achieved consensus. The recommendations are divided into five sections: (1) disease characteristics; (2) diagnostic testing and sampling; (3) acute treatment; (4) treatment in the postacute phase; and (5) research, registries, and future directions in NORSE/FIRES. The detailed results and discussion of all 85 statements are outlined herein. A corresponding summary of findings and practical flowsheets are presented in a companion article.Significance: This detailed analysis offers insight into the supporting evidence and the current gaps in the literature that are associated with expert consensus statements related to NORSE/FIRES. The recommendations generated by this consensus can be used as a guide for the diagnosis, evaluation, and management of patients with NORSE/FIRES, and for planning of future research.

In intensive care units (ICUs), critically ill patients are monitored with electroencephalography (EEG) to prevent serious brain injury. EEG monitoring is constrained by clinician availability, and EEG interpretation can be subjective and prone to interobserver variability. Automated deep-learning systems for EEG could reduce human bias and accelerate the diagnostic process. However, existing uninterpretable (black-box) deep-learning models are untrustworthy, difficult to troubleshoot, and lack accountability in real-world applications, leading to a lack of both trust and adoption by clinicians. We developed an interpretable deep-learning system that accurately classifies six patterns of potentially harmful EEG activity - seizure, lateralized periodic discharges (LPDs), generalized periodic discharges (GPDs), lateralized rhythmic delta activity (LRDA), generalized rhythmic delta activity (GRDA), and other patterns - while providing faithful case-based explanations of its predictions. The model was trained on 50,697 total 50-second continuous EEG samples collected from 2711 patients in the ICU between July 2006 and March 2020 at Massachusetts General Hospital. EEG samples were labeled as one of the six EEG patterns by 124 domain experts and trained annotators. To evaluate the model, we asked eight medical professionals with relevant backgrounds to classify 100 EEG samples into the six pattern categories - once with and once without artificial intelligence (AI) assistance - and we assessed the assistive power of this interpretable system by comparing the diagnostic accuracy of the two methods. The model's discriminatory performance was evaluated with area under the receiver-operating characteristic curve (AUROC) and area under the precision-recall curve. The model's interpretability was measured with task-specific neighborhood agreement statistics that interrogated the similarities of samples and features. In a separate analysis, the latent space of the neural network was visualized by using dimension reduction techniques to examine whether the ictal-interictal injury continuum hypothesis, which asserts that seizures and seizure-like patterns of brain activity lie along a spectrum, is supported by data. The performance of all users significantly improved when provided with AI assistance. Mean user diagnostic accuracy improved from 47 to 71% (P<0.04). The model achieved AUROCs of 0.87, 0.93, 0.96, 0.92, 0.93, and 0.80 for the classes seizure, LPD, GPD, LRDA, GRDA, and other patterns, respectively. This performance was significantly higher than that of a corresponding uninterpretable black-box model (with P<0.0001). Videos traversing the ictal-interictal injury manifold from dimension reduction (a two-dimensional representation of the original high-dimensional feature space) give insight into the layout of EEG patterns within the network's latent space and illuminate relationships between EEG patterns that were previously hypothesized but had not yet been shown explicitly. These results indicate that the ictal-interictal injury continuum hypothesis is supported by data. Users showed significant pattern classification accuracy improvement with the assistance of this interpretable deep-learning model. The interpretable design facilitates effective human-AI collaboration; this system may improve diagnosis and patient care in clinical settings. The model may also provide a better understanding of how EEG patterns relate to each other along the ictal-interictal injury continuum. (Funded by the National Science Foundation, National Institutes of Health, and others.).