Medidas de inteligibilidad para predicción del grado de Parkinson

La comunicación ha sido un instinto básico en el desarrollo del hombre, las personas tendemos a interactuar con el medio, y, por tanto, con nuestros iguales, es por ello, que es imprescindible lograr un proceso comunicativo donde prime el entendimiento. Unos de los factores para conseguir un correcto entendimiento entre interlocutores a través de la comunicación oral, es la inteligibilidad del habla, que en ocasiones puede verse afectada a causa de la denominada disartria. A lo largo de esta memoria, se hablará de dicha disartria y de las implicaciones que tiene en personas con enfermedad de Parkinson. Es la segunda enfermedad más extendida después del Alzheimer, y por tanto, afecta a más de 300.000 personas tan solo en España. Cifra que irá aumentando debido al envejecimiento de la población. Con este Trabajo Fin de Grado, se pretende elaborar un predictor que sea capaz de estimar el grado de inteligibilidad de señales de voz. Se ha utilizado la base de datos “Universal Access” que contiene audios de diversos interlocutores con disartria y sus correspondientes etiquetas con el grado de inteligibilidad que se obtuvieron de forma subjetiva por una serie de evaluadores. La disartria se presenta como síntoma habitual en personas con Parkinson, por ello se ha elegido esta base de datos para el desarrollo y evaluación del sistema. El sistema predictor de inteligibilidad que se ha desarrollado consta de una serie de procesos como la extracción de las características acústicas o features, selección de características, regresión y evaluación de los resultados, entre otros. Tras insertar las señales por el predictor, se obtiene una salida concreta con la predicción del grado de inteligibilidad del paciente, que se evalúa en base a la correlación de Pearson y la raíz del error cuadrático medio. Se han realizado diferentes tipos de pruebas, comparadas con artículos relacionados o de forma independiente. En todas ellas, los resultados han presentado un alto grado de aproximación, alcanzando los objetivos planteados en el proyecto.Communication has been a basic instinct in the development of human, people tend to interact with the environment, and therefore with our peers, that is why it is essential to achieve a communicative process where the understanding prevails. One of the factors to achieve a correct understanding between interlocutors through oral communication is speech intelligibility, which can sometimes be affected by the so-called dysarthria. Throughout this report, we will discuss such dysarthria and the implications it has on people with Parkinson's disease. It is the second most widespread disease after Alzheimer's disease, and therefore affects more than 300,000 people just in Spain. This figure will increase due to the aging of the population. With this Final Degree Project, we pretend to elaborate a predictor that is capable of estimating the degree of intelligibility of speech signals. We have used the “Universal Access” database that contains audios of several speakers with dysarthria and their corresponding labels with the intelligibility score that were subjectively obtained by a set of evaluators. Dysarthria presents as a common symptom in people with Parkinson's disease, so this database has been chosen for the development and assessment of the system. The intelligibility prediction system that has been developed consists of several processes as the extraction of acoustic characteristics or features, feature selection, regression and results evaluation, among others. After feeding the signals into the predictor, we obtain an output with the prediction of the intelligibility degree of the patient, which is evaluated according to the Pearson correlation and the root mean square error. Different types of tests have been performed, compared to related papers or independently. In all of them, the results have presented a high degree of approximation, achieving the objectives of the project.Ingeniería de Sistemas de Comunicacione

Usefulness of multidetector computed tomography to differentiate between renal cell carcinoma and oncocytoma. A model validation

OBJECTIVE: The purpose of this study is to validate a multivariable predictive model previously developed to differentiate between renal cell carcinoma (RCC) and oncocytoma using CT parameters. METHODS AND MATERIALS: We included 100 renal lesions with final diagnosis of RCC or oncocytoma studied before surgery with 4-phase multidetector CT (MDCT). We evaluated the characteristics of the tumors and the enhancement patterns at baseline, arterial, nephrographic and excretory MDCT phases. RESULTS: Histopathologically 15 tumors were oncocytomas and 85 RCCs. RCCs were significantly larger (median 4.4 cm vs 2.8 cm, p = 0.006). There were significant differences in nodule attenuation in the excretory phase compared to baseline (median: 31 vs 42, p = 0.015), with RCCs having lower values. Heterogeneous enhancement patterns were also more frequent in RCCs (85.9% vs 60%, p = 0.027).Multivariable analysis showed that the independent predictors of malignancy were the enhancement pattern, with oncocytomas being more homogeneous in the nephrographic phase [Odds Ratio (OR) 0.16 (95% CI 0.03 to 0.75, p = 0.02)], nodule enhancement in the excretory phase compared to baseline, with RCCs showing lower enhancement [OR 0.96 (95% CI 0.93 to 0.99, p = 0.005)], and a size > 4 cm, with RCCs being larger [OR 5.89 (95% CI 1.10 to 31.58), p = 0.038]. CONCLUSION: The multivariable predictive model previously developed which combines different MDCT parameters, including lesion size > 4 cm, lesion enhancement in the excretory phase compared to baseline and enhancement heterogeneity, can be successfully applied to distinguish RCC from oncocytoma. ADVANCES IN KNOWLEDGE: This study confirms that multiparametric assessment using MDCT (including parameters such as size, homogeneity and enhancement differences between the excretory and the baseline phases) can help distinguish between RCCs and oncocytomas. While it is true that this multiparametric predictive model may not always correctly classify renal tumors such as RCC or oncocytoma, it can be used to determine which patients would benefit from pre-surgical biopsy to confirm that the tumor is in fact an oncocytoma, and thereby avoid unnecessary surgical treatments

Utilidad de la tomografía computarizada para la caracterización de tumores renales sólidos

[spa] HIPÓTESIS: En pacientes con lesiones renales sólidas expansivas, la valoración multiparamétrica utilizando parámetros morfológicos y dinámicos mediante TC permitir la diferenciación entre CCR y oncocitomas. OBJETIVOS: - Determinar qué combinación de parámetros valorados mediante TC (morfológicos y dinámicos) son los más útiles para la diferenciación entre CCR y oncocitomas. - Desarrollar un modelo predictivo que sea capaz de diferenciarlos

Imaging Characterization of Renal Masses

The detection of a renal mass is a relatively frequent occurrence in the daily practice of any Radiology Department. The diagnostic approaches depend on whether the lesion is cystic or solid. Cystic lesions can be managed using the Bosniak classification, while management of solid lesions depends on whether the lesion is well-defined or infiltrative. The approach to well-defined lesions focuses mainly on the differentiation between renal cancer and benign tumors such as angiomyolipoma (AML) and oncocytoma. Differential diagnosis of infiltrative lesions is wider, including primary and secondary malignancies and inflammatory disease, and knowledge of the patient history is essential. Radiologists may establish a possible differential diagnosis based on the imaging features of the renal masses and the clinical history. The aim of this review is to present the contribution of the different imaging techniques and image guided biopsies in the diagnostic management of cystic and solid renal lesions

Accuracy of unenhanced magnetic resonance angiography for the assessment of renal artery stenosis

Purpose: To evaluate the accuracy of unenhanced magnetic resonance angiography (U-MRA) using balanced steady-state free precession (SSFP) sequences with inversion recovery (IR) pulses for the evaluation of renal artery stenosis. Materials and methods: U-MRA was performed in 24 patients with suspected main renal artery stenosis. Two radiologists evaluated the quality of the imaging studies and the ability of U-MRA to identify hemodynamically significant main renal artery stenosis (RAS) defined as a stenosis ≥50% when compared to gold standard tests: contrast-enhanced magnetic resonance angiography (CE-MRA) (18 patients) or digital subtraction arteriography (DSA) (6 patients). Results: A total of 44 main renal arteries were evaluated. Of them, 32 renal arteries could be assessed with U-MRA. When CE-MRA or DSA was used as the reference standard, nine renal arteries had hemodynamically significant RAS. U-MRA correctly identified eight out of nine arteries as having ≥50% RAS, and correctly identified 22 out of 23 arteries as not having significant RAS, with a sensitivity of 88.8%, a specificity of 95.65%, positive and negative predictive value of 88.8% and 95.65%, respectively, and an accuracy of 93.75%. Renal artery fibromuscular dysplasia (FMD) was observed in the two misclassified arteries. Conclusion: U-MRA is a reliable diagnostic method to depict normal and stenotic main renal arteries. U-MRA can be used as an alternative to contrast-enhanced magnetic resonance angiography or computer tomography angiography in patients with renal insufficiency unless FMD is suspected. Keywords: Unenhanced magnetic resonance angiography (U-MRA), Renal artery stenosis (RAS), Contrast-enhanced magnetic resonance angiography (CE-MRA), Fibromuscular dysplasia (FMD), Hypertension (HTA