ArticleScientific reports2021
Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning.
Article in Scientific reports, 2021. 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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Who cites it
14 citing papers in PubMed.
- Article
- Designing a computer-assisted diagnosis system for cardiomegaly detection and radiology report generation.PLOS digital health · 2025Article
- Measurement of Cardiothoracic Ratio on Chest X-rays Using Artificial Intelligence-A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2024Review
- Segmentation-based cardiomegaly detection based on semi-supervised estimation of cardiothoracic ratio.Scientific reports · 2024Article
- The Performance of a Deep Learning-Based Automatic Measurement Model for Measuring the Cardiothoracic Ratio on Chest Radiographs.Bioengineering (Basel, Switzerland) · 2023Article
- A deep learning approach for fully automated measurements of lower extremity alignment in radiographic images.Scientific reports · 2023Article
- Requirement of image standardization for AI-based macroscopic diagnosis for surgical specimens of gastric cancer.Journal of cancer research and clinical oncology · 2023Article
- Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients.Diagnostics (Basel, Switzerland) · 2023Article
- Unsupervised anomaly detection for posteroanterior chest X-rays using multiresolution patch-based self-supervised learning.Scientific reports · 2023Article
- Validation of a Deep Learning Model for Detecting Chest Pathologies from Digital Chest Radiographs.Diagnostics (Basel, Switzerland) · 2023Article
- Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images.Frontiers in artificial intelligence · 2023Article
- Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans.Sensors (Basel, Switzerland) · 2022Article
- Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs.Acta radiologica open · 2022Article
- Article
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8 authors.
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Abstract
We examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior-anterior (PA) chest X-ray images (CXRs) of 793 patients were used to train deep learning (DL) models for lung and heart segmentation. The training dataset included PA CXRs from two public datasets and in-house PA CXRs. Two fully automated segmentation-based methods using state-of-the-art DL models for lung and heart segmentation were developed. The diagnostic performance was assessed and the reliability of the automatic cardiothoracic ratio (CTR) calculation was determined using the mean absolute error and paired t-test. The effects of thoracic pathological conditions on performance were assessed using subgroup analysis. One thousand PA CXRs of 1000 patients (480 men, 520 women; mean age 63 ± 23 years) were included. The CTR values derived from the DL models and diagnostic performance exhibited excellent agreement with reference standards for the whole test dataset. Performance of segmentation-based methods differed based on thoracic conditions. When tested using CXRs with lesions obscuring heart borders, the performance was lower than that for other thoracic pathological findings. Thus, segmentation-based methods using DL could detect cardiomegaly; however, the feasibility of computer-aided detection of cardiomegaly without human intervention was limited.
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