Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Suri, Jasjit S.; Puvvula, Anudeep; Majhail, Misha; Biswas, Mainak; Jamthikar, Ankush D.; Saba, Luca; Faa, Gavino; Singh, Inder M.; Oberleitner, Ronald; Turk, Monika; Srivastava, Saurabh; Chadha, Paramjit S.; Suri, Harman S.; Johri, Amer M.; Nambi, Vijay; Sanches, J Miguel; Khanna, Narendra N.; Viskovic, Klaudija; Mavrogeni, Sophie; Laird, John R.; Bit, Arindam; Pareek, Gyan; Miner, Martin; Balestrieri, Antonella; Sfikakis, Petros P.; Tsoulfas, George; Protogerou, Athanasios; Misra, Durga Prasanna; Agarwal, Vikas; Kitas, George D.; Kolluri, Raghu; Teji, Jagjit; Porcu, Michele; Al-Maini, Mustafa; Agbakoba, Ann; Sockalingam, Meyypan; Sexena, Ajit; Nicolaides, Andrew; Sharma, Aditya; Rathore, Vijay; Viswanathan, Vijay; Naidu, Subbaram; Bhatt, Deepak L.

doi:10.31083/j.rcm.2020.04.236

Cited by 31 publications

(17 citation statements)

References 230 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, other neurological diseases such as Alzheimer’s and Adrenoleukodystrophy (A.L.D.) [ 112 , 113 ], when aligned to PD, can be explored for more robust scoring, ranking, and classification using advanced neural imaging tools [ 69 , 114 , 115 ]. Currently, the world is facing a COVID-19 pandemic, where 26 million people are affected and 5.2 million have died due to the coronavirus.…”

Section: Discussionmentioning

confidence: 99%

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

et al. 2022

View full text Add to dashboard Cite

Background and Motivation: Diagnosis of Parkinson’s disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically. Method: The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. Result: The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were “deep learning with sketches as outcomes” and “machine learning with Electroencephalography,” respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests. Conclusion: The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.

show abstract

Section: Discussionmentioning

confidence: 99%

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…One such typical system for DL design is shown in Figure 14 . This architecture consists of: (a) a training model design utilizing the risk variables taken from six sources such as office-based biomarkers (OBBM), laboratory-based biomarkers (LBBM), carotid image-based phenotypes (CUSIP), medication consumption (MedUSE), PD, and COVID-19, derived from the training dataset, and (b) risk prediction labels as part of the gold standard which are either heart failure (cardiovascular events) or stroke (cerebrovascular events) [ 220 ]. Such a training system can be non-linearly adjusted and has been shown recently in the context of cardiovascular risk stratification [ 38 , 185 , 207 , 221 , 222 ].…”

Section: Deep Learning For Cvd/stroke Risk Assessment In Pd Patients ...mentioning

confidence: 99%

“…In the non-AI-based methods for CVD/stroke the risk was determined by computing the digital total of all the normalized risk values for the image-based biomarkers and then compartmentalized into different risk classes such as no-risk, low-risk, low-moderate risk, high-moderate risk, low-of-high risk, and high-of-high risk. This was computed using the AtheroEdge™ 2.0 system (AtheroPoint, Roseville, CA, USA) [ 36 , 220 , 221 , 258 , 259 , 260 , 261 , 262 , 263 , 264 , 265 ]. Image-based biomarkers, such as TPA, have shown to have a strong link with eGFR [ 266 ], and thus AI-based solution have adapted the usage of TPA in the modeling process.…”

Section: Deep Learning For Cvd/stroke Risk Assessment In Pd Patients ...mentioning

confidence: 99%

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Suri¹,

Maindarkar

Sauseng

et al. 2022

Diagnostics

View full text Add to dashboard Cite

Background and Motivation: Parkinson’s 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 COVID-19 causes the ML systems to become severely non-linear 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 well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 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 COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 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) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term 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 COVID-19.Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

show abstract

“…Artificial intelligence (AI) is a brand new tool rising in helping the daily medical practice and clinical imaging [ 180 ]. The use of AI has been also proposed for COVID-19 [ 181 , 182 ] treatment. In CT, CMR and ultrasound modalities, AI has been applied for data retrieval, segmentation of medical organs and diagnosis for COVID-19 [ 183 ].…”

Section: Covid-19 Specificity Advanced Imagingmentioning

confidence: 99%

Imaging Evaluation of Pulmonary and Non-Ischaemic Cardiovascular Manifestations of COVID-19

et al. 2021

View full text Add to dashboard Cite

Coronavirus Disease 2019 (COVID-19) has been a pandemic challenge for the last year. Cardiovascular disease is the most described comorbidity in COVID-19 patients, and it is related to the disease severity and progression. COVID-19 induces direct damage on cardiovascular system, leading to arrhythmias and myocarditis, and indirect damage due to endothelial dysfunction and systemic inflammation with a high inflammatory burden. Indirect damage leads to myocarditis, coagulation abnormalities and venous thromboembolism, Takotsubo cardiomyopathy, Kawasaki-like disease and multisystem inflammatory syndrome in children. Imaging can support the management, assessment and prognostic evaluation of these patients. Ultrasound is the most reliable and easy to use in emergency setting and in the ICU as a first approach. The focused approach is useful in management of these patients due its ability to obtain quick and focused results. This tool is useful to evaluate cardiovascular disease and its interplay with lungs. However, a detailed echocardiography evaluation is necessary in a complete assessment of cardiovascular involvement. Computerized tomography is highly sensitive, but it might not always be available. Cardiovascular magnetic resonance and nuclear imaging may be helpful to evaluate COVID-19-related myocardial injury, but further studies are needed. This review deals with different modalities of imaging evaluation in the management of cardiovascular non-ischaemic manifestations of COVID-19, comparing their use in emergency and in intensive care.

show abstract

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Cited by 31 publications

References 230 publications

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Imaging Evaluation of Pulmonary and Non-Ischaemic Cardiovascular Manifestations of COVID-19

Contact Info

Product

Resources

About