Abstract
PurposeThe aim of this review is to discuss the most significant contributions about the role of Artificial Intelligence (AI) techniques to support the diagnosis of movement disorders through nuclear medicine modalities.MethodsThe work is based on a selection of papers available on PubMed, Scopus and Web of Sciences. Articles not written in English were not considered in this study.ResultsMany papers are available concerning the increasing contribution of machine learning techniques to classify Parkinson’s disease (PD), Parkinsonian syndromes and Essential Tremor (ET) using data derived from brain SPECT with dopamine transporter radiopharmaceuticals. Other papers investigate by AI techniques data obtained by 123I-MIBG myocardial scintigraphy to differentially diagnose PD and other Parkinsonian syndromes.ConclusionThe recent literature provides strong evidence that AI techniques can play a fundamental role in the diagnosis of movement disorders by means of nuclear medicine modalities, therefore paving the way towards personalized medicine.
Highlights
IntroductionArtificial Intelligence (AI) is an extremely active research area based on computer programs able to mimic human activities, such as decision-making, learning, processing and understanding natural language and images [3, 10]
In recent years, Artificial Intelligence (AI) techniques have been applied to the diagnosis of many nosological entities by means of data derived from radiological and nuclear medicine modalities [1–10].asymmetry indices (AIs) is an extremely active research area based on computer programs able to mimic human activities, such as decision-making, learning, processing and understanding natural language and images [3, 10]
Brain SPECT through 123I-FP-CIT, a pre-synaptic radiopharmaceutical of the dopaminergic transporters (DAT), proved able to provide a significant contribution to the differential diagnosis of early Parkinson’s disease (PD) and non-Parkinsonian syndromes (i.e. Essential Tremor (ET)) [12–16]
Summary
AI is an extremely active research area based on computer programs able to mimic human activities, such as decision-making, learning, processing and understanding natural language and images [3, 10]. The use of AI techniques in the diagnostic process of neurodegenerative diseases as for instance dementia and movement disorders, represents a promising approach [10]. The diagnosis of Parkinson’s disease (PD) and Parkinsonian syndromes versus Essential Tremor (ET) was considerably supported by the increasing role of SPECT scan with presynaptic radiopharmaceuticals, such as the widely diffuse 123I‐2β‐carbomethoxy‐3β‐4‐iodophenyl‐ N‐3‐fluoropropyl nortropane (123I-FP-CIT) [12–16]. A relevant role in the differential diagnosis of PD and atypical Parkinsonian syndromes associated with dementia, i.e. multiple-system atrophy, progressive supranuclear palsy and corticobasal degeneration, is played by 18Fluorodeoxy-glucose (18FDG) PET, as metabolic patterns of regional glucose metabolism of these nosological entities are different and disease-specific [21, 22]. Patients with PD dementia (PDD) present more severe metabolic deficits in the parietal and frontal regions comparing with PD patients without cognitive impairment, while metabolic patterns in PDD patients and patients with Lewy body dementia were broadly similar [22]
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