Artificial neural networks in pathology and medical laboratories

Dybowski, Richard; Gant, Vanya

doi:10.1016/s0140-6736(95)92904-5

Cited by 109 publications

(40 citation statements)

References 13 publications

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“…By a learning algorithm, the neural net achieves a form of learning by modifying weights proportional to the difference between the target and the gained output [9]. Artificial neural network have been applied to diagnosis and decision-making in various medical fields [12,13,14,15,16].…”

Section: Figure 1 the Neural Connection In Animals (Biological Neuromentioning

confidence: 99%

Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status

Rahman¹,

Nesha²,

Akter³

et al. 2013

SJPH

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Abstract:Various methods can be applied to build predictive models for the clinical data with binary outcome variables.This research aims to compare and explore the process of constructing common predictive models. Models based on an artificial neural network (the multilayer perceptron) and binary logistic regression were applied and compared in their ability to classifying disease-free subjects and those with diabetes mellitus(DM) diagnosed by glucose level. Demographic, enthropometric and clinical data were collected based on a total of 460 participants aged over 30 years from six villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic. Out of 460 participants 133 (28.91%) suffered from DM, 116 (25.27%) had impaired glucose tolerance (IGT) and the remainder 211 (45.86%) were disease free. Among other factors, family history of diabetes and arsenic exposure were found as significant risk factors for developing diabetes mellitus (DM), with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 73.79% of cases with IGT or DM in the training datasets, 70.96% in testing datasets and 70.4% of all subjects. On the other hand, the sensitivities of artificial neural network architecture for training and testing datasets and for all subjects were 83.4%, 82.25% and 84.33% respectively, indicate better performance than binary logistic regression model.

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Section: Figure 1 the Neural Connection In Animals (Biological Neuromentioning

confidence: 99%

Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status

Rahman¹,

Nesha²,

Akter³

et al. 2013

SJPH

View full text Add to dashboard Cite

show abstract

“…A more general description of artificial neural networks can be found elsewhere (17)(18)(19)). An ensemble of 30 single artificial neural networks was used for each classification task.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Computer-Assisted Interpretation of Planar Whole-Body Bone Scans

Sadik¹,

Hamadeh²,

Nordblom³

et al. 2008

J Nucl Med

108

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The purpose of this study was to develop a computer-assisted diagnosis (CAD) system based on image-processing techniques and artificial neural networks for the interpretation of bone scans performed to determine the presence or absence of metastases. Methods: A training group of 810 consecutive patients who had undergone bone scintigraphy due to suspected metastatic disease were included in the study. Whole-body images, anterior and posterior views, were obtained after an injection of 99m Tcmethylene diphosphonate. The image-processing techniques included algorithms for automatic segmentation of the skeleton and automatic detection and feature extraction of hot spots. Two sets of artificial neural networks were used to classify the images, 1 classifying each hot spot separately and the other classifying the whole bone scan. A test group of 59 patients with breast or prostate cancer was used to evaluate the CAD system. The patients in the test group were selected to reflect the spectrum of pathology found in everyday clinical work. As the gold standard for the test group, we used the final clinical assessment of each case. This assessment was based on follow-up scans and other clinical data, including the results of laboratory tests, and available diagnostic images, such as from MRI, CT, and radiography, from a mean follow-up period of 4.8 y. Results: The CAD system correctly identified 19 of the 21 patients with metastases in the test group, showing a sensitivity of 90%. Falsepositive classification of metastases was made in 4 of the 38 patients not classified as having metastases by the gold standard, resulting in a specificity of 89%. Conclusion: A completely automated CAD system can be used to detect metastases in bone scans. Application of the method as a clinical decision support tool appears to have significant potential.

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“…Medical data are diverse and, at times, highly complex [4], but MLPs are universal approximators. The flexibility of MLPs has enabled them to be applied to a wide variety of medical fields [5,6,7,8,9,10,11], including oncology [12], dentistry [13], bacteriology [14], and sleep research [15].…”

Section: Multilayer Perceptronsmentioning

confidence: 99%

Perspectives and Prospects

Mareschal¹

2016

Introduction to Drug Disposition and Pharmacokinetics

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In 1998, over 400 papers on artificial neural networks (ANNs) were published in the context of medicine, but why is there this interest in ANNs? And how do ANNs compare with traditional statistical methods? We propose some answers to these questions, and go on to consider the 'black box' issue. Finally, we briefly look at two directions in which ANNs are likely to develop, namely the use of Bayesian statistics and knowledgedata fusion.

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Artificial neural networks in pathology and medical laboratories

Cited by 109 publications

References 13 publications

Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status

Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status

Computer-Assisted Interpretation of Planar Whole-Body Bone Scans

Perspectives and Prospects

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