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Advanced Ultrasound and Photoacoustic Imaging in Cardiology

Min Wu, Navchetan Awasthi, Nastaran Mohammadian Rad, Josien P. W. Pluim, Richard G. P. Lopata
2021 Sensors  
Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. An effective management and treatment of CVDs highly relies on accurate diagnosis of the disease. As the most common imaging technique for clinical diagnosis of the CVDs, US imaging has been intensively explored. Especially with the introduction of deep learning (DL) techniques, US imaging has advanced tremendously in recent years. Photoacoustic imaging (PAI) is one of the most promising new imaging methods in addition
more » ... o the existing clinical imaging methods. It can characterize different tissue compositions based on optical absorption contrast and thus can assess the functionality of the tissue. This paper reviews some major technological developments in both US (combined with deep learning techniques) and PA imaging in the application of diagnosis of CVDs.
doi:10.3390/s21237947 pmid:34883951 fatcat:guktergujjatnfz6jjdj34qjqu

Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders [article]

Nastaran Mohammadian Rad, Seyed Mostafa Kia, Calogero Zarbo, Twan van Laarhoven, Giuseppe Jurman, Paola Venuti, Elena Marchiori, Cesare Furlanello
2017 arXiv   pre-print
Autism Spectrum Disorders are associated with atypical movements, of which stereotypical motor movements (SMMs) interfere with learning and social interaction. The automatic SMM detection using inertial measurement units (IMU) remains complex due to the strong intra and inter-subject variability, especially when handcrafted features are extracted from the signal. We propose a new application of the deep learning to facilitate automatic SMM detection using multi-axis IMUs. We use a convolutional
more » ... neural network (CNN) to learn a discriminative feature space from raw data. We show how the CNN can be used for parameter transfer learning to enhance the detection rate on longitudinal data. We also combine the long short-term memory (LSTM) with CNN to model the temporal patterns in a sequence of multi-axis signals. Further, we employ ensemble learning to combine multiple LSTM learners into a more robust SMM detector. Our results show that: 1) feature learning outperforms handcrafted features; 2) parameter transfer learning is beneficial in longitudinal settings; 3) using LSTM to learn the temporal dynamic of signals enhances the detection rate especially for skewed training data; 4) an ensemble of LSTMs provides more accurate and stable detectors. These findings provide a significant step toward accurate SMM detection in real-time scenarios.
arXiv:1709.05956v1 fatcat:uz3h6ctyrvhcfm32nhav72ako4

Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson's Disease and Autism Spectrum Disorders

Nastaran Mohammadian Rad, Twan van Laarhoven, Cesare Furlanello, Elena Marchiori
2018 Sensors  
[8] for the FOG dataset and the CNN architecture proposed by Rad et al. [7] for the SMM datasets.  ...  [8] and Rad et al. [7] on the FOG and SMM datasets, respectively, in a fully-supervised scenario.  ... 
doi:10.3390/s18103533 fatcat:il3crauryfct7ogsn7xflypnfy

Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism [article]

Nastaran Mohammadian Rad, Andrea Bizzego, Seyed Mostafa Kia, Giuseppe Jurman, Paola Venuti, Cesare Furlanello
2016 arXiv   pre-print
Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility. While the identification and the quantification of SMM patterns remain complex, its automation would provide support to accurate tuning of the intervention in the therapy of autism. Therefore, it is essential to develop automatic SMM detection systems in a real world setting, taking care of strong inter-subject and
more » ... ra-subject variability. Wireless accelerometer sensing technology can provide a valid infrastructure for real-time SMM detection, however such variability remains a problem also for machine learning methods, in particular whenever handcrafted features extracted from accelerometer signal are considered. Here, we propose to employ the deep learning paradigm in order to learn discriminating features from multi-sensor accelerometer signals. Our results provide preliminary evidence that feature learning and transfer learning embedded in the deep architecture achieve higher accurate SMM detectors in longitudinal scenarios.
arXiv:1511.01865v3 fatcat:64dumjm42vdntdg6ya5wqiseeq

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge [article]

Solveig K Sieberts, Jennifer Schaff, Marlena Duda, Bálint Ármin Pataki, Ming Sun, Phil Snyder, Jean-Francois Daneault, Federico Parisi, Gianluca Costante, Udi Rubin, Peter Banda, Yooree Chae (+31 others)
2020 biorxiv/medrxiv   pre-print
Mobile health, the collection of data using wearables and sensors, is a rapidly growing field in health research with many applications. Deriving validated measures of disease and severity that can be used clinically or as outcome measures in clinical trials, referred to as digital biomarkers, has proven difficult. In part due to the complicated analytical approaches necessary to develop these metrics. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features
more » ... ved from accelerometer and gyroscope data in two different datasets to predict the presence of Parkinson's Disease (PD) and severity of three PD symptoms: tremor, dyskinesia and bradykinesia. 40 teams from around the world submitted features, and achieved drastically improved predictive performance for PD (best AUROC=0.87), as well as severity of tremor (best AUPR=0.75), dyskinesia (best AUPR=0.48) and bradykinesia (best AUPR=0.95).
doi:10.1101/2020.01.13.904722 fatcat:cdczq2az7jdkrb2n5xt6lftety

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Solveig K. Sieberts, the Parkinson's Disease Digital Biomarker Challenge Consortium, Jennifer Schaff, Marlena Duda, Bálint Ármin Pataki, Ming Sun, Phil Snyder, Jean-Francois Daneault, Federico Parisi, Gianluca Costante, Udi Rubin, Peter Banda (+32 others)
2021 npj Digital Medicine  
AbstractConsumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived
more » ... from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).
doi:10.1038/s41746-021-00414-7 pmid:33742069 fatcat:ftjqo4tpq5holhmkzjyir5uh7q

A Review of Machine Learning Models for Predicting Autism Spectrum Disorder

Kanchanamala P, G.Leela Sagar
2019 Helix  
Nastaran Mohammadian Rad et al., have proposed a Deep Neural Network with two CNN layers and one pooling layer to classify ASD children using accelerometer data with an accuracy of 82% [21] .  ... 
doi:10.29042/2019-4797-4801 fatcat:3qyltbuuqnamjmbbx5ywtbozpi

Contents [chapter]

2021 Digital Health  
Machine learning for healthcare using wearable sensors 137 Nastaran Mohammadian Rad and Elena Marchiori 9.1 Introduction 137 9.2 Machine learning on wearable sensors 137 9.2.1 Decision tree  ... 
doi:10.1016/b978-0-12-818914-6.00031-4 fatcat:lul57btdr5ertbqcj2a5x6tgme