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- Holst, Holger, et al.
(författare)
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An independent evaluation of a new method for automated interpretation of lung scintigrams using artificial neural networks
- 2001
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Ingår i: European Journal Of Nuclear Medicine. - : Springer Science and Business Media LLC. - 0340-6997 .- 1619-7089. ; 28:1, s. 33-38
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Tidskriftsartikel (refereegranskat)abstract
- The purpose of this study was to evaluate a new automated method for the interpretation of lung perfusion scintigrams using patients from a hospital other than that where the method was developed, and then to compare the performance of the technique against that of experienced physicians. A total of 1,087 scintigrams from patients with suspected pulmonary embolism comprised the training group. The test group consisted of scintigrams from 140 patients collected in a hospital different to that from which the training group had been drawn. An artificial neural network was trained using 18 automatically obtained features from each set of perfusion scintigrams. The image processing techniques included alignment to templates, construction of quotient images based on the perfusion/template images, and finally calculation of features describing segmental perfusion defects in the quotient images. The templates represented lungs of normal size and shape without any pathological changes. The performance of the neural network was compared with that of three experienced physicians who read the same test scintigrams according to the modified PIOPED criteria using, in addition to perfusion images, ventilation images when available and chest radiographs for all patients. Performances were measured as area under the receiver operating characteristic curve. The performance of the neural network evaluated in the test group was 0.88 (95% confidence limits 0.81–0.94). The performance of the three experienced experts was in the range 0.87–0.93 when using the perfusion images, chest radiographs and ventilation images when available. Perfusion scintigrams can be interpreted regarding the diagnosis of pulmonary embolism by the use of an automated method also in a hospital other than that where it was developed. The performance of this method is similar to that of experienced physicians even though the physicians, in addition to perfusion images, also had access to ventilation images for most patients and chest radiographs for all patients. These results show the high potential for the method as a clinical decision support system.
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- Toft, Jens, et al.
(författare)
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The optimal reference population for cardiac normality in myocardial SPET in the detection of coronary artery stenoses: patients with normal coronary angiography or subjects with low likelihood of coronary artery disease?
- 2001
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Ingår i: European Journal Of Nuclear Medicine. - : Springer Science and Business Media LLC. - 1432-105X .- 0340-6997 .- 1619-7089. ; 28:7, s. 831-835
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Tidskriftsartikel (refereegranskat)abstract
- Both subjects with a low likelihood for coronary artery disease (CAD) and patients with normal findings on coronary angiography have been used as reference populations in non-invasive stress testing, including myocardial perfusion scintigraphy. Both of these criteria of normality--low likelihood of CAD and normal coronary angiography--have been criticised, and consensus on this issue is lacking. The aim of this study was to compare two different reference populations by testing the performance of artificial neural networks designed to interpret myocardial scintigrams. The networks were trained on myocardial perfusion scintigrams from 87 patients with angiographically documented CAD and on studies from one of two different reference groups: 48 patients with no signs of CAD based on angiography or 128 healthy volunteers with a likelihood for CAD <5%. The performance of the two different networks was then tested using scintigrams from a separate test group of 68 patients. Coronary angiography was used as the gold standard in this group. The network trained on patients with no signs of CAD based on angiography showed an area under the receiver operating characteristic (ROC) curve of 93%. The ROC area for the network trained on healthy volunteers was 72%, and this difference was statistically significant (P=0.03). The results of this study using artificial neural networks suggest that normal angiography should be preferred as the reference standard in myocardial scintigraphy when a patient is examined for CAD prior to possible angiography. Whether the same is true for other indications, e.g. in prognostic evaluation, is unknown.
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