Outcomes and Complications After Endovascular Treatment of Brain Arteriovenous Malformations: A Prognostication Attempt Using Artificial Intelligence

Asadi, Hamed; Kok, Hong Kuan; Looby, Seamus; Brennan, Paul; O’Hare, Alan; Thornton, John

doi:10.1016/j.wneu.2016.09.086

Cited by 57 publications

(37 citation statements)

References 30 publications

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“…They also used ML techniques to identify factors influencing outcome in brain arteriovenous malformation treated with endovascular embolisation. 60 While standard regression analysis model could only achieve a 43% accuracy rate, their methods worked much better with 97.5% accuracy.…”

Section: Ai Applications In Strokementioning

confidence: 99%

Artificial intelligence in healthcare: past, present and future

et al. 2017

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Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

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Section: Ai Applications In Strokementioning

confidence: 99%

Artificial intelligence in healthcare: past, present and future

et al. 2017

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“…In recent years, some studies demonstrated that ML tools provide better accuracy and discrimination for the prediction of outcomes of endovascular embolization for brain arteriovenous malformations [23], allogeneic hematopoietic stem transplantation-related mortality [24], 30-day mortality after ST elevation myocardial infraction [19], and 10-year all-cause mortality among patients undergoing exercise stress testing [17], etc. However, no previous studies have used ML algorithms for the prediction of the outcomes after TSS.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Recurrence after Transsphenoidal Surgery for Cushing’s Disease: The Use of Machine Learning Algorithms

Liu

Hong

et al. 2019

Neuroendocrinology

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Background: There are no reliable predictive models for recurrence after transsphenoidal surgery (TSS) for Cushing’s disease (CD). Objectives: This study aimed to develop machine learning (ML)-based predictive models for CD recurrence after initial TSS and to evaluate their performance. Method: A total of 354 CD patients were included in this retrospective, supervised learning, data mining study. Predictive models for recurrence were developed according to 17 variables using 7 algorithms. Models were evaluated based on the area under the receiver operating characteristic curve (AUC). Results: All patients were followed up for over 12 months (mean ± SD 43.80 ± 35.61). The recurrence rate was 13.0%. Age (p < 0.001), postoperative morning serum cortisol nadir (p = 0.002), and postoperative (p < 0.001) and preoperative (p = 0.04) morning adrenocorticotropin (ACTH) level were significantly related to recurrence. AUCs of the 7 models ranged from 0.608 to 0.781. The best performance (AUC = 0.781, 95% CI 0.706, 0.856) appeared when 8 variables were introduced to the random forest (RF) algorithm, which was much better than that of logistic regression (AUC = 0.684, p = 0.008) and that of using only postoperative morning serum cortisol (AUC = 0.635, p < 0.001). According to the feature selection algorithms, the top 3 predictors were age, postoperative serum cortisol, and postoperative ACTH. Conclusions: Using ML-based models for prediction of the recurrence after initial TSS for CD is feasible, and RF performs best. The performance of most of ML-based models was significantly better than that of some conventional models.

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“…Arterial feeders' injury and perforation occur when a microcatheter or wire is introduced through tiny tortuous pial arteries. Accidental closure of draining veins before elimination of the nidus during embolization may cause nidal rupture and ICH [23,35,36].…”

Section: Complications Of Embolizationmentioning

confidence: 99%

Cerebral Arteriovenous Malformations (cAVMs): What Is New?

Ganaw¹,

Tharayil²,

Ehfeda³

et al. 2020

Vascular Malformations of the Central Nervous System

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Cerebral arteriovenous malformations (cAVMs) are rare congenital anomalies of cerebral blood vessels that result from maldevelopment of the capillary bed, permitting direct communication between cerebral arteries and veins. It usually occurs in the supratentorial area of the brain; however, it can occur anywhere in the brain and spinal cord. Most of the patients with cAVMs present with a variety of complaints such as seizures, intracerebral hemorrhage, headache, and progressive focal neurological deficit. Imaging such as CT, MRI, and angiography plays a vital role in diagnosis, grading, risk assessment, and posttherapeutic follow-up. The multidisciplinary team use three therapeutic modalities in the treatment of cAVMs. This chapter reviews the clinical presentations, diagnosis, classification, and treatment of cAVMs.

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Outcomes and Complications After Endovascular Treatment of Brain Arteriovenous Malformations: A Prognostication Attempt Using Artificial Intelligence

Cited by 57 publications

References 30 publications

Artificial intelligence in healthcare: past, present and future

Artificial intelligence in healthcare: past, present and future

Prediction of Recurrence after Transsphenoidal Surgery for Cushing’s Disease: The Use of Machine Learning Algorithms

Cerebral Arteriovenous Malformations (cAVMs): What Is New?

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