ArticleClinics (Sao Paulo, Brazil)2023
Deep learning for diagnosis of malign pleural effusion on computed tomography images.
Article in Clinics (Sao Paulo, Brazil), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 14 citations in OpenAlex.
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Classifying diagnostic pitfalls in juxtapleural CT lesions: A root cause.European journal of radiology open · 2026Article
- Development and validation of a diagnostic model for malignant pleural effusion based on random forest.Journal of thoracic disease · 2026Article
- Automated Detection and Classification of Pleural Effusion on Computed Tomography Using Deep Learning.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.Thoracic research and practice · 2026Article
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Deep learning-based classification of pleural malignancy using medical thoracoscopic images.Translational lung cancer research · 2025Article
- Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH.Respiratory research · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- A novel MRI-based deep learning-radiomics framework for evaluating cerebrospinal fluid signal in central nervous system infection.Frontiers in medicine · 2025Article
- The Use of "Cancer Ratio" in Differentiating Malignant and Tuberculous Pleural Effusions: Protocol for a Prospective Observational Study.JMIR research protocols · 2024Observational
- Comparative Evaluation of Chest Ultrasonography and Computed Tomography as Predictors of Malignant Pleural Effusion: A Prospective Study.Diagnostics (Basel, Switzerland) · 2024Article
- Unveiling Insights: A Comprehensive Review of the Role of Medical Thoracoscopy in Pleural Effusion Assessment.Cureus · 2024Review
- Development and validation of a machine learning model for differential diagnosis of malignant pleural effusion using routine laboratory data.Therapeutic advances in respiratory diseaseObservational
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe pleura is a serous membrane that surrounds the lungs. The visceral surface secretes fluid into the serous cavity and the parietal surface ensures a regular absorption of this fluid. If this balance is disturbed, fluid accumulation occurs in the pleural space called "Pleural Effusion". Today, accurate diagnosis of pleural diseases is becoming more critical, as advances in treatment protocols have contributed positively to prognosis. Our aim is to perform computer-aided numerical analysis of Computed Tomography (CT) images from patients showing pleural effusion images on CT and to examine the prediction of malignant/benign distinction using deep learning by comparing with the cytology results.
methodsThe authors classified 408 CT images from 64 patients whose etiology of pleural effusion was investigated using the deep learning method. 378 of the images were used for the training of the system; 15 malignant and 15 benign CT images, which were not included in the training group, were used as the test.
resultsAmong the 30 test images evaluated in the system; 14 of 15 malignant patients and 13 of 15 benign patients were estimated with correct diagnosis (PPD: 93.3%, NPD: 86.67%, Sensitivity: 87.5%, Specificity: 92.86%).
conclusionAdvances in computer-aided diagnostic analysis of CT images and obtaining a pre-diagnosis of pleural fluid may reduce the need for interventional procedures by guiding physicians about which patients may have malignancies. Thus, it is cost and time-saving in patient management, allowing earlier diagnosis and treatment.
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