Development of the Story Telling Examination for Early Mild Cognitive Impairment (Pre-Mild Cognitive Impairment) Screening.

INTRODUCTION: Cognitive function prior to mild cognitive impairment (MCI) has become a burgeoning interest. Tools used to detect this early period before MCI are being pilot-tested. This study aimed to develop a new test to detect pre-MCI and to examine its content validity and feasibility. METHODS: The Story Telling Examination for Early MCI Screening (STEEMS), an audio cognitive test, was developed. It covers ten cognitive domains, e.g., executive function, language fluency, abstract reasoning. Face and content validity were examined by experts in geriatric psychiatry and psychology. The content validity index was 1.00. STEEMS comprised 12 items with 2-4 types of scoring. The tool was further examined in 16 pilot samples for feasibility among healthy participants having no cognitive impairment (Montreal Cognitive Assessment [MoCA] test score ≥25, Mini-Cog ≥3) and no depressive symptoms (Geriatric Depression Scale <6). RESULTS: The 16 healthy older individuals aged 59-73 years, mean age was 65.06 ± 4.07 years, were predominantly males (68.8%). STEEMS scores ranged from 10 to 25, with a mean of 18.38 (SD = 4.2). Thirteen percent obtained 100% correct on the STEEMS, 63% scored 68-92% correct, and 25% scored 40-60% correct. The pre-MCI scores are illustrated by a bell curve's graphical depiction, suggesting a normal distribution probability distribution. Correlation between STEEMS and MoCA test scores was observed. STEEMS showed to be feasible for early elderly or late adults as being brief and easy to understand. The time spent to administer was predictably less than 7 min. DISCUSSION/CONCLUSION: STEEMS could potentially serve as a tool for pre-MCI screening. Further study and investigation in a larger population are required.

A comparative study of visual assessment between dry bone, 2-dimensional photograph, and deep learning methods in sex classification on the auricular area of the os coxae in a Thai population / Un estudio comparativo de la evaluación visual entre el hueso seco, la fotografía bidimensional y los métodos de aprendizaje profundo en la clasificación del sexo en el área auricular del hueso coxal en una población tailandesa

SUMMARY: Sex assessment is an important process in forensic identification. A pelvis is the best skeletal element for identifying sexes due to its sexually dimorphic morphology. This study aimed to compare the accuracy of the visual assessment in dry bones as well as 2D images and to test the accuracy of using a deep convolutional neural network (GoogLeNet) for increasing the performance of a sex determination tool in a Thai population. The total samples consisted of 250 left os coxa that were divided into 200 as a 'training' group (100 females, 100 males) and 50 as a 'test' group. In this study, we observed the auricular area, both hands-on and photographically, for visual assessment and classified the images using GoogLeNet. The intra-inter observer reliabilities were tested for each visual assessment method. Additionally, the validation and test accuracies were 85, 72 percent and 79.5, 60 percent, for dry bone and 2D image methods, respectively. The intra- and inter-observer reliabilities showed moderate agreement (Kappa = 0.54 - 0.67) for both visual assessments. The deep convolutional neural network method showed high accuracy for both validation and test sets (93.33 percent and 88 percent, respectively). Deep learning performed better in classifying sexes from auricular area images than other visual assessment methods. This study suggests that deep learning has advantages in terms of sex classification in Thai samples.

RESUMEN: La evaluación del sexo es un proceso importante en la identificación forense. La pelvis es el mejor elemento esquelético para identificar sexos debido a su morfología sexualmente dimórfica. Este estudio tuvo como objetivo comparar la precisión de la evaluación visual en huesos secos, así como imágenes 2D y probar la precisión del uso de una red neuronal convolucional profunda (GoogLeNet) para aumentar el rendimiento de una herramienta de determinación de sexo en una población tailandesa. Las muestras consistieron en 250 huesos coxales izquierdos, los que fueron dividi- das de la siguiente manera: 200 como un grupo de "entrenamiento" (100 mujeres, 100 hombres) y 50 como un grupo de "prueba". En este estudio, observamos el área auricular, tanto de forma práctica como fotográfica, para una evaluación visual y clasificamos las imágenes utilizando GoogLeNet. Se analizó la confiabilidad intra-interobservador para cada método de evaluación visual. Además, las precisiones de validación y prueba fueron del 85, 72 por ciento y 79,5, 60 por ciento, para los métodos de hueso seco y de imágenes 2D, respectivamente. Las confiabilidades intra e interobservador mostraron un acuerdo moderado (Kappa = 0.54 - 0.67) para ambas evaluaciones visuales. El método de red neuronal convolucional profunda mostró una alta precisión tanto para la validación como para los conjuntos de prueba (93,33 por ciento y 88 por ciento, respectivamente). El aprendizaje se desempeñó mejor en la clasificación de sexos a partir de imágenes del área auricular que otros métodos de evaluación visual. Este estudio sugiere que el aprendizaje profundo tiene ventajas en términos de clasificación por sexo en muestras tailandesas.