Detection and Semiquantitative Analysis of Cardiomegaly,                     Pneumothorax, and Pleural Effusion on Chest Radiographs

Zhou, Leilei; Yin, Xindao; Zhang, Tao; Feng, Yuan; Zhao, Ying; Jin, Mingxu; Peng, Mingyang; Xing, Chunhua; Li, Fengfang; Wang, Ziteng; Wei, Guoliang; Xiao, Jia; Liu, Yujun; Wu, Xinying; Lü, Ling

doi:10.1148/ryai.2021200172

Cited by 19 publications

(23 citation statements)

References 27 publications

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“…Such improvement in detection of findings with AI has been reported in several prior studies . Prior studies with our AI as well as other research and commercial AI algorithms have reported comparable or lower diagnostic performance for detection of pneumothorax, pleural effusion, pneumonia, and pulmonary nodules. Although the stand-alone performance of our AI algorithm was comparable to that of the radiologists and trainees in terms of sensitivity, it had substantially lower specificity.…”

Section: Discussionsupporting

confidence: 81%

Association of Artificial Intelligence–Aided Chest Radiograph Interpretation With Reader Performance and Efficiency

Ahn¹,

Ebrahimian

McDermott

et al. 2022

JAMA Netw Open

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IMPORTANCEThe efficient and accurate interpretation of radiologic images is paramount. OBJECTIVE To evaluate whether a deep learning-based artificial intelligence (AI) engine used concurrently can improve reader performance and efficiency in interpreting chest radiograph abnormalities. DESIGN, SETTING, AND PARTICIPANTS This multicenter cohort study was conducted from April to November 2021 and involved radiologists, including attending radiologists, thoracic radiology fellows, and residents, who independently participated in 2 observer performance test sessions. The sessions included a reading session with AI and a session without AI, in a randomized crossover manner with a 4-week washout period in between. The AI produced a heat map and the image-level probability of the presence of the referrable lesion. The data used were collected at 2 quaternary academic hospitals in Boston, Massachusetts: Beth Israel Deaconess Medical Center (The Medical Information Mart for Intensive Care Chest X-Ray [MIMIC-CXR]) and Massachusetts General Hospital (MGH). MAIN OUTCOMES AND MEASURESThe ground truths for the labels were created via consensual reading by 2 thoracic radiologists. Each reader documented their findings in a customized report template, in which the 4 target chest radiograph findings and the reader confidence of the presence of each finding was recorded. The time taken for reporting each chest radiograph was also recorded.Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were calculated for each target finding. RESULTSA total of 6 radiologists (2 attending radiologists, 2 thoracic radiology fellows, and 2 residents) participated in the study. The study involved a total of 497 frontal chest radiographs-247 from the MIMIC-CXR data set (demographic data for patients were not available) and 250 chest radiographs from MGH (mean [SD] age, 63 [16] years; 133 men [53.2%])-from adult patients with and without 4 target findings (pneumonia, nodule, pneumothorax, and pleural effusion). The target findings were found in 351 of 497 chest radiographs. The AI was associated with higher sensitivity for all findings compared with the readers (nodule, 0.

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Section: Discussionsupporting

confidence: 81%

Association of Artificial Intelligence–Aided Chest Radiograph Interpretation With Reader Performance and Efficiency

Ahn¹,

Ebrahimian

McDermott

et al. 2022

JAMA Netw Open

View full text Add to dashboard Cite

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“…Then, the CTR can be calculated accordingly by traditional methods. Similar methods have been shown to be accurate and effective [ 12 , 13 , 14 , 15 , 16 ] in previous works. However, hemodialysis patients are predisposed to a considerable prevalence of pulmonary complications (e.g., pulmonary edema 8.20–20.50%, pleural effusions 22.95–33.80%) [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 69%

Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients

et al. 2023

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Cardiomegaly is associated with poor clinical outcomes and is assessed by routine monitoring of the cardiothoracic ratio (CTR) from chest X-rays (CXRs). Judgment of the margins of the heart and lungs is subjective and may vary between different operators. Methods: Patients aged > 19 years in our hemodialysis unit from March 2021 to October 2021 were enrolled. The borders of the lungs and heart on CXRs were labeled by two nephrologists as the ground truth (nephrologist-defined mask). We implemented AlbuNet-34, a U-Net variant, to predict the heart and lung margins from CXR images and to automatically calculate the CTRs. Results: The coefficient of determination (R2) obtained using the neural network model was 0.96, compared with an R2 of 0.90 obtained by nurse practitioners. The mean difference between the CTRs calculated by the nurse practitioners and senior nephrologists was 1.52 ± 1.46%, and that between the neural network model and the nephrologists was 0.83 ± 0.87% (p < 0.001). The mean CTR calculation duration was 85 s using the manual method and less than 2 s using the automated method (p < 0.001). Conclusions: Our study confirmed the validity of automated CTR calculations. By achieving high accuracy and saving time, our model can be implemented in clinical practice.

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“…The performance of the technique was validated in the 2015 International Symposium on Biomedical Imaging Cell-Tracking Challenge [ 18 ]. However, we did not re-evaluate the model because many studies have tested the method [ 26 , 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images

et al. 2022

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Early detection increases overall survival among patients with lung cancer. This study formulated a machine learning method that processes chest X-rays (CXRs) to detect lung cancer early. After we preprocessed our dataset using monochrome and brightness correction, we used different kinds of preprocessing methods to enhance image contrast and then used U-net to perform lung segmentation. We used 559 CXRs with a single lung nodule labeled by experts to train a You Only Look Once version 4 (YOLOv4) deep-learning architecture to detect lung nodules. In a testing dataset of 100 CXRs from patients at Taipei Veterans General Hospital and 154 CXRs from the Japanese Society of Radiological Technology dataset, the sensitivity of the AI model using a combination of different preprocessing methods performed the best at 79%, with 3.04 false positives per image. We then tested the AI by using 383 sets of CXRs obtained in the past 5 years prior to lung cancer diagnoses. The median time from detection to diagnosis for radiologists assisted with AI was 46 (3–523) days, longer than that for radiologists (8 (0–263) days). The AI model can assist radiologists in the early detection of lung nodules.

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Detection and Semiquantitative Analysis of Cardiomegaly, Pneumothorax, and Pleural Effusion on Chest Radiographs

Cited by 19 publications

References 27 publications

Association of Artificial Intelligence–Aided Chest Radiograph Interpretation With Reader Performance and Efficiency

Association of Artificial Intelligence–Aided Chest Radiograph Interpretation With Reader Performance and Efficiency

Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients

Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images

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