RESUMEN
Metabolic Syndrome (MetS) is associated with the risk of developing chronic disease (atherosclerotic cardiovascular disease, type 2 diabetes, cancers and chronic kidney disease) and has an important role in early prevention. Previous research showed that an artificial neural network (ANN) is a suitable tool for algorithmic MetS diagnostics, that includes solely non-invasive, low-cost and easily-obtainabled (NI&LC&EO) diagnostic methods. This paper considers using four well-known machine learning methods (linear regression, artificial neural network, decision tree and random forest) for MetS predictions and provides their comparison, in order to induce and facilitate development of appropriate medical software by using these methods. Training, validation and testing are conducted on the large dataset that includes 3000 persons. Input vectors are very simple and contain the following parameters: gender, age, body mass index, waist-to-height ratio, systolic and diastolic blood pressures, while the output is MetS diagnosis in true/false form, made in accordance with International Diabetes Federation (IDF). Comparison leads to the conclusion that random forest achieves the highest specificity (SPC=0.9436), sensitivity (SNS=0.9154), positive (PPV=0.9379) and negative (NPV=0.9150) predictive values. Algorithmic diagnosis of MetS could be beneficial in everyday clinical practice since it can easily identify high risk patients.
Asunto(s)
Algoritmos , Síndrome Metabólico/diagnóstico , Femenino , Humanos , Masculino , Persona de Mediana Edad , Redes Neurales de la Computación , Factores de RiesgoRESUMEN
The diagnosis of metabolic syndrome (MetS) has a leading role in the early prevention of chronic disease, such as cardiovascular disease, type 2 diabetes, cancers and chronic kidney disease. It would be very greatful that MetS diagnosis can be predicted in everyday clinical practice. This paper presents artificial neural network (ANN) prediction of the diagnosis of MetS that includes solely non-invasive, low-cost and easily-obtained diagnostic methods. This solution can extract the risky persons and suggests complete tests only on them by saving money and time. ANN input vectors are very simple and contain solely non-invasive, low-cost and easily-obtained parameters: gender, age, body mass index, waist-to-height ratio, systolic and diastolic blood pressures. ANN output is M e t S-coefficient in true/false form, obtained from MetS definition of International Diabetes Federation (IDF). ANN training, validation and testing are conducted on the large dataset that includes 2928 persons. Feed-forward ANNs with 1-100 hidden neurons were considered and an optimal architecture were determinated. Comparison with other authors leads to the conclusion that our solution achieves the highest positive predictive value P P V = 0.8579. Further, obtained negative predictive value N P V = 0.8319 is also high and close to PPV, which means that our ANN solution is suitable both for positive and negative MetS prediction.
Asunto(s)
Síndrome Metabólico/diagnóstico , Redes Neurales de la Computación , Adolescente , Adulto , Factores de Edad , Anciano , Glucemia , Presión Sanguínea , Índice de Masa Corporal , Diagnóstico Precoz , Femenino , Predisposición Genética a la Enfermedad , Humanos , Lípidos/sangre , Masculino , Síndrome Metabólico/fisiopatología , Persona de Mediana Edad , Reproducibilidad de los Resultados , Factores Sexuales , Relación Cintura-Estatura , Adulto JovenRESUMEN
It is widely recognized that the JPEG2000 facilitates issues in medical imaging: storage, communication, sharing, remote access, interoperability, and presentation scalability. Therefore, JPEG2000 support was added to the DICOM standard Supplement 61. Two approaches to support JPEG2000 medical image are explicitly defined by the DICOM standard: replacing the DICOM image format with corresponding JPEG2000 codestream, or by the Pixel Data Provider service, DICOM supplement 106. The latest one supposes two-step retrieval of medical image: DICOM request and response from a DICOM server, and then JPIP request and response from a JPEG2000 server. We propose a novel strategy for transmission of scalable JPEG2000 images extracted from a single codestream over DICOM network using the DICOM Private Data Element without sacrificing system interoperability. It employs the request redirection paradigm: DICOM request and response from JPEG2000 server through DICOM server. The paper presents programming solution for implementation of request redirection paradigm in a DICOM transparent manner.
Asunto(s)
Compresión de Datos/normas , Diagnóstico por Imagen/normas , Adhesión a Directriz , Guías como Asunto , Interpretación de Imagen Asistida por Computador/normas , Sistemas de Información Radiológica/normas , InternacionalidadRESUMEN
The idea for softcopy viewing of medical image outside the radiology reading room spread among the scientists in various fields for several years. An image could be read on workstation of all types, from desktop across movable to handheld. Benefits are numerous and continue to grow as physicians use them discovering new usage cases. Proposed solutions vary with PACS architecture invasion level, communication and storage image formats, and utilization. We employ JPEG2000 standard because of its high (lossy/lossless) compression ratio with minimal spatial distortion, retrieval-oriented storage, and streaming. It is embedded in PACS as the DICOM Private Data Element containing JPIP parameter string, so-called DICOM2000. The DICOM2000 message is transparent for standard DICOM devices at the slightest level of invasion. Thanks to sophisticated JPEG2000 streaming, medical image becomes suitable for any resolution and quality display and (wireless) networks. The solution is validated on the ACR/NEMA standard test set of PACS images.