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1.
Nat Med ; 2024 Jul 04.
Artículo en Inglés | MEDLINE | ID: mdl-38965435

RESUMEN

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.

2.
bioRxiv ; 2024 Jun 17.
Artículo en Inglés | MEDLINE | ID: mdl-38948787

RESUMEN

Background: Transmission electron microscopy (TEM) images can visualize kidney glomerular filtration barrier ultrastructure, including the glomerular basement membrane (GBM) and podocyte foot processes (PFP). Podocytopathy is associated with glomerular filtration barrier morphological changes observed experimentally and clinically by measuring GBM or PFP width. However, these measurements are currently performed manually. This limits research on podocytopathy disease mechanisms and therapeutics due to labor intensiveness and inter-operator variability. Methods: We developed a deep learning-based digital pathology computational method to measure GBM and PFP width in TEM images from the kidneys of Integrin-Linked Kinase (ILK) podocyte-specific conditional knockout (cKO) mouse, an animal model of podocytopathy, compared to wild-type (WT) control mouse. We obtained TEM images from WT and ILK cKO littermate mice at 4 weeks old. Our automated method was composed of two stages: a U-Net model for GBM segmentation, followed by an image processing algorithm for GBM and PFP width measurement. We evaluated its performance with a 4-fold cross-validation study on WT and ILK cKO mouse kidney pairs. Results: Mean (95% confidence interval) GBM segmentation accuracy, calculated as Jaccard index, was 0.54 (0.52-0.56) for WT and 0.61 (0.56-0.66) for ILK cKO TEM images. Automated and corresponding manual measured PFP widths differed significantly for both WT (p<0.05) and ILK cKO (p<0.05), while automated and manual GBM widths differed only for ILK cKO (p<0.05) but not WT (p=0.49) specimens. WT and ILK cKO specimens were morphologically distinguishable by manual GBM (p<0.05) and PFP (p<0.05) width measurements. This phenotypic difference was reflected in the automated GBM (p=0.06) more than PFP (p=0.20) widths. Conclusions: These results suggest that certain automated measurements enabled via deep learning-based digital pathology tools could distinguish healthy kidneys from those with podocytopathy. Our proposed method provides high-throughput, objective morphological analysis and could facilitate podocytopathy research and translate into clinical diagnosis.

3.
BMC Med Inform Decis Mak ; 24(1): 152, 2024 Jun 04.
Artículo en Inglés | MEDLINE | ID: mdl-38831432

RESUMEN

BACKGROUND: Machine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains. The assessment of dataset quality stands as a pivotal precursor to the successful deployment of ML models. In this study, we introduce DREAMER (Data REAdiness for MachinE learning Research), an algorithmic framework leveraging supervised and unsupervised machine learning techniques to autonomously evaluate the suitability of tabular datasets for ML model development. DREAMER is openly accessible as a tool on GitHub and Docker, facilitating its adoption and further refinement within the research community.. RESULTS: The proposed model in this study was applied to three distinct tabular datasets, resulting in notable enhancements in their quality with respect to readiness for ML tasks, as assessed through established data quality metrics. Our findings demonstrate the efficacy of the framework in substantially augmenting the original dataset quality, achieved through the elimination of extraneous features and rows. This refinement yielded improved accuracy across both supervised and unsupervised learning methodologies. CONCLUSION: Our software presents an automated framework for data readiness, aimed at enhancing the integrity of raw datasets to facilitate robust utilization within ML pipelines. Through our proposed framework, we streamline the original dataset, resulting in enhanced accuracy and efficiency within the associated ML algorithms.


Asunto(s)
Aprendizaje Automático , Humanos , Conjuntos de Datos como Asunto , Aprendizaje Automático no Supervisado , Algoritmos , Aprendizaje Automático Supervisado , Programas Informáticos
5.
Alzheimers Dement ; 2024 Jun 25.
Artículo en Inglés | MEDLINE | ID: mdl-38924662

RESUMEN

INTRODUCTION: Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. METHODS: We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. RESULTS: Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years. DISCUSSION: The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment. HIGHLIGHTS: Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.

6.
Hum Brain Mapp ; 45(8): e26707, 2024 Jun 01.
Artículo en Inglés | MEDLINE | ID: mdl-38798082

RESUMEN

Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational value. The efficacy of these models often encounters challenges due to variabilities arising from different data generation protocols, imaging equipment, radiological artifacts, and shifts in demographic distributions. Domain generalization (DG) techniques show promise in addressing these challenges by enabling the model to learn from one or more source domains and apply this knowledge to new, unseen target domains. Here we present a framework that utilizes model interpretability to enhance the generalizability of classification models across various cohorts. We used MRI scans and clinical diagnoses from four independent cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 1821), the Framingham Heart Study (FHS, n = 304), the Australian Imaging Biomarkers & Lifestyle Study of Ageing (AIBL, n = 661), and the National Alzheimer's Coordinating Center (NACC, n = 4647). With this data, we trained a deep neural network to focus on areas of the brain identified as relevant to the disease for model training. Our approach involved training a classifier to differentiate between structural neurodegeneration in individuals with normal cognition (NC), mild cognitive impairment (MCI), and dementia due to Alzheimer's disease (AD). This was achieved by aligning class-wise attention with a unified visual saliency prior, which was computed offline for each class using all the training data. Our method not only competes with state-of-the-art approaches but also shows improved correlation with postmortem histology. This alignment with the gold standard evidence is a significant step towards validating the effectiveness of DG frameworks, paving the way for their broader application in the field.


Asunto(s)
Enfermedad de Alzheimer , Disfunción Cognitiva , Aprendizaje Profundo , Imagen por Resonancia Magnética , Neuroimagen , Humanos , Enfermedad de Alzheimer/diagnóstico por imagen , Enfermedad de Alzheimer/patología , Anciano , Imagen por Resonancia Magnética/métodos , Imagen por Resonancia Magnética/normas , Femenino , Masculino , Neuroimagen/métodos , Neuroimagen/normas , Anciano de 80 o más Años , Disfunción Cognitiva/diagnóstico por imagen , Disfunción Cognitiva/patología , Estudios de Cohortes
7.
Am J Pathol ; 194(7): 1285-1293, 2024 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-38588853

RESUMEN

Bronchial premalignant lesions (PMLs) precede the development of invasive lung squamous cell carcinoma (LUSC), posing a significant challenge in distinguishing those likely to advance to LUSC from those that might regress without intervention. This study followed a novel computational approach, the Graph Perceiver Network, leveraging hematoxylin and eosin-stained whole slide images to stratify endobronchial biopsies of PMLs across a spectrum from normal to tumor lung tissues. The Graph Perceiver Network outperformed existing frameworks in classification accuracy predicting LUSC, lung adenocarcinoma, and nontumor lung tissue on The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium datasets containing lung resection tissues while efficiently generating pathologist-aligned, class-specific heatmaps. The network was further tested using endobronchial biopsies from two data cohorts, containing normal to carcinoma in situ histology. It demonstrated a unique capability to differentiate carcinoma in situ lung squamous PMLs based on their progression status to invasive carcinoma. The network may have utility in stratifying PMLs for chemoprevention trials or more aggressive follow-up.


Asunto(s)
Carcinoma de Células Escamosas , Neoplasias Pulmonares , Lesiones Precancerosas , Humanos , Lesiones Precancerosas/patología , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/genética , Carcinoma de Células Escamosas/patología
8.
ACS Appl Bio Mater ; 7(5): 3041-3049, 2024 05 20.
Artículo en Inglés | MEDLINE | ID: mdl-38661721

RESUMEN

Drug-coated balloon (DCB) therapy is a promising endovascular treatment for obstructive arterial disease. The goal of DCB therapy is restoration of lumen patency in a stenotic vessel, whereby balloon deployment both mechanically compresses the offending lesion and locally delivers an antiproliferative drug, most commonly paclitaxel (PTX) or derivative compounds, to the arterial wall. Favorable long-term outcomes of DCB therapy thus require predictable and adequate PTX delivery, a process facilitated by coating excipients that promotes rapid drug transfer during the inflation period. While a variety of excipients have been considered in DCB design, there is a lack of understanding about the coating-specific biophysical determinants of essential device function, namely, acute drug transfer. We consider two hydrophilic excipients for PTX delivery, urea (UR) and poly(ethylene glycol) (PEG), and examine how compositional and preparational variables in the balloon surface spray-coating process impact resultant coating microstructure and in turn acute PTX transfer to the arterial wall. Specifically, we use scanning electron image analyses to quantify how coating microstructure is altered by excipient solid content and balloon-to-nozzle spray distance during the coating procedure and correlate obtained microstructural descriptors of coating aggregation to the efficiency of acute PTX transfer in a one-dimensional ex vivo model of DCB deployment. Experimental results suggest that despite the qualitatively different coating surface microstructures and apparent PTX transfer mechanisms exhibited with these excipients, the drug delivery efficiency is generally enhanced by coating aggregation on the balloon surface. We illustrate this microstructure-function relation with a finite element-based computational model of DCB deployment, which along with our experimental findings suggests a general design principle to increase drug delivery efficiency across a broad range of DCB designs.


Asunto(s)
Materiales Biocompatibles Revestidos , Interacciones Hidrofóbicas e Hidrofílicas , Paclitaxel , Paclitaxel/química , Paclitaxel/farmacología , Paclitaxel/administración & dosificación , Materiales Biocompatibles Revestidos/química , Ensayo de Materiales , Polietilenglicoles/química , Tamaño de la Partícula , Humanos , Urea/química , Angioplastia de Balón , Sistemas de Liberación de Medicamentos , Propiedades de Superficie
9.
Vasc Med ; : 1358863X241231942, 2024 Apr 16.
Artículo en Inglés | MEDLINE | ID: mdl-38623630

RESUMEN

BACKGROUND: Paclitaxel (PTX) is touted as an essential medicine due to its extensive use as a chemotherapeutic agent for various cancers and an antiproliferative agent for endovascular applications. Emerging studies in cardio-oncology implicate various vascular complications of chemotherapeutic agents. METHODS: We evaluated the inflammatory response induced by the systemic administration of PTX. The investigation included RNAseq analysis of primary human endothelial cells (ECs) treated with PTX to identify transcriptional changes in pro-inflammatory mediators. Additionally, we used dexamethasone (DEX), a well-known antiinflammatory compound, to assess its effectiveness in counteracting these PTX-induced changes. Further, we studied the effects of PTX on monocyte chemoattractant protein-1 (MCP-1) levels in the media of ECs. The study also extended to in vivo analysis, where a group of mice was injected with PTX and subsequently harvested at different times to assess the immediate and delayed effects of PTX on inflammatory mediators in blood and aortic ECs. RESULTS: Our RNAseq analysis revealed that PTX treatment led to significant transcriptional perturbations in pro-inflammatory mediators such as MCP-1 and CD137 within primary human ECs. These changes were effectively abrogated when DEX was administered. In vitro experiments showed a marked increase in MCP-1 levels in EC media following PTX treatment, which returned to baseline upon treatment with DEX. In vivo, we observed a threefold increase in MCP-1 levels in blood and aortic ECs 12 h post-PTX administration. Similar trends were noted for CD137 and other downstream mediators like tissue factor, vascular cell adhesion molecule 1, and E-selectin in aortic ECs. CONCLUSION: Our findings illustrate that PTX exposure induces an upregulation of atherothrombotic mediators, which can be alleviated with concurrent administration of DEX. Considering these observations, further long-term investigations should focus on understanding the systemic implications associated with PTX-based therapies and explore the clinical relevance of DEX in mitigating such risks.

10.
IEEE Trans Med Imaging ; PP2024 Apr 08.
Artículo en Inglés | MEDLINE | ID: mdl-38587959

RESUMEN

Multimodal machine learning models are being developed to analyze pathology images and other modalities, such as gene expression, to gain clinical and biological insights. However, most frameworks for multimodal data fusion do not fully account for the interactions between different modalities. Here, we present an attention-based fusion architecture that integrates a graph representation of pathology images with gene expression data and concomitantly learns from the fused information to predict patient-specific survival. In our approach, pathology images are represented as undirected graphs, and their embeddings are combined with embeddings of gene expression signatures using an attention mechanism to stratify tumors by patient survival. We show that our framework improves the survival prediction of human non-small cell lung cancers, outperforming existing state-of-the-art approaches that leverage multimodal data. Our framework can facilitate spatial molecular profiling to identify tumor heterogeneity using pathology images and gene expression data, complementing results obtained from more expensive spatial transcriptomic and proteomic technologies.

11.
Am J Speech Lang Pathol ; 33(4): 2091-2128, 2024 Jul 03.
Artículo en Inglés | MEDLINE | ID: mdl-38652820

RESUMEN

PURPOSE: Speech fluency has important diagnostic implications for individuals with poststroke aphasia (PSA) as well as primary progressive aphasia (PPA), and quantitative assessment of connected speech has emerged as a widely used approach across both etiologies. The purpose of this review was to provide a clearer picture on the range, nature, and utility of individual quantitative speech/language measures and methods used to assess connected speech fluency in PSA and PPA, and to compare approaches across etiologies. METHOD: We conducted a scoping review of literature published between 2012 and 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Forty-five studies were included in the review. Literature was charted and summarized by etiology and characteristics of included patient populations and method(s) used for derivation and analysis of speech/language features. For a subset of included articles, we also charted the individual quantitative speech/language features reported and the level of significance of reported results. RESULTS: Results showed that similar methodological approaches have been used to quantify connected speech fluency in both PSA and PPA. Two hundred nine individual speech-language features were analyzed in total, with low levels of convergence across etiology on specific features but greater agreement on the most salient features. The most useful features for differentiating fluent from nonfluent aphasia in both PSA and PPA were features related to overall speech quantity, speech rate, or grammatical competence. CONCLUSIONS: Data from this review demonstrate the feasibility and utility of quantitative approaches to index connected speech fluency in PSA and PPA. We identified emergent trends toward automated analysis methods and data-driven approaches, which offer promising avenues for clinical translation of quantitative approaches. There is a further need for improved consensus on which subset of individual features might be most clinically useful for assessment and monitoring of fluency. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25537237.


Asunto(s)
Accidente Cerebrovascular , Humanos , Accidente Cerebrovascular/complicaciones , Medición de la Producción del Habla , Afasia Progresiva Primaria/diagnóstico , Afasia/etiología , Habla , Masculino , Femenino
12.
medRxiv ; 2024 Mar 26.
Artículo en Inglés | MEDLINE | ID: mdl-38585870

RESUMEN

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.

13.
Arthritis Care Res (Hoboken) ; 76(7): 984-992, 2024 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-38523250

RESUMEN

OBJECTIVE: The objective of this study was to identify gait alterations related to worsening knee pain and worsening physical function, using machine learning approaches applied to wearable sensor-derived data from a large observational cohort. METHODS: Participants in the Multicenter Osteoarthritis Study (MOST) completed a 20-m walk test wearing inertial sensors on their lower back and ankles. Parameters describing spatiotemporal features of gait were extracted from these data. We used an ensemble machine learning technique ("super learning") to optimally discriminate between those with and without worsening physical function and, separately, those with and without worsening pain over two years. We then used log-binomial regression to evaluate associations of the top 10 influential variables selected with super learning with each outcome. We also assessed whether the relation of altered gait with worsening function was mediated by changes in pain. RESULTS: Of 2,324 participants, 29% and 24% had worsening knee pain and function over two years, respectively. From the super learner, several gait parameters were found to be influential for worsening pain and for worsening function. After adjusting for confounders, greater gait asymmetry, longer average step length, and lower dominant frequency were associated with worsening pain, and lower cadence was associated with worsening function. Worsening pain partially mediated the association of cadence with function. CONCLUSION: We identified gait alterations associated with worsening knee pain and those associated with worsening physical function. These alterations could be assessed with wearable sensors in clinical settings. Further research should determine whether they might be therapeutic targets to prevent worsening pain and worsening function.


Asunto(s)
Artralgia , Marcha , Aprendizaje Automático , Osteoartritis de la Rodilla , Dispositivos Electrónicos Vestibles , Humanos , Femenino , Masculino , Osteoartritis de la Rodilla/fisiopatología , Anciano , Persona de Mediana Edad , Marcha/fisiología , Artralgia/fisiopatología , Artralgia/diagnóstico , Articulación de la Rodilla/fisiopatología , Dimensión del Dolor , Progresión de la Enfermedad , Estado Funcional , Prueba de Paso , Análisis de la Marcha/instrumentación , Estados Unidos/epidemiología , Valor Predictivo de las Pruebas
14.
Front Neurol ; 15: 1340710, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38426173

RESUMEN

Introduction: Although the growth of digital tools for cognitive health assessment, there's a lack of known reference values and clinical implications for these digital methods. This study aims to establish reference values for digital neuropsychological measures obtained through the smartphone-based cognitive assessment application, Defense Automated Neurocognitive Assessment (DANA), and to identify clinical risk factors associated with these measures. Methods: The sample included 932 cognitively intact participants from the Framingham Heart Study, who completed at least one DANA task. Participants were stratified into subgroups based on sex and three age groups. Reference values were established for digital cognitive assessments within each age group, divided by sex, at the 2.5th, 25th, 50th, 75th, and 97.5th percentile thresholds. To validate these values, 57 cognitively intact participants from Boston University Alzheimer's Disease Research Center were included. Associations between 19 clinical risk factors and these digital neuropsychological measures were examined by a backward elimination strategy. Results: Age- and sex-specific reference values were generated for three DANA tasks. Participants below 60 had median response times for the Go-No-Go task of 796 ms (men) and 823 ms (women), with age-related increases in both sexes. Validation cohort results mostly aligned with these references. Different tasks showed unique clinical correlations. For instance, response time in the Code Substitution task correlated positively with total cholesterol and diabetes, but negatively with high-density lipoprotein and low-density lipoprotein cholesterol levels, and triglycerides. Discussion: This study established and validated reference values for digital neuropsychological measures of DANA in cognitively intact white participants, potentially improving their use in future clinical studies and practice.

15.
medRxiv ; 2024 Feb 05.
Artículo en Inglés | MEDLINE | ID: mdl-38370740

RESUMEN

The escalating incidence of kidney biopsies providing insufficient tissue for diagnosis poses a dual challenge, straining the healthcare system and jeopardizing patients who may require rebiopsy or face the prospect of an inaccurate diagnosis due to an unsampled disease. Here, we introduce a web-based tool that can provide real-time, quantitative assessment of kidney biopsy adequacy directly from photographs taken with a smartphone camera. The software tool was developed using a deep learning-driven automated segmentation technique, trained on a dataset comprising nephropathologist-confirmed annotations of the kidney cortex on digital biopsy images. Our framework demonstrated favorable performance in segmenting the cortex via 5-fold cross-validation (Dice coefficient: 0.788±0.130) (n=100). Offering a bedside tool for kidney biopsy adequacy assessment has the potential to provide real-time guidance to the physicians performing medical kidney biopsies, reducing the necessity for re-biopsies. Our tool can be accessed through our web-based platform: http://www.biopsyadequacy.org.

16.
Skeletal Radiol ; 53(8): 1541-1552, 2024 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-38388702

RESUMEN

OBJECTIVE: Use subchondral bone length (SBL), a new MRI-derived measure that reflects the extent of cartilage loss and bone flattening, to predict the risk of progression to total knee replacement (TKR). METHODS: We employed baseline MRI data from the Osteoarthritis Initiative (OAI), focusing on 760 men and 1214 women with bone marrow lesions (BMLs) and joint space narrowing (JSN) scores, to predict the progression to TKR. To minimize bias from analyzing both knees of a participant, only the knee with a higher Kellgren-Lawrence (KL) grade was considered, given its greater potential need for TKR. We utilized the Kaplan-Meier survival curves and Cox proportional hazards models, incorporating raw and normalized values of SBL, JSN, and BML as predictors. The study included subgroup analyses for different demographics and clinical characteristics, using models for raw and normalized SBL (merged, femoral, tibial), BML (merged, femoral, tibial), and JSN (medial and lateral compartments). Model performance was evaluated using the time-dependent area under the curve (AUC), Brier score, and Concordance index to gauge accuracy, calibration, and discriminatory power. Knee joint and region-level analyses were conducted to determine the effectiveness of SBL, JSN, and BML in predicting TKR risk. RESULTS: The SBL model, incorporating data from both the femur and tibia, demonstrated a predictive capacity for TKR that closely matched the performance of the BML score and the JSN grade. The Concordance index of the SBL model was 0.764, closely mirroring the BML's 0.759 and slightly below JSN's 0.788. The Brier score for the SBL model stood at 0.069, showing comparability with BML's 0.073 and a minor difference from JSN's 0.067. Regarding the AUC, the SBL model achieved 0.803, nearly identical to BML's 0.802 and slightly lower than JSN's 0.827. CONCLUSION: SBL's capacity to predict the risk of progression to TKR highlights its potential as an effective imaging biomarker for knee osteoarthritis.


Asunto(s)
Artroplastia de Reemplazo de Rodilla , Progresión de la Enfermedad , Imagen por Resonancia Magnética , Osteoartritis de la Rodilla , Humanos , Femenino , Masculino , Osteoartritis de la Rodilla/diagnóstico por imagen , Osteoartritis de la Rodilla/cirugía , Imagen por Resonancia Magnética/métodos , Anciano , Persona de Mediana Edad , Análisis de Supervivencia , Articulación de la Rodilla/diagnóstico por imagen , Articulación de la Rodilla/cirugía , Articulación de la Rodilla/patología
17.
J Am Heart Assoc ; 13(2): e031348, 2024 Jan 16.
Artículo en Inglés | MEDLINE | ID: mdl-38226510

RESUMEN

BACKGROUND: Smartphone-based digital technology is increasingly being recognized as a cost-effective, scalable, and noninvasive method of collecting longitudinal cognitive and behavioral data. Accordingly, a state-of-the-art 3-year longitudinal project focused on collecting multimodal digital data for early detection of cognitive impairment was developed. METHODS AND RESULTS: A smartphone application collected 2 modalities of cognitive data, digital voice and screen-based behaviors, from the FHS (Framingham Heart Study) multigenerational Generation 2 (Gen 2) and Generation 3 (Gen 3) cohorts. To understand the feasibility of conducting a smartphone-based study, participants completed a series of questions about their smartphone and app use, as well as sensory and environmental factors that they encountered while completing the tasks on the app. Baseline data collected to date were from 537 participants (mean age=66.6 years, SD=7.0; 58.47% female). Across the younger participants from the Gen 3 cohort (n=455; mean age=60.8 years, SD=8.2; 59.12% female) and older participants from the Gen 2 cohort (n=82; mean age=74.2 years, SD=5.8; 54.88% female), an average of 76% participants agreed or strongly agreed that they felt confident about using the app, 77% on average agreed or strongly agreed that they were able to use the app on their own, and 81% on average rated the app as easy to use. CONCLUSIONS: Based on participant ratings, the study findings are promising. At baseline, the majority of participants are able to complete the app-related tasks, follow the instructions, and encounter minimal barriers to completing the tasks independently. These data provide evidence that designing and collecting smartphone application data in an unsupervised, remote, and naturalistic setting in a large, community-based population is feasible.


Asunto(s)
Aplicaciones Móviles , Teléfono Inteligente , Humanos , Femenino , Anciano , Persona de Mediana Edad , Masculino , Estudios de Factibilidad , Encuestas y Cuestionarios , Estudios Longitudinales , Cognición
18.
J Am Heart Assoc ; 13(2): e031247, 2024 Jan 16.
Artículo en Inglés | MEDLINE | ID: mdl-38226518

RESUMEN

Most research using digital technologies builds on existing methods for staff-administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home-based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early-stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow-up possible. Through the American Heart Association-funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.


Asunto(s)
Enfermedad de Alzheimer , Disfunción Cognitiva , Humanos , Enfermedad de Alzheimer/diagnóstico , Enfermedad de Alzheimer/terapia , Cognición , Boston
19.
J Am Heart Assoc ; 13(2): e032733, 2024 Jan 16.
Artículo en Inglés | MEDLINE | ID: mdl-38226519

RESUMEN

BACKGROUND: Smartphone-based cognitive assessments have emerged as promising tools, bridging gaps in accessibility and reducing bias in Alzheimer disease and related dementia research. However, their congruence with traditional neuropsychological tests and usefulness in diverse cohorts remain underexplored. METHODS AND RESULTS: A total of 406 FHS (Framingham Heart Study) and 59 BHS (Bogalusa Heart Study) participants with traditional neuropsychological tests and digital assessments using the Defense Automated Neurocognitive Assessment (DANA) smartphone protocol were included. Regression models investigated associations between DANA task digital measures and a neuropsychological global cognitive Z score (Global Cognitive Score [GCS]), and neuropsychological domain-specific Z scores. FHS participants' mean age was 57 (SD, 9.75) years, and 44% (179) were men. BHS participants' mean age was 49 (4.4) years, and 28% (16) were men. Participants in both cohorts with the lowest neuropsychological performance (lowest quartile, GCS1) demonstrated lower DANA digital scores. In the FHS, GCS1 participants had slower average response times and decreased cognitive efficiency scores in all DANA tasks (P<0.05). In BHS, participants in GCS1 had slower average response times and decreased cognitive efficiency scores for DANA Code Substitution and Go/No-Go tasks, although this was not statistically significant. In both cohorts, GCS was significantly associated with DANA tasks, such that higher GCS correlated with faster average response times (P<0.05) and increased cognitive efficiency (all P<0.05) in the DANA Code Substitution task. CONCLUSIONS: Our findings demonstrate that smartphone-based cognitive assessments exhibit concurrent validity with a composite measure of traditional neuropsychological tests. This supports the potential of using smartphone-based assessments in cognitive screening across diverse populations and the scalability of digital assessments to community-dwelling individuals.


Asunto(s)
Enfermedad de Alzheimer , Disfunción Cognitiva , Masculino , Humanos , Persona de Mediana Edad , Femenino , Teléfono Inteligente , Cognición/fisiología , Pruebas Neuropsicológicas , Estudios Longitudinales , Disfunción Cognitiva/diagnóstico
20.
Neurology ; 101(23): 1058-1067, 2023 Dec 04.
Artículo en Inglés | MEDLINE | ID: mdl-37816646

RESUMEN

Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. In addition, we explore use cases for LLMs, such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurologic disorders.


Asunto(s)
Inteligencia Artificial , Neurología , Humanos , Lenguaje , Registros Médicos , Investigadores
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