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1.
Neuroimage ; 111: 562-79, 2015 May 01.
Artículo en Inglés | MEDLINE | ID: mdl-25652394

RESUMEN

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.


Asunto(s)
Algoritmos , Enfermedad de Alzheimer/diagnóstico , Disfunción Cognitiva/diagnóstico , Diagnóstico por Computador/métodos , Interpretación de Imagen Asistida por Computador/métodos , Imagen por Resonancia Magnética/métodos , Anciano , Anciano de 80 o más Años , Enfermedad de Alzheimer/clasificación , Disfunción Cognitiva/clasificación , Diagnóstico por Computador/normas , Femenino , Humanos , Interpretación de Imagen Asistida por Computador/normas , Imagen por Resonancia Magnética/normas , Masculino , Persona de Mediana Edad , Sensibilidad y Especificidad
2.
J Exp Psychol Learn Mem Cogn ; 40(5): 1287-306, 2014 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-24911135

RESUMEN

In order to examine metacognitive accuracy (i.e., the relationship between metacognitive judgment and memory performance), researchers often rely on by-participant analysis, where metacognitive accuracy (e.g., resolution, as measured by the gamma coefficient or signal detection measures) is computed for each participant and the computed values are entered into group-level statistical tests such as the t test. In the current work, we argue that the by-participant analysis, regardless of the accuracy measurements used, would produce a substantial inflation of Type I error rates when a random item effect is present. A mixed-effects model is proposed as a way to effectively address the issue, and our simulation studies examining Type I error rates indeed showed superior performance of mixed-effects model analysis as compared to the conventional by-participant analysis. We also present real data applications to illustrate further strengths of mixed-effects model analysis. Our findings imply that caution is needed when using the by-participant analysis, and recommend the mixed-effects model analysis.


Asunto(s)
Cognición/fisiología , Juicio/fisiología , Memoria/fisiología , Modelos Psicológicos , Detección de Señal Psicológica/fisiología , Simulación por Computador , Humanos
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