Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Occupational exposure to aflatoxin (AFB1) in poultry production

Aflatoxin B1 (AFB1) has been recognized to cause cancer in the humans liver. Epidemiological and laboratory evidence also point towards the respiratory system as target for carcinogenesis. Exposure occurs mainly through food chain but inhalation represent an additional route of exposure. Agricultural workers have the greatest risk of occupational exposure due to their exposition to airborne aflatoxin through inhalation of dust.

Predicted VO2 on 6 minutes walk test for FAP liver transplanted patients

Physical function is limited post-liver transplantation and exercise training can improve exercise capacity following transplantation but previously assessment of functional capacity is necessary. The 6 minute walk test (6MWT) is routinely used for studying patient’s exercise capacity and is less expensive and time consuming than a Cardiopulmonary Exercise Test (CPXT). There have been some studies looking at the relationship between 6MWT and peak VO2 in patients but few presented an adequate equation for predicted peak VO2 from 6MWT to transplanted patients and none for Familial Amyloidotic Polyneuropathy (FAP) liver transplanted patients (FAPTx). The aim of this study was to compare the validity of 6 equations as predictors of VO2 from the 6MWT and compare it with directly measured VO2 in patients FAPTx.