Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis

Sugibayashi, Toshio; Walston, Shannon L; Matsumoto, T; Mitsuyama, Yasuhito; Miki, Yukio; Ueda, Daiju

doi:10.1183/16000617.0259-2022

Cited by 20 publications

(4 citation statements)

References 89 publications

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“…One similar review study found a pooled sensitivity of 84% and specificity of 96% for the performance of DL for pneumothorax detection across all imaging modalities, in close agreement with our results. 40 Our study focused specifically on pneumothorax detection in CXRs (which are the first line imaging technique for pneumothorax) and used meta-regression to further evaluate several factors that may have an impact on performance. DL algorithms have also been developed to examine a variety of other common CXR pathologies, such as pneumonia.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Pneumothorax Detection on Chest Radiograph: A Diagnostic Test Accuracy Systematic Review and Meta Analysis

Katzman,

Alabousi,

Islam

et al. 2024

Can Assoc Radiol J

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Background: Pneumothorax is a common acute presentation in healthcare settings. A chest radiograph (CXR) is often necessary to make the diagnosis, and minimizing the time between presentation and diagnosis is critical to deliver optimal treatment. Deep learning (DL) algorithms have been developed to rapidly identify pathologic findings on various imaging modalities. Purpose: The purpose of this systematic review and meta-analysis was to evaluate the overall performance of studies utilizing DL algorithms to detect pneumothorax on CXR. Methods: A study protocol was created and registered a priori (PROSPERO CRD42023391375). The search strategy included studies published up until January 10, 2023. Inclusion criteria were studies that used adult patients, utilized computer-aided detection of pneumothorax on CXR, dataset was evaluated by a qualified physician, and sufficient data was present to create a 2 × 2 contingency table. Risk of bias was assessed using the QUADAS-2 tool. Bivariate random effects meta-analyses and meta-regression modeling were performed. Results: Twenty-three studies were selected, including 34 011 patients and 34 075 CXRs. The pooled sensitivity and specificity were 87% (95% confidence interval, 81%, 92%) and 95% (95% confidence interval, 92%, 97%), respectively. The study design, use of an institutional/public data set and risk of bias had no significant effect on the sensitivity and specificity of pneumothorax detection. Conclusions: The relatively high sensitivity and specificity of pneumothorax detection by deep-learning showcases the vast potential for implementation in clinical settings to both augment the workflow of radiologists and assist in more rapid diagnoses and subsequent patient treatment.

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Section: Discussionmentioning

confidence: 99%

Deep Learning for Pneumothorax Detection on Chest Radiograph: A Diagnostic Test Accuracy Systematic Review and Meta Analysis

Katzman,

Alabousi,

Islam

et al. 2024

Can Assoc Radiol J

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“…Therefore, the deep learning model can automatically cover falsenegative cases that human experts may miss, helping to diagnose diseases. In a recent meta-analysis of deep learning for pneumothorax diagnosis, both sensitivity and speci city were improved by increasing the number of training samples 55 . However, there is a possibility of a high risk of bias, indicating that additional studies with participants or study designs to reduce this risk are needed.…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Temporomandibular Joint Effusion with Deep Learning Algorithm

Lee,

Jeon,

Won

et al. 2023

Preprint

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This study investigated the usefulness of deep learning-based automatic detection of temporomandibular joint (TMJ) effusion using magnetic resonance imaging (MRI) in patients with temporomandibular joint disorder (TMD) and whether the diagnostic accuracy of the model improved when patients’ clinical information was provided in addition to MRI images. The sagittal MR images of 2,948 TMJs were collected from 1,017 women and 457 men (mean age 37.19 ± 18.64 years). The TMJ effusion diagnostic performances of three convolutional neural networks (scratch, fine-tuning, and freeze schemes) were compared with those of human experts based on areas under the curve (AUCs) and diagnosis accuracies. The fine-tuning model with proton density (PD) images showed acceptable prediction performance (AUC = 0.7895), and the from-scratch (0.6193) and freeze (0.6149) models showed lower performances (p < 0.05). The fine-tuning model had excellent specificity compared to the human experts (87.25% vs. 58.17%). However, the human experts were superior in sensitivity (80.00% vs. 57.43%) (all p < 0.001). In Grad-CAM visualizations, the fine-tuning scheme focused more on effusion than on other structures of the TMJ, and the sparsity was higher than that of the from-scratch scheme (82.40% vs. 49.83%, p < 0.05). The Grad-CAM visualizations agreed with the model learned through important features in the TMJ area, particularly around the articular disc. Two fine-tuning models on PD and T2-weighted images showed that the diagnostic performance did not improve compared with using PD alone (p < 0.05). Diverse AUCs were observed across each group when the patients were divided according to age (0.7083–0.8375) and sex (male:0.7576, female:0.7083). The prediction accuracy of the ensemble model was higher than that of the human experts when all the data were used (74.21% vs. 67.71%, p < 0.05). A deep neural network (DNN) was developed to process multimodal data, including MRI and patient clinical data. Analysis of four age groups with the DNN model showed that the 41–60 age group had the best performance (AUC = 0.8258). There was no significant difference between the prediction performances of the fine-tuning model and the DNN (p > 0.05). The fine-tuning model and DNN were optimal for judging TMJ effusion and may be used to prevent true negative cases and aid in human diagnostic performance. Assistive automated diagnostic methods have the potential to increase clinicians’ diagnostic accuracy.

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“…Deep learning techniques have been effectively applied in diagnosing pneumothorax (Sugibayashi et al 2023), aiding in the recognition and classification of its various forms. For traumatic pneumothorax, caused by physical damage to lung tissues, algorithms can automatically detect and localize the condition from CT images (Majkowska et al 2020, Tran et al 2021.…”

Section: Objectivesmentioning

confidence: 99%

Developing an explainable diagnosis system utilizing deep learning model: a case study of spontaneous pneumothorax

Lin,

Wei,

Bai

et al. 2024

Phys. Med. Biol.

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Objective.The trend in the medical field is towards intelligent detection-based medical diagnostic systems. However, these methods are often seen as "black boxes" due to their lack of interpretability. This situation presents challenges in identifying reasons for misdiagnoses and improving accuracy, which leads to potential risks of misdiagnosis and delayed treatment. Therefore, how to enhance the interpretability of diagnostic models is crucial for improving patient outcomes and reducing treatment delays. So far, only limited researches exist on deep learning-based prediction of spontaneous pneumothorax, a pulmonary disease that affects lung ventilation and venous return. Approach. This study develops an integrated medical image analysis system using explainable deep learning model for image recognition and visualization to achieve an interpretable automatic diagnosis process.Main results.The system achieves an impressive 95.56% accuracy in pneumothorax classification, which emphasizes the significance of the blood vessel penetration defect in clinical judgment.Significance.This would lead to improve model trustworthiness, reduce uncertainty, and accurate diagnosis of various lung diseases, which results in better medical outcomes for patients and better utilization of medical resources. Future research can focus on implementing new deep learning models to detect and diagnose other lung diseases that can enhance the generalizability of this system.

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Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis

Cited by 20 publications

References 89 publications

Deep Learning for Pneumothorax Detection on Chest Radiograph: A Diagnostic Test Accuracy Systematic Review and Meta Analysis

Deep Learning for Pneumothorax Detection on Chest Radiograph: A Diagnostic Test Accuracy Systematic Review and Meta Analysis

Automatic Detection of Temporomandibular Joint Effusion with Deep Learning Algorithm

Developing an explainable diagnosis system utilizing deep learning model: a case study of spontaneous pneumothorax

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