scholarly journals Abdominal Computed Tomography Imaging Findings in Hospitalized COVID-19 Patients: A Year-Long Experience and Associations Revealed by Explainable Artificial Intelligence

2021 ◽  
Vol 7 (12) ◽  
pp. 258
Author(s):  
Alice Scarabelli ◽  
Massimo Zilocchi ◽  
Elena Casiraghi ◽  
Pierangelo Fasani ◽  
Guido Giovanni Plensich ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Pulmonary Embolism ◽  
Statistical Analysis ◽  
Patient Outcomes ◽  
Strong Relationship ◽  
Active Bleeding ◽  
Free Fluid ◽  
Icu Admission ◽  
Explainable Artificial Intelligence ◽  
Radiological Stage

The aim of this retrospective study is to assess any association between abdominal CT findings and the radiological stage of COVID-19 pneumonia, pulmonary embolism and patient outcomes. We included 158 adult hospitalized COVID-19 patients between 1 March 2020 and 1 March 2021 who underwent 206 abdominal CTs. Two radiologists reviewed all CT images. Pathological findings were classified as acute or not. A subset of patients with inflammatory pathology in ACE2 organs (bowel, biliary tract, pancreas, urinary system) was identified. The radiological stage of COVID pneumonia, pulmonary embolism, overall days of hospitalization, ICU admission and outcome were registered. Univariate statistical analysis coupled with explainable artificial intelligence (AI) techniques were used to discover associations between variables. The most frequent acute findings were bowel abnormalities (n = 58), abdominal fluid (n = 42), hematomas (n = 28) and acute urologic conditions (n = 8). According to univariate statistical analysis, pneumonia stage > 2 was significantly associated with increased frequency of hematomas, active bleeding and fluid-filled colon. The presence of at least one hepatobiliary finding was associated with all the COVID-19 stages > 0. Free abdominal fluid, acute pathologies in ACE2 organs and fluid-filled colon were associated with ICU admission; free fluid also presented poor patient outcomes. Hematomas and active bleeding with at least a progressive stage of COVID pneumonia. The explainable AI techniques find no strong relationship between variables.

scholarly journals COVID-19-induced vascular angiopathy: CTPA signs in critically ill patients other than acute pulmonary embolism and high-lung opacity scores

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Mohamed Hossameldin khalifa ◽  
Ahmed Samir ◽  
Ayman Ibrahim Baess ◽  
Sara Samy Hendawi
Keyword(s):  
Artificial Intelligence ◽  
Pulmonary Embolism ◽  
Lung Volume ◽  
Acute Pulmonary Embolism ◽  
Pulmonary Angiography ◽  
Vascular Tree ◽  
Ct Pulmonary Angiography ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Severity Scores

Abstract Background Vascular angiopathy is suggested to be the major cause of silent hypoxia among COVID-19 patients without severe parenchymal involvement. However, pulmonologists and clinicians in intensive care units become confused when they encounter acute respiratory deterioration with neither severe parenchymal lung involvement nor acute pulmonary embolism. Other radiological vascular signs might solve this confusion. This study investigated other indirect vascular angiopathy signs on CT pulmonary angiography (CTPA) and involved a novel statistical analysis that was performed to determine the significance of associations between these signs and the CT opacity score of the pathological lung volume, which is calculated by an artificial intelligence system. Results The study was conducted retrospectively, during September and October 2020, on 73 patients with critical COVID-19 who were admitted to the ICU with progressive dyspnea and low O2 saturation on room air (PaO2 < 93%). They included 53 males and 20 females (73%:27%), and their age ranged from 18 to 88 years (mean ± SD=53.3 ± 13.5). CT-pulmonary angiography was performed for all patients, and an artificial intelligence system was utilized to quantitatively assess the diseased lung volume. The radiological data were analyzed by three expert consultant radiologists to reach consensus. A low CT opacity score (≤10) was found in 18 patients (24.7%), while a high CT opacity score (>10) was found in 55 patients (75.3%). Pulmonary embolism was found in 24 patients (32.9%); three of them had low CT opacity scores. Four other indirect vasculopathy CTPA signs were identified: (1) pulmonary vascular enlargement (57 patients—78.1%), (2) pulmonary hypertension (14 patients—19.2%), (3) vascular tree-in-bud pattern (10 patients—13.7%), and (4) pulmonary infarction (three patients—4.1%). There were no significant associations between these signs and the CT opacity score (0.3205–0.7551, all >0.05). Furthermore, both pulmonary vascular enlargement and the vascular tree-in-bud sign were found in patients without pulmonary embolism and low CT-severity scores (13/15–86.7% and 2/15–13.3%, respectively). Conclusion Pulmonary vascular enlargement or, less commonly, vascular tree-in-bud pattern are both indirect vascular angiopathy signs on CTPA that can explain the respiratory deterioration which complicates COVID-19 in the absence of severe parenchymal involvement or acute pulmonary embolism.

scholarly journals Artificial Intelligence to Get Insights of Multi-Drug Resistance Risk Factors during the First 48 Hours from ICU Admission

Antibiotics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 239
Author(s):  
Inmaculada Mora-Jiménez ◽  
Jorge Tarancón-Rey ◽  
Joaquín Álvarez-Rodríguez ◽  
Cristina Soguero-Ruiz
Keyword(s):  
Artificial Intelligence ◽  
Risk Factors ◽  
Drug Resistance ◽  
Multi Drug Resistance ◽  
Time Interval ◽  
Health Records ◽  
Resistance Risk ◽  
Icu Admission ◽  
Selection Strategies ◽  
Patient Will

Multi-drug resistance (MDR) is one of the most current and greatest threats to the global health system nowadays. This situation is especially relevant in Intensive Care Units (ICUs), where the critical health status of these patients makes them more vulnerable. Since MDR confirmation by the microbiology laboratory usually takes 48 h, we propose several artificial intelligence approaches to get insights of MDR risk factors during the first 48 h from the ICU admission. We considered clinical and demographic features, mechanical ventilation and the antibiotics taken by the patients during this time interval. Three feature selection strategies were applied to identify statistically significant differences between MDR and non-MDR patient episodes, ending up in 24 selected features. Among them, SAPS III and Apache II scores, the age and the department of origin were identified. Considering these features, we analyzed the potential of machine learning methods for predicting whether a patient will develop a MDR germ during the first 48 h from the ICU admission. Though the results presented here are just a first incursion into this problem, artificial intelligence approaches have a great impact in this scenario, especially when enriching the set of features from the electronic health records.

An explainable Artificial Intelligence approach to study MCI to AD conversion via HD-EEG processing

2021 ◽  
pp. 155005942110636
Author(s):  
Francesco Carlo Morabito ◽  
Cosimo Ieracitano ◽  
Nadia Mammone
Keyword(s):  
Artificial Intelligence ◽  
Frontal Lobe ◽  
Parietal Lobe ◽  
Classification Performance ◽  
Head Region ◽  
Left Frontal Lobe ◽  
Performance Accuracy ◽  
Power Spectral ◽  
Explainable Artificial Intelligence

An explainable Artificial Intelligence (xAI) approach is proposed to longitudinally monitor subjects affected by Mild Cognitive Impairment (MCI) by using high-density electroencephalography (HD-EEG). To this end, a group of MCI patients was enrolled at IRCCS Centro Neurolesi Bonino Pulejo of Messina (Italy) within a follow-up protocol that included two evaluations steps: T0 (first evaluation) and T1 (three months later). At T1, four MCI patients resulted converted to Alzheimer’s Disease (AD) and were included in the analysis as the goal of this work was to use xAI to detect individual changes in EEGs possibly related to the degeneration from MCI to AD. The proposed methodology consists in mapping segments of HD-EEG into channel-frequency maps by means of the power spectral density. Such maps are used as input to a Convolutional Neural Network (CNN), trained to label the maps as “T0” (MCI state) or “T1” (AD state). Experimental results reported high intra-subject classification performance (accuracy rate up to 98.97% (95% confidence interval: 98.68–99.26)). Subsequently, the explainability of the proposed CNN is explored via a Grad-CAM approach. The procedure allowed to detect which EEG-channels (i.e., head region) and range of frequencies (i.e., sub-bands) resulted more active in the progression to AD. The xAI analysis showed that the main information is included in the delta sub-band and that, limited to the analyzed dataset, the highest relevant areas are: the left-temporal and central-frontal lobe for Sb01, the parietal lobe for Sb02, the left-frontal lobe for Sb03 and the left-frontotemporal region for Sb04.

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