Development of Artificial Intelligence System for Dangerous Object Recognition in X-ray Baggage Images

2020 ◽  
Vol 69 (7) ◽  
pp. 1067-1072
Author(s):  
Jeong-nam Lee ◽  
Hyun-chong Cho
2021 ◽  
Author(s):  
Avinash Nanivadekar ◽  
Kapil Zirpe ◽  
Ashutosh Dwivedi ◽  
Rajan Patel ◽  
Richa Pant ◽  
...  

Background: Early prediction of disease severity in COVID-19 patients is essential. Chest X-ray (CXR) is a faster, widely available, and less expensive imaging modality that may be useful in predicting mortality in COVID-19 patients. Artificial Intelligence (AI) may help expedite CXR reading times, and improve mortality prediction. We sought to develop and assess an artificial intelligence system that used chest X-rays and clinical parameters to predict mortality in COVID-19 patients. Methods: A retrospective study was conducted in Ruby Hall Clinic, Pune, India. The study included patients who had a positive real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test for COVID-19 and at least one available chest X-ray at the time of their initial presentation or admission. Features from CXR images and clinical parameters were used to train the Random Forest model. Results: Clinical data from a total of 201 patients was assessed retrospectively. The average age of the cohort was 51.4 ± 14.8 years, with 29.4% of the patients being over the age of 60. The model, which used CXRs and clinical parameters as inputs, had a sensitivity of 0.83 [95% CI: 0.7, 0.95] and a specificity of 0.7 [95% CI: 0.64, 0.77]. The area under the curve (AUC) on receiver operating characteristics (ROC) was increased from 0.74 [95% CI: 0.67, 0.8] to 0.79 [95% CI: 0.72, 0.85] when the model included features of CXRs in addition to clinical parameters. Conclusion: An Artificial Intelligence (AI) model based on CXRs and clinical parameters demonstrated high sensitivity and can be used as a rapid and reliable tool for COVID-19 mortality prediction.


1989 ◽  
Vol 33 ◽  
pp. 531-536
Author(s):  
Erland P. Wittig ◽  
Carl E. Rechsteiner

SummaryAutomation of X-ray fluorescence equipment has proliferated during the last 10 years. However, the focus has been on data collection and data massage. Data presentation has been limited to a few rudimentary formats. These formats ‘ generally involve printing the results in some arbitrary fashion, followed by manual transfer to the client.This paper describes a system (using rudimentary artificial intelligence techniques) that automates data presentation. It handles data from 14 different methods; evaluates the data against the requirements of the method (i.e. mass balance, detection and reporting limits, and matrix interferences). Further, a data report is generated in a consistent format, including the reporting of significant figures. Additionally, an exception report is printed when the measured results are outside the applicable range of the method or violate quality assurance constraints. In such cases, alternative methods are recommended in the exception report.


2021 ◽  
Vol 160 (6) ◽  
pp. S-64-S-65
Author(s):  
Ethan A. Chi ◽  
Gordon Chi ◽  
Cheuk To Tsui ◽  
Yan Jiang ◽  
Karolin Jarr ◽  
...  

Author(s):  
Mohamed Hossameldin khalifa ◽  
Ahmed Samir ◽  
Ayman Ibrahim Baess ◽  
Sara Samy Hendawi

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.


Sign in / Sign up

Export Citation Format

Share Document