Dataset Information

NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation.

ABSTRACT: Cognitive impairments are a prevalent consequence of acquired brain injury, dementia, and age-related cognitive decline, hampering individuals' daily functioning and independence, with significant societal and economic implications. While neurorehabilitation represents a promising avenue for addressing these deficits, traditional rehabilitation approaches face notable limitations. First, they lack adaptability, offering one-size-fits-all solutions that may not effectively meet each patient's unique needs. Furthermore, the resource-intensive nature of these interventions, often confined to clinical settings, poses barriers to widespread, cost-effective, and sustained implementation, resulting in suboptimal outcomes in terms of intervention adaptability, intensity, and duration. In response to these challenges, this paper introduces NeuroAIreh@b, an innovative cognitive profiling and training methodology that uses an AI-driven framework to optimize neurorehabilitation prescription. NeuroAIreh@b effectively bridges the gap between neuropsychological assessment and computational modeling, thereby affording highly personalized and adaptive neurorehabilitation sessions. This approach also leverages virtual reality-based simulations of daily living activities to enhance ecological validity and efficacy. The feasibility of NeuroAIreh@b has already been demonstrated through a clinical study with stroke patients employing a tablet-based intervention. The NeuroAIreh@b methodology holds the potential for efficacy studies in large randomized controlled trials in the future.

SUBMITTER: Faria AL

PROVIDER: S-EPMC10846403 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation.

Faria Ana Lúcia AL Almeida Yuri Y Branco Diogo D Câmara Joana J Cameirão Mónica M Ferreira Luis L Moreira André A Paulino Teresa T Rodrigues Pedro P Spinola Mónica M Vilar Manuela M Bermúdez I Badia Sergi S Simões Mario M Fermé Eduardo E

Frontiers in neurology 20240119

Cognitive impairments are a prevalent consequence of acquired brain injury, dementia, and age-related cognitive decline, hampering individuals' daily functioning and independence, with significant societal and economic implications. While neurorehabilitation represents a promising avenue for addressing these deficits, traditional rehabilitation approaches face notable limitations. First, they lack adaptability, offering one-size-fits-all solutions that may not effectively meet each patient's uni ...[more]

PMID: 38322797

Similar Datasets

Project description:BackgroundSurgery and radiotherapy are primary nonconservative treatments for prostate cancer (PCa). However, personalizing treatment options between these treatment modalities is challenging due to unclear criteria. We developed an artificial intelligence (AI)-based model that can identify patients with localized PCa who would benefit more from either radiotherapy or surgery, thereby providing personalized clinical decision-making.Material and methodsData from consecutive patients with localized PCa who received radiotherapy or surgery with complete records of clinicopathological variables and follow-up results in 12 registries of the Surveillance, Epidemiology, and End Results database were analyzed. Patients from 7 registries were randomly assigned to training (TD) and internal validation datasets (IVD) at a 9:1 ratio. The remaining 5 registries constituted the external validation dataset (EVD). TD was divided into training-radiotherapy (TRD) and training-surgery (TSD) datasets, and IVD was divided into internal-radiotherapy (IRD) and internal-surgery (ISD) datasets. Six models for radiotherapy and surgery were trained using TRD and TSD to predict radiotherapy survival probability (RSP) and surgery survival probability (SSP), respectively. The models with the highest concordance index (C-index) on IRD and ISD were chosen to form the final treatment recommendation model (FTR). FTR recommendations were based on the higher value between RSP and SSP. Kaplan-Meier curves were generated for patients receiving recommended (consistent group) and nonrecommended treatments (inconsistent group), which were compared using the log-rank test.ResultsThe study included 118 236 patients, categorized into TD (TRD: 44 621; TSD: 41 500), IVD (IRD: 4949; ISD: 4621), and EVD (22 545). Both radiotherapy and surgery models accurately predicted RSP and SSP (C-index: 0.735-0.787 and 0.769-0.797, respectively). The consistent group exhibited higher survival rates than the inconsistent group, particularly among patients not suitable for active surveillance (P < .001).ConclusionFTR accurately identifies patients with localized PCa who would benefit more from either radiotherapy or surgery, offering clinicians an effective AI tool to make informed choices between these 2 treatments.

Project description:PurposeTo evaluate the performance of a disease activity (DA) model developed to detect DA in participants with neovascular age-related macular degeneration (nAMD).DesignPost hoc analysis.ParticipantsPatient dataset from the phase III HAWK and HARRIER (H&H) studies.MethodsAn artificial intelligence (AI)-based DA model was developed to generate a DA score based on measurements of OCT images and other parameters collected from H&H study participants. Disease activity assessments were classified into 3 categories based on the extent of agreement between the DA model's scores and the H&H investigators' decisions: agreement ("easy"), disagreement ("noisy"), and close to the decision boundary ("difficult"). Then, a panel of 10 international retina specialists ("panelists") reviewed a sample of DA assessments of these 3 categories that contributed to the training of the final DA model. A panelists' majority vote on the reviewed cases was used to evaluate the accuracy, sensitivity, and specificity of the DA model.Main outcome measuresThe DA model's performance in detecting DA compared with the DA assessments made by the investigators and panelists' majority vote.ResultsA total of 4472 OCT DA assessments were used to develop the model; of these, panelists reviewed 425, categorized as "easy" (17.2%), "noisy" (20.5%), and "difficult" (62.4%). False-positive and false negative rates of the DA model's assessments decreased after changing the assessment in some cases reviewed by the panelists and retraining the DA model. Overall, the DA model achieved 80% accuracy. For "easy" cases, the DA model reached 96% accuracy and performed as well as the investigators (96% accuracy) and panelists (90% accuracy). For "noisy" cases, the DA model performed similarly to panelists and outperformed the investigators (84%, 86%, and 16% accuracies, respectively). The DA model also outperformed the investigators for "difficult" cases (74% and 53% accuracies, respectively) but underperformed the panelists (86% accuracy) owing to lower specificity. Subretinal and intraretinal fluids were the main clinical parameters driving the DA assessments made by the panelists.ConclusionsThese results demonstrate the potential of using an AI-based DA model to optimize treatment decisions in the clinical setting and in detecting and monitoring DA in patients with nAMD.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Dataset Information

NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation.

Publications

NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets