Artificial intelligence for brain diseases: A systematic review

Segato, Alice; Marzullo, Aldo; Calimeri, Francesco; Momi, Elena De

doi:10.1063/5.0011697

Cited by 101 publications

(50 citation statements)

References 183 publications

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“…In efforts to meet the healthcare demands, there are interest in the possibility of using CAD tools based on artificial intelligence methods, namely machine learning (which potentially involves the more conventional pattern recognition approaches) or deep learning (which may involve sophisticated multi-layered neuronal systems), to perform an automated diagnosis of PD [11][12][13]. These CAD tools can perform automated detection using the biomarkers of PD, such as Electroencephalogram (EEG) signals, posture analysis in the gait cycle, voice aberration, or brain imaging such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) [14].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Loh

Hong²,

Ooi

et al. 2021

Sensors

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Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.

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Section: Introductionmentioning

confidence: 99%

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Loh

Hong²,

Ooi

et al. 2021

Sensors

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“…Although there is no consensus definition, AI is now commonly defined as the development of computer systems able to perform tasks that normally require human intelligence 6 . AI is transforming the fields of computer vision, language processing, image analysis and big data interpretation and has already been found to have considerable application potential to dementia diagnosis and care 7 . AI allows for rapid pattern analysis of large data sets, of which significant quantities have been collated over the past three decades.…”

Section: Figurementioning

confidence: 99%

“…Despite the clear potential advantages of AI technology, as noted by Segato et al . (2020) 7 major challenges lie with data quality, data inconsistency and instability, and limitations in data size and diversity. The development of large databases such as ADNI are helping to overcome some of these problems.…”

Section: Conclusion and Future Challengesmentioning

confidence: 99%

“…There is a need to establish trust in AI and consider issues such as accountability, privacy, data governance, cybersecurity and non‐discrimination. This will likely require the development of ethical and legal systems for the implementation, validation and control of AI in clinical care 7 . It is important to acknowledge that AI is best utilised in combination with the clinical expertise from a multidisciplinary team, and it is not a replacement for them.…”

Section: Conclusion and Future Challengesmentioning

confidence: 99%

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Artificial intelligence as an aid to diagnosing dementia: an overview

Dashwood

Churchhouse

Young

et al. 2021

Prog Neurol Psychiatry

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“…The use of artificial intelligence techniques is gradually bringing efficient theoretical solutions to a large number of real-world clinical problems related to the brain (4). Specifically, recently, thanks to the accumulation of relevant data and the development of increasingly effective algorithms, it has been possible to significantly increase the understanding of complex brain mechanisms.…”

mentioning

confidence: 99%

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Oliveira¹

2020

Arch Pediat Neurosurg

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The human brain contains around 86 billion nerve cells and about as many glial cells [1]. In addition, there are about 100 trillion connections between the nerve cells alone. While mapping all the connections of a human brain remains out of reach, scientists have started to address the problem on a smaller scale. The term artificial neural networks (ANNs or simply neural networks (NNs), encompassing a family of nonlinear computational methods that, at least in the early stage of their development, were inspired by the functioning of the human brain. Indeed, the first ANNs were nothing more than integrated circuits devised to reproduce and understand the transmission of nerve stimuli and signals in the human central nervous system [2]. The correct way of doing it is to the first study human behavior. The human brain has a biological neural network that has billions of interconnections. As the brain learns, these connections are either formed, changed or removed, similar to how an artificial neural network adjusts its weights to account for a new training example. This complexity is the reason why it is said that practice makes one perfect since a greater number of learning instances allow the biological neural network to become better at whatever it is doing. Depending upon the stimulus, only a certain subset of neurons are activated in the nervous system. Recently, Moreau et al., [3] published an interesting paper studying how artificial intelligence can help doctors and patients with meningiomas make better treatment decisions, according to a new study. They demonstrated that their models were capable of predicting meaningful individual-specific clinical outcome variables and show good generalizability across the Surveillance, Epidemiology, and End Results (SEER) database to predict meningioma malignancy and survival after specific treatments. Statistical learning models were trained and validated on 62,844 patients from the SEER database and a model scoring for the malignancy model was performed using a series of metrics. A free smartphone and web application were also provided for readers to access and test the predictive models (www.meningioma.app). The use of artificial intelligence techniques is gradually bringing efficient theoretical solutions to a large number of real-world clinical problems related to the brain (4). Specifically, recently, thanks to the accumulation of relevant data and the development of increasingly effective algorithms, it has been possible to significantly increase the understanding of complex brain mechanisms. The researchers' efforts are creating increasingly sophisticated and interpretable algorithms, which could favor a more intensive use of “intelligent” technologies in practical clinical contexts. Brain and machine working together will improve the power of these methods to make individual-patient predictions could lead to improved diagnosis, patient counseling, and outcomes.

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Artificial intelligence for brain diseases: A systematic review

Cited by 101 publications

References 183 publications

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Artificial intelligence as an aid to diagnosing dementia: an overview

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