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An artificial intelligence program for the radiologic diagnosis of brain lesions.
Rasuli P, Rasouli F, Hammond DI, Amiri F.
Department of Radiology, Ottawa General Hospital, University of Ottawa, Ontario, Canada.
Radiographics. 1996 Sep;16(5):1207-13
A software program has been developed that uses a frame-based expert system for differential diagnosis in neuroradiology. A frame-based expert system is used to store the magnetic resonance (MR) and computed tomographic (CT) imaging characteristics of over 100 known brain disorders in object-like entities. The frames are organized in a hierarchic structure in which lower order frames inherit attributes from higher order frames, with the highest frame containing information that applies to all the other frames. Program execution follows a consultation paradigm with a dynamic database. A decision tree menu provides a user-friendly interface with which to navigate through the network, based on features of the lesion as depicted on MR and CT images. The system can provide a differential diagnosis based on the MR imaging findings alone with information criteria including the signal intensity of the lesion on T1- and T2-weighted images, the location of the lesion, and the presence or absence of mass effect. The differential diagnosis may be further refined by adding CT-related information, including CT attenuation and the presence or absence of calcification and contrast enhancement.
Posted via PubMed for educational and discussion purposes only.
Link to PubMed Reference
Department of Radiology, Ottawa General Hospital, University of Ottawa, Ontario, Canada.
Radiographics. 1996 Sep;16(5):1207-13
A software program has been developed that uses a frame-based expert system for differential diagnosis in neuroradiology. A frame-based expert system is used to store the magnetic resonance (MR) and computed tomographic (CT) imaging characteristics of over 100 known brain disorders in object-like entities. The frames are organized in a hierarchic structure in which lower order frames inherit attributes from higher order frames, with the highest frame containing information that applies to all the other frames. Program execution follows a consultation paradigm with a dynamic database. A decision tree menu provides a user-friendly interface with which to navigate through the network, based on features of the lesion as depicted on MR and CT images. The system can provide a differential diagnosis based on the MR imaging findings alone with information criteria including the signal intensity of the lesion on T1- and T2-weighted images, the location of the lesion, and the presence or absence of mass effect. The differential diagnosis may be further refined by adding CT-related information, including CT attenuation and the presence or absence of calcification and contrast enhancement.
Posted via PubMed for educational and discussion purposes only.
Link to PubMed Reference
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