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Purpose: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound., Theory and Methods: We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications., Results: In simulations, the proposed strategy reduces the estimates' bias throughout parameter space and achieves a variance close to the Cramér-Rao bound. In vivo, we observe good concordance between parameter maps estimated with the proposed NNs and traditional estimators, such as nonlinear least-squares fitting, while state-of-the-art NNs show larger deviations., Conclusion: The proposed NNs have greatly reduced bias compared to those trained using the mean squared error and offer significantly improved computational efficiency over traditional estimators with comparable or better accuracy., (© 2024 International Society for Magnetic Resonance in Medicine.)

Objectives: To describe the application of a data-mining statistical algorithm for calculation of clinical laboratory tests reference intervals., Methods: Reference intervals for eight different analytes and different age and sex groups (a total of 11 separate reference intervals) for tests that are unlikely to be ordered during routine screening of disease-free populations were calculated using the modified algorithm for data mining of test results stored in the laboratory database and compared with published peer-reviewed studies that used direct sampling. The selection of analytes was based on the predefined criteria that include comparability of analytical methods with a statistically significant number of observations., Results: Of the 11 calculated reference intervals, having upper and lower limits for each, 21 of 22 reference interval limits were not statistically different from the reference studies., Conclusions: The presented statistical algorithm is shown to be an accurate and practical tool for reference interval calculations., (Copyright© by the American Society for Clinical Pathology.)

Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden states) is typically assumed to be known or predetermined by some model selection criterion in conventional analysis. As prior information about the order frequently lacks, pairwise comparisons under criterion-based methods become computationally expensive with the model space growing. A few studies have conducted order selection and parameter estimation simultaneously, but they only considered homogeneous parametric instances. This study proposes a Bayesian double penalization (BDP) procedure for simultaneous order selection and parameter estimation of heterogeneous semiparametric HMMs. To overcome the difficulties in updating the order, we create a brand-new Markov chain Monte Carlo algorithm coupled with an effective adjust-bound reversible jump strategy. Simulation results reveal that the proposed BDP procedure performs well in estimation and works noticeably better than the conventional criterion-based approaches. Application of the suggested method to the Alzheimer's Disease Neuroimaging Initiative research further supports its usefulness., (© 2024 John Wiley & Sons Ltd.)

Literature screening is the process of identifying all relevant records from a pool of candidate paper records in systematic review, meta-analysis, and other research synthesis tasks. This process is time consuming, expensive, and prone to human error. Screening prioritization methods attempt to help reviewers identify most relevant records while only screening a proportion of candidate records with high priority. In previous studies, screening prioritization is often referred to as automatic literature screening or automatic literature identification. Numerous screening prioritization methods have been proposed in recent years. However, there is a lack of screening prioritization methods with reliable performance. Our objective is to develop a screening prioritization algorithm with reliable performance for practical use, for example, an algorithm that guarantees an 80% chance of identifying at least 80 % of the relevant records. Based on a target-based method proposed in Cormack and Grossman, we propose a screening prioritization algorithm using sampling with replacement. The algorithm is a wrapper algorithm that can work with any current screening prioritization algorithm to guarantee the performance. We prove, with mathematics and probability theory, that the algorithm guarantees the performance. We also run numeric experiments to test the performance of our algorithm when applied in practice. The numeric experiment results show this algorithm achieve reliable performance under different circumstances. The proposed screening prioritization algorithm can be reliably used in real world research synthesis tasks., (© 2024 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

Analysing data from educational tests allows governments to make decisions for improving the quality of life of individuals in a society. One of the key responsibilities of statisticians is to develop models that provide decision-makers with pertinent information about the latent process that educational tests seek to represent. Mixtures of t $$ t $$ factor analysers (MtFA) have emerged as a powerful device for model-based clustering and classification of high-dimensional data containing one or several groups of observations with fatter tails or anomalous outliers. This paper considers an extension of MtFA for robust clustering of censored data, referred to as the MtFAC model, by incorporating external covariates. The enhanced flexibility of including covariates in MtFAC enables cluster-specific multivariate regression analysis of dependent variables with censored responses arising from upper and/or lower detection limits of experimental equipment. An alternating expectation conditional maximization (AECM) algorithm is developed for maximum likelihood estimation of the proposed model. Two simulation experiments are conducted to examine the effectiveness of the techniques presented. Furthermore, the proposed methodology is applied to Peruvian data from the 2007 Early Grade Reading Assessment, and the results obtained from the analysis provide new insights regarding the reading skills of Peruvian students., (© 2023 British Psychological Society.)

The gig economy and gig work have grown quickly in recent years and have drawn much attention from researchers in different fields. Because the platform mediated gig economy is a relatively new phenomenon, studies have produced a range of interesting findings; of interest here are the socio‐technical issues that this work has surfaced. This systematic literature review (SLR) provides a snapshot of a range of socio‐technical issues raised in the last 12 years of literature focused on the platform mediated gig economy. Based on a sample of 515 papers gathered from nine databases in multiple disciplines, 132 were coded that specifically studied the gig economy, gig work, and gig workers. Three main socio‐technical themes were identified: (1) the digital workplace, which includes information infrastructure and digital labor that are related to the nature of gig work and the user agency; (2) algorithmic management, which includes platform governance, performance management, information asymmetry, power asymmetry, and system manipulation, relying on a diverse set of technological tools including algorithms and big data analytics; (3) ethical design, as a relevant value set that gig workers expect from the platform, which includes trust, fairness, equality, privacy, and transparency. A social informatics perspective is used to rethink the relationship between gig workers and platforms, extract the socio‐technical issues noted in prior research, and discuss the underexplored aspects of the platform mediated gig economy. The results draw attention to understudied yet critically important socio‐technical issues in the gig economy that suggest short‐ and long‐term opportunities for future research directions. [ABSTRACT FROM AUTHOR]

Methods to rank documents in large-scale citation data are increasingly assessed in terms of their ability to identify small sets of expert-selected papers. Here, we propose an algorithm for the accurate identification of milestone papers from citation networks. The algorithm combines an influence propagation process with game theory concepts. It outperforms state-of-the-art metrics in the identification of milestone papers in aggregate citation network data, while potentially mitigating the ranking's temporal bias compared with metrics that have similar milestone identification performance. The proposed method sheds light on the interplay between ranking accuracy and temporal bias. • We transform citation networks into influence propagation networks by reversing the citation relationships. • We propose a novel diffusion-based metric for ranking papers, combining influence propagation and game theory. • Experiments on APS and DBLP datasets demonstrate the effectiveness of our metric in identifying milestone papers. [ABSTRACT FROM AUTHOR]

The rapid development of technology has resulted in a surge in the volume of digital data available. This situation creates a problem for users who need assistance in locating specific information within this massive collection of data, resulting in a time-consuming process. Automatic Text Summarization systems have been developed as a more effective solution than traditional summarization techniques to address this problem and improve user access to relevant information. It is well known that researchers in the health sciences find it difficult to keep up with the latest literature due to their busy schedules. This study aims to produce comprehensive abstracts of Turkish-language scientific papers in the field of health sciences. Although abstracts of scientific papers are already available, more thorough summaries are still needed. To the best of our knowledge, no previous attempt has been made to automatically summarize Turkish language health science papers. For this purpose, a dataset of 105 Turkish papers was collected from DergiPark. Term Frequency, Term Frequency-Inverse Document Frequency, Latent Semantic Analysis, TextRank, and Latent Dirichlet Allocation algorithms were chosen as extractive text summarization methods due to their frequent use in this field. The performance of the text summarization models was evaluated using recall, precision, and F-score metrics, and the algorithms gave satisfactory results for Turkish. [ABSTRACT FROM AUTHOR]

The term irritant-induced (occupational) asthma (IIA) has been used to denote various clinical forms of asthma related to irritant exposure at work. The causal relationship between irritant exposure(s) and the development of asthma can be substantiated by the temporal association between the onset of asthma symptoms and a single or multiple high-level exposure(s) to irritants, whereas this relationship can only be inferred from epidemiological data for workers chronically exposed to moderate levels of irritants. Accordingly, the following clinical phenotypes should be distinguished within the wide spectrum of irritant-related asthma: (i) definite IIA, that is acute-onset IIA characterized by the rapid onset of asthma within a few hours after a single exposure to very high levels of irritant substances; (ii) probable IIA, that is asthma that develops in workers with multiple symptomatic high-level exposures to irritants; and (iii) possible IIA, that is asthma occurring with a delayed-onset after chronic exposure to moderate levels of irritants. This document prepared by a panel of experts summarizes our current knowledge on the diagnostic approach, epidemiology, pathophysiology, and management of the various phenotypes of IIA., (© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

This document is a special issue of the journal Algorithms, featuring selected papers from the journal's editorial board members from 2022 and 2023. The issue includes 16 research papers covering a range of topics such as game theory, fault detection in cellular networks, optimization algorithms, machine learning, cryptocurrency trading, and more. Each paper presents its own unique research findings and methodologies. The issue aims to showcase the diverse research interests and expertise of the journal's editorial board members. [Extracted from the article]

We study the static and dynamic properties of networks of crumpled creases formed in hand crushed sheets of paper. The fractal dimensionalities of crumpling networks in the unfolded (flat) and folded configurations are determined. Some other noteworthy features of crumpling networks are established. The physical implications of these findings are discussed. Specifically, we state that self-avoiding interactions introduce a characteristic length scale of sheet crumpling. A framework to model the crumpling phenomena is suggested. Mechanics of sheet crushing under external confinement is developed. The effect of compaction geometry on the crushing mechanics is revealed.

Objective: The use of continuous intravenous regular human insulin (RHI) infusion is often necessary to achieve glycemic control in critically ill patients. Because insulin is a high-risk medication owing to the potential for severe hypoglycemia, it is imperative that insulin infusion algorithms are designed to be safe, effective, and instructionally clear. The safety and efficacy of our intravenous RHI infusion algorithm protocol has been previously established (Nutrition 2008;24:536-45); however, the protocol violations by nursing personnel were not examined. The objective of this study was to assess nursing adherence to our RHI infusion algorithm., Methods: Continuous RHI infusion algorithm violations were retrospectively evaluated in adult patients admitted to a trauma intensive care unit who received concurrent continuous enteral and/or parenteral nutrition therapy and our algorithm for at least 3 d. Blood glucose (BG) monitoring was done every 1 to 2 h with the target BG at 70 to 149 mg/dL (3.9 to 8.3 mmol/L). Nursing adherence to the RHI infusion protocol was evaluated for each patient by comparing the adjustments in insulin infusion rates documented by the nursing personnel with the prescribed adjustments per our graduated continuous intravenous RHI infusion algorithm., Results: A total of 4150 BG measurements necessitating the determination of the appropriate RHI dosage rate by nursing personnel in 40 patients occurred during the observational period. The target BG was achieved for a mean of 20 h/d and none of the patients had an episode of severe hypoglycemia (BG <40 mg/dL or 2.2 mmol/L). The overall rate of algorithm violations was 12.1%. The algorithm violations accounted for a single episode of mild to moderate hypoglycemia (BG 40 to 60 mg/dL or 2.2 to 3.3 mmol/L) in 4 patients and 65 total episodes of hyperglycemia (BG ≥150 mg/dL or 8.3 mmol/L) in 18 patients., Conclusion: An adherence rate of nearly 90% is indicative of excellent nursing adherence compared with other published paper-based algorithms that examined protocol adherence. These data, combined with our previously published glycemic control data, indicate that this RHI infusion algorithm is an effective one for hyperglycemic trauma patients receiving continuous enteral and/or parenteral nutritional therapy., (Copyright © 2012 Elsevier Inc. All rights reserved.)

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60–70% and the average correlation of 3-by-1 ECGs achieved 80–90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects. [ABSTRACT FROM AUTHOR]

Interaction between proteins and nucleic acids is crucial to many cellular activities. Accurately detecting nucleic acid-binding residues (NABRs) in proteins can help researchers better understand the interaction mechanism between proteins and nucleic acids. Structure-based methods can generally make more accurate predictions than sequence-based methods. However, the existing structure-based methods are sensitive to protein conformational changes, causing limited generalizability. More effective and robust approaches should be further explored. In this study, we propose iNucRes-ASSH to identify nucleic acid-binding residues with a self-attention-based structure-sequence hybrid neural network. It improves the generalizability and robustness of NABR prediction from two levels: residue representation and prediction model. Experimental results show that iNucRes-ASSH can predict the nucleic acid-binding residues even when the experimentally validated structures are unavailable and outperforms five competing methods on a recent benchmark dataset and a widely used test dataset., (© 2023 Wiley Periodicals LLC.)

Background: Radiation dose reduction has been the focus of many research activities in x-ray CT. Various approaches were taken to minimize the dose to patients, ranging from the optimization of clinical protocols, refinement of the scanner hardware design, and development of advanced reconstruction algorithms. Although significant progress has been made, more advancements in this area are needed to minimize the radiation risks to patients., Purpose: Reconstruction algorithm-based dose reduction approaches focus mainly on the suppression of noise in the reconstructed images while preserving detailed anatomical structures. Such an approach effectively produces synthesized high-dose images (SHD) from the data acquired with low-dose scans. A representative example is the model-based iterative reconstruction (MBIR). Despite its widespread deployment, its full adoption in a clinical environment is often limited by an undesirable image texture. Recent studies have shown that deep learning image reconstruction (DLIR) can overcome this shortcoming. However, the limited availability of high-quality clinical images for training and validation is often the bottleneck for its development. In this paper, we propose a novel approach to generate SHD with existing low-dose clinical datasets that overcomes both the noise texture issue and the data availability issue., Methods: Our approach is based on the observation that noise in the image can be effectively reduced by performing image processing orthogonal to the imaging plane. This process essentially creates an equivalent thick-slice image (TSI), and the characteristics of TSI depend on the nature of the image processing. An advantage of this approach is its potential to reduce impact on the noise texture. The resulting image, however, is likely corrupted by the anatomical structural degradation due to partial volume effects. Careful examination has shown that the differential signal between the original and the processed image contains sufficient information to identify regions where anatomical structures are modified. The differential signal, unfortunately, contains significant noise and has to be removed. The noise removal can be accomplished by performing iterative noise reduction to preserve structural information. The processed differential signal is subsequently subtracted from TSI to arrive at SHD., Results: The algorithm was evaluated extensively with phantom and clinical datasets. For better visual inspection, difference images between the original and SHD were generated and carefully examined. Negligible residual structure could be observed. In addition to the qualitative inspection, quantitative analyses were performed on clinical images in terms of the CT number consistency and the noise reduction characteristics. Results indicate that no CT number bias is introduced by the proposed algorithm. In addition, noise reduction capability is consistent across different patient anatomical regions. Further, simulated water phantom scans were utilized in the generation of the noise power spectrum (NPS) to demonstrate the preservation of the noise-texture., Conclusions: We present a method to generate SHD datasets from regularly acquired low-dose CT scans. Images produced with the proposed approach exhibit excellent noise-reduction with the desired noise-texture. Extensive clinical and phantom studies have demonstrated the efficacy and robustness of our approach. Potential limitations of the current implementation are discussed and further research topics are outlined., (© 2023 American Association of Physicists in Medicine.)

The development and maintenance of the item bank is a critical element to a CD-CAT (cognitive diagnostic computerized adaptive testing; Cheng, 2009) system. For continuous testing, it is important to replenish the item bank with new items that have been calibrated. This requires pretesting to estimate the parameters of the new items. For CD-CAT, the structural parameters that need to be estimated include both item parameters and attribute vectors. In this paper, we propose three residual-statistic-based methods: RMA, ROEM, and RMEM, to estimate the attribute vectors and item parameters all together for new items. An iterative two-step online calibration procedure is developed to estimate the attribute vectors for the new items in the first step, and estimate the item parameters in the second step, then proceed iteratively until convergence is reached. An extensive simulation study was conducted to evaluate the performance of the three proposed methods and compare them with two existing methods, namely the Joint Estimation Algorithm (JEA; Chen & Xin, 2011) and Single Item Estimation (SIE; Chen et al., 2015) methods. In terms of the estimation of the attribute vector, the RMEM method performs the best in most of the cases. In terms of item parameter estimation, RMEM still has some advantages, and RMA outperforms JEA and SIE. Taken together, results suggest that the RMEM is superior to the other methods, especially when sample size is relatively small. A real-data example is provided to illustrate the application of RMEM in practice., (© 2022. The Psychonomic Society, Inc.)

Image processing (IP) in digital radiography has been steadily refined to improve image quality. Adaptable settings enable users to adjust systems to their specific requirements. This prospective, analytical study aimed to investigate the influence of different IP settings and dose reductions on image quality. Included were 20 cadaveric equine limb specimens distal to the metacarpophalangeal and metatarsophalangeal joints. Images were processed with the Dynamic Visualization II system (Fujifilm) using five different IP settings including multiobjective frequency processing, flexible noise control (FNC), and virtual grid processing (VGP). Seven criteria were assessed by three veterinary radiology Diplomates and one veterinary radiology resident in a blinded study using a scoring system. Algorithm comparison was performed using an absolute visual grading analysis. The rating of bone structures was improved by VGP at full dose (P < .05; AUC VGC  = 0.45). Überschwinger artifact perception was enhanced by VGP (P < .001; AUC VGC  = 0.66), whereas image noise perception was suppressed by FNC (P < .001; AUC VGC  = 0.29). The ratings of bone structures were improved by FNC at 50% dose (P < .05; AUC VGC  = 0.44), and 25% dose (P < .001; AUC VGC  = 0.32), and clinically acceptable image quality was maintained at 50% dose (mean rating 2.16; 95.8% ratings sufficient or better). The favored IP setting varied among observers, with higher agreement at lower dose levels. These findings supported using individualized IP settings based on the radiologist's preferences and situational image requirements, rather than using default settings., (© 2023 The Authors. Veterinary Radiology & Ultrasound published by Wiley Periodicals LLC on behalf of American College of Veterinary Radiology.)

The pipeline of drug discovery consists of a number of processes; drug-target interaction determination is one of the salient steps among them. Computational prediction of drug-target interactions can facilitate in reducing the search space of experimental wet lab-based verifications steps, thus considerably reducing time and other resources dedicated to the drug discovery pipeline. While machine learning-based methods are more widespread for drug-target interaction prediction, network-centric methods are also evolving. In this chapter, we focus on the process of the drug-target interaction prediction from the perspective of using machine learning algorithms and the various stages involved for developing an accurate predictor., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Data from natural sources show counter-intuitive distribution patterns for the leading digits to the left of the decimal point and the digit 1 is observed more frequently than all other numbers. This pattern, which was first described by Newcomb and later confirmed by Benford, is used in financial and tax auditing to detect fraud. Deviations from the pattern indicate possible falsifications. Anesthesiology journals are affected not only by ghostwriting and plagiarism but also by counterfeiting. In the present study 20 publications in anesthesiology known to be falsified by an author were investigated for irregularities with respect to Benford's law using the χ(2)-test and the Z-test. In the 20 retracted publications an average first-digit frequency of 243.1 (standard deviation SD ± 118.2, range: 30-592) and an average second-digit frequency of 132.3 (SD ± 72.2, range: 15-383) were found. The observed distribution of the first and second digits to the left of the decimal point differed significantly (p< 0.01) from the expected distribution described by Benford. Only the observed absolute frequencies for digits 3, 4 and 5 did not differ significantly from the expected values. In an analysis of each paper 17 out of 20 studies differed significantly from the expected value for the first digit and 18 out of 20 studies varied significantly from the expected value of the second digit. Only one paper did not vary significantly from expected values for the digits to the left of the decimal. For comparison, a meta-analysis using complex mathematical procedures was chosen as a control. The analysis showed a first-digit distribution consistent with the Benford distribution. Thus, the method used in the present study seems to be sensitive for detecting fraud. Additional statements of specificity cannot yet be made as this requires further analysis of data that is definitely not falsified. Future studies exploring conformity might help prevent falsified studies from being published.

The aim of this paper is to introduce and assess three algorithms for the identification of overlapping thematic structures in networks of papers. We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles, abstracts, and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three predefined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.

We study the cyclic dominance of three species in two-dimensional constrained Newman-Watts networks with a four-state variant of the rock-paper-scissors game. By limiting the maximal connection distance Rmax in Newman-Watts networks with the long-range connection probability p , we depict more realistically the stochastic interactions among species within ecosystems. When we fix mobility and vary the value of p or Rmax, the Monte Carlo simulations show that the spiral waves grow in size, and the system becomes unstable and biodiversity is lost with increasing p or Rmax. These results are similar to recent results of Reichenbach et al. [Nature (London) 448, 1046 (2007)], in which they increase the mobility only without including long-range interactions. We compared extinctions with or without long-range connections and computed spatial correlation functions and correlation length. We conclude that long-range connections could improve the mobility of species, drastically changing their crossover to extinction and making the system more unstable.

The language of radiology has gradually evolved from "the film" (the foundation of radiology since Wilhelm Roentgen's 1895 discovery of x-rays) to "the image," an electronic manifestation of a radiologic examination that exists within the bits and bytes of a computer. Rather than simply storing and displaying radiologic images in a static manner, the computational power of the computer may be used to enhance a radiologist's ability to visually extract information from the image through image processing and image manipulation algorithms. Image processing tools provide a broad spectrum of opportunities for image enhancement. Gray-level manipulations such as histogram equalization, spatial alterations such as geometric distortion correction, preprocessing operations such as edge enhancement, and enhanced radiography techniques such as temporal subtraction provide powerful methods to improve the diagnostic quality of an image or to enhance structures of interest within an image. Furthermore, these image processing algorithms provide the building blocks of more advanced computer vision methods. The prominent role of medical physicists and the AAPM in the advancement of medical image processing methods, and in the establishment of the "image" as the fundamental entity in radiology and radiation oncology, has been captured in 35 volumes of Medical Physics.

We examined the quality of anticoagulation produced by two paper-based warfarin dosing algorithms in a randomized clinical trial of warfarin therapy. Fifty-eight patients were randomized to receive warfarin at a target international normalized ratio (INR) range of 2.1-3.0 and were followed for an average of 2.7 years. As a proportion of total patient-time, the percentage of time spent above, within, and below the therapeutic range was 11%, 71%, and 19% respectively. Fifty-six patients were randomized to receive warfarin at a higher target INR range (3.1-4.0) and had INRs within the therapeutic range for 40% of total patient time. We conclude that the performance, minimal cost, and ease-of-use of these algorithms make them well-suited for patient management within primary-care and research settings.

This paper promotes the concept of active models in image-guided interventions. We outline the limitations of the rigid body assumption in image-guided interventions and describe how intraoperative imaging provides a rich source of information on spatial location of anatomical structures and therapy devices, allowing a preoperative plan to be updated during an intervention. Soft tissue deformation and variation from an atlas to a particular individual can both be determined using non-rigid registration. Established methods using free-form deformations have a very large number of degrees of freedom. Three examples of deformable models--motion models, biomechanical models and statistical shape models--are used to illustrate how prior information can be used to restrict the number of degrees of freedom of the registration algorithm and thus provide active models for image-guided interventions. We provide preliminary results from applications for each type of model.