Comparison of different radiography systems in an experimental study for detection of forearm fractures and evaluation of the Müller-AO and Frykman classification for distal radius fractures

OBJECTIVES: We sought to compare the diagnostic performance of screen-film radiography, storage-phosphor radiography, and a flat-panel detector system in detecting forearm fractures and to classify distal radius fractures according to the Müller-AO and Frykman classifications compared with the true extent, depicted by anatomic preparation. MATERIALS AND METHODS: A total of 71 cadaver arms were fractured in a material testing machine creating different fractures of the radius and ulna as well as of the carpal bones. Radiographs of the complete forearm were evaluated by 3 radiologists, and anatomic preparation was used as standard of reference in a receiver operating curve analysis. RESULTS: The highest diagnostic performance was obtained for the detection of distal radius fractures with area under the receiver operating curve (AUC) values of 0.959 for screen-film radiography, 0.966 for storage-phosphor radiography, and 0.971 for the flat-panel detector system (P > 0.05). Exact classification was slightly better for the Frykman (kappa values of 0.457-0.478) compared with the Müller-AO classification (kappa values of 0.404-0.447), but agreement can be considered as moderate for both classifications. CONCLUSIONS: The 3 imaging systems showed a comparable diagnostic performance in detecting forearm fractures. A high diagnostic performance was demonstrated for distal radius fractures and conventional radiography can be routinely performed for fracture detection. However, compared with anatomic preparation, depiction of the true extent of distal radius fractures was limited and the severity of distal radius fractures tends to be underestimated.

Developing ICF Core Sets for persons with sleep disorders based on the International Classification of Functioning, Disability and Health

BACKGROUND: With the International Classification of Functioning, Disability and Health (ICF), we can now rely on a globally agreed-upon framework and system for classifying the typical spectrum of problems in the functioning of persons given the environmental context in which they live. ICF Core Sets are subgroups of ICF items selected to capture those aspects of functioning that are most likely to be affected by sleep disorders. OBJECTIVE: The objective of this paper is to outline the developmental process for the ICF Core Sets for Sleep. METHODS: The ICF Core Sets for Sleep will be defined at an ICF Core Sets Consensus Conference, which will integrate evidence from preliminary studies, namely (a) a systematic literature review regarding the outcomes used in clinical trials and observational studies, (b) focus groups with people in different regions of the world who have sleep disorders, (c) an expert survey with the involvement of international clinical experts, and (d) a cross-sectional study of people with sleep disorders in different regions of the world. CONCLUSION: The ICF Core Sets for Sleep are being designed with the goal of providing useful standards for research, clinical practice and teaching. It is hypothesized that the ICF Core Sets for Sleep will stimulate research that leads to an improved understanding of functioning, disability, and health in sleep medicine. It is of further hope that such research will lead to interventions and accommodations that improve the restoration and maintenance of functioning and minimize disability among people with sleep disorders throughout the world.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

A fuzzy risk attitude classification based on prospect theory

Traditional methods do not actually measure peoples’ risk attitude naturally and precisely. Therefore, a fuzzy risk attitude classification method is developed. Since the prospect theory is usually considered as an effective model of decision making, the personalized parameters in prospect theory are firstly fuzzified to distinguish people with different risk attitudes, and then a fuzzy classification database schema is applied to calculate the exact value of risk value attitude and risk be- havior attitude. Finally, by applying a two-hierarchical clas- sification model, the precise value of synthetical risk attitude can be acquired.

Hierarchical segmentation-assisted multimodal registration for MR brain images

Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

A Texture-based land cover classification for the delineation of a shifting cultivation landscape in the Lao PDR using landscape metrics

The delineation of shifting cultivation landscapes using remote sensing in mountainous regions is challenging. On the one hand, there are difficulties related to the distinction of forest and fallow forest classes as occurring in a shifting cultivation landscape in mountainous regions. On the other hand, the dynamic nature of the shifting cultivation system poses problems to the delineation of landscapes where shifting cultivation occurs. We present a two-step approach based on an object-oriented classification of Advanced Land Observing Satellite, Advanced Visible and Near-Infrared Spectrometer (ALOS AVNIR) and Panchromatic Remote-sensing Instrument for Stereo Mapping (ALOS PRISM) data and landscape metrics. When including texture measures in the object-oriented classification, the accuracy of forest and fallow forest classes could be increased substantially. Based on such a classification, landscape metrics in the form of land cover class ratios enabled the identification of crop-fallow rotation characteristics of the shifting cultivation land use practice. By classifying and combining these landscape metrics, shifting cultivation landscapes could be delineated using a single land cover dataset.

An optimized lead system for long-term esophageal electrocardiography

Long-term electrocardiography (ECG) featuring adequate atrial and ventricular signal quality is highly desirable. Routinely used surface leads are limited in atrial signal sensitivity and recording capability impeding complete ECG delineation, i.e. in the presence of supraventricular arrhythmias. Long-term esophageal ECG might overcome these limitations but requires a dedicated lead system and recorder design. To this end, we analysed multiple-lead esophageal ECGs with respect to signal quality by describing the ECG waves as a function of the insertion level, interelectrode distance, electrode shape and amplifier's input range. The results derived from clinical data show that two bipolar esophageal leads, an atrial lead with short (15 mm) interelectrode distance and a ventricular lead with long (80 mm) interelectrode distance provide non-inferior ventricular signal strength and superior atrial signal strength compared to standard surface lead II. High atrial signal slope in particular is observed with the atrial esophageal lead. The proposed esophageal lead system in combination with an increased recorder input range of ±20 mV minimizes signal loss due to excessive electrode motion typically observed in esophageal ECGs. The design proposal might help to standardize long-term esophageal ECG registrations and facilitate novel ECG classification systems based on the independent detection of ventricular and atrial electrical activity.

A web-based non-intrusive ambient system to measure and classify activities of daily living.

BACKGROUND The number of older adults in the global population is increasing. This demographic shift leads to an increasing prevalence of age-associated disorders, such as Alzheimer's disease and other types of dementia. With the progression of the disease, the risk for institutional care increases, which contrasts with the desire of most patients to stay in their home environment. Despite doctors' and caregivers' awareness of the patient's cognitive status, they are often uncertain about its consequences on activities of daily living (ADL). To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline. The occurrence, performance, and duration of different ADL are important indicators of functional ability. The patient's ability to cope with these activities is traditionally assessed with questionnaires, which has disadvantages (eg, lack of reliability and sensitivity). Several groups have proposed sensor-based systems to recognize and quantify these activities in the patient's home. Combined with Web technology, these systems can inform caregivers about their patients in real-time (e.g., via smartphone). OBJECTIVE We hypothesize that a non-intrusive system, which does not use body-mounted sensors, video-based imaging, and microphone recordings would be better suited for use in dementia patients. Since it does not require patient's attention and compliance, such a system might be well accepted by patients. We present a passive, Web-based, non-intrusive, assistive technology system that recognizes and classifies ADL. METHODS The components of this novel assistive technology system were wireless sensors distributed in every room of the participant's home and a central computer unit (CCU). The environmental data were acquired for 20 days (per participant) and then stored and processed on the CCU. In consultation with medical experts, eight ADL were classified. RESULTS In this study, 10 healthy participants (6 women, 4 men; mean age 48.8 years; SD 20.0 years; age range 28-79 years) were included. For explorative purposes, one female Alzheimer patient (Montreal Cognitive Assessment score=23, Timed Up and Go=19.8 seconds, Trail Making Test A=84.3 seconds, Trail Making Test B=146 seconds) was measured in parallel with the healthy subjects. In total, 1317 ADL were performed by the participants, 1211 ADL were classified correctly, and 106 ADL were missed. This led to an overall sensitivity of 91.27% and a specificity of 92.52%. Each subject performed an average of 134.8 ADL (SD 75). CONCLUSIONS The non-intrusive wireless sensor system can acquire environmental data essential for the classification of activities of daily living. By analyzing retrieved data, it is possible to distinguish and assign data patterns to subjects' specific activities and to identify eight different activities in daily living. The Web-based technology allows the system to improve care and provides valuable information about the patient in real-time.

Towards the introduction of the 'Immunoscore' in the classification of malignant tumours.

The American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) TNM staging system provides the most reliable guidelines for the routine prognostication and treatment of colorectal carcinoma. This traditional tumour staging summarizes data on tumour burden (T), the presence of cancer cells in draining and regional lymph nodes (N) and evidence for distant metastases (M). However, it is now recognized that the clinical outcome can vary significantly among patients within the same stage. The current classification provides limited prognostic information and does not predict response to therapy. Multiple ways to classify cancer and to distinguish different subtypes of colorectal cancer have been proposed, including morphology, cell origin, molecular pathways, mutation status and gene expression-based stratification. These parameters rely on tumour-cell characteristics. Extensive literature has investigated the host immune response against cancer and demonstrated the prognostic impact of the in situ immune cell infiltrate in tumours. A methodology named 'Immunoscore' has been defined to quantify the in situ immune infiltrate. In colorectal cancer, the Immunoscore may add to the significance of the current AJCC/UICC TNM classification, since it has been demonstrated to be a prognostic factor superior to the AJCC/UICC TNM classification. An international consortium has been initiated to validate and promote the Immunoscore in routine clinical settings. The results of this international consortium may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).

Tumour border configuration in colorectal cancer: proposal for an alternative scoring system based on the percentage of infiltrating margin.

AIMS Information on tumour border configuration (TBC) in colorectal cancer (CRC) is currently not included in most pathology reports, owing to lack of reproducibility and/or established evaluation systems. The aim of this study was to investigate whether an alternative scoring system based on the percentage of the infiltrating component may represent a reliable method for assessing TBC. METHODS AND RESULTS Two hundred and fifteen CRCs with complete clinicopathological data were evaluated by two independent observers, both 'traditionally' by assigning the tumours into pushing/infiltrating/mixed categories, and alternatively by scoring the percentage of infiltrating margin. With the pushing/infiltrating/mixed pattern method, interobserver agreement (IOA) was moderate (κ = 0.58), whereas with the percentage of infiltrating margins method, IOA was excellent (intraclass correlation coefficient of 0.86). A higher percentage of infiltrating margin correlated with adverse features such as higher grade (P = 0.0025), higher pT (P = 0.0007), pN (P = 0.0001) and pM classification (P = 0.0063), high-grade tumour budding (P < 0.0001), lymphatic invasion (P < 0.0001), vascular invasion (P = 0.0032), and shorter survival (P = 0.0008), and was significantly associated with an increased probability of lymph node metastasis (P < 0.001). CONCLUSIONS Information on TBC gives additional prognostic value to pathology reports on CRC. The novel proposed scoring system, by using the percentage of infiltrating margin, outperforms the 'traditional' way of reporting TBC. Additionally, it is reproducible and simple to apply, and can therefore be easily integrated into daily diagnostic practice.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

Efficiency and Continuity in Public Finance: The Ottoman System of Taxation

Economic historians have recently emphasized the importance of integrating economic and historical approaches in studying institutions. The literature on the Ottoman system of taxation, however, has continued to adopt a primarily historical approach, using ad hoc categories of classification and explaining the system through its continuities with the historical precedent. This paper integrates economic and historical approaches to examine the structure, efficiency, and regional diversity of the tax system. The structure of the system made it possible for the Ottomans to economize on the transaction cost of measuring the tax base. Regional variations resulted from both efficient adaptations and institutional rigidities.

Beilage zur Classification der Rosen : Aufzählung der besten älteren und neueren Sorten aus den betreffenden Gruppen

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Classification der Rosen nebst Eintheilung derselben in natürliche Gruppen

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Beilage zur Classification der Rosen : Aufzählung der besten älteren und neueren Sorten aus den betreffenden Gruppen

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