Biomedical subjects

Lars Edenbrandt

Publications and source records attributed to Lars Edenbrandt.

At least 19 recordsLinked to original sources

PubMed2007

Automated interpretation of PET/CT images in patients with lung cancer.

PURPOSE: To develop a completely automated method based on image processing techniques and artificial neural networks for the interpretation of combined [(18)F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) images for the diagnosis and staging of lung cancer. METHODS: A total of 87 patients who underwent PET/CT examinations due to suspected lung cancer comprised the training group. The test group consisted of PET/CT images from 49 patients suspected with lung cancer. The consensus interpretations by two experienced physicians were used as the 'gold standard' image interpretation. The training group was used in the development of the automated method. The image processing techniques included algorithms for segmentation of the lungs based on the CT images and detection of lesions in the PET images. Lung boundaries from the CT images were used for localization of lesions in the PET images in the feature extraction process. Eight features from each examination were used as inputs to artificial neural networks trained to classify the images. Thereafter, the performance of the network was evaluated in the test set. RESULTS: The performance of the automated method measured as the area under the receiver operating characteristic curve, was 0.97 in the test group, with an accuracy of 92%. The sensitivity was 86% at a specificity of 100%. CONCLUSIONS: A completely automated method using artificial neural networks can be used to detect lung cancer with such a high accuracy that the application as a clinical decision support tool appears to have significant potential.

Adult↗

PubMed2006

Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room.

OBJECTIVE: Patients with suspicion of acute coronary syndrome (ACS) are difficult to diagnose and they represent a very heterogeneous group. Some require immediate treatment while others, with only minor disorders, may be sent home. Detecting ACS patients using a machine learning approach would be advantageous in many situations. METHODS AND MATERIALS: Artificial neural network (ANN) ensembles and logistic regression models were trained on data from 634 patients presenting an emergency department with chest pain. Only data immediately available at patient presentation were used, including electrocardiogram (ECG) data. The models were analyzed using receiver operating characteristics (ROC) curve analysis, calibration assessments, inter- and intra-method variations. Effective odds ratios for the ANN ensembles were compared with the odds ratios obtained from the logistic model. RESULTS: The ANN ensemble approach together with ECG data preprocessed using principal component analysis resulted in an area under the ROC curve of 80%. At the sensitivity of 95% the specificity was 41%, corresponding to a negative predictive value of 97%, given the ACS prevalence of 21%. Adding clinical data available at presentation did not improve the ANN ensemble performance. Using the area under the ROC curve and model calibration as measures of performance we found an advantage using the ANN ensemble models compared to the logistic regression models. CONCLUSION: Clinically, a prediction model of the present type, combined with the judgment of trained emergency department personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.

Acute Disease↗

PubMed2006

A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department.

BACKGROUND: Several models for prediction of acute coronary syndrome (ACS) among chest pain patients in the emergency department (ED) have been presented, but many models predict only the likelihood of acute myocardial infarction, or include a large number of variables, which make them less than optimal for implementation at a busy ED. We report here a simple statistical model for ACS prediction that could be used in routine care at a busy ED. METHODS: Multivariable analysis and logistic regression were used on data from 634 ED visits for chest pain. Only data immediately available at patient presentation were used. To make ACS prediction stable and the model useful for personnel inexperienced in electrocardiogram (ECG) reading, simple ECG data suitable for computerized reading were included. RESULTS: Besides ECG, eight variables were found to be important for ACS prediction, and included in the model: age, chest discomfort at presentation, symptom duration and previous hypertension, angina pectoris, AMI, congestive heart failure or PCI/CABG. At an ACS prevalence of 21% and a set sensitivity of 95%, the negative predictive value of the model was 96%. CONCLUSION: The present prediction model, combined with the clinical judgment of ED personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.

Acute Disease↗

PubMed2006

Direct hospital costs of chest pain patients attending the emergency department: a retrospective study.

BACKGROUND: Chest pain is one of the most common complaints in the Emergency Department (ED), but the cost of ED chest pain patients is unclear. The aim of this study was to describe the direct hospital costs for unselected chest pain patients attending the emergency department (ED). METHODS: 1,000 consecutive ED visits of patients with chest pain were retrospectively included. Costs directly following the ED visit were retrieved from the hospital economy system. RESULTS: The mean cost per patient visit was 26.8 thousand Swedish kronar (kSEK) (median 7.2 kSEK), with admission time accounting for 73% of all costs. Mean cost for patients discharged from the ED was 1.4 kSEK (median 1.3 kSEK), and for patients without ACS admitted 1 day or less 7.6 kSEK (median 6.9 kSEK). The practice in the present study to admit 67% of the patients, of whom only 31% proved to have ACS, was estimated to give a cost per additional life-year saved by hospital admission, compared to theoretical strategy of discharging all patients home, of about 350 kSEK (39 kEUR or 42 kUSD). CONCLUSION: Costs for chest pain patients are large and primarily due to admission time. The present admission practice seems to be cost-effective, but the substantial overadmission indicates that better ED diagnostics and triage could decrease costs considerably.

Journal Article↗

PubMed2006

A new computer-based decision-support system for the interpretation of bone scans.

OBJECTIVE: To develop a completely automated method, based on image processing techniques and artificial neural networks, for the interpretation of bone scans regarding the presence or absence of metastases. METHODS: A total of 200 patients, all of whom had the diagnosis of breast or prostate cancer and had undergone bone scintigraphy, were studied retrospectively. Whole-body images, anterior and posterior, were obtained after injection of 99mTc-methylene diphosphonate. The study material was randomly divided into a training group and a test group, with 100 patients in each group. The training group was used in the process of developing the image analysis techniques and to train the artificial neural networks. The test group was used to evaluate the automated method. The image processing techniques included algorithms for segmentation of the head, chest, spine, pelvis and bladder, automatic thresholding and detection of hot spots. Fourteen features from each examination were used as input to artificial neural networks trained to classify the images. The interpretations by an experienced physician were used as the 'gold standard'. RESULTS: The automated method correctly identified 28 of the 31 patients with metastases in the test group, i.e., a sensitivity of 90%. A false positive classification of metastases was made in 18 of the 69 patients not classified as having metastases by the experienced physician, resulting in a specificity of 74%. CONCLUSION: A completely automated method can be used to detect metastases in bone scans. Future developments in this field may lead to clinically valuable decision-support tools.

Adult↗

PubMed2006

A model that accounts for the interdependence of extent and severity in the automatic evaluation of myocardial defects.

BACKGROUND: The extent and severity are two important parameters when describing a regional defect in myocardial single-photon emission computed tomography (SPECT) perfusion imaging. Intuitively, these two parameters should be independent of each other, but we have shown in a previous study that there is an interdependence. This interdependence has been investigated in two commercially available analysis programs (AutoQUANT and 4D-MSPECT) using Monte Carlo-simulated SPECT data. METHODS: An anthropomorphic digital computer phantom and a Monte Carlo program were used to generate SPECT data. Several defects of different volumes and lesion activity uptake reductions were simulated and evaluated. Comparison of the measures of extent and severity with their corresponding known values led to the development of a correction model based on least-squares parameter estimation. This model was then applied to a test group consisting of 10 different computer-simulated patients. RESULTS: Our results showed that the interdependence was reduced significantly for most of our test cases after applying the correction method. The application of the correction model to the test group demonstrated that the model was robust with respect to different patient geometries. A further test with projections that simulated a perfect SPECT system revealed that the interdependence between the extent and severity was not caused by the limited spatial resolution of the SPECT system, but rather the inherent design of the algorithms of the analysis programs. CONCLUSIONS: A model has been developed to take into account the interdependence of the extent and severity.

Algorithms↗

PubMed2006

Validation of a new automated method for analysis of gated-SPECT images.

We recently presented a new method for quantification of CArdiac FUnction--denoted CAFU--as the first step in the development of an automated method for integrated interpretation of gated myocardial perfusion single photon emission computed tomography (SPECT) images. The aim of this study was to validate CAFU in the assessment of global and regional function of the left ventricle. Quantitative gated-SPECT (QGS), the most widely used software package for quantification of gated-SPECT images, was used as reference method for the measurements of ejection fraction (EF) and ventricular volumes, and visual analysis by an experienced physician was used as reference method for the measurements of regional wall motion and thickening. Two different groups of consecutive patients referred for myocardial perfusion scintigraphy were studied. Global function was evaluated in 316 patients and regional function in 49 other patients. The studies were performed using a 2-day stress/rest 99 m-Tc-sestamibi protocol. A good correlation was found between EF values from QGS and CAFU (EF CAFU = 0.84 EF QGS + 13, r = 0.94), but CAFU values were on average 4 EF points higher than QGS values. With CAFU the segments with normal thickening according to the physician showed significantly higher thickening values (in all parts of the myocardium) compared to the segments classified as having abnormal thickening. In conclusion, this study demonstrates that CAFU can be used to quantify global and regional function in gated-SPECT images. This is an important step in our development of an automated method for integrated interpretation of gated-SPECT myocardial perfusion scintigraphy studies.

Adenosine↗

PubMed2006

Patient gender and radiopharmaceutical tracer is of minor importance for the interpretation of myocardial perfusion images using an artificial neural network.

The purpose of this study was to assess the influence of patient gender and choice of perfusion tracer on computer-based interpretation of myocardial perfusion images. For the image interpretation, an automated method was used based on image processing and artificial neural network techniques. A total of 1000 patients were studied, all referred to the Royal Brompton Hospital in London for myocardial perfusion scintigraphy over a period of 1 year. The patients were randomized to receive either thallium or one of the two technetium tracers, methoxyisobutylisonitrile or tetrofosmin. Artificial neural networks were trained with either mixed gender or gender-specific and mixed tracer or tracer-specific training sets of different sizes. The performance of the networks was assessed in separate test sets, with the interpretation of experienced physicians regarding the presence or absence of fixed or reversible defects in the images as the gold standard. The neural networks trained with large mixed gender training sets were as good as the networks trained with gender-specific data sets. In addition, the neural networks trained with large mixed tracer training sets were as good as or better than the networks trained with tracer-specific data sets. Our results indicate that the influence of patient gender and perfusion tracer are of minor importance for the computer-based interpretation of the myocardial perfusion images. The differences that occur can be compensated for by larger training sets.

Adult↗

PubMed2006

Decision support for the initial triage of patients with acute coronary syndromes.

Early revascularization of acute coronary syndromes improves the prognosis. It is of vital importance that the decision to treat the patient is taken as early as possible. The aim of this study was (i) to develop an automated tool for the analysis of electrocardiograms (ECGs) with regard to changes that indicate possible transmural ischaemia and (ii) to assess the influence of the tool on the ECG classifications of three interns with less than 12 months of experience in ECG reading. An artificial neural network was trained to automatically interpret ECGs using 3000 ECGs recorded at an emergency department. Thereafter, the performance of the network was evaluated using 1000 test ECGs. In the second step, three interns classified these test ECGs twice on different occasions, with and without the advice of the neural network. The gold standard was the classification made by two experienced cardiologists. On average, the three interns showed a sensitivity of 68% at a specificity of 92% without the advice of the neural network and a sensitivity of 93% at a specificity of 87% with the advice. The neural network itself showed a sensitivity of 95% at a specificity of 88%. The increase in sensitivity of 23-26% was significant (P<0.001) for all three interns. In conclusion, an artificial neural network can be trained to the improve performance in the interpretation of ST-segment changes in accordance with that of the experienced cardiologists.

Automation↗

PubMed2006

The added value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy and artificial neural networks.

To assess the value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy (MPS) and an artificial neural network. A total of 422 patients referred for MPS were studied using a one day (99m)Tc-tetrofosmin protocol. Adenosine stress combined with submaximal dynamic exercise was used. The images were interpreted by one of three experienced clinicians and these interpretations regarding the presence or absence of myocardial infarction were used as the standard. A fully automated method using artificial neural networks was compared with the clinical interpretation. Either perfusion data alone or a combination of perfusion and function from ECG-gated images were used as input to different artificial neural networks. After a training session, the two types of neural networks were evaluated in separate test groups using an eightfold cross-validation procedure. The neural networks trained with both perfusion and ECG-gated images had a 4-7% higher specificity compared with the corresponding networks using perfusion data only, in four of five segments compared at the same level of sensitivity. The greatest improvement in specificity, from 70% to 77%, was seen in the inferior segment. In the septal and lateral segments the specificity rose from 73% to 77% and from 81% to 85%, respectively. In the anterior segment, the increase in specificity from 93% to 94% by adding functional data was not significant. The addition of functional information from ECG-gated MPS is of value for the diagnosis of myocardial infarction using an automated method of interpreting myocardial perfusion images.

Adenosine↗

PubMed2006

Subendocardial versus transmural ischaemia in myocardial perfusion SPECT--a Monte Carlo study.

UNLABELLED: Myocardial perfusion imaging with single-photon emission computed tomography (SPECT) is useful for the evaluation of patients with known or suspected coronary artery disease. Parameters of interest are the reduction in the blood perfusion (severity) and the lesion volume (extent). The aim of this study was to evaluate these parameters, as calculated by automatic quantification software, for different cases of subendocardial and transmural myocardial lesions. METHODS: A computer phantom was used to simulate 32 male patients with different defect locations and activity uptakes, which were based on clinical patient studies. The Monte Carlo program SIMIND was used to simulate realistic SPECT projections which were reconstructed to give short-axis images, analysed by the AutoQUANT program using the same procedure as for a real patient. RESULTS: The results showed a disparity between the quantification of transmural and subendocardial lesions with the same lesion activity uptake reduction and this could be confirmed by visual interpretation. Neither the parameters given by the quantification program nor visual interpretation could distinguish between the transmural lesions and the subendocardial lesions with activity uptake reduction twice as high as in the corresponding transmural lesions. CONCLUSION: Transmural lesions and the corresponding subendocardial lesions with the same activity uptake reduction could be separated by the quantification software for SPECT imaging and visual analysis. The subendocardial lesions with activity uptake reduction twice as high as in the corresponding transmural lesions could not be differentiated neither by the quantification software nor by visual interpretation. Thus these lesions will get the same scoring when analysed by the AutoQUANT program.

Artificial Intelligence↗

PubMed2005

Toward personal eHealth in cardiology. Results from the EPI-MEDICS telemedicine project.

Despite many attempts to improve the management of acute myocardial infarction, only small trends to shorter time intervals before treatment have been reported. The self-care solution developed by the European EPI-MEDICS project (2001-2004) is a novel, very affordable, easy-to-use, portable, and intelligent Personal ECG Monitor (PEM) for the early detection of cardiac ischemia and arrhythmia that is able to record a professional-quality, 3-lead electrocardiogram (ECG) based on leads I, II, and V2; derive the missing leads of the standard 12-lead ECG (thanks to either a generic or a patient-specific transform), compare each ECG with a reference ECG by means of advanced neural network-based decision-making methods taking into account the serial ECG measurements and the patient risk factors and clinical data; and generate different levels of alarms and forward the alarm messages with the recorded ECGs and the patient's Personal electronic Health Record (PHR) to the relevant health care providers by means of a standard Bluetooth-enabled, GSM/GPRS-compatible mobile phone. The ECG records are SCP-ECG encoded and stored with the PHR on a secure personal SD Card embedded in the PEM device. The alarm messages and the PHR are XML encoded. Major alarm messages are automatically transmitted to the nearest emergency call center. Medium or minor alarms are sent on demand to a central PEM Alarm Web Server. Health professionals are informed by a Short Message Service. The PEM embeds itself a Web server to facilitate the reviewing and/or update of the PHR during a routine visit at the office of the general physician or cardiologist. Eighty PEM prototypes have been finalized and tested for several weeks on 697 citizens/patients in different clinical and self-care situations involving end users (188 patients), general physicians (10), and cardiologists (9). The clinical evaluation indicates that the EPI-MEDICS concept may save lives and is very valuable for prehospitalization triage.

Allied Health Personnel↗

PubMed2005

Interdependence between measures of extent and severity of myocardial perfusion defects provided by automatic quantification programs.

AIM: To evaluate the accuracy of the values of lesion extent and severity provided by the two automatic quantification programs AutoQUANT and 4D-MSPECT using myocardial perfusion images generated by Monte Carlo simulation of a digital phantom. The combination between a realistic computer phantom and an accurate scintillation camera simulation tool allows the generation of realistic single-photon emission computed tomography (SPECT) images similar to those obtained in clinical patient studies. METHODS: The NCAT phantom and the SIMIND Monte Carlo program were used to simulate myocardial perfusion studies. Perfusion defects with sizes ranging from 5 to 17% of the left ventricular wall volume and reductions in tracer uptake of 20, 60 and 100% were simulated in three vascular territories. RESULTS: The values of the extent provided by the programs were dependent on the reduction in tracer uptake, i.e. the severity. Similarly, the measures of severity were dependent on the size of the lesions. The severity provided by AutoQUANT for different defects was not dependent on the location, whereas 4D-MSPECT presented different values depending on the location in the left ventricle. The measures of extent and severity of the defects with the same true extent and activity uptake reduction provided by the two programs were different. CONCLUSIONS: The NCAT phantom and the SIMIND Monte Carlo program were shown to be useful in simulating clinical myocardial SPECT studies. The quantification programs gave values of lesion extent that were dependent on the magnitude of the severity. Users should therefore consider this dependence when interpreting results from these programs.

Automation↗

PubMed2005

Comparison of 1- and 2-day protocols for myocardial SPECT: a Monte Carlo study.

BACKGROUND: Myocardial perfusion single-photon emission computed tomography (SPECT) is carried out by combining a rest and a stress study that are performed either on one day or two separate days. A problem when performing the two studies on 1 day is that the residual activity from the first study contributes to the activity measured in the second study. AIM: Our aim was to identify and evaluate trends in the quantification parameters of myocardial perfusion images as a function of separation time between rest and stress. METHODS: A digital phantom was used for the generation of heart images and a Monte Carlo-based scintillation camera program was used to simulate SPECT projection images. In our simulations, the rest images were normal and the stress images included lesions of different types and localization. Two programs for quantification of myocardial perfusion images were used to assess the different images in an automated and objective way. RESULTS: The summed difference scores observed with the 2-day protocol were 3 +/- 1 (mean +/- SD) higher for AutoQUANT and 2 +/- 1 higher for 4D-MSPECT compared with those observed with the 1-day protocol. The extent values were 2% points higher for the 2-day protocol compared with the 1-day protocol for both programs. CONCLUSIONS: There are differences in the quantitative assessment of perfusion defects depending on the type of protocol used. The contribution of residual activity is larger when a 1-day protocol is used compared with the 2-day protocol. The differences, although small, are of a magnitude that results in a clear shift in quantification parameters.

Coronary Artery Disease↗

PubMed2005

A new automated method for analysis of gated-SPECT images based on a three-dimensional heart shaped model.

A new automated method for quantification of left ventricular function from gated-single photon emission computed tomography (SPECT) images has been developed. The method for quantification of cardiac function (CAFU) is based on a heart shaped model and the active shape algorithm. The model contains statistical information of the variability of left ventricular shape. CAFU was adjusted based on the results from the analysis of five simulated gated-SPECT studies with well defined volumes of the left ventricle. The digital phantom NURBS-based Cardiac-Torso (NCAT) and the Monte-Carlo method SIMIND were used to simulate the studies. Finally CAFU was validated on ten rest studies from patients referred for routine stress/rest myocardial perfusion scintigraphy and compared with Cedar-Sinai quantitative gated-SPECT (QGS), a commercially available program for quantification of gated-SPECT images. The maximal differences between the CAFU estimations and the true left ventricular volumes of the digital phantoms were 11 ml for the end-diastolic volume (EDV), 3 ml for the end-systolic volume (ESV) and 3% for the ejection fraction (EF). The largest differences were seen in the smallest heart. In the patient group the EDV calculated using QGS and CAFU showed good agreement for large hearts and higher CAFU values compared with QGS for the smaller hearts. In the larger hearts, ESV was much larger for QGS than for CAFU both in the phantom and patient studies. In the smallest hearts there was good agreement between QGS and CAFU. The findings of this study indicate that our new automated method for quantification of gated-SPECT images can accurately measure left ventricular volumes and EF.

Artificial Intelligence↗

PubMed2005

Interpretation of captopril renography using artificial neural networks.

The purpose of this study was to develop a method based on artificial neural networks for interpretation of captopril renography tests for the detection of renovascular hypertension caused by renal artery stenosis and to assess the value of different measurements from the test. A total of 250 99mTc-MAG3 captopril renography tests were used in the study. The material was collected from two different patient groups. One group consisted of 101 patients who also had undergone a renal angiography. The angiographies, which were used as gold standard, showed a significant renal artery stenosis in 53 of the 101 cases. The second group consisted of 149 patients, who's captopril renography tests all were interpreted as not compatible with significant renal artery stenosis by an experienced nuclear medicine physician. Artificial neural networks were trained for the diagnosis of renal artery stenosis using eight measures from each renogram. The neural network was then evaluated in separate test groups using an eightfold cross validation procedure. The performance of the neural networks, measured as the area under the receiver operating characteristic curve, was 0.93. The sensitivity was 91% at a specificity of 90%. The lowest performance was found for the network trained without use of a parenchymal transit measure, indicating the importance of this feature. Artificial neural networks can be trained to interpret captopril renography tests for detection of renovascular hypertension caused by renal artery stenosis. The result almost equals that of human experts shown in previous studies.

Angiotensin-Converting Enzyme Inhibitors↗

PubMed2004

Regional cerebral blood flow in Alzheimer's disease: classification and analysis of heterogeneity.

Neural networks have been successfully applied to brain perfusion images to classify patients with Alzheimer's disease from normal or other patient populations. Given the recognition that Alzheimer's disease constitutes a heterogeneous disorder, the identification of subgroups sharing common functional brain deficits would constitute a further improvement in the utility of such methods. Therefore, we aimed to investigate whether neural networks could discriminate cortical perfusion deficits of patients with Alzheimer's disease from normal brain perfusion. A second step was to identify subgroups of patients sharing similar perfusion deficits. The study population consisted of one group of 92 normal healthy subjects and one group of 132 patients with mild-to-moderate Alzheimer's disease. The patients were diagnosed according to established criteria (DSM-IV and NINCDS-ADRDA). Regional cerebral blood flow was assessed by the non-invasive (133)Xe inhalation method, using a 64-detector system for measurements of blood flow in superficial cortical areas. The regional blood flow values were used as the only input to artificial neural networks with multilayer Perceptron architecture. The networks were trained using the back-propagation updating algorithm. A fourfold cross validation procedure was used in order to obtain the most reliable performance of the networks. The performance of the neural network, measured as the area under the receiver-operating characteristic curve, was 0.94, with a sensitivity for Alzheimer's disease of 86% at a specificity of 90%. An analysis of the relative importance of cortical areas in the discrimination showed that left parietal areas were more important than the right homologous ones. A clustering analysis of the Alzheimer patients identified three or four subgroups of patients with clearly different combinations of blood flow pathology. A consistent finding in all subgroups was a significant deficit in temporoparietal blood flow of both hemispheres. Distinct group differences were seen in frontal, central and occipital areas with different combinations of involvement. This is the first study in which neural networks have been applied to brain perfusion images obtained with the (133)Xe inhalation method. The results demonstrate that a classification of patients with Alzheimer's disease obtained with this method is compatible with the best results obtained with other brain imaging methods. The identification of clearly distinguishable patterns of blood flow pathology in subgroups of patients lends further support to the notion that Alzheimer's disease is a heterogeneous disorder.

Adolescent↗

PubMed2004

Quantification of myocardial perfusion defects using three different software packages.

Software packages are widely used for quantification of myocardial perfusion defects. The quantification is used to assist the physician in his/her interpretation of the study. The purpose of this study was to compare the quantification of reversible perfusion defects by three different commercially available software packages. We included 50 consecutive patients who underwent myocardial perfusion single-photon emission tomography (SPET) with a 2-day technetium-99m tetrofosmin protocol. Two experienced technologists processed the studies using the following three software packages: Cedars Quantitative Perfusion SPECT, Emory Cardiac Toolbox and 4D-MSPECT. The same sets of short axis slices were used as input to all three software packages. Myocardial uptake was scored in 20 segments for both the rest and the stress studies. The summed difference score (SDS) was calculated for each patient and the SDS values were classified into: normal (< 4), mildly abnormal (4-8), moderately abnormal (9-13), and severely abnormal (> 13). All three software packages were in agreement that 21 patients had a normal SDS, four patients had a mildly abnormal SDS and one patient had a severely abnormal SDS. In the remaining 24 patients (48%) there was disagreement between the software packages regarding SDS classification. A difference in classification of more than one step between the highest and lowest scores, for example from normal to moderately abnormal or from mildly to severely abnormal, was found in six of these 24 patients. Widely used software packages commonly differ in their quantification of myocardial perfusion defects. The interpreting physician should be aware of these differences when using scoring systems.

Adult↗