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When performing a systematic review, researchers screen the articles retrieved after a broad search strategy one by one, which is time-consuming. Computerised support of this screening process has been applied with varying success. This is partly due to the dependency on large amounts of data to develop models that predict inclusion. In this paper, we present an approach to choose which data to use in model training and compare it with established approaches. We used a dataset of 50 Cochrane diagnostic test accuracy reviews, and each was used as a target review. From the remaining 49 reviews, we selected those that most closely resembled the target review's clinical topic using the cosine similarity metric. Included and excluded studies from these selected reviews were then used to develop our prediction models. The performance of models trained on the selected reviews was compared against models trained on studies from all available reviews. The prediction models performed best with a larger number of reviews in the training set and on target reviews that had a research subject similar to other reviews in the dataset. Our approach using cosine similarity may reduce computational costs for model training and the duration of the screening process.
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Pruebas Diagnósticas de Rutina , Investigación , Automatización , Revisiones Sistemáticas como AsuntoRESUMEN
BACKGROUND AND PURPOSE: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in clinical practice. OBJECTIVE: To assess the value of convolutional neural networks (CNNs) in the automatic segmentation of infarct volume in follow-up CT images in a large population of patients with acute ischemic stroke. MATERIALS AND METHODS: We included CT images of 1026 patients from a large pooling of patients with acute ischemic stroke. A reference standard for the infarct segmentation was generated by manual delineation. We introduce three CNN models for the segmentation of subtle, intermediate, and severe hypodense lesions. The fully automated infarct segmentation was defined as the combination of the results of these three CNNs. The results of the three-CNNs approach were compared with the results from a single CNN approach and with the reference standard segmentations. RESULTS: The median infarct volume was 48 mL (IQR 15-125 mL). Comparison between the volumes of the three-CNNs approach and manually delineated infarct volumes showed excellent agreement, with an intraclass correlation coefficient (ICC) of 0.88. Even better agreement was found for severe and intermediate hypodense infarcts, with ICCs of 0.98 and 0.93, respectively. Although the number of patients used for training in the single CNN approach was much larger, the accuracy of the three-CNNs approach strongly outperformed the single CNN approach, which had an ICC of 0.34. CONCLUSION: Convolutional neural networks are valuable and accurate in the quantitative assessment of infarct volumes, for both subtle and severe hypodense infarcts in follow-up CT images. Our proposed three-CNNs approach strongly outperforms a more straightforward single CNN approach.
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Infarto Cerebral/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Redes Neurales de la Computación , Tomografía Computarizada por Rayos X/métodos , Isquemia Encefálica/diagnóstico por imagen , Femenino , Estudios de Seguimiento , Humanos , Masculino , Accidente Cerebrovascular/diagnóstico por imagenRESUMEN
BACKGROUND AND PURPOSE: Delayed cerebral ischemia (DCI) is a severe complication in patients with aneurysmal subarachnoid hemorrhage. Several associated predictors have been previously identified. However, their predictive value is generally low. We hypothesize that Machine Learning (ML) algorithms for the prediction of DCI using a combination of clinical and image data lead to higher predictive accuracy than previously applied logistic regressions. MATERIALS AND METHODS: Clinical and baseline CT image data from 317 patients with aneurysmal subarachnoid hemorrhage were included. Three types of analysis were performed to predict DCI. First, the prognostic value of known predictors was assessed with logistic regression models. Second, ML models were created using all clinical variables. Third, image features were extracted from the CT images using an auto-encoder and combined with clinical data to create ML models. Accuracy was evaluated based on the area under the curve (AUC), sensitivity and specificity with 95% CI. RESULTS: The best AUC of the logistic regression models for known predictors was 0.63 (95% CI 0.62 to 0.63). For the ML algorithms with clinical data there was a small but statistically significant improvement in the AUC to 0.68 (95% CI 0.65 to 0.69). Notably, aneurysm width and height were included in many of the ML models. The AUC was highest for ML models that also included image features: 0.74 (95% CI 0.72 to 0.75). CONCLUSION: ML algorithms significantly improve the prediction of DCI in patients with aneurysmal subarachnoid hemorrhage, particularly when image features are also included. Our experiments suggest that aneurysm characteristics are also associated with the development of DCI.
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Isquemia Encefálica/diagnóstico , Isquemia Encefálica/etiología , Aprendizaje Automático , Hemorragia Subaracnoidea/complicaciones , Isquemia Encefálica/diagnóstico por imagen , Estudios de Cohortes , Humanos , Valor Predictivo de las Pruebas , Pronóstico , Estudios Prospectivos , Sensibilidad y Especificidad , Tomografía Computarizada por Rayos XRESUMEN
The increasing size of medical imaging data, in particular time series such as CT perfusion (CTP), requires new and fast approaches to deliver timely results for acute care. Cloud architectures based on graphics processing units (GPUs) can provide the processing capacity required for delivering fast results. However, the size of CTP datasets makes transfers to cloud infrastructures time-consuming and therefore not suitable in acute situations. To reduce this transfer time, this work proposes a fast and lossless compression algorithm for CTP data. The algorithm exploits redundancies in the temporal dimension and keeps random read-only access to the image elements directly from the compressed data on the GPU. To the best of our knowledge, this is the first work to present a GPU-ready method for medical image compression with random access to the image elements from the compressed data.
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Gráficos por Computador , Compresión de Datos/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Perfusión , Tomografía Computarizada por Rayos X/métodos , Algoritmos , Humanos , Factores de TiempoRESUMEN
BACKGROUND: This study set out to determine whether structural changes are present outside the thalamus after thalamotomy in patients with essential tremor (ET), specifically in the cerebellorubrothalamic tracts. We hypothesized that diffusion tensor imaging (DTI) would detect these changes. METHODS: We collected DTI scans and analyzed differences in Fractional Anisotropy (FA) and Mean Diffusivity (MD) between the left and right superior and middle cerebellar peduncle in ET patients that have undergone unilateral, left, thalamotomy and ET patients that did not undergo thalamotomy (control group). We used classical ROI-based statistics to determine whether changes are present. RESULTS: We found decreased FA and increased MD values in the right superior cerebellar peduncle leading to the left, lesioned thalamus, only in the thalamotomy group. CONCLUSIONS: Our study suggests long-term structural changes in the cerebellorubrothalamic tract after thalamotomy. This contributes to further understanding of the biological mechanism following surgical lesions in the basal ganglia.