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General General

Pulmonary Embolism in Acute Asthma Exacerbation: Clinical Characteristics, Prediction Model and Hospital Outcomes.

In Lung

PURPOSE : Little is known about the characteristics and impact of acute pulmonary embolism (PE) during episodes of asthma exacerbation. We aimed to characterize patients diagnosed with acute PE in the setting of asthma exacerbation, develop a prediction model to help identify future patients and assess the impact of acute PE on hospital outcomes.

METHODS : We included 758 patients who were treated for asthma exacerbation and underwent a computed tomographic pulmonary angiography (CTA) during the same encounter at a university-based hospital between June 2011 and October 2018. We compared clinical characteristics of patients with and without acute PE and developed a machine learning prediction model to classify the PE status based on the clinical variables. We used multivariable regression analysis to evaluate the impact of acute PE on hospital outcomes.

RESULTS : Twenty percent of the asthma exacerbation patients who underwent CTA had an acute PE. Factors associated with acute PE included previous history of PE, high CHA2DS2-VASc score, hyperlipidemia, history of deep vein thrombosis, malignancy, chronic systemic corticosteroids use, high body mass index and atrial fibrillation. Using these factors, we developed a random forest machine learning prediction model which had an 88% accuracy in classifying the acute PE status of the patients (area under the receiver operating characteristic curve = 0.899; 95% confidence interval: 0.885-0.913). Acute PE in asthma exacerbation was associated with longer hospital stay and intensive care unit stay.

CONCLUSION : It is important to consider acute PE, a potentially life-threatening event, in the setting of asthma exacerbation especially when other risk factors are present.

Alzghoul Bashar N, Reddy Raju, Chizinga Mwelwa, Innabi Ayoub, Zou Baiming, Papierniak Eric S, Faruqi Ibrahim

2020-May-18

Asthma, Asthma exacerbation, Pulmonary embolism, Pulmonary vascular disease

Public Health Public Health

Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve.

In International journal of environmental research and public health ; h5-index 73.0

The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9-30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.

Simsek Murat, Kantarci Burak

2020-May-14

Artificial Intelligence, COVID-19, epidemics, mobile assessment centers, neural networks, optimum route planning, pandemics, public health, self-organizing feature map

Public Health Public Health

Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve.

In International journal of environmental research and public health ; h5-index 73.0

The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9-30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.

Simsek Murat, Kantarci Burak

2020-May-14

Artificial Intelligence, COVID-19, epidemics, mobile assessment centers, neural networks, optimum route planning, pandemics, public health, self-organizing feature map

Surgery Surgery

Deep learning with 4D spatio-temporal data representations for OCT-based force estimation

ArXiv Preprint

Estimating the forces acting between instruments and tissue is a challenging problem for robot-assisted minimally-invasive surgery. Recently, numerous vision-based methods have been proposed to replace electro-mechanical approaches. Moreover, optical coherence tomography (OCT) and deep learning have been used for estimating forces based on deformation observed in volumetric image data. The method demonstrated the advantage of deep learning with 3D volumetric data over 2D depth images for force estimation. In this work, we extend the problem of deep learning-based force estimation to 4D spatio-temporal data with streams of 3D OCT volumes. For this purpose, we design and evaluate several methods extending spatio-temporal deep learning to 4D which is largely unexplored so far. Furthermore, we provide an in-depth analysis of multi-dimensional image data representations for force estimation, comparing our 4D approach to previous, lower-dimensional methods. Also, we analyze the effect of temporal information and we study the prediction of short-term future force values, which could facilitate safety features. For our 4D force estimation architectures, we find that efficient decoupling of spatial and temporal processing is advantageous. We show that using 4D spatio-temporal data outperforms all previously used data representations with a mean absolute error of 10.7mN. We find that temporal information is valuable for force estimation and we demonstrate the feasibility of force prediction.

Nils Gessert, Marcel Bengs, Matthias Schlüter, Alexander Schlaefer

2020-05-20

General General

A machine learning-based approach to directly compare the diagnostic accuracy of myocardial perfusion imaging by conventional and cadmium-zinc telluride SPECT.

In Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology

BACKGROUND : We evaluated the performance of conventional (C) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride (CZT)-SPECT in a large cohort of patients with suspected or known coronary artery disease (CAD) and compared the diagnostic accuracy of the two systems using machine learning (ML) algorithms.

METHODS AND RESULTS : A total of 517 consecutive patients underwent stress myocardial perfusion imaging (MPI) by both C-SPECT and CZT-SPECT. In the overall population, an excellent correlation between stress MPI data and left ventricular (LV) functional parameters measured by C-SPECT and by CZT-SPECT was observed (all P < .001). ML analysis performed through the implementation of random forest (RF) and k-nearest neighbors (NN) algorithms proved that CZT-SPECT has greater accuracy than C-SPECT in detecting CAD. For both algorithms, the sensitivity of CZT-SPECT (96% for RF and 60% for k-NN) was greater than that of C-SPECT (88% for RF and 53% for k-NN).

CONCLUSIONS : MPI data and LV functional parameters obtained by CZT-SPECT are highly reproducible and provide good correlation with those obtained by C-SPECT. ML approach showed that the accuracy and sensitivity of CZT-SPECT is greater than C-SPECT in detecting CAD.

Cantoni Valeria, Green Roberta, Ricciardi Carlo, Assante Roberta, Zampella Emilia, Nappi Carmela, Gaudieri Valeria, Mannarino Teresa, Genova Andrea, De Simini Giovanni, Giordano Alessia, D’Antonio Adriana, Acampa Wanda, Petretta Mario, Cuocolo Alberto

2020-May-18

CAD, MPI, SPECT, diagnostic and prognostic application

Radiology Radiology

A closer look to the new frontier of artificial intelligence in the percutaneous treatment of primary lesions of the liver.

In Medical oncology (Northwood, London, England)

The purpose of thermal ablation is induction of tumor death by means of localized hyperthermia resulting in irreversible cellular damage. Ablative therapies are well-recognized treatment modalities for HCC lesions and are considered standard of care for HCC nodules < 3 cm in diameter in patients not suitable for surgery. Effective lesion treatment rely on complete target volume ablation. Technical limitations are represented by large (> 3 cm) or multicentric nodules as well as complex nodule location and poor lesion conspicuity. Artificial Intelligence (AI) is a general term referred to computational algorithms that can analyze data and perform complex tasks otherwise prerogative of Human Intelligence. AI has a variety of application in percutaneous ablation procedures such as Navigational software, Fusion Imaging, and robot-assisted ablation tools. Those instruments represent relative innovations in the field of Interventional Oncology and promising strategies to overcome actual limitations of ablative therapy in order to increase feasibility and technical results. This work aims to review the principal application of Artificial Intelligence in the percutaneous ablation of primary lesions of the liver with special focus on how AI can impact in the treatment of HCC especially on potential advantages on the drawbacks of the conventional technique.

Citone M, Fanelli F, Falcone G, Mondaini F, Cozzi D, Miele V

2020-May-18

Artificial intelligence, Fusion imaging, HCC, Liver ablation, Percutaneous ablation