219 research outputs found
Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation
Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS 1.0-Unseen" proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL
Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinsonâs Disease Affected by COVIDâ19: A Narrative Review
Background and Motivation: Parkinsonâs disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVIDâ19 causes the ML systems to be-come severely nonâlinear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no wellâexplained ML paradigms. Deep neural networks are powerful learning machines that generalize nonâlinear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVIDâ19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVIDâ19 framework. We study the hypothesis that PD in the presence of COVIDâ19 can cause more harm to the heart and brain than in nonâ COVIDâ19 conditions. COVIDâ19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVIDâ19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVIDâ19 lesions, office and laboratory arterial atherosclerotic imageâbased biomarkers, and medicine usage for the PD patients for the design of DL pointâbased models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVIDâ 19 environment and this was also verified. DL architectures like long shortâterm memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVIDâ19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVIDâ19. Š 2022 by the authors. Licensee MDPI, Basel, Switzerland
Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinsonâs Disease Affected by COVIDâ19: A Narrative Review
Background and Motivation: Parkinsonâs disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVIDâ19 causes the ML systems to be-come severely nonâlinear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no wellâexplained ML paradigms. Deep neural networks are powerful learning machines that generalize nonâlinear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVIDâ19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVIDâ19 framework. We study the hypothesis that PD in the presence of COVIDâ19 can cause more harm to the heart and brain than in nonâ COVIDâ19 conditions. COVIDâ19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVIDâ19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVIDâ19 lesions, office and laboratory arterial atherosclerotic imageâbased biomarkers, and medicine usage for the PD patients for the design of DL pointâbased models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVIDâ 19 environment and this was also verified. DL architectures like long shortâterm memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVIDâ19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVIDâ19
Recent Advances in Machine Learning Applied to Ultrasound Imaging
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. Š 2022 by the authors. Licensee MDPI, Basel, Switzerland
Basic Science to Clinical Research: Segmentation of Ultrasound and Modelling in Clinical Informatics
The world of basic science is a world of minutia; it boils down to improving even a fraction of a percent over the baseline standard. It is a domain of peer reviewed fractions of seconds and the world of squeezing every last ounce of efficiency from a processor, a storage medium, or an algorithm. The field of health data is based on extracting knowledge from segments of data that may improve some clinical process or practice guideline to improve the time and quality of care. Clinical informatics and knowledge translation provide this information in order to reveal insights to the world of improving patient treatments, regimens, and overall outcomes.
In my world of minutia, or basic science, the movement of blood served an integral role. The novel detection of sound reverberations map out the landscape for my research. I have applied my algorithms to the various anatomical structures of the heart and artery system. This serves as a basis for segmentation, active contouring, and shape priors. The algorithms presented, leverage novel applications in segmentation by using anatomical features of the heart for shape priors and the integration of optical flow models to improve tracking. The presented techniques show improvements over traditional methods in the estimation of left ventricular size and function, along with plaque estimation in the carotid artery.
In my clinical world of data understanding, I have endeavoured to decipher trends in Alzheimerâs disease, Sepsis of hospital patients, and the burden of Melanoma using mathematical modelling methods. The use of decision trees, Markov models, and various clustering techniques provide insights into data sets that are otherwise hidden. Finally, I demonstrate how efficient data capture from providers can achieve rapid results and actionable information on patient medical records. This culminated in generating studies on the burden of illness and their associated costs.
A selection of published works from my research in the world of basic sciences to clinical informatics has been included in this thesis to detail my transition. This is my journey from one contented realm to a turbulent one
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