Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage
  Using A Deep Learning Model Based On The Initial CT Scan
Sergio Garcia-Garcia Santiago Cepeda Dominik Muller Alejandra Mosteiro Ramon Torne Silvia Agudo Natalia de la Torre Ignacio Arrese Rosario Sarabia
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.