RESUMEN
BACKGROUND: Amnestic mild cognitive impairment (aMCI) is the most common preclinical stage of Alzheimer's disease (AD). A strategy to reduce the impact of AD is the early aMCI diagnosis and clinical intervention. Neuroimaging, neurobiological, and genetic markers have proved to be sensitive and specific for the early diagnosis of AD. However, the high cost of these procedures is prohibitive in low-income and middle-income countries (LIMCs). The neuropsychological assessments currently aim to identify cognitive markers that could contribute to the early diagnosis of dementia. OBJECTIVE: Compare machine learning (ML) architectures classifying and predicting aMCI and asset the contribution of cognitive measures including binding function in distinction and prediction of aMCI. METHODS: We conducted a two-year follow-up assessment of a sample of 154 subjects with a comprehensive multidomain neuropsychological battery. Statistical analysis was proposed using complete ML architectures to compare subjects' performance to classify and predict aMCI. Additionally, permutation importance and Shapley additive explanations (SHAP) routines were implemented for feature importance selection. RESULTS: AdaBoost, gradient boosting, and XGBoost had the highest performance with over 80%success classifying aMCI, and decision tree and random forest had the highest performance with over 70%success predictive routines. Feature importance points, the auditory verbal learning test, short-term memory binding tasks, and verbal and category fluency tasks were used as variables with the first grade of importance to distinguish healthy cognition and aMCI. CONCLUSION: Although neuropsychological measures do not replace biomarkers' utility, it is a relatively sensitive and specific diagnostic tool for aMCI. Further studies with ML must identify cognitive performance that differentiates conversion from average MCI to the pathological MCI observed in AD.
Asunto(s)
Enfermedad de Alzheimer/fisiopatología , Disfunción Cognitiva/fisiopatología , Diagnóstico Precoz , Aprendizaje Automático , Memoria a Corto Plazo/fisiología , Anciano , Enfermedad de Alzheimer/diagnóstico , Enfermedad de Alzheimer/genética , Cognición/fisiología , Disfunción Cognitiva/diagnóstico , Disfunción Cognitiva/psicología , Femenino , Envejecimiento Saludable/fisiología , Humanos , Masculino , Pruebas NeuropsicológicasRESUMEN
Electrosurgery with argon plasma coagulation is a widespread technique used in various medical fields for applications which range from hemostasis to devitalization processes. Developing tools which provide feedback concerning tissue condition during these surgeries is fundamental for improving the safety and success of this treatment. We present here a method based on diffuse reflectance spectroscopy to monitor gastric mucosal devitalization treatments. The analysis of the diffusely reflected spectra of the tissue allows us to differentiate between ablation states by using linear discriminant analysis (LDA) as a classification algorithm. An ex vivo pilot study on several swine stomachs showed promising results, with 97.8% of correctly classified ablation states on a new unseen stomach, encouraging further tests with human tissue.