ArticleSurgical neurology international2021
Postsurgical functional outcome prediction model using deep learning framework (Prediction One, Sony Network Communications Inc.) for hypertensive intracerebral hemorrhage.
Article in Surgical neurology international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- AI Models for Surgical Decision Support in Spontaneous Intracerebral Hemorrhage: A Systematic Review in Relation to Trials and Guidelines.Neurocritical care · 2026Pooled it
- Artificial intelligence applications in intracerebral hemorrhage care: implications for clinical and nursing practice - a narrative literature review.Frontiers in rehabilitation sciences · 2025Pooled it
- Can Artificial Intelligence Be Applied to Diagnose Intracerebral Hemorrhage under the Background of the Fourth Industrial Revolution? A Novel Systemic Review and Meta-Analysis.International journal of clinical practice · 2022Pooled it
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- Predicting Motor FIM at Discharge for Recovery Stroke Patients in a Rehabilitation Ward Using an AI Predictive Analysis Tool.Japanese journal of comprehensive rehabilitation science · 2026Article
- Preliminary approach to creation of a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques on computed tomography images of the parotid glands.Imaging science in dentistry · 2025Article
- Predicting 90-day postoperative outcomes in spontaneous supratentorial intracerebral hemorrhage: a double-center study of clinical-radiomics integration using non-contrast CT.Frontiers in neurology · 2025Article
- Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.Journal of the American Heart Association · 2024Article
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage.European radiology · 2024Article
- Brain is also time: good short-term outcome predictions of artificial intelligence in spontaneous intracerebral hemorrhage pave the way for the long-term assessment.European radiology · 2024Article
- Can AI predict walking independence in patients with stroke upon admission to a recovery-phase rehabilitation ward?Japanese journal of comprehensive rehabilitation science · 2024Article
- Article
- Risk factors and a prediction model for the prognosis of intracerebral hemorrhage using cerebral microhemorrhage and clinical factors.Frontiers in neurology · 2023Article
- Changes in diet, exercise and psychology of the quarantined population during the COVID-19 outbreak in Shanghai.PloS one · 2023Article
- Machine learning prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis: a cross-cultural validation in Caucasian and Han Chinese cohort.Therapeutic advances in neurological disorders · 2022Article
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundReliable prediction models of intracerebral hemorrhage (ICH) outcomes are needed for decision-making of the treatment. Statistically 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 DL-based functional outcome prediction models for ICH outcomes after surgery. We herein made a functional outcome prediction model using DLframework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), and compared it to original ICH score, ICH Grading Scale, and FUNC score.
methodsWe used 140 consecutive hypertensive ICH patients' data in our hospital between 2012 and 2019. All patients were surgically treated. Modified Rankin Scale 0-3 at 6 months was defined as a favorable outcome. We randomly divided them into 100 patients training dataset and 40 patients validation dataset. Prediction One made the prediction model using the training dataset with 5-fold cross-validation. We calculated area under the curves (AUCs) regarding the outcome using the DL-based model, ICH score, ICH Grading Scale, and FUNC score. The AUCs were compared.
resultsThe model made by Prediction One using 64 variables had AUC of 0.997 in the training dataset and that of 0.884 in the validation dataset. These AUCs were superior to those derived from ICH score, ICH Grading Scale, and FUNC score.
conclusionWe easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the DL-based model was superior to those of previous statistically calculated models.
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