Prediction of Pulmonary Function Parameters Based on a Combination Algorithm

Zhou, Ruishi; Wang, Peng; Li, Yueqi; Mou, Xiuying; Zhao, Zongci; Chen, Xianxiang; Du, Lidong; Yang, Ting; Zhan, Qingyuan; Fang, Zhen

doi:10.3390/bioengineering9040136

Cited by 4 publications

(3 citation statements)

References 38 publications

(49 reference statements)

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“…Lung function parameters play a pivotal role in the assessment of respiratory diseases ( 32 ). Early lung infections are negatively associated with most lung function parameters ( 33 ).…”

Section: Discussionmentioning

confidence: 99%

Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model

Xu,

Tao,

Zhu

et al. 2023

Front. Pediatr.

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BackgroundPneumonia remains the leading cause of death among children aged 1–59 months. The early prediction of poor outcomes (PO) is of critical concern. This study aimed to explore the risk factors relating to PO in severe community-acquired pneumonia (SCAP) and build a PO-predictive nomogram model for children with SCAP.MethodsWe retrospectively identified 300 Chinese pediatric patients diagnosed with SCAP who were hospitalized in the Affiliated Hospital of Southwest Medical University from August 1, 2018, to October 31, 2021. Children were divided into the PO and the non-PO groups. The occurrence of PO was designated as the dependent variable. Univariate and multivariate logistic regression analyses were used to identify the risk factors of PO. A nomogram model was constructed from the multivariate logistic regression analysis and internally validated for model discrimination and calibration. The performance of the nomogram was estimated using the concordance index (C-index).ResultsAccording to the efficacy evaluation criteria, 56 of 300 children demonstrated PO. The multivariate logistic regression analysis resulted in the following independent risk factors for PO: co-morbidity (OR: 8.032, 95% CI: 3.556–18.140, P < 0.0001), requiring invasive mechanical ventilation (IMV) (OR: 7.081, 95% CI: 2.250–22.282, P = 0.001), and ALB < 35 g/L (OR: 3.203, 95% CI: 1.151–8.912, P = 0.026). Results of the internal validation confirmed that the model provided good discrimination (concordance index [C-index], 0.876 [95% CI: 0.828–0.925]). The calibration plots in the nomogram model were of high quality.ConclusionThe nomogram facilitated accurate prediction of PO in children diagnosed with SCAP and could be helpful for clinical decision-making.

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“…Lung function parameters play a pivotal role in the assessment of respiratory diseases ( 32 ). Early lung infections are negatively associated with most lung function parameters ( 33 ).…”

Section: Discussionmentioning

confidence: 99%

Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model

Xu,

Tao,

Zhu

et al. 2023

Front. Pediatr.

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“…In previous work [ 23 ], we designed and implemented a portable sensing device to accurately collect volumetric capnography data. This device primarily consists of a differential pressure sensor and a carbon dioxide concentration sensor based on non-dispersive infrared (NDIR) technology.…”

Section: Methodsmentioning

confidence: 99%

A Novel Approach for the Detection and Severity Grading of Chronic Obstructive Pulmonary Disease Based on Transformed Volumetric Capnography

Mou,

Wang,

Sun

et al. 2024

Bioengineering

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Chronic Obstructive Pulmonary Disease (COPD), as the third leading cause of death worldwide, is a major global health issue. The early detection and grading of COPD are pivotal for effective treatment. Traditional spirometry tests, requiring considerable physical effort and strict adherence to quality standards, pose challenges in COPD diagnosis. Volumetric capnography (VCap), which can be performed during natural breathing without requiring additional compliance, presents a promising alternative tool. In this study, the dataset comprised 279 subjects with normal pulmonary function and 148 patients diagnosed with COPD. We introduced a novel quantitative analysis method for VCap. Volumetric capnograms were converted into two-dimensional grayscale images through the application of Gramian Angular Field (GAF) transformation. Subsequently, a multi-scale convolutional neural network, CapnoNet, was conducted to extract features and facilitate classification. To improve CapnoNet’s performance, two data augmentation techniques were implemented. The proposed model exhibited a detection accuracy for COPD of 95.83%, with precision, recall, and F1 measures of 95.21%, 95.70%, and 95.45%, respectively. In the task of grading the severity of COPD, the model attained an accuracy of 96.36%, complemented by precision, recall, and F1 scores of 88.49%, 89.99%, and 89.15%, respectively. This work provides a new perspective for the quantitative analysis of volumetric capnography and demonstrates the strong performance of the proposed CapnoNet in the diagnosis and grading of COPD. It offers direction and an effective solution for the clinical application of capnography.

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“…The development of medical imaging is also changing with each passing day. More and more cases have shown that the combination of artificial intelligence and medical imaging has good results, such as breast cancer (BC) [ 15 ], arrhythmia [ 16 ], and lung function prediction [ 17 ]. There are also related studies in dentistry, for example, adding machine learning to the color recognition of dentures [ 18 ] or adding deep learning to the detection of tooth decay [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph

et al. 2022

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Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold preprocessing technique for image segmentation, which can achieve an accuracy rate of more than 96%; (2) a better and more intuitive apical lesions symptom enhancement technique; and (3) a model for apical lesions detection with an accuracy as high as 96.21%. Compared with existing state-of-the-art technology, the proposed model has improved the accuracy by more than 5%. The proposed model has successfully improved the automatic diagnosis of apical lesions. With the help of automation, dentists can focus more on technical and medical diagnoses, such as treatment, tooth cleaning, or medical communication. This proposal has been certified by the Institutional Review Board (IRB) with the certification number 202002030B0.

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Prediction of Pulmonary Function Parameters Based on a Combination Algorithm

Cited by 4 publications

References 38 publications

Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model

Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model

A Novel Approach for the Detection and Severity Grading of Chronic Obstructive Pulmonary Disease Based on Transformed Volumetric Capnography

A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph

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