A Deep Learning Method for Alerting Emergency Physicians about the Presence of Subphrenic Free Air on Chest Radiographs

Su, Chien Wei; Tsai, Tsung-Yu; Tseng, Cheng-Yen; Liu, Keng-Hao; Lee, Chi‐Wei

doi:10.3390/jcm10020254

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…Su et al [ 49 ] proposed a DL method for alerting emergency physicians about the presence of subphrenic free air on frontal CXR, and achieved 0.875, 0.825, and 0.889 in sensitivity, specificity, and AUC scores, respectively. This tool may provide a sensitive additional screening to detect pneumoperitoneum.…”

Section: Ai Applications In Oncologic Abdominal Emergenciesmentioning

confidence: 99%

“…However, as above-mentioned, many emergency physicians lack sufficient experience to recognize pneumoperitoneum promptly and for this reason, it is essential to develop an automated method for frontal review of the X-ray images of the chest to warn of the danger of the clinical picture and to have a second look. Su et al [49] proposed a DL method for alerting emergency physicians about the presence of subphrenic free air on frontal CXR, and achieved 0.875, 0.825, and 0.889 in sensitivity, specificity, and AUC scores, respectively. This tool may provide a sensitive additional screening to detect pneumoperitoneum.…”

Section: Ai Applications In Oncologic Abdominal Emergenciesmentioning

confidence: 99%

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Role of artificial intelligence in oncologic emergencies: a narrative review

Fanni

Greco

Rossi

et al. 2023

Exploration of Targeted Anti-Tumor Therapy

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Oncologic emergencies are a wide spectrum of oncologic conditions caused directly by malignancies or their treatment. Oncologic emergencies may be classified according to the underlying physiopathology in metabolic, hematologic, and structural conditions. In the latter, radiologists have a pivotal role, through an accurate diagnosis useful to provide optimal patient care. Structural conditions may involve the central nervous system, thorax, or abdomen, and emergency radiologists have to know the characteristics imaging findings of each one of them. The number of oncologic emergencies is growing due to the increased incidence of malignancies in the general population and also to the improved survival of these patients thanks to the advances in cancer treatment. Artificial intelligence (AI) could be a solution to assist emergency radiologists with this rapidly increasing workload. To our knowledge, AI applications in the setting of the oncologic emergency are mostly underexplored, probably due to the relatively low number of oncologic emergencies and the difficulty in training algorithms. However, cancer emergencies are defined by the cause and not by a specific pattern of radiological symptoms and signs. Therefore, it can be expected that AI algorithms developed for the detection of these emergencies in the non-oncological field can be transferred to the clinical setting of oncologic emergency. In this review, a craniocaudal approach was followed and central nervous system, thoracic, and abdominal oncologic emergencies have been addressed regarding the AI applications reported in literature. Among the central nervous system emergencies, AI applications have been reported for brain herniation and spinal cord compression. In the thoracic district the addressed emergencies were pulmonary embolism, cardiac tamponade and pneumothorax. Pneumothorax was the most frequently described application for AI, to improve sensibility and to reduce the time-to-diagnosis. Finally, regarding abdominal emergencies, AI applications for abdominal hemorrhage, intestinal obstruction, intestinal perforation, and intestinal intussusception have been described.

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Section: Ai Applications In Oncologic Abdominal Emergenciesmentioning

confidence: 99%

Section: Ai Applications In Oncologic Abdominal Emergenciesmentioning

confidence: 99%

Role of artificial intelligence in oncologic emergencies: a narrative review

Fanni

Greco

Rossi

et al. 2023

Exploration of Targeted Anti-Tumor Therapy

View full text Add to dashboard Cite

show abstract

“…For prediction at test stage, region proposal task was still an issue that required further improvements. A segmentation and classification model proposed to compare with radiologist cohort Private [82] A CNN model proposed for identification of abnormal CXRs and localization of abnormalities Private [83] Localizing COVID-19 opacity and severity detection on CXRs Private [84] Use of Lung cropped CXR in DenseNet for cardiomegaly detection Open-I, PadChest [85] Applied multiple models and combinations of CXR datasets to detect COVID-19 ChestX-ray14 JSRT + SCR, COVID-CXR [86] Multiple architectures evaluated for two-stage classification of pneumonia Ped-pneumonia [87] Inception-v3 based pneumoconiosis detection and evaluation against two radiologists Private [88] VGG-16 architecture adapted for classification of pediatric pneumonia types Ped-pneumonia [89] Used ResNet-50 as backbone for segmentation model to detect healthy, pneumonia, and COVID-19 COVID-CXR [90] CNN employed to detect the presence of subphrenic free air from CXR Private [91] Binary classification vs One-class identification of viral pneumonia cases Private [92] Applied a weighting scheme to improve abnormality for classification ChestX-ray14 [93] To improve image-level classification, a Lesion detection network has been employed Private [94] An ensemble scheme has been used for DenseNet-121 networks for COVID-19 classification ChestX-ray14…”

Section: Fast R-cnnmentioning

confidence: 99%

“…Two of the important utilities of these datasets are (1) validity of proposed work and (2) further advancements. Examples of such work can be found in [81][82][83]87,90,91,93,107]. They trained their models on private data which may not be re-producible by other researchers.…”

Section: Disclosure Of Training Datamentioning

confidence: 99%

A Survey on Tools and Techniques for Localizing Abnormalities in X-ray Images Using Deep Learning

et al. 2022

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Deep learning is expanding and continues to evolve its capabilities toward more accuracy, speed, and cost-effectiveness. The core ingredients for getting its promising results are appropriate data, sufficient computational resources, and best use of a particular algorithm. The application of these algorithms in medical image analysis tasks has achieved outstanding results compared to classical machine learning approaches. Localizing the area-of-interest is a challenging task that has vital importance in computer aided diagnosis. Generally, radiologists interpret the radiographs based on their knowledge and experience. However, sometimes, they can overlook or misinterpret the findings due to various reasons, e.g., workload or judgmental error. This leads to the need for specialized AI tools that assist radiologists in highlighting abnormalities if exist. To develop a deep learning driven localizer, certain alternatives are available within architectures, datasets, performance metrics, and approaches. Informed decision for selection within the given alternative can lead to batter outcome within lesser resources. This paper lists the required components along-with explainable AI for developing an abnormality localizer for X-ray images in detail. Moreover, strong-supervised vs weak-supervised approaches have been majorly discussed in the light of limited annotated data availability. Likewise, other correlated challenges have been presented along-with recommendations based on a relevant literature review and similar studies. This review is helpful in streamlining the development of an AI based localizer for X-ray images while extendable for other radiological reports.

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“…A few studies have applied DL to detect pneumoperitoneum on abdominal radiographs [ 11 12 13 14 ]; however, their value has been limited because they have been based on a traditional supervised learning approach with minimal labeled data, which is insufficient to guarantee generalizability. Additionally, these studies have neither used their model for the challenging task of detecting pneumoperitoneum in supine radiographs nor showed the clinical benefit of its application.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enabled Detection of Pneumoperitoneum in Supine and Erect Abdominal Radiography: Modeling Using Transfer Learning and Semi-Supervised Learning

Park

Lee

et al. 2023

Korean J Radiol

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Objective Detection of pneumoperitoneum using abdominal radiography, particularly in the supine position, is often challenging. This study aimed to develop and externally validate a deep learning model for the detection of pneumoperitoneum using supine and erect abdominal radiography. Materials and Methods A model that can utilize “pneumoperitoneum” and “non-pneumoperitoneum” classes was developed through knowledge distillation. To train the proposed model with limited training data and weak labels, it was trained using a recently proposed semi-supervised learning method called distillation for self-supervised and self-train learning (DISTL), which leverages the Vision Transformer. The proposed model was first pre-trained with chest radiographs to utilize common knowledge between modalities, fine-tuned, and self-trained on labeled and unlabeled abdominal radiographs. The proposed model was trained using data from supine and erect abdominal radiographs. In total, 191212 chest radiographs (CheXpert data) were used for pre-training, and 5518 labeled and 16671 unlabeled abdominal radiographs were used for fine-tuning and self-supervised learning, respectively. The proposed model was internally validated on 389 abdominal radiographs and externally validated on 475 and 798 abdominal radiographs from the two institutions. We evaluated the performance in diagnosing pneumoperitoneum using the area under the receiver operating characteristic curve (AUC) and compared it with that of radiologists. Results In the internal validation, the proposed model had an AUC, sensitivity, and specificity of 0.881, 85.4%, and 73.3% and 0.968, 91.1, and 95.0 for supine and erect positions, respectively. In the external validation at the two institutions, the AUCs were 0.835 and 0.852 for the supine position and 0.909 and 0.944 for the erect position. In the reader study, the readers’ performances improved with the assistance of the proposed model. Conclusion The proposed model trained with the DISTL method can accurately detect pneumoperitoneum on abdominal radiography in both the supine and erect positions.

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A Deep Learning Method for Alerting Emergency Physicians about the Presence of Subphrenic Free Air on Chest Radiographs

Cited by 5 publications

References 33 publications

Role of artificial intelligence in oncologic emergencies: a narrative review

Role of artificial intelligence in oncologic emergencies: a narrative review

A Survey on Tools and Techniques for Localizing Abnormalities in X-ray Images Using Deep Learning

Deep Learning-Enabled Detection of Pneumoperitoneum in Supine and Erect Abdominal Radiography: Modeling Using Transfer Learning and Semi-Supervised Learning

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