Dataset Information

Pleural thickening on screening chest X-rays: a single institutional study.

ABSTRACT: Although pleural thickening is a common finding on routine chest X-rays, its radiological and clinical features remain poorly characterized. Our investigation of 28,727 chest X-rays obtained from annual health examinations confirmed that pleural thickening was the most common abnormal radiological finding. In most cases (92.2%), pleural thickening involved the apex of the lung, particularly on the right side; thus, it was defined as a pulmonary apical cap. Pleural thickening was more common in males than in females and in current smokers or ex-smokers than in never smokers. The prevalence increased with age, ranging from 1.8% in teenagers to 9.8% in adults aged 60?years and older. Moreover, pleural thickening was clearly associated with greater height and lower body weight and body mass index, suggesting that a tall, thin body shape may predispose to pleural thickening. These observations allowed us to speculate about the causative mechanisms of pleural thickening that are attributable to disproportionate perfusion, ventilation, or mechanical forces in the lungs.

SUBMITTER: Saito A

PROVIDER: S-EPMC6612134 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Pleural thickening on screening chest X-rays: a single institutional study.

Saito Akira A Hakamata Yukichika Y Yamada Yukiko Y Sunohara Mitsuhiro M Tarui Megumi M Murano Yoko Y Mitani Akihisa A Tanaka Kimie K Nagase Takahide T Yanagimoto Shintaro S

Respiratory research 20190705 1

Although pleural thickening is a common finding on routine chest X-rays, its radiological and clinical features remain poorly characterized. Our investigation of 28,727 chest X-rays obtained from annual health examinations confirmed that pleural thickening was the most common abnormal radiological finding. In most cases (92.2%), pleural thickening involved the apex of the lung, particularly on the right side; thus, it was defined as a pulmonary apical cap. Pleural thickening was more common in m ...[more]

PMID: 31277661

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Project description:The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.

Dataset Information

Pleural thickening on screening chest X-rays: a single institutional study.

Publications

Pleural thickening on screening chest X-rays: a single institutional study.

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