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PubMed1998

Vulvovaginal candidiasis: clinical manifestations, risk factors, management algorithm.

OBJECTIVE: To correlate symptoms, signs, and risk factors with positive wet mounts or cultures for Candida albicans and to develop an algorithm to diagnose vulvovaginal candidiasis. METHODS: This cross-sectional study of 774 randomly selected women from an urban sexually transmitted disease (STD) clinic evaluated symptoms, signs, and risk factors associated with C albicans, detected by wet mount and culture, and constructed an algorithm. RESULTS: C albicans, recovered from 186 (24%) of the 774 women, was associated with chief complaints of vulvar pruritus or burning. Elicited symptoms were vulvar pruritus, pain or burning, and external dysuria; signs were vulvar erythema, edema, fissures, vaginal erythema, and thick, curdy vaginal discharge. Among 545 women with symptoms of either increased vaginal discharge or vulvar pruritus or burning, only 155 (28%) had positive C albicans cultures, whereas bacterial vaginosis or other sexually transmitted infections were found in 288 (53%). In multivariate analysis, risk factors for positive C albicans culture included condom use, presentation after the 14th menstrual cycle day, sexual intercourse more than four times per month, recent antibiotic use, young age, past gonococcal infection, and absence of current gonorrhea or bacterial vaginosis. A clinical algorithm based on symptoms, signs, and selective use of wet mounts and cultures would have provided prompt treatment to 150 of 167 (90%) women with vulvovaginal candidiasis while minimizing the number of cultures performed. CONCLUSION: A simple algorithm using symptoms, signs, wet mounts, and selective cultures can identify 90% of women with vulvovaginal candidiasis. In this STD clinic, vulvovaginal symptoms also require assessment for bacterial vaginosis, trichomoniasis, and cervical infection.

Adolescent↗

PubMed1993

Screening and managing abdominal aortic aneurysms at the Ochsner Clinic: suggested algorithm and method of derivation. Department of Surgery and Ochsner Clinic Quality Assurance Committee.

An algorithm for screening and management of abdominal aortic aneurysms was developed at the Ochsner Medical Institutions to address the considerable variation identified in clinical practice. A consensus panel of physicians whose opinions differed regarding the management of abdominal aortic aneurysms was convened to develop the algorithm. Based on a literature review and clinical experience, the panel established criteria to determine how frequently and by which methodologies patients with abdominal aortic aneurysms should be followed and when a referral to a vascular surgeon is appropriate. The algorithm developed by the consensus panel method was used to establish practice guidelines that are flexible enough to address individual patient needs yet structured enough to eliminate inappropriate care. Data are being collected and analyzed in real time to determine whether elements of the algorithm should be revised.

Aftercare↗

PubMed2000

A simple algorithm for a digital three-pole Butterworth filter of arbitrary cut-off frequency: application to digital electroencephalography.

Algorithms for low-pass and high-pass three-pole recursive Butterworth filters of a given cut-off frequency have been developed. A band-pass filter can be implemented by sequential application of algorithms for low- and high-pass filters. The algorithms correspond to infinite impulse-response filters that have been designed by applying the bilinear transformation to the transfer functions of the corresponding analog filters, resulting in a recursive digital filter with seven real coefficients. Expressions for filter coefficients as a function of the cut-off frequency and the sampling period are derived. Filter performance is evaluated and discussed. As in the case of their analog counterparts, their transfer function shows marked flattening over the pass band and gradually higher attenuation can be seen at frequencies above or below the cut-off frequency, with a slope of around 60 dB/decade. There is a 3 dB attenuation at the cut-off frequency and a gradual increase in phase shift over one decade above or below the cut-off frequency. Low-pass filters show a maximum overshoot of 8% and high-pass filters show a maximum downwards overshoot of approximately 35%. The filter is mildly under-damped, with a damping factor of 0.5. On an IBM 300GL personal computer at 600 MH with 128 MB RAM, filtering time with MATLAB 5.2 running under Windows 98 is of the order of 50 ms for 60000 samples. This will be adequate for on-line electroencephalography (EEG) applications. The simplicity of the algorithm to calculate filter coefficients for an arbitrary cut-off frequency can be useful to modern EEG laboratories and software designers for electrophysiological applications.

Algorithms↗

PubMed2001

Synthesizing spatially complex sound in virtual space: an accurate offline algorithm.

The study of spatial processing in the auditory system usually requires complex experimental setups, using arrays of speakers or speakers mounted on moving arms. These devices, while allowing precision in the presentation of the spatial attributes of sound, are complex, expensive and limited. Alternative approaches rely on virtual space sound delivery. In this paper, we describe a virtual space algorithm that enables accurate reconstruction of eardrum waveforms for arbitrary sound sources moving along arbitrary trajectories in space. A physical validation of the synthesis algorithm is performed by comparing waveforms recorded during real motion with waveforms synthesized by the algorithm. As a demonstration of possible applications of the algorithm, virtual motion stimuli are used to reproduce psychophysical results in humans and for studying responses of barn owls to auditory motion stimuli.

Acoustic Stimulation↗

PubMed2001

A general algorithm for optimal sampling schedule design in nuclear medicine imaging.

Optimal sampling schedule (OSS) is of great interest in biomedical experiment design, as it can improve the physiological parameter estimation precision and significantly reduce the samples required. A number of well designed algorithms and software packages have been developed, which deal with the instantaneous measurements at discrete times. However, in nuclear medicine tracer kinetic studies, the imaging systems, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT), take measurements (images) based on continuous accumulation over time intervals. In this case, the existing algorithms cannot be used to design OSS so as to reduce the image frame numbers. In this paper, a general OSS design algorithm for the accumulative measurement is proposed. The potential usefulness of the algorithm is demonstrated by its designing OSS in [18F] fluoro-2-deoxy-D-glucose (FDG) studies with PET to estimate the local cerebral metabolic rate of glucose. The robustness of parameter estimation using the OSS with respect to intra-subject and inter-subject parameter variations is also presented.

Algorithms↗

PubMed2002

Generalized linear least squares algorithms for modeling glucose metabolism in the human brain with corrections for vascular effects.

The generalized linear least squares (GLLS) algorithm has been found useful in image-wide parameter estimation for the generation of parametric images with positron emission tomography (PET) as it is computationally efficient and statistically reliable. However, the original algorithm was designed for parameter estimation with non-uniformly sampled instantaneous measurements. When dynamic PET data are sampled with the optimal image sampling schedule (OISS) to reduce memory and storage space, only a few temporal image frames are recorded. As a result, the direct application of GLLS is no longer appropriate. In this paper, we extend the GLLS algorithm to a five parameter model for the study of human brain metabolism, which accounts for the effect of cerebral blood volume (CBV), using OISS sampled data, with as few as five temporal samples. The formulation for this new GLLS algorithm is developed, and its computational efficiency and statistical reliability are investigated and validated using computer simulations and clinical PET [18F]-2-fluoro-2-deoxy-D-glucose (FDG) data.

Algorithms↗

PubMed2003

'Minimum average risk' as a new peak-detection algorithm applied to myofibrillar dynamics.

We present a new peak-detection algorithm based on the method of 'minimum average risk' proposed by Kolmogorov and developed for signal processing in various fields. In this method, translations of features within a signal scan are quantified by minimizing the integrated pointwise product of each scan relative to the first derivative of the immediately previous scan. We have adapted this method for use in a new algorithm to monitor dynamic changes of sarcomere length in single myofibrillar sarcomeres of striated muscles, but the algorithm can also be used more generally for peak localization. We find that this method results in sub-nanometer precision and higher signal-to-noise ratio than current methods. At an equal noise level, the RMS deviation of the minimum average risk algorithm was 1.3 times lower than that of the center of mass method with modeled data and 3-4 times lower with actual data.

Algorithms↗

PubMed1999

Rapid algorithms for the construction of cerebral blood flow and oxygen utilization images with oxygen-15 and dynamic positron emission tomography.

Two rapid estimation algorithms for construction of cerebral blood flow (CBF) and oxygen utilization (CMRO) images with dynamic positron emission tomography (PET) are presented. These algorithms are based on the linear least squares (LLS) and generalized linear least squares (GLLS) methodologies. Using the conventional two-compartmental model and multiple tracer studies, we derived a linear relationship for brain tissue activity to arterial blood activity, time-integrated arterial blood activity and time-integrated brain tissue activity. The LLS technique is computationally efficient as no regression analysis is required, while GLLS is used to refine the estimates obtained from LLS. A comparative study using non-linear least squares regression (NLS) revealed excellent correlation between the new algorithms for various noise levels expected in clinical applications. A sensitivity analysis was performed to examine reliability and identifiability of the parameter estimates. In view of the results, LLS and GLLS provide rapid and reliable estimates of CBF and CMRO when applied to dynamic PET data. These algorithms are particularly suitable for pixel-by-pixel construction of high resolution and highly accurate PET functional images.

Algorithms↗

PubMed1987

Developing a clinical algorithm for early management of cervical spine injury in child trauma victims.

To define a subset of injured children for whom emergency cervical spine radiography may be unnecessary, we performed a retrospective chart and radiologic review. Two entry methods were used: All injured children, from birth through 16 years, who had received cervical spine radiographs at The Children's Memorial Hospital from September 1983, to September 1984, were included. All patients from birth to 16 years with proven or suspected cases of cervical spine injury who had received cervical spine radiographs and who had been treated at either the Children's Memorial Hospital or the Northwestern University Spine Trauma Unit during period 1974 to 1984 also were included. Each child's chart was reviewed, and 84 clinical variables were recorded. All radiographs were reviewed by a pediatric neuroradiologist. Of 206 children studied, 59 had cervical spine injuries. A clinical algorithm was derived using the following eight variables: neck pain; neck tenderness; limitation of neck mobility; history of trauma to the neck; and abnormalities of reflexes, strength, sensation, or mental status. The following decision rule was selected: Positive findings in any of these eight variables mandates cervical spine radiography. This algorithm correctly identified 58 of 59 children with cervical spine injury, yielding a sensitivity of 98% and specificity of 54%. Cervical spine radiographs could have been avoided in 79 children (38% of the entire sample). This algorithm performed better than did models derived from logistic regression analysis of the same data. Validation trials are required prior to the implementation of this or other clinical decision algorithms in practice.

Accidents↗

PubMed1989

Development of a decision algorithm for a semiautomatic defibrillator.

A decision algorithm was developed for a semiautomatic defibrillator. The function of the algorithm is to evaluate the ECG of a patient and determine whether a defibrillation shock should be delivered. The development process included establishment of defibrillation criteria, creation of ECG databases, algorithm design, development of test protocols, and clinical testing. The result was an algorithm with sensitivity and specificity sufficiently accurate to allow a defibrillation shock to be delivered safely outside the hospital.

Algorithms↗

PubMed1998

Multilevel and motion model-based ultrasonic speckle tracking algorithms.

A multilevel motion model-based approach to ultrasonic speckle tracking has been developed that addresses the inherent trade-offs associated with traditional single-level block matching (SLBM) methods. The multilevel block matching (MLBM) algorithm uses variable matching block and search window sizes in a coarse-to-fine scheme, preserving the relative immunity to noise associated with the use of a large matching block while preserving the motion field detail associated with the use of a small matching block. To decrease further the sensitivity of the multilevel approach to noise, speckle decorrelation and false matches, a smooth motion model-based block matching (SMBM) algorithm has been implemented that takes into account the spatial inertia of soft tissue elements. The new algorithms were compared to SLBM through a series of experiments involving manual translation of soft tissue phantoms, motion field computer simulations of rotation, compression and shear deformation, and an experiment involving contraction of human forearm muscles. Measures of tracking accuracy included mean squared tracking error, peak signal-to-noise ratio (PSNR) and blinded observations of optical flow. Measures of tracking efficiency included the number of sum squared difference calculations and the computation time. In the phantom translation experiments, the SMBM algorithm successfully matched the accuracy of SLBM using both large and small matching blocks while significantly reducing the number of computations and computation time when a large matching block was used. For the computer simulations, SMBM yielded better tracking accuracies and spatial resolution when compared with SLBM using a large matching block. For the muscle experiment, SMBM outperformed SLBM both in terms of PSNR and observations of optical flow. We believe that the smooth motion model-based MLBM approach represents a meaningful development in ultrasonic soft tissue motion measurement.

Algorithms↗

PubMed2003

Improved Hidden Markov Model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid.

Multiple sequence alignment (MSA) is one of the basic problems in computational biology. Realistic problem instances of MSA are computationally intractable for exact algorithms. One way to tackle MSA is to use Hidden Markov Models (HMMs), which are known to be very powerful in the related problem domain of speech recognition. However, the training of HMMs is computationally hard and there is no known exact method that can guarantee optimal training within reasonable computing time. Perhaps the most powerful training method is the Baum-Welch algorithm, which is fast, but bears the problem of stagnation at local optima. In the study reported in this paper, we used a hybrid algorithm combining particle swarm optimization with evolutionary algorithms to train HMMs for the alignment of protein sequences. Our experiments show that our approach yields better alignments for a set of benchmark protein sequences than the most commonly applied HMM training methods, such as Baum-Welch and Simulated Annealing.

Algorithms↗

PubMed2003

Analysis of gene expression profiles: an application of memetic algorithms to the minimum sum-of-squares clustering problem.

Microarrays have become a key technology in experimental molecular biology since they allow monitoring of gene expression for more than 10,000 genes in parallel producing huge amounts of data. In the exploration of transcriptional regulatory networks, an important task is to cluster gene expression data to identify groups of genes with similar patterns and hence similar function. In this paper, memetic algorithms (MAs)-evolutionary algorithms incorporating local search-are proposed for minimum sum-of-squares clustering (MSSC). In a fitness landscape analysis, it is shown that the MSSC problem has correlation structure exploitable by MAs. The proposed MAs are shown to be superior to multi-start k-means as well as five other clustering algorithms from the bioinformatics literature including hierarchical algorithms and self-organizing maps. Although the fitness values of the different clustering solutions lie close together, it is shown that the solutions differ significantly from each other in terms of cluster memberships which is extremely important for the biological interpretation of the clustering results.

Algorithms↗

PubMed1998

Emergence of algorithmic language in genetic systems.

In genetic systems there is a non-trivial interface between the sequence of symbols which constitutes the chromosome, or 'genotype', and the products which this sequence encodes--the 'phenotype'. This interface can be thought of as a 'computer'. In this case the chromosome is viewed as an algorithm and the phenotype as the result of the computation. In general, only a small fraction of all possible sequences of symbols makes any sense for a given computer. The difficulty of finding meaningful algorithms by random mutation is known as the brittleness problem. In this paper we show that mutation and crossover favor the emergence of an algorithmic language which facilitates the production of meaningful sequences following random mutations of the genotype. We base our conclusions on an analysis of the population dynamics of a variant of Kitano's neurogenetic model wherein the chromosome encodes the rules for cellular division and the phenotype is a 16-cell organism interpreted as a connectivity matrix for a feed-forward neural network. We show that an algorithmic language emerges, describe this language in extenso, and show how it helps to solve the brittleness problem.

Algorithms↗

PubMed1978

Resuscitation algorithm for management of acute emergencies.

Assuming that unrecognized or inadequately corrected hypovolemia results in higher mortality and morbidity rates, we developed a systematic approach to resuscitation that would: 1) identify criteria to aid in the recognition of hypovolemia and ensure the expeditious correction of this defect without interfering with diagnostic workup and management; 2) define criteria to prevent fluid overload which may jeopardize the patient's course, and 3) express these criteria in an explicit, systematic, patient care algorithm, ie, protocol, useful to both the resident and the practicing physician. We are now conducting prospective clinical trials with one service using the algorithm and the others acting as the control group. Preliminary results comparing patient outcomes suggest that the algorithm improves patient care by shortening resuscitation time and results in fewer hospital days, intensive care unit days, febrile days, and days on mechanical ventilation as well as reduced mortality. The algorithm provides a systematic plan to organize patient care so that the most urgently needed procedures are not delayed or overlooked.

Algorithms↗

PubMed2001

A quantitative comparison of motion detection algorithms in fMRI.

An important step in the analysis of fMRI time-series data is to detect, and as much as possible, correct for subject motion during the course of the scanning session. Several public domain algorithms are currently available for motion detection in fMRI. This paper compares the performance of four commonly used programs: AIR 3.08, SPM99, AFNI98, and the pyramid method of Thévenaz, Ruttimann, and Unser (TRU). The comparison is based on the performance of the algorithms in correcting a range of simulated known motions in the presence of various degrees of noise. SPM99 provided the most accurate motion detection amongst the algorithms studied. AFNI98 provided only slightly less accurate results than SPM99, however, it was several times faster than the other programs. This algorithm represents a good compromise between speed and accuracy. AFNI98 was also the most robust program in presence of noise. It yielded reasonable results for very low signal to noise levels. For small initial misalignments, TRU's performance was similar to SPM99 and AFNI98. However, its accuracy diminished rapidly for larger misalignments. AIR was found to be the least accurate program studied.

Algorithms↗

PubMed2002

Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms.

This paper presents MRI segmentation techniques to differentiate abnormal and normal tissues in Ophthalmology using fuzzy clustering algorithms. Applying the best-known fuzzy c-means (FCM) clustering algorithm, a newly proposed algorithm, called an alternative fuzzy c-mean (AFCM), was used for MRI segmentation in Ophthalmology. These unsupervised segmentation algorithms can help Ophthalmologists to reduce the medical imaging noise effects originating from low resolution sensors and/or the structures that move during the data acquisition. They may be particularly helpful in the clinical oncological field as an aid to the diagnosis of Retinoblastoma, an inborn oncological disease in which symptoms usually show in early childhood. For the purpose of early treatment with radiotherapy and surgery, the newly proposed AFCM is preferred to provide more information for medical images used by Ophthalmologists. Comparisons between FCM and AFCM segmentations are made. Both fuzzy clustering segmentation techniques provide useful information and good results. However, the AFCM method has better detection of abnormal tissues than FCM according to a window selection. Overall, the newly proposed AFCM segmentation technique is recommended in MRI segmentation.

Algorithms↗

PubMed2001

A pruning method for the recursive least squared algorithm.

The recursive least squared (RLS) algorithm is an effective online training method for neural networks. However, its conjunctions with weight decay and pruning have not been well studied. This paper elucidates how generalization ability can be improved by selecting an appropriate initial value of the error covariance matrix in the RLS algorithm. Moreover, how the pruning of neural networks can be benefited by using the final value of the error covariance matrix will also be investigated. Our study found that the RLS algorithm is implicitly a weight decay method, where the weight decay effect is controlled by the initial value of the error covariance matrix; and that the inverse of the error covariance matrix is approximately equal to the Hessian matrix of the network being trained. We propose that neural networks are first trained by the RLS algorithm and then some unimportant weights are removed based on the approximate Hessian matrix. Simulation results show that our approach is an effective training and pruning method for neural networks.

Algorithms↗