A deep learning model for prognosis prediction after intracranial hemorrhage. Issue 2 (31st December 2022)

• Record Type: Journal Article
• Authors: Pérez del Barrio, Amaia; Esteve Domínguez, Anna Salut; Menéndez Fernández‐Miranda, Pablo; Sanz Bellón, Pablo; Rodríguez González, David; Lloret Iglesias, Lara; Marqués Fraguela, Enrique; González Mandly, Andrés A.; Vega, José A.
• Is Part Of: Journal of neuroimaging. Volume 33:Issue 2(2023)
• Page Start: 218
• Page End: 226
• Publication Date: 2022-12-31
• Subjects: deep learning -- head CT -- hybrid -- intracranial hemorrhage -- medical image -- prediction -- prognosis; Diagnostic imaging -- Periodicals; Nervous system -- Diseases -- Diagnosis -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Système nerveux -- Maladies -- Diagnostic -- Périodiques; Imagerie médicale; Neuroimagerie; Neurologie; Système nerveux; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.804754
• DOI: 10.1111/jon.13078
• Languages: English
• ISSNs: 1051-2284

Abstract

Abstract: 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.