BMC’s Post

Our researchers have used machine learning to define three subtypes of Parkinson’s disease, a finding that may inform the development of customized treatment strategies for patients. “Parkinson’s disease is highly heterogeneous, which means that people with the same disease can have very different symptoms,” said Dr. Feng Wei of Weill Cornell Medicine. “This indicates there is not likely to be a one-size-fits-all approach to treating it." Using deep learning-based approaches to analyze database records, a team led by Dr. Wang and Dr. Chang Su have identified three subtypes based on disease progression. They named them the Inching Pace subtype (PD-I, about 36% of patients) for disease with a mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M, about 51% of patients) for cases that have mild baseline severity but advance at a moderate rate; and Rapid Pace subtype (PD-R), for cases with the most rapid symptom progression rate. They also explored the molecular mechanism associated with each subtype and found distinct brain imaging and cerebrospinal fluid biomarkers for the three subtypes. https://bit.ly/4cH5jOb

I am glad to share that one of my recent articles got published in "Journal of electrocardiology" by Elsevier This article provides an in-depth approach to use of AI enhanced ECG for diagnosing Cardiovascular Diseases.

We are well aware that cardiovascular diseases (CVDs) remain the leading cause of death globally so how can Artificial Intelligence and Machine Learning approaches help us in this fight? In fact these "intelligences" have the potential to revolutionise research and treatment by enabling personalised medicine, early detection, optimised therapies, & accelerated drug discovery. Accordingly, COST Action AtheroNET are here to connect experts from different fields and transfer novel omic technologies from bench to bedside. Learn more 🔗 https://lnkd.in/eWReHf8P With Paolo Magni, Yvan Devaux, George Kararigas, Dimitris Kardassis, ALBERICO CATAPANO, & Aleksandra Gruca #COSTactions #ScienceWithoutBorders #CVD

An efficient tool for Parkinson's disease detection and severity grading based on time-frequency and fuzzy features of cumulative gait signals through improved LSTM networks https://lnkd.in/gw8nq8Ya Parkinson's disease (PD) is a widespread neurodegenerative condition that affects many individuals annually. Early identification and monitoring of disease progression are crucial to effectively managing symptoms and preventing motor complications. This research proposes an automated PD diagnosis and severity-grading model based on time-frequency and fuzzy features using improved uni-directional and bi-directional long short-term memory networks with sensitive hyperparameters optimization. We utilize vertical ground reaction force signals collected from Physionet's publicly available dataset recorded during regular and dual-task clinical trials of walking measurements. Only the cumulative signal of both feet was then utilized and segmented into 30-s windows without further pre-processing. Subsequently, we extracted only four key time-frequency and fuzzy features from each segment, effectively capturing the signal's inherent uncertainty. Bayesian optimization is employed in both detection and grading approaches to fine-tune the two critical hyperparameters: the initial learning rate and the number of hidden units in the network. The detection phase yields an exceptional accuracy of 99.19%, surpassing state-of-the-art studies with the same dataset. In the grading phase, classification based on the unified PD rating scale values achieves an accuracy of 92.28%. The proposed study delves into the potential of cumulative gait signals as a powerful diagnostic tool for PD, aiming to extract precise and intricate information by implementing straightforward and minimal processing endeavors. This method demonstrates significant efficiency in terms of complexity, cost, and energy consumption by utilizing a single-dimensional signal, eliminating the need for pre-processing steps, and limiting the features used for training. #Parkinson's disease grading #Cumulative gait signal #Bayesian optimization #Long short-term memory #technology #research #innovation

Artificial intelligence (AI) applied to electrocardiogram (ECG) interpretation shows high accuracy for early detection of valvular heart diseases (VHDs), based on a meta-analysis of data from 713,537 patients, with pooled accuracy and sensitivity of 81% and 83%, respectively. However, the low positive predictive value (PPV) #CardiacSurgery #ECG #HeartDisease

At Empowered Health, we level up from generic population recommendations to deliver a higher caliber of care through a Precision Medicine approach. At its core, precision medicine is about identifying the unique characteristics of each patient and using this information to make more accurate diagnoses, predict treatment outcomes, and develop targeted therapies that are tailored to the individual. We use advanced technologies such as genomic sequencing, biometrics and big data analytics, which enable us to gather and analyze vast amounts of information about a patient’s health status, medical history, genetic profile and other factors. One of the key benefits of precision medicine is that it allows clinicians to identify and treat diseases earlier, before they have a chance to progress and cause serious damage. By understanding the genetic and molecular underpinnings of disease, doctors can more accurately diagnose and treat conditions such as cancer, cardiovascular disease, and neurological disorders. If you're ready to level up your care, schedule an Inquiry Call with us on our website: https://lnkd.in/gXuChqNx

Artificial intelligence (AI) applied to electrocardiogram (ECG) interpretation shows high accuracy for early detection of valvular heart diseases (VHDs), based on a meta-analysis of data from 713,537 patients, with pooled accuracy and sensitivity of 81% and 83%, respectively. However, the low positive predictive value (PPV) #CardiacSurgery #ECG #HeartDisease

A recent study unveils the potential of utilizing a tablet-based artificial intelligence system to assess movement disorders, including Parkinson's Disease!  The study evaluated the discriminatory features among Parkinson's Disease patients, healthy subjects, and diverse movement disorders, achieving an impressive diagnostic accuracy of 94.0% for distinguishing Parkinson's Disease patients from healthy controls. At Magnes, we recognize the importance of advancing research and innovative solutions in the field of neurodegenerative conditions. This study highlights the potential of digital biomarkers and the collection of real-world data for diagnosis and disease management. Read more about the study at the link! #ParkinsonsResearch #NeurodegenerativeConditions #DigitalBiomarkers #Innovation #gait #nushu #healthcare 

A recent publication delves into the innovative use of a deep-learning model, known as CXR CVD-Risk, to estimate the 10-year risk for major adverse cardiovascular events (MACE) from routine chest radiographs (CXRs). The study compares its performance with that of the traditional atherosclerotic cardiovascular disease (ASCVD) risk score, shedding light on its implications for statin eligibility. The model is particularly useful because necessary inputs are often missing to make these assessments using traditional methods. Although the model was developed using retrospective studies, it showcases promising results for opportunistic screening using CXRs, which are the most frequent imaging studies. Leveraging this robust dataset, the deep-learning model holds potential for accurately estimating the 10-year risk for MACE, paving the way for more effective cardiovascular risk assessment strategies. 🩺💡 #DeepLearning #CardiovascularHealth #Innovation #AI #MedTech #Healthcare https://lnkd.in/gMSGBSqr

Monitoring the balance between oxidants and anti-oxidants, which is defined as a redox status, may help with providing a more efficient treatment of tumours, but it may also allow for a better understanding of neurodegenerative diseases. Read more in the article!  #electronparamagneticresonance #EPR