{"id":"4a573eba-dcab-43e9-98ec-6a6dbaf50fd9","slug":"easily-created-prediction-model-using-deep-learning-software-prediction-one-sony-network-communications-inc-for-subarachnoid-hemorrhage-outcomes-from-small-dataset-at-admission","title":"Easily created prediction model using deep learning software (Prediction One, Sony Network Communications Inc.) for subarachnoid hemorrhage outcomes from small dataset at admission","authors":["Masahito Katsuki","Yukinari Kakizawa","Akihiro Nishikawa","Yasunaga Yamamoto","Toshiya Uchiyama"],"abstract":"Background: Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes are needed for decision-making of the treatment. SAFIRE score using only four variables is a good prediction scoring system. However, making such prediction models needs a large number of samples and time-consuming statistical analysis. Deep learning (DL), one of the artificial intelligence, is attractive, but there were no reports on prediction models for SAH outcomes using DL. We herein made a prediction model using DL software, Prediction One (Sony Network Communications Inc., Tokyo, Japan) and compared it to SAFIRE score. Methods: We used 153 consecutive aneurysmal SAH patients data in our hospital between 2012 and 2019. Modified Rankin Scale (mRS) 0–3 at 6 months was defined as a favorable outcome. We randomly divided them into 102 patients training dataset and 51 patients external validation dataset. Prediction one made the prediction model using the training dataset with internal cross-validation. We used both the created model and SAFIRE score to predict the outcomes using the external validation set. The areas under the curve (AUCs) were compared. Results: The model made by Prediction One using 28 variables had AUC of 0.848, and its AUC for the validation dataset was 0.953 (95%CI 0.900–1.000). AUCs calculated using SAFIRE score were 0.875 for the training dataset and 0.960 for the validation dataset, respectively. Conclusion: We easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the model was not so inferior to those of previous statistically calculated prediction models.","thumbnailUrl":"https://sni-digital-videos.s3.amazonaws.com/articles/4a573eba-dcab-43e9-98ec-6a6dbaf50fd9/featured/hero-cropped-1781563004468.jpg","publishDate":"2020-11-06T00:00:00.000Z","doi":"10.25259/SNI_636_2020","categories":["Neurovascular","Original Article"],"fullTextUrl":"https://surgicalneurologyint.com/api/articles/4a573eba-dcab-43e9-98ec-6a6dbaf50fd9/pdf"}