Efficient performance of neural network models as artificial intelligence prediction tools in cardiology

D. Cianflone, O. Carandente, M. Carlino, C. Meloni, S. L. Chierchia

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Pattern generalization capabilities of neural networks are evaluated in two models: 1) prediction of coronary lesions from myocardial perfusion SPECT data; and 2) determination of comments to stress test results (EST). The SPECT network correctly predicted the stenosed/occluded vessel in all single-vessel disease cases, while the success rate was 83% for multi-vessel diseases. The EST network provided 106/125 correct interpretations. It is concluded that the functional success on software simulated neural networks is derived from the underlying computing model. Input values are comparable to multidimensional vectors since they are sequences of values wherein value position in the sequence is as important as the value itself. Interneural connections are numerical matrices instead. Network knowledge is included in the matrices that define each particular network. This structure allows some generalization ability since the response can always be computed for unexpected or incomplete data clusters.

Original languageEnglish
Title of host publicationComputers in Cardiology
PublisherPubl by IEEE
Number of pages3
ISBN (Print)0818622253
Publication statusPublished - May 1991
EventComputers in Cardiology - Proceedings - Chicago, IL, USA
Duration: Sep 23 1990Sep 26 1990


OtherComputers in Cardiology - Proceedings
CityChicago, IL, USA

ASJC Scopus subject areas

  • Software
  • Cardiology and Cardiovascular Medicine


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