Holger Holst

Detection of frequently overlooked electrocardiographic lead reversals using artificial neural networks

Artificial neural networks can be used to recognize lead reversals in the 12-lead electrocardiogram at very high specificity, and the sensitivity is much higher than that of a conventional interpretation program. The neural networks developed in this and an earlier study for detection of lead reversals, in combination with an algorithm for the right arm/right foot lead reversal, would recognize approximately 75% of lead reversals encountered in clinical practice.

Automated interpretation of ventilation-perfusion lung scintigrams for the diagnosis of pulmonary embolism using artificial neural networks.

Abstract.The purpose of this study was to develop a completely automated method for the interpretation of ventilation-perfusion (V-P) lung scintigrams used in the diagnosis of pulmonary embolism. An artificial neural network was trained for the diagnosis of pulmonary embolism using 18 automatically obtained features from each set of V-P scintigrams. The techniques used to process the images included their alignment to templates, the construction of quotient images based on the ventilation and perfusion images, and the calculation of measures describing V-P mismatches in the quotient images. The templates represented lungs of normal size and shape without any pathological changes. Images that could not be properly aligned to the templates were detected and excluded automatically. After exclusion of those V-P scintigrams not properly aligned to the templates, 478 V-P scintigrams remained in a training group of consecutive patients with suspected pulmonary embolism, and a further 87 V-P scintigrams formed a separate test group comprising patients who had undergone pulmonary angiography. The performance of the neural network, measured as the area under the receiver operating characteristic curve, was 0.87 (95% confidence limits 0.82–0.92) in the training group and 0.79 (0.69–0.88) in the test group. It is concluded that a completely automated method can be used for the interpretation of V-P scintigrams. The performance of this method is similar to others previously presented, whereby features were extracted manually.

An independent evaluation of a new method for automated interpretation of lung scintigrams using artificial neural networks.

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.

Role of ventilation scintigraphy in diagnosis of acute pulmonary embolism: an evaluation using artificial neural networks.

The purpose of this study was to assess the value of the ventilation study in the diagnosis of acute pulmonary embolism using a new automated method. Either perfusion scintigrams alone or two different combinations of ventilation/perfusion scintigrams were used as the only source of information regarding pulmonary embolism. A completely automated method based on computerised image processing and artificial neural networks was used for the interpretation. Three artificial neural networks were trained for the diagnosis of pulmonary embolism. Each network was trained with 18 automatically obtained features. Three different sets of features originating from three sets of scintigrams were used. One network was trained using features obtained from each set of perfusion scintigrams, including six projections. The second network was trained using features from each set of (joint) ventilation and perfusion studies in six projections. A third network was trained using features from the perfusion study in six projections combined with a single ventilation image from the posterior view. A total of 1,087 scintigrams from patients with suspected pulmonary embolism were used for network training. The test group consisted of 102 patients who had undergone both scintigraphy and pulmonary angiography. Performances in the test group were measured as area under the receiver operation characteristic curve. The performance of the neural network in interpreting perfusion scintigrams alone was 0.79 (95% confidence limits 0.71–0.86). When one ventilation image (posterior view) was added to the perfusion study, the performance was 0.84 (0.77–0.90). This increase was statistically significant (P=0.022). The performance increased to 0.87 (0.81–0.93) when all perfusion and ventilation images were used, and the increase in performance from 0.79 to 0.87 was also statistically significant (P=0.016). The automated method presented here for the interpretation of lung scintigrams shows a significant increase in performance when one or all ventilation images are added to the six perfusion images. Thus, the ventilation study has a significant role in the diagnosis of acute lung embolism.

Acute Myocardial Infarction: Analysis of the ECG Using Artificial Neural Networks

This paper presents a neural network classifier for the diagnosis of acute myocardial infarction, using the 12-lead ECG. Features from the ECGs were extracted using principal component analysis, which allows for a small number of effective indicators. A total of 4724 pairs of ECGs, recorded at the emergency department, was used in this study. It was found (empirically) that a previous ECG, recorded on the same patient, has a small positive effect on the performance for the neural network classifier.

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, whos 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.

Automated interpretation of ventilation-perfusion lung scintigrams for the diagnosis of pulmonary embolism using support vector machines

The purpose of this study was to develop a new completely automated method for the interpretation of ventilation-perfusion (V-P) lung scintigrams for the diagnosis of pulmonary embolism. A new way of extracting features, characteristic for pulmonary embolism is presented. These features are then used as input to a Support Vector Machine, which discriminates between pulmonary embolism or no embolism. Using a material of 509 training cases and 104 test cases, the performance of the system, measured as the area under the ROC curve, was 0.86 in the test group. It is concluded that a completely automatic method can be used for interpretation of V-P scintigrams. It is faster and more robust than a previously presented method [4,5] and the accuracy is at the same level as the the previous method. It also handles abnormalities in the lungs. (Less)

A New Artificial Neural Network Method for the Interpretation of ECGs

Acute myocardial infarct could be a life-threatening disease. Early treatment could be life saving, but treatment of patients not suffering from infarct may cause serious complications. Therefore a rapid decision regarding diagnosis and treatment is of great importance. The physician at the emergency department has to rely on the electrocardiogram (ECG) for the diagnosis. A reliable computer-aided interpretation would be of great value. The purpose of this study was to develop a decision-support system for the diagnosis of acute myocardial infarct using a method that can estimate the error of an artificial neural network.