Publications

Abstract BACKGROUND AND AIMS Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. APPROACH AND RESULTS Learning data were […]

Abstract BACKGROUND AND AIMS With metabolic dysfunction-associated fatty liver disease (MAFLD) incidence and prevalence sharply increasing globally, there is an urgent need for non-invasive diagnostic tests to accurately screen high-risk MAFLD patients for liver inflammation and fibrosis. We aimed to develop a novel sequential algorithm based on N-terminal propeptide of type 3 collagen (PRO-C3) for […]

Abstract BACKGROUND Alcohol is a main cause of preventable deaths and frequently leads to the development of alcohol-related liver disease. Due to the lack of diagnostics, patients are commonly diagnosed after developing clinical manifestations. Recently, the biomarker PRO-C3 was shown to accurately identify fibrosis due to non-alcoholic fatty liver disease. AIM To assess the diagnostic […]

Abstract INTRODUCTION Metabolic-associated fatty liver disease is common, with fibrosis the major determinant of adverse outcomes. Population-based screening tools with high diagnostic accuracy for the staging of fibrosis are lacking. METHODS Three independent cohorts, 2 with both liver biopsy and liver stiffness measurements (LSMs, n = 254 and 65) and a population sample (n = […]

Abstract Given the high global prevalence of nonalcoholic fatty liver disease (NAFLD), the need for relevant noninvasive biomarkers and algorithms to accurately stage disease severity is a critical unmet medical need. Identifying those with advanced fibrosis (≥ F3) is the most crucial, as these individuals have the greatest risk of adverse, long-term, liver-related outcomes. We […]

Abstract Breast density is considered a structural property of a mammogram that can change in various ways explaining different effects of medicinal treatments. The aim of the present work is to provide a framework for obtaining more accurate and sensitive measurements of breast density changes related to specific effects like Hormonal Replacement Therapy (HRT) and […]

Abstract Present study has brought out a comparison of PCA and fuzzy clustering techniques in classifying protein profiles (chromatogram) of homogenates of different tissue origins: Ovarian, Cervix, Oral cancers, which were acquired using HPLC-LIF (High Performance Liquid Chromatography-Laser Induced Fluorescence) method developed in our laboratory. Study includes 11 chromatogram spectra each from oral, cervical, ovarian […]

Abstract Segmentation methods based on pixel classification are powerful but often slow. We introduce two general algorithms, based on sparse classification, for optimizing the computation while still obtaining accurate segmentations. The computational costs of the algorithms are derived, and they are demonstrated on real 3-D magnetic resonance imaging and 2-D radiograph data. We show that […]

Abstract We present a novel method for automatic shape model building from a collection of training shapes. The result is a shape model consisting of the mean model and the major modes of variation with a dense correspondence map between individual shapes. The framework consists of iterations where a medial shape representation is deformed into […]

Abstract A core part of many medical image segmentation techniques is the point distribution model, i.e., the landmark-based statistical shape model which describes the type of shapes under consideration. To build a proper model, that is flexible and generalizes well, one typically needs a large amount of landmarked training data, which can be hard to […]