A primer on deep learning and convolutional neural networks for clinicians

Deep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the machine learning field, such as adaptive momentum algorithms or dropout regularization, have dramatically improved the convolutional neural networks predicting ability, outperforming that of conventional fully connected neural networks. This work summarizes, in an intended didactic way, the main aspects of these cutting-edge techniques from a medical imaging perspective.We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).Consejo Superior de Investigaciones Cientificas (JS-CSIC-BMCSO-0920) Deep-Hybrid DataCloud (H2020—Grant agreement No 777435) Servicio Cantabro de Salud.Peer reviewe

A deep learning model for prognosis prediction after intracranial hemorrhage

[Background and purpose]: Intracranial hemorrhage (ICH) is a common life-threatening condition that must be rapidly diagnosed and treated. However, there is still a lack of consensus regarding treatment, driven to some extent by prognostic uncertainty. While several prediction models for ICH detection have already been published, here we present a deep learning predictive model for ICH prognosis.[Methods]: We included patients with ICH (n = 262), and we trained a custom model for the classification of patients into poor prognosis and good prognosis, using a hybrid input consisting of brain CT images and other clinical variables. We compared it with two other models, one trained with images only (I-model) and the other with tabular data only (D-model).[Results]: Our hybrid model achieved an area under the receiver operating characteristic curve (AUC) of .924 (95% confidence interval [CI]: .831-.986), and an accuracy of .861 (95% CI: .760-.960). The I- and D-models achieved an AUC of .763 (95% CI: .622-.902) and .746 (95% CI: .598-.876), respectively.[Conclusions]: The proposed hybrid model was able to accurately classify patients into good and poor prognosis. To the best of our knowledge, this is the first ICH prognosis prediction deep learning model. We concluded that deep learning can be applied for prognosis prediction in ICH that could have a great impact on clinical decision-making. Further, hybrid inputs could be a promising technique for deep learning in medical imaging.Peer reviewe

De novo headache in ischemic stroke patients treated with thrombectomy: a prospective study

Background and aim Headache attributed to intracranial endovascular procedures is described in the ICHD-3. Our aim was to study the frequency and characteristics of headache specifically related to thrombectomy in patients with ischemic stroke. Methods Prospective evaluation of clinical features of headache after thrombectomy using an ad hoc questionnaire. Results One hundred seventeen patients were included (52.1% females). Most had an anterior circulation artery occlusion (91.5%). 93 (79.5%) received general anaesthesia. 111 (94.9%) required stent retriever, 21 (24.4%) angioplasty and 19 (16.2%) aspiration thrombectomy. 31 (26.5%; 95% CI 18.8?35.5%) had headache related to thrombectomy, and it was associated with a history of primary headache (p =?0.004). No differences about sex, initial NIHSS score, or the type or complexity of the procedure were observed. Headache was usually moderate and oppressive, ipsilateral to the artery occlusion and usually lasted less than 48?hours. Conclusions Almost one-third of patients with ischemic stroke who undergo endovascular thrombectomy experience headache in the first 24?hours, occurring more frequently in patients who had a previous history of headaches regardless of the procedure complexity.Acknowledgments: The authors received no financial suppor