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Biomedical subjects

G D Tourassi

Publications and source records attributed to G D Tourassi.

14 recordsLinked to original sources
PubMed2001

Multifractal texture analysis of perfusion lung scans as a potential diagnostic tool for acute pulmonary embolism.

A computer-assisted diagnostic (CAD) tool was developed for the diagnosis of acute pulmonary embolism (PE) in perfusion lung scans. Forty-five scans (with angiographic proof) were included in the study. The CAD tool was composed of two modules. The first module performs multifractal texture analysis on the posterior view of the perfusion scan. The second module is a decision algorithm that merges the multifractal parameters into a diagnosis regarding the presence or absence of PE. Linear and non-linear decision models were evaluated for the diagnostic task. A consensus neural network significantly outperformed all decision models including the physicians.

Acute Disease↗
PubMed2001

A neural network approach to breast cancer diagnosis as a constraint satisfaction problem.

A constraint satisfaction neural network (CSNN) approach is proposed for breast cancer diagnosis using mammographic and patient history findings. Initially, the diagnostic decision to biopsy was formulated as a constraint satisfaction problem. Then, an associative memory type neural network was applied to solve the problem. The proposed network has a flexible, nonhierarchical architecture that allows it to operate not only as a predictive tool but also as an analysis tool for knowledge discovery of association rules. The CSNN was developed and evaluated using a database of 500 nonpalpable breast lesions with definitive histopathological diagnosis. The CSNN diagnostic performance was evaluated using receiver operating characteristic analysis (ROC). The results of the study showed that the CSNN ROC area index was 0.84+/-0.02. The CSNN predictive performance is competitive with that achieved by experienced radiologists and backpropagation artificial neural networks (BP-ANNs) presented before. Furthermore, the study illustrates how CSNN can be used as a knowledge discovery tool overcoming some of the well-known limitations of BP-ANNs.

Age Factors↗
PubMed2001

Application of the mutual information criterion for feature selection in computer-aided diagnosis.

The purpose of this study was to investigate an information theoretic approach to feature selection for computer-aided diagnosis (CAD). The approach is based on the mutual information (MI) concept. MI measures the general dependence of random variables without making any assumptions about the nature of their underlying relationships. Consequently, MI can potentially offer some advantages over feature selection techniques that focus only on the linear relationships of variables. This study was based on a database of statistical texture features extracted from perfusion lung scans. The ultimate goal was to select the optimal subset of features for the computer-aided diagnosis of acute pulmonary embolism (PE). Initially, the study addressed issues regarding the approximation of MI in a limited dataset as it is often the case in CAD applications. The MI selected features were compared to those features selected using stepwise linear discriminant analysis and genetic algorithms for the same PE database. Linear and nonlinear decision models were implemented to merge the selected features into a final diagnosis. Results showed that the MI is an effective feature selection criterion for nonlinear CAD models overcoming some of the well-known limitations and computational complexities of other popular feature selection techniques in the field.

Diagnosis, Computer-Assisted↗
PubMed2000

Fractal texture analysis of perfusion lung scans.

The purpose of this study is to investigate if fractal texture analysis can assist in the diagnostic interpretation of perfusion lung scans. Forty-five perfusion scans were acquired from patients with clinical suspicion of acute pulmonary embolism (PE) who underwent pulmonary angiography for final diagnosis. Fractal texture analysis was performed on 270 regions of interest (ROIs) extracted from the posterior view of the lung scans. Specifically, there were 94 normally perfused ROIs and 176 abnormal ROIs representing various lung diseases including PE and obstructive pulmonary disease (OPD). The average fractal dimension (FD) of normal ROIs was statistically significantly higher than that of abnormal ROIs. Furthermore, the FDs of abnormal ROIs with PE were significantly lower than the FDs of ROIs with OPD present.

Angiography↗
PubMed2000

Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions.

OBJECTIVE: We present case-based reasoning computer software developed from mammographic findings to provide support for the clinical decision to perform biopsy of the breast. SUBJECTS AND METHODS: The case-based reasoning system is designed to support the decision to perform biopsy in those patients who have suspicious findings on diagnostic mammography. Currently, between 66% and 90% of biopsies are performed on benign lesions. Our system is designed to help decrease the number of benign biopsies without missing malignancies. Clinicians interpret the mammograms using a standard reporting lexicon. The case-based reasoning system compares these findings with a database of cases with known outcomes (from biopsy) and returns the fraction of similar cases that were malignant. This malignancy fraction is an intuitive response that the clinician can then consider when making the decision regarding biopsy. RESULTS: The system was evaluated using a round-robin sampling scheme and performed with an area under the receiver operating characteristic curve of 0.83, comparable with the performance of a neural network model. If only the cases returning a malignancy fraction of greater than a threshold of 0.10 are sent to biopsy, no malignancies would be missed, and the number of benign biopsies would be decreased by 25%. At a threshold of 0.21, 98%, of the malignancies would be biopsied, and the number of benign biopsies would be decreased by 41%. CONCLUSION: This preliminary investigation indicates that the case-based reasoning approach to computer-aided diagnosis has the potential to improve the accuracy of breast cancer diagnosis on mammography.

Adult↗
PubMed1998

Acute pulmonary embolism: cost-effectiveness analysis of the effect of artificial neural networks on patient care.

PURPOSE: To evaluate the cost-effectiveness of artificial neural networks for diagnosis in patients suspected of having acute pulmonary embolism who are typically referred for pulmonary angiography. MATERIALS AND METHODS: Four diagnostic strategies were explored to help define the diagnostic role of neural networks in patients suspected of having pulmonary embolism in whom nondiagnostic ventilation-perfusion lung scans were obtained. First, a network was used to determine which patients could be directly referred for treatment without angiography. Second, the network was applied to determine in which patients treatment could be withheld. Third, the network was used to distinguish patients in whom the network gave indeterminate responses and who should proceed to angiography. Each strategy was compared with use of angiography in terms of morbidity, mortality, and cost per life saved. RESULTS: The use of the neural network reduced the average cost per patient by more than one-half relative to the cost of angiography. Morbidity and mortality rates were also comparable to or lower than those associated with angiography. The results were consistent regardless of the prevalence of disease. CONCLUSION: The use of neural networks in the diagnosis of pulmonary embolism is a promising way to improve cost-effectiveness in the care of patients with nondiagnostic lung scans.

Acute Disease↗
PubMed1996

Improved noninvasive diagnosis of acute pulmonary embolism with optimally selected clinical and chest radiographic findings.

RATIONALE AND OBJECTIVES: The authors improved the noninvasive diagnosis of acute pulmonary embolism (PE) by studying the clinical and chest radiographic findings of patients suspected of having PE and correlating those findings with the physicians' clinical impression. METHODS: A stepwise linear discriminant algorithm was developed on the basis of 1,064 patients from the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) study to select clinical and chest radiographic findings with the highest diagnostic power in patients suspected of having PE. Subsequently, a linear classifier and a nonlinear artificial neural network were developed to help diagnose PE on the basis of the reduced number of findings. RESULTS: Both classifiers produced a statistically significant improvement (Az = 0.77 +/- 0.02) in the clinical performance of the PIOPED physicians (Az = 0.72 +/- 0.02). Results are also presented separately for groups of patients classified on the basis of the difficulty level of their ventilation-perfusion lung scans. CONCLUSION: Two computer-aided diagnostic tools were developed to assist physicians in the assessment of the pretest likelihood of PE by using an optimally reduced number of findings.

Acute Disease↗
PubMed1995

Lesion size quantification in SPECT using an artificial neural network classification approach.

An artificial neural network (ANN) has been developed to determine the size of lesions detected in single photon emission computed tomographic images. The network is the Learning Vector Quantizer and is trained to perform size quantification based on image neighborhoods extracted around the lesions. The ANN is compared to the optimal, Bayesian algorithm developed to perform the same task using the unreconstructed, projection data. The performance of the neural network is evaluated at two different noise levels. The Bayesian algorithm provides the upper bound for size quantification performance against which the ANN is compared. In the ideal case where the Bayesian algorithm has explicit knowledge of the underlying distributions, its performance is superior to that of the neural network. However, in the more realistic case where the distributions need to be estimated from the same learning sample the ANN was trained on, the two algorithms have comparable performances.

Algorithms↗
PubMed1995

Artificial neural network for diagnosis of acute pulmonary embolism: effect of case and observer selection.

PURPOSE: To compare the diagnostic performance of an artificial neural network (ANN) with that of physicians in patients with suspected pulmonary embolism (PE). MATERIALS AND METHODS: An ANN was developed to predict PE by using findings from ventilation-perfusion lung scans and chest radiographs. First, the network was evaluated on 1,064 cases from the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) study that had a definitive angiographic outcome. An upper and lower bound of its diagnostic performance was provided depending on case difficulty. Then, the network was tested on 104 patients with suspected PE in whom pulmonary angiography was essential for diagnosis. The diagnostic performance of the ANN was compared with that of (a) two nuclear medicine physicians who read the scans for the needs of this study and (b) the nuclear medicine physicians who originally read the scans. The effects of case and observer selection on performance were addressed. RESULTS: The ANN outperformed the physicians when they used the PIOPED criteria for categoric assessment, and it performed as well as the two study physicians on the basis of their probability assessments. CONCLUSION: The ANN can detect or exclude PE in a highly selected group of difficult cases with a consistency equivalent to that of very experienced physicians.

Algorithms↗
PubMed1993

Artificial neural networks for single photon emission computed tomography. A study of cold lesion detection and localization.

RATIONALE AND OBJECTIVES: An artificial neural network was developed for cold lesion detection and localization in single photon emission computed tomography (SPECT) images. METHODS: The network was trained for several noise levels and lesion sizes to identify lesions located in the center of small image neighborhoods. When scrolled across an image the trained network was able to identify cold abnormalities. The diagnostic performance of the technique was evaluated at two noise levels (50,000 and 100,000 counts/slice) and for two lesion sizes (radius: 1.0 cm and 1.5 cm) using the free-response operating characteristic (FROC) analysis. Furthermore, the same network was tested on a situation it was not trained on (80,000 counts/slice and a different reconstruction filter). RESULTS: The neural network showed high sensitivity and small false-positive rates per image for all test situations. These results suggest that neural networks are promising tools for computer-aided clinical diagnosis in SPECT:

Equipment Design↗
PubMed1993

Acute pulmonary embolism: artificial neural network approach for diagnosis.

PURPOSE: To investigate use of an artificial neural network (ANN) as a computer-aided diagnostic (CAD) tool for predicting pulmonary embolism (PE) from ventilation-perfusion lung scans and chest radiographs. MATERIALS AND METHODS: The data base consisted of cases extracted from the collaborative study of the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED). Initially, scan findings from 1,064 patients (383 with PE, 681 without PE) were used to train and test the network by using the "jackknife" method. Then, a receiver-operating-characteristic analysis was applied to compare the performance of the network with that of the physicians involved in the PIOPED study. RESULTS: The ANN significantly outperformed the physicians involved in the PIOPED study (two-tailed P value = .01). CONCLUSION: The findings suggest that an ANN can form the basis of a CAD system to assist physicians with the diagnosis of PE.

Acute Disease↗
PubMed1992

An artificial neural network for lesion detection on single-photon emission computed tomographic images.

RATIONALE AND OBJECTIVES: An artificial neural network (ANN) has been developed to detect nonactive circular lesions on single-slice, single-photon emission computed tomographic (SPECT) images reconstructed using filtered back projection (FBP). METHODS: The neural network is a single-layer perception which learns to identify features on the SPECT image using supervised training with a modified delta rule. The network was trained on a set of SPECT images containing clinically realistic levels of noise. The trained network was applied to a set of 120 images, and the detection performance was evaluated at several decision thresholds using receiver operating characteristic (ROC) analysis. RESULTS: The trained neural network performed better than human observers for the same detection task with the same images as reflected by a significantly larger ROC curve area. CONCLUSIONS: ANN can be trained successfully to perform lesion detection on reconstructed SPECT images.

Equipment Design↗
PubMedDate not supplied

The effect of data sampling on the performance evaluation of artificial neural networks in medical diagnosis.

PURPOSE: To study the effect of data sampling on the predictive assessment of artificial neural networks (ANNs) for medical diagnostic tasks. METHODS: Three statistical techniques were used to evaluate the diagnostic performances of ANNs: 1) cross validation, 2) round robin, and 3) bootstrap. These techniques are different sampling plans designed to reduce the small-sample estimation bias and variance contributions. The study was based on two networks, one developed for the diagnosis of pulmonary embolism (1,064 cases) and the other developed for the diagnosis of breast cancer (206 cases). RESULTS: The three sampling techniques produced different performance estimates for both networks. The estimates varied substantially depending on the training sample size and the training-stopping criterion. CONCLUSION: The predictive assessment of ANNs in medical diagnosis can vary substantially based on the complexity of the problem, the data sampling technique, and the number of cases available.

Breast Neoplasms↗