Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Williams, Simon C.; Horsfall, Hugo Layard; Funnell, Jonathan P.; Hanrahan, John; Khan, Danyal Z.; Muirhead, William; Stoyanov, Danail; Marcus, Hani J.

doi:10.3390/cancers13195010

Cited by 46 publications

(31 citation statements)

References 225 publications

(298 reference statements)

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“…Machine learning (ML) Process by which an algorithm encodes statistical regularities from a database of examples into parameter weights for future predictions [11] Deep learning (DL) Multilayered complex ML platform comprised of numerous computational layers able to make accurate predictions [6] Supervised learning Training an ML algorithm using previously labeled training examples, consisting of inputs and desired outputs provided by an expert [7,11] Figure 1. Relationship between AI, ML, and DL.…”

Section: Term Description Referencesmentioning

confidence: 99%

“…An ML technique that processes information in an architecture comprising many layers ("neurons"), each inter-neuronal connection extracting the desired parameters incrementally from the training data [6,11] Deep neural network (DNN) A DL architecture with multiple layers between the input and output layers [11] Convolutional neural network (CNN)…”

Section: Term Description Referencesmentioning

confidence: 99%

“…Despite not having an official definition, AI is generally recognized as the ability to imitate human cognitive functions using machines. Through an ingenious association of computer science, algorithms, machine learning, and data science, AI can solve tasks with a comparable performance to humans or above their level [3,[5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Clinical Applications of Artificial Intelligence—An Updated Overview

Busnatu

Niculescu

Bolocan

et al. 2022

JCM

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Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.

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Section: Term Description Referencesmentioning

confidence: 99%

Section: Term Description Referencesmentioning

confidence: 99%

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Clinical Applications of Artificial Intelligence—An Updated Overview

Busnatu

Niculescu

Bolocan

et al. 2022

JCM

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show abstract

“…AI is regarded as a set of technological solutions (e.g., information and communication infrastructure, software, data processing, decision-making services, and tools) that can imitate intelligent human behavior through the convergence of computer science, statistics, algorithms, information retrieval, and data science. AI provides machines with intelligent problem-solving capabilities, such as planning, reasoning, perception, independent learning, or decision making, when presented with numerous data forms [ 27 , 28 , 29 , 30 ]. On the other hand, ML represents a subset of AI that uses statistical techniques that enable computers to improve their predictions and performance concerning a specific task.…”

Section: Defining Conceptsmentioning

confidence: 99%

A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment

Busnatu

Niculescu

Bolocan

et al. 2022

JPM

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With the prevalence of digitalization in all aspects of modern society, health assessment is becoming digital too. Taking advantage of the most recent technological advances and approaching medicine from an interdisciplinary perspective has allowed for important progress in healthcare services. Digital health technologies and biotelemetry devices have been more extensively employed for preventing, detecting, diagnosing, monitoring, and predicting the evolution of various diseases, without requiring wires, invasive procedures, or face-to-face interaction with medical personnel. This paper aims to review the concepts correlated to digital health, classify and describe biotelemetry devices, and present the potential of digitalization for remote health assessment, the transition to personalized medicine, and the streamlining of clinical trials.

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“…Deep learning (DL), a component of AI, uses a variety of methods to accurately diagnose diseases. AI approaches are applied to the automated segmentation of MR images to detect brain tumours [1]. The segmentation of the brain tumours had been conducted on many studies.…”

Section: Introductionmentioning

confidence: 99%

A Modified U-Net Based Architecture for Brain Tumour Segmentation on BRATS 2020

Kajal

Mittal

2022

Preprint

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The segmentation of brain tumours plays a significant role in the analysis of medical imaging. For a precise diagnosis of the condition, radiologists employ medical imaging. In order to recognise brain tumours from medical imaging, the radiologist's work must be challenging and complex. There are various distinct steps that may be used to identify brain tumours using magnetic resonance imaging (MRI). In the field of medical imaging, segmentation is the key stage. Segmentation is carried out after classification and image analysis. The appropriate segmentation is crucial since a brain tumour's incorrect detection might have a number of negative effects Method: In this work, the multimodal Brain tumour segmentation challenge was employed (MICCAI BRATS). We removed the brain tumour from the MRI images using the BRATS 2020 dataset, which is openly accessible. In this collection, there are 371 NiFTI-format folders. Convolutional neural networks (CNNs), a kind of deep learning based on an encoder-decoder model, are used in the proposed method to separate the tumours. Results: Accuracy = 0.9759, loss = 0.8240, and IOU = 0.6413 indicate that the proposed model is successful. The proposed model performs better when compared to the state-of-art segmentation models used in this study.

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Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Cited by 46 publications

References 225 publications

Clinical Applications of Artificial Intelligence—An Updated Overview

Clinical Applications of Artificial Intelligence—An Updated Overview

A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment

A Modified U-Net Based Architecture for Brain Tumour Segmentation on BRATS 2020

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