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CHAMPAIGN, Ill. Monitoring the progression of multiple sclerosis-related gait issues can be challenging in adults over 50 years old, requiring a clinician to differentiate between problems related to MS and other age-related issues. To address this problem, researchers are integrating gait data and machine learning to advance the tools used to monitor and predict disease progression.

A new study of this approach led by University of Illinois Urbana Champaign graduate student Rachneet Kaur, kinesiology and community health professor Manuel Hernandez and industrial and enterprise engineering and mathematics professor Richard Sowers is published in the journal Institute of Electrical and Electronics Engineers Transactions on Biomedical Engineering.

Multiple sclerosis can present itself in many ways in the approximately 2 million people that it affects globally, and walking problems are a common symptom. About half of the patients need walking assistance within 15 years of onset, the study reports.

We wanted to get a sense of the interactions between aging and concurrent MS disease-related changes, and whether we can also differentiate between the two in older adults with MS, Hernandez said. Machine-learning techniques seem to work particularly well at spotting complex hidden changes in performance. We hypothesized that these analysis techniques might also be useful in predicting sudden gait changes in persons with MS.

Using an instrumented treadmill, the team collected gait data normalized for body size and demographics from 20 adults with MS and 20 age-, weight-, height- and gender-matched older adults without MS. The participants walked at a comfortable pace for up to 75 seconds while specialized software captured gait events, corresponding ground reaction forces and center-of-pressure positions during each walk. The team extracted each participants characteristic spatial, temporal and kinetic features in their strides to examine variations in gait during each trial.

Changes in various gait features, including a data feature called the butterfly diagram, helped the team detect differences in gait patterns between participants. The diagram gains its name from the butterfly-shaped curve created from the repeated center-of-pressure trajectory for multiple continuous strides during a subjects walk and is associated with critical neurological functions, the study reports.

We study the effectiveness of a gait dynamics-based machine-learning framework to classify strides of older persons with MS from healthy controls to generalize across different walking tasks and over new subjects, Kaur said. This proposed methodology is an advancement toward developing an assessment marker for medical professionals to predict older people with MS who are likely to have a worsening of symptoms in the near term.

Future studies can provide more thorough examinations to manage the studys small cohort size, Sowers said.

Biomechanical systems, such as walking, are poorly modeled systems, making it difficult to spot problems in a clinical setting, Sowers said. In this study, we are trying to extract conclusions from data sets that include many measurements of each individual, but a small number of individuals. The results of this study make significant headway in the area of clinical machine learning-based disease-prediction strategies.

Hernandez also is affiliated with the Beckman Institute of Advanced Science and Technology and the theCarle Illinois College of Medicine.

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Machine learning helps spot gait problems in individuals with multiple sclerosis - University of Illinois News

Scope: Global Machine Learning MarketThe global Machine Learning market report includes the analysis of all the important aspects associated with the Machine Learning market. The detailed study on the CAGR at which the market is anticipated to expand in the future is provided in the study. The detailed information regarding market valuation at different times is included in the report. The market study also covers the study of varying dynamics of the Machine Learning industry.

Vendor Landscape and Profiling:Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP SE (Walldorf, Germany), SAS Institute Inc. (North Carolina, US), Google, Inc. (California, US), Amazon Web Services Inc. (Washington, US), Baidu, Inc. (Beijing, China), BigML, Inc. (Oregon, US), Fair Isaac Corporation (FICO) (California, US), Hewlett Packard Enterprise Development LP (HPE) (California, US), Intel Corporation (California, US), KNIME.com AG (Zurich, Switzerland), RapidMiner, Inc. (Massachusetts, US), Angoss Software Corporation (Toronto, Canada), H2O.ai (California, US), Alpine Data (California, US), Domino Data Lab, Inc. (California, US), Dataiku (Paris, France), Luminoso Technologies, Inc. (Massachusetts, US), TrademarkVision (Pennsylvania, US), Fractal Analytics Inc. (New Jersey, US), TIBCO Software Inc. (California, US), Teradata (Ohio, US), Dell Inc. (Texas, US), and Oracle Corporation (California, US)

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Machine Learning Market Predominant Trends and Growth Opportunities by 2028: Microsoft Corporation (Washington, US), IBM Corporation (New York, US),...

This article was originally published here

IEEE Rev Biomed Eng. 2021 Mar 26;PP. doi: 10.1109/RBME.2021.3069213. Online ahead of print.

ABSTRACT

COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.

PMID:33769936 | DOI:10.1109/RBME.2021.3069213

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A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis - DocWire News

AI and healthcare professionals are warning of concerns over a machine learning algorithm made for diagnosing COVID-19.

The idea behind these technologies was to help health professionals tell the difference between coronavirus and other similarly-presenting ailments like pneumonia.

But concerned professionals say changes need to be made before the COVID diagnosis machine learning is used in a clinical environment.

More from a recent VentureBeat article:

Of those 62 papers included in the analysis, roughly half made no attempt to perform external validation of training data, did not assess model sensitivity or robustness, and did not report the demographics of people represented in training data.

Frankenstein datasets, the kind made with duplicate images obtained from other datasets, were also found to be a common problem, and only one in five COVID-19 diagnosis or prognosis models shared their code so others can reproduce results claimed in literature.

In their current reported form, none of the machine learning models included in this review are likely candidates for clinical translation for the diagnosis/prognosis of COVID-19, the paper reads. Despite the huge efforts of researchers to develop machine learning models for COVID-19 diagnosis and prognosis, we found methodological flaws and many biases throughout the literature, leading to highly optimistic reported performance.

Publicly available datasets also commonly suffered from lower quality image formats and werent large enough to train reliable AI models.

How does that old expression go? the problem with computers is that they doexactlywhat you tell them to do.

I love that saying because, despite the fact that AI is growing the point of teaching itself without as much human intervention, its still a glorified computer. A model is still only as good as the data being fed to it, and instances like this only underline just how much of a strict science machine learning is.

My TechDecisions Podcast Episode 107: Artificial Intelligence in the Enterprise

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AI Researchers Say There are Problems with Machine Learning COVID Diagnosis - TechDecisions

Healthcare professionals and AI experts alike are warning about some issues theyve identified in a machine learning model made for diagnosing the coronavirus.

The idea behind these technologies was to help health professionals tell the difference between coronavirus and other similarly-presenting ailments like pneumonia.

But concerned professionals say changes need to be made before the COVID diagnosis machine learning is used in a clinical environment.

More from a recentVentureBeat article:

Of those 62 papers included in the analysis, roughly half made no attempt to perform external validation of training data, did not assess model sensitivity or robustness, and did not report the demographics of people represented in training data.

Frankenstein datasets, the kind made with duplicate images obtained from other datasets, were also found to be a common problem, and only one in five COVID-19 diagnosis or prognosis models shared their code so others can reproduce results claimed in literature.

In their current reported form, none of the machine learning models included in this review are likely candidates for clinical translation for the diagnosis/prognosis of COVID-19, the paper reads. Despite the huge efforts of researchers to develop machine learning models for COVID-19 diagnosis and prognosis, we found methodological flaws and many biases throughout the literature, leading to highly optimistic reported performance.

Publicly available datasets also commonly suffered from lower quality image formats and werent large enough to train reliable AI models.

How does that old expression go? the problem with computers is that they doexactlywhat you tell them to do.

I love that saying because, despite the fact that AI is growing the point of teaching itselfwithout as much human intervention, its still a glorified computer.

After all, a model is still only as good as the data being fed to it, and instances like this only underline just how much of a strict science machine learning is.

Read Next: Artificial Intelligence Speculates on Whether Shakespeare Had Help with Henry VIII

Excerpt from:
There Are Issues with the COVID-19 Diagnosis Machine Learning Algorithms - Commercial Integrator