Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage Using A Deep Learning Model Based On The Initial CT Scan

• URL: http://arxiv.org/abs/2308.13373v1
• Date: Fri, 25 Aug 2023 13:33:56 GMT
• Title: Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage Using A Deep Learning Model Based On The Initial CT Scan
• Authors: Sergio Garcia-Garcia, Santiago Cepeda, Dominik Muller, Alejandra Mosteiro, Ramon Torne, Silvia Agudo, Natalia de la Torre, Ignacio Arrese, Rosario Sarabia
• Abstract summary: Convolutional neural networks (CNN) are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in Subarachnoid hemorrhage patients by processing the initial CT scan on a CNN based algorithm.
• Score: 34.86503928854081
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: PURPOSE: Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. Convolutional neural networks (CNN), a form of deep learning, are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in SAH patients by processing the initial CT scan on a CNN based algorithm. METHODS: Retrospective multicentric study of a consecutive cohort of patients with SAH between 2011-2022. Demographic, clinical and radiological variables were analyzed. Pre-processed baseline CT scan images were used as the input for training a CNN using AUCMEDI Framework. Our model's architecture leverages the DenseNet-121 structure, employing transfer learning principles. The output variable was mortality in the first three months. Performance of the model was evaluated by statistical parameters conventionally used in studies involving artificial intelligence methods. RESULTS: Images from 219 patients were processed, 175 for training and validation of the CNN and 44 for its evaluation. 52%(115/219) of patients were female, and the median age was 58(SD=13.06) years. 18.5%(39/219) were idiopathic SAH. Mortality rate was 28.5%(63/219). The model showed good accuracy at predicting mortality in SAH patients exclusively using the images of the initial CT scan (Accuracy=74%, F1=75% and AUC=82%). CONCLUSION: Modern image processing techniques based on AI and CNN make possible to predict mortality in SAH patients with high accuracy using CT scan images as the only input. These models might be optimized by including more data and patients resulting in better training, development and performance on tasks which are beyond the skills of conventional clinical knowledge.

Related papers

• LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model [0.0]
  We proposed a deep learning model-based Convolutional Neural Network framework for the early detection of lung cancer using CT scan images. It achieved an accuracy of 92%, AUC of 98.21%, recall of 91.72%, and loss of 0.328.
  arXiv  Detail & Related papers  (2023-04-10T18:47:20Z)
• Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation [31.51725965329019]
  Deep learning methods have been proposed to assist clinicians in automatic COVID-19 diagnosis based on computed tomography imaging. DenseNet121 achieves an average test accuracy of 99.44% in three trials for three-category classification, including normal, non-COVID-19 pneumonia, and COVID-19 pneumonia.
  arXiv  Detail & Related papers  (2022-08-09T12:24:53Z)
• Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders [65.95959936242993]
  We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent skeletal related events (SREs) in breast cancer patients.
  arXiv  Detail & Related papers  (2022-03-20T21:00:10Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections [46.414990784180546]
  Convolutional neural networks (CNNs) are the state-of-the-art image segmentation technique. CNNs require large annotated datasets for training. Self-supervised learning strategies can leverage unlabeled data for training.
  arXiv  Detail & Related papers  (2021-05-24T12:27:06Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data [0.0]
  We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.
  arXiv  Detail & Related papers  (2021-05-13T08:39:56Z)
• CNN AE: Convolution Neural Network combined with Autoencoder approach to detect survival chance of COVID 19 patients [11.121959969774327]
  We propose a novel method named CNN-AE to predict survival chance of COVID-19 patients using a CNN trained on clinical information. To further increase the prediction accuracy, we use the CNN in combination with an autoencoder.
  arXiv  Detail & Related papers  (2021-04-18T20:31:17Z)
• Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Classification in Imbalanced Data Sets [0.6802401545890961]
  Machine learning techniques for accurate classification of skin cancer from medical images have been reported. Many techniques are based on pre-trained convolutional neural networks (CNNs), which enable training the models based on limited amounts of training data. We propose a novel ensemble-based CNN architecture where multiple CNN models, some of which are pre-trained and some are trained only on the data at hand, along with patient information (meta-data) are combined using a meta-learner.
  arXiv  Detail & Related papers  (2021-03-22T06:04:45Z)
• Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
  We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
  arXiv  Detail & Related papers  (2020-07-20T19:08:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.