Telemonitoring predicts in advance heart failure admissions.

BACKGROUND: Heart failure (HF) is increasingly common and characterised by frequent admissions to hospital. To reduce the risk of HF hospitalisation (HFH), approaches as telemonitoring (TM) have been introduced. This study aimed to develop an algorithm for detecting patients at high risk of HFH, using daily collected physiological data (blood pressure, heart rate, weight) by non-invasive TM. METHODS: The analysis was based on home-TM data collected from a single centre as part of HF care. The prediction of HFH was considered as a signal processing and classification problem. Signal processing aimed to transform the signals to enhance the information relevant to HFH. We attempted to construct an algorithm that could identify such patterns and classify them as abnormal by assessing the predictive value of each of the monitored signals and their combinations using analysis of vectors (e.g. vectors of raw signal values, vectors of signals obtained by Multi-Resolution Analysis). RESULTS: The best predictive results were achieved with the combined used of weight and diastolic BP. The highest predictive performance was achieved using 8-day TM data (area under the receiver operator characteristic curve (AUC) 0.82±0.02). Prediction based on 4-day TM data was slightly less accurate with an AUC of 0.77±0.01. CONCLUSION: We have found that using an algorithm based on weight and diastolic blood pressure measured over 8days predicts heart failure admissions with a high degree of accuracy. The value of such an algorithm should be tested in clinical trials.

Ratsnake: a versatile image annotation tool with application to computer-aided diagnosis.

Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

Image analysis framework for infection monitoring.

We present a novel framework for automatic extraction of the progress of an infection from time-series medical images, with application to pneumonia monitoring. In each image of a series, the lungs, which are the body components of interest in our study, are detected and delineated by a modified active shape model-based algorithm that is constrained by binary approximation masks. This algorithm offers resistance in the presence of infection manifestations that may distort the typical appearance of the body components of interest. The relative extent of the infection manifestations is assessed by supervised classification of samples acquired from the respective image regions. The samples are represented by multiple dissimilarity features fused according to a novel entropy-based weighted voting scheme offering nonparametric operation and robustness to outliers. The output of the proposed framework is a time series of structured data quantifying the relative extent of infection manifestations at the body components of interest over time. The results obtained indicate an improved performance over relevant state-of-the-art methods. The overall accuracy quantified by the area under receiver operating characteristic reaches 90.0 ± 2.1%. The effectiveness of the proposed framework to pneumonia monitoring, the generality, and the adaptivity of its methods open perspectives for application to other medical imaging domains.

Reduction of capsule endoscopy reading times by unsupervised image mining.

The screening of the small intestine has become painless and easy with wireless capsule endoscopy (WCE) that is a revolutionary, relatively non-invasive imaging technique performed by a wireless swallowable endoscopic capsule transmitting thousands of video frames per examination. The average time required for the visual inspection of a full 8-h WCE video ranges from 45 to 120min, depending on the experience of the examiner. In this paper, we propose a novel approach to WCE reading time reduction by unsupervised mining of video frames. The proposed methodology is based on a data reduction algorithm which is applied according to a novel scheme for the extraction of representative video frames from a full length WCE video. It can be used either as a video summarization or as a video bookmarking tool, providing the comparative advantage of being general, unbounded by the finiteness of a training set. The number of frames extracted is controlled by a parameter that can be tuned automatically. Comprehensive experiments on real WCE videos indicate that a significant reduction in the reading times is feasible. In the case of the WCE videos used this reduction reached 85% without any loss of abnormalities.

Microarray-MD: a system for exploratory analysis of microarray gene expression data.

In this paper, we present Microarray Medical Data explorer (Microarray-MD), a novel software system that is able to assist in the exploratory analysis of gene expression microarray data. It implements a combination scheme of multiple Support Vector Machines, which integrates a variety of gene selection criteria and allows for the discrimination of multiple diseases or subtypes of a disease. The system can be trained and automatically tune its parameters with the provision of pathologically characterized gene expression data to its input. Given a set of new, uncharacterized, patient's data as input, it outputs a decision on the type or the subtype of a disease. A graphical user interface provides easy access to the system operations and direct adjustment of its parameters. It has been tested on various publicly available datasets. The overall accuracy it achieves was estimated to exceed 90%.

CoLD: a versatile detection system for colorectal lesions in endoscopy video-frames.

In this paper, we present CoLD (colorectal lesions detector) an innovative detection system to support colorectal cancer diagnosis and detection of pre-cancerous polyps, by processing endoscopy images or video frame sequences acquired during colonoscopy. It utilizes second-order statistical features that are calculated on the wavelet transformation of each image to discriminate amongst regions of normal or abnormal tissue. An artificial neural network performs the classification of the features. CoLD integrates the feature extraction and classification algorithms under a graphical user interface, which allows both novice and expert users to utilize effectively all system's functions. It has been developed in close cooperation with gastroenterology specialists and has been tested on various colonoscopy videos. The detection accuracy of the proposed system has been estimated to be more than 95%. As it has been resulted, it can be used as a supplementary diagnostic tool for colorectal lesions.