Thalamic atrophy measured by artificial intelligence in a multicentre clinical routine real-world study is associated with disability progression

Zivadinov, Robert; Bergsland, Niels; Jakimovski, Dejan; Weinstock‐Guttman, Bianca; Benedict, Ralph H. B.; Silva, Diego Augusto Santos; Dwyer, Michael G.

doi:10.1136/jnnp-2022-329333

Cited by 13 publications

(3 citation statements)

References 42 publications

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“…The thalamus is one of those structures where atrophy is first measurable in MS patients. It has a particularly strong clinical correlation and enables clinicians to predict the progression of disability [ 15 , 21 ]. An association can thus be expected between thalamic atrophy and NfL levels.…”

Section: Discussionmentioning

confidence: 99%

CSF Markers of Oxidative Stress Are Associated with Brain Atrophy and Iron Accumulation in a 2-Year Longitudinal Cohort of Early MS

Burgetova,

Dusek,

Uher

et al. 2023

IJMS

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In this prospective longitudinal study, we quantified regional brain volume and susceptibility changes during the first two years after the diagnosis of multiple sclerosis (MS) and identified their association with cerebrospinal fluid (CSF) markers at baseline. Seventy patients underwent MRI (T1 and susceptibility weighted images processed to quantitative susceptibility maps, QSM) with neurological examination at the diagnosis and after two years. In CSF obtained at baseline, the levels of oxidative stress, products of lipid peroxidation, and neurofilaments light chain (NfL) were determined. Brain volumetry and QSM were compared with a group of 58 healthy controls. In MS patients, regional atrophy was identified in the striatum, thalamus, and substantia nigra. Magnetic susceptibility increased in the striatum, globus pallidus, and dentate and decreased in the thalamus. Compared to controls, MS patients developed greater atrophy of the thalamus, and a greater increase in susceptibility in the caudate, putamen, globus pallidus and a decrease in the thalamus. Of the multiple calculated correlations, only the decrease in brain parenchymal fraction, total white matter, and thalamic volume in MS patients negatively correlated with increased NfL in CSF. Additionally, negative correlation was found between QSM value in the substantia nigra and peroxiredoxin-2, and QSM value in the dentate and lipid peroxidation levels.

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Section: Discussionmentioning

confidence: 99%

CSF Markers of Oxidative Stress Are Associated with Brain Atrophy and Iron Accumulation in a 2-Year Longitudinal Cohort of Early MS

Burgetova,

Dusek,

Uher

et al. 2023

IJMS

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“…Others combined advanced MLMs and sensitive techniques, such as diffusion tensor imaging to estimate MS patients' disability along with regional grey matter volumes and functional connectivity [67][68][69]. Some papers confirm that thalamic volume loss was a significant predictor of disability and cognitive decline, they have used deep learning approach based on Deep Gray Rating via AI [70][71][72].…”

Section: Disease Progression and Prognosis Prediction Of Ms Diseasementioning

confidence: 99%

Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

Naji,

Mahdaoui,

Klevor

et al. 2023

Cureus

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Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including “demographics, genetics, clinical and radiological presentation.” These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.

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“…Clinical practice is quickly changing following digital and technological advances, including artificial intelligence (AI) [ 1 – 4 ]. ChatGPT [ 5 ] is an open-source generative processing AI software that proficiently replies to users’ queries, in a human-like modality [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

ChatGPT vs. neurologists: a cross-sectional study investigating preference, satisfaction ratings and perceived empathy in responses among people living with multiple sclerosis

Maida,

Moccia,

Palladino

et al. 2024

J Neurol

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Background ChatGPT is an open-source natural language processing software that replies to users’ queries. We conducted a cross-sectional study to assess people living with Multiple Sclerosis’ (PwMS) preferences, satisfaction, and empathy toward two alternate responses to four frequently-asked questions, one authored by a group of neurologists, the other by ChatGPT. Methods An online form was sent through digital communication platforms. PwMS were blind to the author of each response and were asked to express their preference for each alternate response to the four questions. The overall satisfaction was assessed using a Likert scale (1–5); the Consultation and Relational Empathy scale was employed to assess perceived empathy. Results We included 1133 PwMS (age, 45.26 ± 11.50 years; females, 68.49%). ChatGPT’s responses showed significantly higher empathy scores (Coeff = 1.38; 95% CI = 0.65, 2.11; p > z < 0.01), when compared with neurologists’ responses. No association was found between ChatGPT’ responses and mean satisfaction (Coeff = 0.03; 95% CI = − 0.01, 0.07; p = 0.157). College graduate, when compared with high school education responder, had significantly lower likelihood to prefer ChatGPT response (IRR = 0.87; 95% CI = 0.79, 0.95; p < 0.01). Conclusions ChatGPT-authored responses provided higher empathy than neurologists. Although AI holds potential, physicians should prepare to interact with increasingly digitized patients and guide them on responsible AI use. Future development should consider tailoring AIs’ responses to individual characteristics. Within the progressive digitalization of the population, ChatGPT could emerge as a helpful support in healthcare management rather than an alternative.

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Thalamic atrophy measured by artificial intelligence in a multicentre clinical routine real-world study is associated with disability progression

Cited by 13 publications

References 42 publications

CSF Markers of Oxidative Stress Are Associated with Brain Atrophy and Iron Accumulation in a 2-Year Longitudinal Cohort of Early MS

CSF Markers of Oxidative Stress Are Associated with Brain Atrophy and Iron Accumulation in a 2-Year Longitudinal Cohort of Early MS

Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

ChatGPT vs. neurologists: a cross-sectional study investigating preference, satisfaction ratings and perceived empathy in responses among people living with multiple sclerosis

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