Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review

Buchlak, Quinlan D.; Esmaili, Nazanin; Lévêque, Jean; Farrokhi, Farrokh; Bennett, Christine; Piccardi, Massimo; Sethi, Rajiv K.

doi:10.1007/s10143-019-01163-8

Cited by 170 publications

(136 citation statements)

References 108 publications

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“…This compensates for the difference (error) between the set-point and the system response to the feedback control. As a result, the system can, in principle, utilize artificial intelligence (AI) and machine learning algorithms [40][41][42][43][44]. In the present computational study, the treatment time of the continuous RF procedure was set to 60 s.…”

Section: Numerical Setup and Modeling Detailsmentioning

confidence: 99%

Domain Heterogeneity in Radiofrequency Therapies for Pain Relief: A Computational Study with Coupled Models

Singh

Melnik

2020

Bioengineering

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The objective of the current research work is to study the differences between the predicted ablation volume in homogeneous and heterogeneous models of typical radiofrequency (RF) procedures for pain relief. A three-dimensional computational domain comprising of the realistic anatomy of the target tissue was considered in the present study. A comparative analysis was conducted for three different scenarios: (a) a completely homogeneous domain comprising of only muscle tissue, (b) a heterogeneous domain comprising of nerve and muscle tissues, and (c) a heterogeneous domain comprising of bone, nerve and muscle tissues. Finite-element-based simulations were performed to compute the temperature and electrical field distribution during conventional RF procedures for treating pain, and exemplified here for the continuous case. The predicted results reveal that the consideration of heterogeneity within the computational domain results in distorted electric field distribution and leads to a significant reduction in the attained ablation volume during the continuous RF application for pain relief. The findings of this study could provide first-hand quantitative information to clinical practitioners about the impact of such heterogeneities on the efficacy of RF procedures, thereby assisting them in developing standardized optimal protocols for different cases of interest.

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Section: Numerical Setup and Modeling Detailsmentioning

confidence: 99%

Domain Heterogeneity in Radiofrequency Therapies for Pain Relief: A Computational Study with Coupled Models

Singh

Melnik

2020

Bioengineering

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“…At the same time, the field of robotics continues growing and providing not only physical but also virtual experiences. Both AI and robotics are applied in fields such as medicine (Buchlak et al, 2019;Loh, 2018), marketing (Jarek & Mazurek, 2019), finance (Lee, 2020) and entertainment (Meena, Jingar & Gupta, 2020). However, the field of education needs to keep pace with this changing context.…”

Section: Introductionmentioning

confidence: 99%

The impact of AI and robotics on physical, social‐emotional and intellectual learning outcomes: An integrated analytical framework

Salas‐Pilco¹

2020

Brit J Educational Tech

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This qualitative study examines the use of artificial intelligence (AI) and robotics in learning designs from the perspective of learning sciences. The literature on the topic indicates that there is not enough research on including diverse learning outcomes in the designs for learning. Therefore, the purpose of this study was to understand how AI and robots impact physical, social‐emotional and intellectual learning outcomes through the implementation of learning designs that are guided by selected design principles. In this study, the design‐based research (DBR) methodology was employed for investigating learning in naturalistic contexts. The intervention was implemented in a primary school in which learners used educational robots. The main findings reveal that the development of an integrated analytical framework, which considers a broader spectrum of human potential, allows for analyzing students’ learning outcomes in a more integral, inclusive and balanced way. This, in turn, promotes students’ learning by using AI and robots. Another finding reveals that the impact of using AI and robotics on learning designs is reflected in learners’ personal trajectories having different pathways and paces. Finally, the lessons learned and the challenges to be overcome are summarized, and recommendations are made for future research for the enhancement of learning experiences that use AI and robotics.

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“…Machine learning classification is one of the domains of AI that enables an algorithm or classifier to learn patterns in large, complex datasets and produce useful predictive outputs. The number of published machine learning studies in neurosurgery is increasing exponentially [11][12][13][14]. Some of them have focused on the application of machine learning algorithms to support clinical decision making in neurosurgery.…”

Section: Introductionmentioning

confidence: 99%

Intracranial pressure based decision making: Prediction of suspected increased intracranial pressure with machine learning

2020

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Background Repeated invasive intracranial pressure (ICP) monitoring is desirable because many neurosurgical pathologies are associated with elevated ICP. On the other hand, it could become a risk for children to repeat sedation, anesthesia, or radiation exposure. As a non-invasive method, measurements of optic nerve sheath diameter (ONSD) has been revealed to accurately predict increased ICP. However, no studies have indicated a relationship among age, brain, and ventricular parameters in normal children, nor a prediction of increased ICP with artificial intelligence. Methods and findings This study enrolled 400 normal children for control and 75 children with signs of increased ICP between 2009 and 2019. Measurements of the parameters including ONSD on CT were obtained. A supervised machine learning was applied to predict suspected increased ICP based on CT measurements. A linear correlation was shown between ln(age) and mean ONSD (mONSD) in normal children, revealing mONSD = 0.36ln(age)+2.26 (R 2 = 0.60). This study revealed a linear correlation of mONSD measured on CT with ln(age) and the width of the brain, not the width of the ventricles in 400 normal children based on the univariate analyses. Additionally, the multivariate analyses revealed minimum bicaudate nuclei distance was also associated with mONSD. The results of the group comparison between control and suspected increased ICP revealed a statistical significance in mONSD and the width of the ventricles. The study indicated that supervised machine learning application could be applied to predict suspected increased ICP in children, with an accuracy of 94% for training, 91% for test. Conclusions This study clarified three issues regarding ONSD and ICP. Mean ONSD measured on CT was correlated with ln(age) and the width of the brain, not the width of the ventricles in 400 normal children based on the univariate analyses. The multivariate analyses revealed

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Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review

Cited by 170 publications

References 108 publications

Domain Heterogeneity in Radiofrequency Therapies for Pain Relief: A Computational Study with Coupled Models

Domain Heterogeneity in Radiofrequency Therapies for Pain Relief: A Computational Study with Coupled Models

The impact of AI and robotics on physical, social‐emotional and intellectual learning outcomes: An integrated analytical framework

Intracranial pressure based decision making: Prediction of suspected increased intracranial pressure with machine learning

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