Collaboration between a human group and AI

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Petr75
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Collaboration between a human group and AI

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2017 Dec 22 [revised 2018 Aug 1]
Institute for Complex Systems, National Research Council, Rome, Italy
Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5990125.2/

Abstract
Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen and generalize this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.
https://www.eboro.cz
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Petr75
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Re: Collaboration between a human group and AI

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2020 Jul 7
Pixyl, Research and Development Laboratory, Grenoble, France
Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI
https://pubmed.ncbi.nlm.nih.gov/32651155/

Abstract

Purpose: The purpose of this study was to create an algorithm that combines multiple machine-learning techniques to predict the expanded disability status scale (EDSS) score of patients with multiple sclerosis at two years solely based on age, sex and fluid attenuated inversion recovery (FLAIR) MRI data.

Materials and methods: Our algorithm combined several complementary predictors: a pure deep learning predictor based on a convolutional neural network (CNN) that learns from the images, as well as classical machine-learning predictors based on random forest regressors and manifold learning trained using the location of lesion load with respect to white matter tracts. The aggregation of the predictors was done through a weighted average taking into account prediction errors for different EDSS ranges. The training dataset consisted of 971 multiple sclerosis patients from the "Observatoire français de la sclérose en plaques" (OFSEP) cohort with initial FLAIR MRI and corresponding EDSS score at two years. A test dataset (475 subjects) was provided without an EDSS score. Ten percent of the training dataset was used for validation.

Results: Our algorithm predicted EDSS score in patients with multiple sclerosis and achieved a MSE=2.2 with the validation dataset and a MSE=3 (mean EDSS error=1.7) with the test dataset.

Conclusion: Our method predicts two-year clinical disability in patients with multiple sclerosis with a mean EDSS score error of 1.7, using FLAIR sequence and basic patient demographics. This supports the use of our model to predict EDSS score progression. These promising results should be further validated on an external validation cohort.
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Petr75
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Re: Collaboration between a human group and AI

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2020 Nov
Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
Diverse Applications of Artificial Intelligence in Neuroradiology
https://pubmed.ncbi.nlm.nih.gov/33039000/

Abstract

Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of AI, including workflow optimization, lesion segmentation, and precision education. Given the many modalities used in diagnosing neurologic diseases, AI may be deployed to integrate across modalities (MR imaging, computed tomography, PET, electroencephalography, clinical and laboratory findings), facilitate crosstalk among specialists, and potentially improve diagnosis in patients with trauma, multiple sclerosis, epilepsy, and neurodegeneration. Together, there are myriad applications of AI for neuroradiology."
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Petr75
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Re: Collaboration between a human group and AI

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2020 Oct 28
School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia/Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
The emerging role of artificial intelligence in multiple sclerosis imaging
https://pubmed.ncbi.nlm.nih.gov/33112207/

Abstract

Background: Computer-aided diagnosis can facilitate the early detection and diagnosis of multiple sclerosis (MS) thus enabling earlier interventions and a reduction in long-term MS-related disability. Recent advancements in the field of artificial intelligence (AI) have led to the improvements in the classification, quantification and identification of diagnostic patterns in medical images for a range of diseases, in particular, for MS. Importantly, data generated using AI techniques are analyzed automatically, which compares favourably with labour-intensive and time-consuming manual methods.

Objective: The aim of this review is to assist MS researchers to understand current and future developments in the AI-based diagnosis and prognosis of MS.

Methods: We will investigate a variety of AI approaches and various classifiers and compare the current state-of-the-art techniques in relation to lesion segmentation/detection and prognosis of disease. After briefly describing the magnetic resonance imaging (MRI) techniques commonly used, we will describe AI techniques used for the detection of lesions and MS prognosis.

Results: We then evaluate the clinical maturity of these AI techniques in relation to MS.

Conclusion: Finally, future research challenges are identified in a bid to encourage further improvements of the methods.
https://www.eboro.cz
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Petr75
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Re: Collaboration between a human group and AI

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2023 Dec 13
Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran
Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls
https://pubmed.ncbi.nlm.nih.gov/38088176/

Abstract

Multiple sclerosis (MS) is a complex autoimmune disease characterized by inflammatory processes, demyelination, neurodegeneration, and axonal damage within the central nervous system (CNS). Retinal imaging, particularly Optical coherence tomography (OCT), has emerged as a crucial tool for investigating MS-related retinal injury. The integration of artificial intelligence(AI) has shown promise in enhancing OCT analysis for MS. Researchers are actively utilizing AI algorithms to accurately detect and classify MS-related abnormalities, leading to improved efficiency in diagnosis, monitoring, and personalized treatment planning. The prognostic value of AI in predicting MS disease progression has garnered substantial attention. Machine learning (ML) and deep learning (DL) algorithms can analyze longitudinal OCT data to forecast the course of the disease, providing critical information for personalized treatment planning and improved patient outcomes. Early detection of high-risk patients allows for targeted interventions to mitigate disability progression effectively. As such, AI-driven approaches yielded remarkable abilities in classifying distinct MS subtypes based on retinal features, aiding in disease characterization and guiding tailored therapeutic strategies. Additionally, these algorithms have enhanced the accuracy and efficiency of OCT image segmentation, streamlined diagnostic processes, and reduced human error. This study reviews the current research studies on the integration of AI,including ML and DL algorithms, with OCT in the context of MS. It examines the advancements, challenges, potential prospects, and ethical concerns of AI-powered techniques in enhancing MS diagnosis, monitoring disease progression, revolutionizing patient care, the development of patient screening tools, and supported clinical decision-making based on OCT images.
https://www.eboro.cz
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