Multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19 using pristine ground-truth, versus radiologists

Tan, Tao; Das, Bipul; Soni, Ravi; Fejes, Mate; Yang, Haixiu; Ranjan, Sohan; Szabó, Dániel; Melapudi, Vikram; Shriram, K. S.; Agrawal, Utkarsh; Ruskó, László; Herczeg, Zita; Darázs, Barbara; Tegzes, Pal; Ferenczi, Lehel; Mullick, Rakesh; Avinash, Gopal

doi:10.1016/j.neucom.2022.02.040

Cited by 16 publications

(4 citation statements)

References 48 publications

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“…In addition, the short detection time enables decisionmaking within 15-20 minutes 3 . Throughout the COVID-19 pandemic, numerous studies have arisen focusing on the use of medical imaging, such as chest x-rays, for disease diagnosis and the analysis of multidrug-resistant bacteria [4][5][6] , however, in this paper we focus more on the social security issues raised by SARS-CoV-2. Therefore, the use of RDT for rapid detection of the viral genome of patients in the region can be useful for early triage and rapid management.…”

Section: Introductionmentioning

confidence: 99%

RDT-FSDet: Few-shot Object Detection for Rapid Antigen Test

Duan,

Wang,

Tan

et al. 2023

Interdisciplinary Nursing Research

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Objective: Manual verification of rapid diagnostic test (RDT) results is a time-consuming task; therefore, it is essential to introduce an object detection model into RDT result recognition to reduce the time involved. To address these problems, a detector that can rapidly adapt to different RDT results in various regions is important. Methods: We employed the few-shot object detection strategy and trained the Faster R-CNN detector with the mainland data set as the base class, followed by fine-tuning with the few-shot approach on the Macau RDT result data set. Moreover, we introduced 2 novel data augmentation methods, namely the “light simulation mask method” and “synthetic positive samples” for an unbalanced data set, to increase the sample size and balance the data set of the RDT detection task. Result: Compared with LightR-YOLOv5, RDT-few-shot detector (FSDet) achieved an mean average precision of 91.18 and a recall of 93.59 on the Macau RDT data set, demonstrating that this model can rapidly adapt to RDT results in different regions. The inference time of RDT-FSDet for each RDT result was 0.14 seconds, which can save ~90% of the detection time compared with manual screening. Conclusion: In addition to its application in the context of the coronavirus disease 2019 pandemic, this model can also be used as a general small-sample detection model. RDT-FSDet can be applied to the detection tasks of other small data sets, such as managing and analyzing detection results in other or future epidemics.

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Section: Introductionmentioning

confidence: 99%

RDT-FSDet: Few-shot Object Detection for Rapid Antigen Test

Duan,

Wang,

Tan

et al. 2023

Interdisciplinary Nursing Research

Self Cite

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“… 20 , 21 Compared with the single mode (X-ray only), the AUC of the existing multi-modal X-ray diagnosis in COVID-19 and non-COVID-19 pneumonia increased from 0.89 to 0.93. 22 Therefore, whether multi-modal information including X-ray data can improve the differential diagnosis of gram-positive or gram-negative bacteria requires further investigation.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model for the Diagnosis and Discrimination of Gram-Positive and Gram-Negative Bacterial Pneumonia for Children Using Chest Radiography Images and Clinical Information

Wen¹,

Xu²,

Cai³

et al. 2023

IDR

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Purpose This study aimed to develop a deep learning model based on chest radiography (CXR) images and clinical data to accurately classify gram-positive and gram-negative bacterial pneumonia in children to guide the use of antibiotics. Methods We retrospectively collected CXR images along with clinical information for gram-positive (n=447) and gram-negative (n=395) bacterial pneumonia in children from January 1, 2016, to June 30, 2021. Four types of machine learning models based on clinical data and six types of deep learning algorithm models based on image data were constructed, and multi-modal decision fusion was performed. Results In the machine learning models, CatBoost, which only used clinical data, had the best performance; its area under the receiver operating characteristic curve (AUC) was significantly higher than that of the other models (P<0.05). The incorporation of clinical information improved the performance of deep learning models that relied solely on image-based classification. Consequently, AUC and F1 increased by 5.6% and 10.2% on average, respectively. The best quality was achieved with ResNet101 (model accuracy: 0.75, recall rate: 0.84, AUC: 0.803, F1: 0.782). Conclusion Our study established a pediatric bacterial pneumonia model that utilizes CXR and clinical data to accurately classify cases of gram-negative and gram-positive bacterial pneumonia. The results confirmed that the addition of image data to the convolutional neural network model significantly improved its performance. While the CatBoost-based classifier had greater advantages owing to a smaller dataset, the quality of the Resnet101 model trained using multi-modal data was comparable to that of the CatBoost model, even with a limited number of samples.

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“…The usage of small key points causes increase in the efficiency of the hand-coded approaches but results in missing acquiring the significant aspect of image modality which in turn decreases the classification results. Due to such reasons, these methods are not found to be proficient for the CXR analysis (13). Now, the success of Artificial Intelligence (AI)-based techniques in the automatic diagnosis of medical diseases is astonishing.…”

Section: Introductionmentioning

confidence: 99%

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

Albahli

Nazir

2022

Front. Med.

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Machine learning techniques have lately attracted a lot of attention for their potential to execute expert-level clinical tasks, notably in the area of medical image analysis. Chest radiography is one of the most often utilized diagnostic imaging modalities in medical practice, and it necessitates timely coverage regarding the presence of probable abnormalities and disease diagnoses in the images. Computer-aided solutions for the identification of chest illness using chest radiography are being developed in medical imaging research. However, accurate localization and categorization of specific disorders in chest X-ray images is still a challenging problem due to the complex nature of radiographs, presence of different distortions, high inter-class similarities, and intra-class variations in abnormalities. In this work, we have presented an Artificial Intelligence (AI)-enabled fully automated approach using an end-to-end deep learning technique to improve the accuracy of thoracic illness diagnosis. We proposed AI-CenterNet CXR, a customized CenterNet model with an improved feature extraction network for the recognition of multi-label chest diseases. The enhanced backbone computes deep key points that improve the abnormality localization accuracy and, thus, overall disease classification performance. Moreover, the proposed architecture is lightweight and computationally efficient in comparison to the original CenterNet model. We have performed extensive experimentation to validate the effectiveness of the proposed technique using the National Institutes of Health (NIH) Chest X-ray dataset. Our method achieved an overall Area Under the Curve (AUC) of 0.888 and an average IOU of 0.801 to detect and classify the eight types of chest abnormalities. Both the qualitative and quantitative findings reveal that the suggested approach outperforms the existing methods, indicating the efficacy of our approach.

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Multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19 using pristine ground-truth, versus radiologists

Cited by 16 publications

References 48 publications

RDT-FSDet: Few-shot Object Detection for Rapid Antigen Test

RDT-FSDet: Few-shot Object Detection for Rapid Antigen Test

A Deep Learning Model for the Diagnosis and Discrimination of Gram-Positive and Gram-Negative Bacterial Pneumonia for Children Using Chest Radiography Images and Clinical Information

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

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