Abstract
Artificial intelligence (AI) has been established as a promising technology for computer-assisted medical decision making. Artificial neural networks (ANNs) are by far the most popular AI approach to the diagnostic interpretation of medical images. Several studies have shown that ANNs can be trained to perform diagnostic tasks, offering physicians a fast, consistent, and unbiased second opinion. This paper presents an application of ANNs in the field of nuclear medicine. Specifically, an ANN approach is developed for the diagnostic interpretation of ventilation-perfusion lung scans for patients with clinical suspicion of acute pulmonary embolism.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brady, A.P., Stevenson, G.W., and Stevenson, I. (1994), “Colorectal cancer overlooked at barium enema examination and colonoscopy: continuing perceptual problem,”Radiologyvol. 192, pp. 373–378.
Anderson, R.E., Hill, R.B., and Key, C.R. (1989), “The sensitivity and specificity of clinical diagnostics during five decades: toward an understanding of necessary fallibility,”JAMAvol. 261, pp. 1610–1617.
Renfrew, D.L., Franken Jr., E.A., Berbaum, K.S., Weigelt, F.H., and Abu-Yousef, M.M. (1992), “Error in radiology: classification and lessons in 182 cases presented at a problem case conference,”Radiologyvol. 183, pp. 145–150.
Robinson, P.J. (1997), “Radiology’s Achilles’ heel: error and variation in the interpretation of the Roentgen image,”BJRvol. 70, pp. 1085–1098.
Anttinen, I., Pamilo, M., Soiva, M., and Roiha, M. (1993), “Double reading of mammography screening films — one radiologist or two?,”Clin Radiolvol. 48, no. 6, pp. 414–421.
Gillum, R.F. (1987), “Pulmonary embolism and thrombophlebitis in the United States, 1970–1985,”Am. Heart Ivol. 114, pp. 1262–1264.
Soskolne, C.L., Wong, A.W., and Lilienfeld, D.E. (1990), “Trends in pulmonary embolism death rates for Canada and the United States, 1962–87,”Can. Med. Assoc. J.vol. 142, no. 4, pp. 321–324.
Lilienfeld, D.E., Chan, E., Ehland, J., Godbold, J.H., Landrigan, P.J., and Marsh, G. (1992), “ Mortality from pulmonary embolism in the United States: 1962 to 1984,”Chestvol. 98, no. 5, pp. 1067–1072.
Tapson, V.F. (1997), “Pulmonary embolism: the diagnostic repertoire,”Chestvol. 112, no. 3, pp. 578–580.
The PIOPED Investigators (1990), “Value of the ventilation/ perfusion scan in acute pulmonary embolism: results of the prospective investigation,”JAMAvol. 263, pp. 2753–2759.
Gottschalk, A., Juni, J.E., Sostman, H.D., Coleman, R.E., Thrall, J., McKusick, K.A., Froelich, J.W., and Alavi, A. (1993), “Ventilation-perfusion scintigraphy in the PIOPED study: Part I. Data collection and tabulation,”J. Nucl. Med.vol. 34, no. 7, pp. 1109–1118.
Gottschalk, A., Sostman, H.D., Coleman, R.E., Juni, J.E., Thrall, J., McKusick, K.A., Froelich, J.W., and Alavi, A. (1993), “Ventilation-perfusion scintigraphy in the PIOPED study: Part II. Evaluation of the scintigraphic criteria and interpretation,”J. Nucl. Med.vol. 34, no. 7, pp. 1119–1126.
Palareti, G., Leali, N., Coccheri, S.et al.(1996), “Bleeding complications of oral anticoagulant treatment: an inception-cohort, prospective collaborative study (ISCOAT). Italian Study on Complications of Oral Anticoagulant Therapy,” Lancet, vol. 348, pp. 423–428.
Scott, J.A., and Palmer, E.L (1993), “Neural network analysis of ventilation-perfusion lung scans,”Radiologyvol. 186, pp. 661–664.
Tourassi, G.D., Floyd Jr., C.E., Sostman, H.D., and Coleman, R.E. (1993), “Acute pulmonary embolism: artificial neural network approach for diagnosis,”Radiologyvol. 189, pp. 555–558.
Tourassi, G.D., Floyd Jr., C.E., Sostman, H.D., and Coleman, R.E. (1995), “Performance evaluation of an artificial neural network for the diagnosis of acute pulmonary embolism: effect of case and observer selection,”Radiologyvol. 194, pp. 889–893.
Fisher, R.E., Scott, J.A., and Palmer, E.L. (1996), “Neural networks in ventilation-perfusion imaging,”Radiologyvol. 198, pp. 699–706.
Scott, J.A., Fisher, R.E., and Palmer, E.L. (1996), “Neural networks in ventilation-perfusion imaging. Part II. Effects of interpretive variability,”Radiologyvol. 198, pp. 707–713.
Gabor, F.V., Datz, F.L., and Christian, P.E. (1994), “Image analysis and categorization of ventilation-perfusion scans for the diagnosis of pulmonary embolism using an expert system,”J Nucl Medvol. 35, pp. 797–802.
Scott, J.A. (1999), “Using artificial neural network analysis of global ventilation-perfusion lung scan morphometry as a diagnostic tool,”AJRvol. 173, pp. 943–948.
McCulloch, W.S., Pitts, W.H. (1943), “A logical calculus for the ideas immanent in nervous activity,”Bulletin of Mathematical Biophysicsvol. 5, pp. 115–133.
Rosenblatt, F. (1959), Principles of Neurodynamics, Spartan Books, New York, NY.
Rosenblatt, F. (1958), “The perceptron: a probabilistic model for information storage and organization in the brain,”Psychological Reviewvol. 65, pp. 386–408.
White, H. (1989), “Learning in artificial neural networks: a statistical perspective,”Neural Computationvol. 1, pp. 425–484.
Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986), “Learning internal representations by error propagation,” in Rumelhart, D.E., McClelland, J.L. (Ed.)Parallel Distributed Processing: Explorations in the Microstructures of CognitionCambridge, MA: The MIT Press, vol. I, pp. 318–362.
Fortin, C., Kumaresan, R., and Ohley, W. (1992), “Fractal dimension in the analysis of medical images,”IEEE Eng Med Biolpp. 65–71.
Anguiano, E., Pancorbo, M., and Aguilar, M. (1993), “Fractal characterization by frequency analysis. I: Surfaces,”J Microscopyvol. 172, pp. 223–232.
Aguilar, M., Anguiano, E., and Pancorbo, M. (1993), “Fractal characterization by frequency analysis. II: A new method,”J Microscopyvol. 172, pp. 233–238.
Swets, J.A. (1988) “Measuring the accuracy of diagnostic systems,”Sciencevol. 240, pp. 1285–1293.
Tourassi, G..D., and Floyd Jr., C.E. (1997) “Effect of data sampling on the performance evaluation of artificial neural networks for medical diagnosis,”Med Dec Makingvol. 17, no. 2, pp. 186–192.
Efron, B. (1983) “Estimating the error rate of a prediction rule: improvement on cross-validation,”J American Stat Assocvol. 78, pp. 316–331.
Scott, J.A., and Palmer, E.L. (1993) “Do diagnostic algorithms always produce a uniform lung interpretation?”J Nucl Medvol. 34, pp. 661–665.
Kegelmeyer, W.P., Pruneda, J.M., Bourland, P.D., Hillis, A.H., Riggs, M.W., and Nipper, M.P. (1994) “Computer-aided mammographic screening for spiculated lesions,”Radiolvol. 191, no. 2, pp. 315–317.
Jiang, Y.L., Nishikawa, R.M., Schmidt, R.A., Metz, C.E., Giger, M.L., and Doi, K. (1999), “Improving breast cancer diagnosis with computer-aided diagnosis,”Acad Radiolvol. 6, no. 1, pp. 22–33.
Chan, H.P., Sahiner, B., Helvie, M.A., Petrick, N., Roubidoux, M.A.. Wilson, T.E., Adler, D.D., Paramagu,l C., Newman, J.S., and Sanjay-Gopal, S. (1999) “Improvement of radiologists’ characterization of mammographic masses by using computer-aided diagnosis: an ROC study,”Radiolvol. 212, no. 3, pp. 817–827.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer Science+Business Media New York
About this chapter
Cite this chapter
Tourassi, G.D., Frederick, E.D., Coleman, R.E. (2001). Artificial Neural Networks as a Computer Aid for Lung Disease Detection and Classification in Ventilation-Perfusion Lung Scans. In: Jain, L., De Wilde, P. (eds) Practical Applications of Computational Intelligence Techniques. International Series in Intelligent Technologies, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0678-1_11
Download citation
DOI: https://doi.org/10.1007/978-94-010-0678-1_11
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-3868-3
Online ISBN: 978-94-010-0678-1
eBook Packages: Springer Book Archive