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Ji Z, Mu J, Liu J, Zhang H, Dai C, Zhang X, Ganchev I. ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation. Med Biol Eng Comput 2024; 62:1673-1687. [PMID: 38326677 PMCID: PMC11076390 DOI: 10.1007/s11517-024-03025-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 01/15/2024] [Indexed: 02/09/2024]
Abstract
Early intervention in tumors can greatly improve human survival rates. With the development of deep learning technology, automatic image segmentation has taken a prominent role in the field of medical image analysis. Manually segmenting kidneys on CT images is a tedious task, and due to the diversity of these images and varying technical skills of professionals, segmentation results can be inconsistent. To address this problem, a novel ASD-Net network is proposed in this paper for kidney and kidney tumor segmentation tasks. First, the proposed network employs newly designed Adaptive Spatial-channel Convolution Optimization (ASCO) blocks to capture anisotropic information in the images. Then, other newly designed blocks, i.e., Dense Dilated Enhancement Convolution (DDEC) blocks, are utilized to enhance feature propagation and reuse it across the network, thereby improving its segmentation accuracy. To allow the network to segment complex and small kidney tumors more effectively, the Atrous Spatial Pyramid Pooling (ASPP) module is incorporated in its middle layer. With its generalized pyramid feature, this module enables the network to better capture and understand context information at various scales within the images. In addition to this, the concurrent spatial and channel squeeze & excitation (scSE) attention mechanism is adopted to better comprehend and manage context information in the images. Additional encoding layers are also added to the base (U-Net) and connected to the original encoding layer through skip connections. The resultant enhanced U-Net structure allows for better extraction and merging of high-level and low-level features, further boosting the network's ability to restore segmentation details. In addition, the combined Binary Cross Entropy (BCE)-Dice loss is utilized as the network's loss function. Experiments, conducted on the KiTS19 dataset, demonstrate that the proposed ASD-Net network outperforms the existing segmentation networks according to all evaluation metrics used, except for recall in the case of kidney tumor segmentation, where it takes the second place after Attention-UNet.
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Affiliation(s)
- Zhanlin Ji
- Department of Artificial Intelligence, North China University of Science and Technology, Tangshan, 063009, People's Republic of China
| | - Juncheng Mu
- Department of Artificial Intelligence, North China University of Science and Technology, Tangshan, 063009, People's Republic of China
| | - Jianuo Liu
- Department of Artificial Intelligence, North China University of Science and Technology, Tangshan, 063009, People's Republic of China
| | - Haiyang Zhang
- Department of Computing, Xi'an Jiaotong-Liverpool University, Suzhou, People's Republic of China
| | - Chenxu Dai
- Department of Artificial Intelligence, North China University of Science and Technology, Tangshan, 063009, People's Republic of China
| | - Xueji Zhang
- School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, Guangdong, 518060, People's Republic of China.
| | - Ivan Ganchev
- Telecommunications Research Centre (TRC), University of Limerick, Limerick, V94 T9PX, Ireland.
- Department of Computer Systems, University of Plovdiv "Paisii Hilendarski", Plovdiv, 4000, Bulgaria.
- Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, 1040, Bulgaria.
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Rajinikanth V, Biju R, Mittal N, Mittal V, Askar S, Abouhawwash M. COVID-19 detection in lung CT slices using Brownian-butterfly-algorithm optimized lightweight deep features. Heliyon 2024; 10:e27509. [PMID: 38468955 PMCID: PMC10926136 DOI: 10.1016/j.heliyon.2024.e27509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 02/29/2024] [Accepted: 02/29/2024] [Indexed: 03/13/2024] Open
Abstract
Several deep-learning assisted disease assessment schemes (DAS) have been proposed to enhance accurate detection of COVID-19, a critical medical emergency, through the analysis of clinical data. Lung imaging, particularly from CT scans, plays a pivotal role in identifying and assessing the severity of COVID-19 infections. Existing automated methods leveraging deep learning contribute significantly to reducing the diagnostic burden associated with this process. This research aims in developing a simple DAS for COVID-19 detection using the pre-trained lightweight deep learning methods (LDMs) applied to lung CT slices. The use of LDMs contributes to a less complex yet highly accurate detection system. The key stages of the developed DAS include image collection and initial processing using Shannon's thresholding, deep-feature mining supported by LDMs, feature optimization utilizing the Brownian Butterfly Algorithm (BBA), and binary classification through three-fold cross-validation. The performance evaluation of the proposed scheme involves assessing individual, fused, and ensemble features. The investigation reveals that the developed DAS achieves a detection accuracy of 93.80% with individual features, 96% accuracy with fused features, and an impressive 99.10% accuracy with ensemble features. These outcomes affirm the effectiveness of the proposed scheme in significantly enhancing COVID-19 detection accuracy in the chosen lung CT database.
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Affiliation(s)
- Venkatesan Rajinikanth
- Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, Tamil Nadu, India
| | - Roshima Biju
- Department of Computer Science Engineering, Parul University, Vadodara, 391760, Gujarat, India
| | - Nitin Mittal
- Skill Faculty of Engineering and Technology, Shri Vishwakarma Skill University, Palwal, 121102, Haryana, India
| | - Vikas Mittal
- Department of Electronics and Communication Engineering, Chandigarh University, Mohali, 140413, India
| | - S.S. Askar
- Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Mohamed Abouhawwash
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
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Liu F, Zhu T, Wu X, Yang B, You C, Wang C, Lu L, Liu Z, Zheng Y, Sun X, Yang Y, Clifton L, Clifton DA. A medical multimodal large language model for future pandemics. NPJ Digit Med 2023; 6:226. [PMID: 38042919 PMCID: PMC10693607 DOI: 10.1038/s41746-023-00952-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 10/24/2023] [Indexed: 12/04/2023] Open
Abstract
Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Since most neural networks are supervised, their performance heavily depends on the volume and quality of available labels. However, few such labels exist for rare diseases (e.g., new pandemics). Here we report a medical multimodal large language model (Med-MLLM) for radiograph representation learning, which can learn broad medical knowledge (e.g., image understanding, text semantics, and clinical phenotypes) from unlabelled data. As a result, when encountering a rare disease, our Med-MLLM can be rapidly deployed and easily adapted to them with limited labels. Furthermore, our model supports medical data across visual modality (e.g., chest X-ray and CT) and textual modality (e.g., medical report and free-text clinical note); therefore, it can be used for clinical tasks that involve both visual and textual data. We demonstrate the effectiveness of our Med-MLLM by showing how it would perform using the COVID-19 pandemic "in replay". In the retrospective setting, we test the model on the early COVID-19 datasets; and in the prospective setting, we test the model on the new variant COVID-19-Omicron. The experiments are conducted on 1) three kinds of input data; 2) three kinds of downstream tasks, including disease reporting, diagnosis, and prognosis; 3) five COVID-19 datasets; and 4) three different languages, including English, Chinese, and Spanish. All experiments show that our model can make accurate and robust COVID-19 decision-support with little labelled data.
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Affiliation(s)
- Fenglin Liu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Tingting Zhu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Xian Wu
- Jarvis Research Center, Tencent YouTu Lab, Beijing, China
| | - Bang Yang
- School of Computer Science, Peking University, Beijing, China
| | | | - Chenyang Wang
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Lei Lu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Zhangdaihong Liu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
- Oxford-Suzhou Centre for Advanced Research, Suzhou, China
| | - Yefeng Zheng
- Jarvis Research Center, Tencent YouTu Lab, Beijing, China
| | - Xu Sun
- School of Computer Science, Peking University, Beijing, China
| | - Yang Yang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lei Clifton
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
- Oxford-Suzhou Centre for Advanced Research, Suzhou, China.
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Atceken Z, Celik Y, Atasoy C, Peker Y. The Diagnostic Utility of Artificial Intelligence-Guided Computed Tomography-Based Severity Scores for Predicting Short-Term Clinical Outcomes in Adults with COVID-19 Pneumonia. J Clin Med 2023; 12:7039. [PMID: 38002652 PMCID: PMC10672493 DOI: 10.3390/jcm12227039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
Chest computed tomography (CT) imaging with the use of an artificial intelligence (AI) analysis program has been helpful for the rapid evaluation of large numbers of patients during the COVID-19 pandemic. We have previously demonstrated that adults with COVID-19 infection with high-risk obstructive sleep apnea (OSA) have poorer clinical outcomes than COVID-19 patients with low-risk OSA. In the current secondary analysis, we evaluated the association of AI-guided CT-based severity scores (SSs) with short-term outcomes in the same cohort. In total, 221 patients (mean age of 52.6 ± 15.6 years, 59% men) with eligible chest CT images from March to May 2020 were included. The AI program scanned the CT images in 3D, and the algorithm measured volumes of lobes and lungs as well as high-opacity areas, including ground glass and consolidation. An SS was defined as the ratio of the volume of high-opacity areas to that of the total lung volume. The primary outcome was the need for supplemental oxygen and hospitalization over 28 days. A receiver operating characteristic (ROC) curve analysis of the association between an SS and the need for supplemental oxygen revealed a cut-off score of 2.65 on the CT images, with a sensitivity of 81% and a specificity of 56%. In a multivariate logistic regression model, an SS > 2.65 predicted the need for supplemental oxygen, with an odds ratio (OR) of 3.98 (95% confidence interval (CI) 1.80-8.79; p < 0.001), and hospitalization, with an OR of 2.40 (95% CI 1.23-4.71; p = 0.011), adjusted for age, sex, body mass index, diabetes, hypertension, and coronary artery disease. We conclude that AI-guided CT-based SSs can be used for predicting the need for supplemental oxygen and hospitalization in patients with COVID-19 pneumonia.
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Affiliation(s)
- Zeynep Atceken
- Department of Radiology, Koc University School of Medicine, Istanbul 34010, Turkey; (Z.A.); (C.A.)
| | - Yeliz Celik
- Center for Translational Medicine (KUTTAM), Department of Pulmonary Medicine, Koc University School of Medicine, and Koc University Research, Koc University, Istanbul 34010, Turkey;
| | - Cetin Atasoy
- Department of Radiology, Koc University School of Medicine, Istanbul 34010, Turkey; (Z.A.); (C.A.)
| | - Yüksel Peker
- Center for Translational Medicine (KUTTAM), Department of Pulmonary Medicine, Koc University School of Medicine, and Koc University Research, Koc University, Istanbul 34010, Turkey;
- Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden
- Department of Clinical Sciences, Respiratory Medicine and Allergology, Faculty of Medicine, Lund University, 22185 Lund, Sweden
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Division of Sleep and Circadian Disorders, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, USA
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Feyisa DW, Ayano YM, Debelee TG, Schwenker F. Weak Localization of Radiographic Manifestations in Pulmonary Tuberculosis from Chest X-ray: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:6781. [PMID: 37571564 PMCID: PMC10422452 DOI: 10.3390/s23156781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/03/2023] [Accepted: 07/14/2023] [Indexed: 08/13/2023]
Abstract
Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one of the infectious diseases with the highest global mortalities. Chest radiography is a technique that is often employed in the diagnosis of PTB. Radiologists identify the severity and stage of PTB by inspecting radiographic features in the patient's chest X-ray (CXR). The most common radiographic features seen on CXRs include cavitation, consolidation, masses, pleural effusion, calcification, and nodules. Identifying these CXR features will help physicians in diagnosing a patient. However, identifying these radiographic features for intricate disorders is challenging, and the accuracy depends on the radiologist's experience and level of expertise. So, researchers have proposed deep learning (DL) techniques to detect and mark areas of tuberculosis infection in CXRs. DL models have been proposed in the literature because of their inherent capacity to detect diseases and segment the manifestation regions from medical images. However, fully supervised semantic segmentation requires several pixel-by-pixel labeled images. The annotation of such a large amount of data by trained physicians has some challenges. First, the annotation requires a significant amount of time. Second, the cost of hiring trained physicians is expensive. In addition, the subjectivity of medical data poses a difficulty in having standardized annotation. As a result, there is increasing interest in weak localization techniques. Therefore, in this review, we identify methods employed in the weakly supervised segmentation and localization of radiographic manifestations of pulmonary tuberculosis from chest X-rays. First, we identify the most commonly used public chest X-ray datasets for tuberculosis identification. Following that, we discuss the approaches for weakly localizing tuberculosis radiographic manifestations in chest X-rays. The weakly supervised localization of PTB can highlight the region of the chest X-ray image that contributed the most to the DL model's classification output and help pinpoint the diseased area. Finally, we discuss the limitations and challenges of weakly supervised techniques in localizing TB manifestations regions in chest X-ray images.
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Affiliation(s)
- Degaga Wolde Feyisa
- Ethiopian Artificial Intelligence Institute, Addis Ababa P.O. Box 40782, Ethiopia; (D.W.F.); (Y.M.A.); (T.G.D.)
| | - Yehualashet Megersa Ayano
- Ethiopian Artificial Intelligence Institute, Addis Ababa P.O. Box 40782, Ethiopia; (D.W.F.); (Y.M.A.); (T.G.D.)
| | - Taye Girma Debelee
- Ethiopian Artificial Intelligence Institute, Addis Ababa P.O. Box 40782, Ethiopia; (D.W.F.); (Y.M.A.); (T.G.D.)
- Department of Electrical and Computer Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 120611, Ethiopia
| | - Friedhelm Schwenker
- Institute of Neural Information Processing, Ulm University, 89069 Ulm, Germany
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Wen R, Xu P, Cai Y, Wang F, Li M, Zeng X, Liu C. 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. Infect Drug Resist 2023; 16:4083-4092. [PMID: 37388188 PMCID: PMC10305772 DOI: 10.2147/idr.s404786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/29/2023] [Indexed: 07/01/2023] Open
Abstract
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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Affiliation(s)
- Ru Wen
- Medical College, Guizhou University, Guizhou, 550000, People’s Republic of China
- Department of Medical Imaging, Guizhou Provincial People Hospital, Guiyang City, Guizhou Province, 550000, People’s Republic of China
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, People’s Republic of China
| | - Peng Xu
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, People’s Republic of China
| | - Yimin Cai
- Medical College, Guizhou University, Guizhou, 550000, People’s Republic of China
| | - Fang Wang
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, People’s Republic of China
| | - Mengfei Li
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, People’s Republic of China
| | - Xianchun Zeng
- Department of Medical Imaging, Guizhou Provincial People Hospital, Guiyang City, Guizhou Province, 550000, People’s Republic of China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, People’s Republic of China
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Sfayyih AH, Sulaiman N, Sabry AH. A review on lung disease recognition by acoustic signal analysis with deep learning networks. JOURNAL OF BIG DATA 2023; 10:101. [PMID: 37333945 PMCID: PMC10259357 DOI: 10.1186/s40537-023-00762-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/08/2023] [Indexed: 06/20/2023]
Abstract
Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.
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Affiliation(s)
- Alyaa Hamel Sfayyih
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Nasri Sulaiman
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Ahmad H. Sabry
- Department of Computer Engineering, Al-Nahrain University, Al Jadriyah Bridge, 64074 Baghdad, Iraq
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Tekerek A, Al-Rawe IAM. A Novel Approach for Prediction of Lung Disease Using Chest X-ray Images Based on DenseNet and MobileNet. WIRELESS PERSONAL COMMUNICATIONS 2023:1-15. [PMID: 37360137 PMCID: PMC10177707 DOI: 10.1007/s11277-023-10489-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/24/2023] [Indexed: 06/28/2023]
Abstract
Covid19 corona virus has caused widespread disruption across the world, in terms of the health, economy, and society problems. X-ray images of the chest can be helpful in making an accurate diagnosis because the corona virus typically first manifests its symptoms in patients' lungs. In this study, a classification method based on deep learning is proposed as a means of identifying lung disease from chest X-ray images. In the proposed study, the detection of covid19 corona virus disease from chest X-ray images was made with MobileNet and Densenet models, which are deep learning methods. Several different use cases can be built with the help of MobileNet model and case modelling approach is utilized to achieve 96% accuracy and an Area Under Curve (AUC) value of 94%. According to the result, the proposed method may be able to more accurately identify the signs of an impurity from dataset of chest X-ray images. This research also compares various performance parameters such as precision, recall and F1-Score.
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Affiliation(s)
- Adem Tekerek
- Department of Computer Engineering, Technology Faculty, Gazi University, Ankara, Türkiye
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Ho ML, Arnold CW, Decker SJ, Hazle JD, Krupinski EA, Mankoff DA. Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic. Acad Radiol 2023; 30:631-639. [PMID: 36764883 PMCID: PMC9816088 DOI: 10.1016/j.acra.2022.12.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 01/09/2023]
Abstract
Understanding imaging research experiences, challenges, and strategies for academic radiology departments during and after COVID-19 is critical to prepare for future disruptive events. We summarize key insights and programmatic initiatives at major academic hospitals across the world, based on literature review and meetings of the Radiological Society of North America Vice Chairs of Research (RSNA VCR) group. Through expert discussion and case studies, we provide suggested guidelines to maintain and grow radiology research in the postpandemic era.
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Affiliation(s)
- Mai-Lan Ho
- Nationwide Children's Hospital and The Ohio State University, Columbus, Ohio.
| | | | | | - John D. Hazle
- The University of Texas MD Anderson Cancer Center, Houston, Texas
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Han X, Hu Z, Wang S, Zhang Y. A Survey on Deep Learning in COVID-19 Diagnosis. J Imaging 2022; 9:jimaging9010001. [PMID: 36662099 PMCID: PMC9866755 DOI: 10.3390/jimaging9010001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/05/2022] [Accepted: 12/16/2022] [Indexed: 12/31/2022] Open
Abstract
According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.
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Affiliation(s)
- Xue Han
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Zuojin Hu
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China
| | - Shuihua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
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A Multi-centric Evaluation of Deep Learning Models for Segmentation of COVID-19 Lung Lesions on Chest CT Scans. IRANIAN JOURNAL OF RADIOLOGY 2022. [DOI: 10.5812/iranjradiol-117992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Background: Chest computed tomography (CT) scan is one of the most common tools used for the diagnosis of patients with coronavirus disease 2019 (COVID-19). While segmentation of COVID-19 lung lesions by radiologists can be time-consuming, the application of advanced deep learning techniques for automated segmentation can be a promising step toward the management of this infection and similar diseases in the future. Objectives: This study aimed to evaluate the performance and generalizability of deep learning-based models for the automated segmentation of COVID-19 lung lesions. Patients and Methods: Four datasets (2 private and 2 public) were used in this study. The first and second private datasets included 297 (147 healthy and 150 COVID-19 cases) and 82 COVID-19 subjects. The public datasets included the COVID19-P20 (20 COVID-19 cases from 2 centers) and the MosMedData datasets (50 COVID-19 patients from a single center). Model comparisons were made based on the Dice similarity coefficient (DSC), receiver operating characteristic (ROC) curve, and area under the curve (AUC). The predicted CT severity scores by the model were compared with those of radiologists by measuring the Pearson’s correlation coefficients (PCC). Also, DSC was used to compare the inter-rater agreement of the model and expert against that of 2 experts on an unseen dataset. Finally, the generalizability of the model was evaluated, and a simple calibration strategy was proposed. Results: The VGG16-UNet model showed the best performance across both private datasets, with a DSC of 84.23% ± 1.73% on the first private dataset and 56.61% ± 1.48% on the second private dataset. Similar results were obtained on public datasets, with a DSC of 60.10% ± 2.34% on the COVID19-P20 dataset and 66.28% ± 2.80% on a combined dataset of COVID19-P20 and MosMedData. The predicted CT severity scores of the model were compared against those of radiologists and were found to be 0.89 and 0.85 on the first private dataset and 0.77 and 0.74 on the second private dataset for the right and left lungs, respectively. Moreover, the model trained on the first private dataset was examined on the second private dataset and compared against the radiologist, which revealed a performance gap of 5.74% based on DSCs. A calibration strategy was employed to reduce this gap to 0.53%. Conclusion: The results demonstrated the potential of the proposed model in localizing COVID-19 lesions on CT scans across multiple datasets; its accuracy competed with the radiologists and could assist them in diagnostic and treatment procedures. The effect of model calibration on the performance of an unseen dataset was also reported, increasing the DSC by more than 5%.
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da Costa Avelar PH, Del Coco N, Lamb LC, Tsoka S, Cardoso-Silva J. A Bayesian predictive analytics model for improving long range epidemic forecasting during an infection wave. HEALTHCARE ANALYTICS (NEW YORK, N.Y.) 2022; 2:100115. [PMID: 37520620 PMCID: PMC9533637 DOI: 10.1016/j.health.2022.100115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/17/2022] [Accepted: 09/26/2022] [Indexed: 11/04/2022]
Abstract
Following the outbreak of the coronavirus epidemic in early 2020, municipalities, regional governments and policymakers worldwide had to plan their Non-Pharmaceutical Interventions (NPIs) amidst a scenario of great uncertainty. At this early stage of an epidemic, where no vaccine or medical treatment is in sight, algorithmic prediction can become a powerful tool to inform local policymaking. However, when we replicated one prominent epidemiological model to inform health authorities in a region in the south of Brazil, we found that this model relied too heavily on manually predetermined covariates and was too reactive to changes in data trends. Our four proposed models access data of both daily reported deaths and infections as well as take into account missing data (e.g., the under-reporting of cases) more explicitly, with two of the proposed versions also attempting to model the delay in test reporting. We simulated weekly forecasting of deaths from the period from 31/05/2020 until 31/01/2021, with first week data being used as a cold-start to the algorithm, after which we use a lighter variant of the model for faster forecasting. Because our models are significantly more proactive in identifying trend changes, this has improved forecasting, especially in long-range predictions and after the peak of an infection wave, as they were quicker to adapt to scenarios after these peaks in reported deaths. Assuming reported cases were under-reported greatly benefited the model in its stability, and modelling retroactively-added data (due to the "hot" nature of the data used) had a negligible impact on performance.
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Affiliation(s)
- Pedro Henrique da Costa Avelar
- Data Science Brigade, Porto Alegre, Rio Grande do Sul, Brazil
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
- Department of Informatics, King's College London, London, United Kingdom
- Machine Intellection Department, Institute for Infocomm Research, A*STAR, Singapore
| | | | - Luis C Lamb
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Sophia Tsoka
- Department of Informatics, King's College London, London, United Kingdom
| | - Jonathan Cardoso-Silva
- Data Science Brigade, Porto Alegre, Rio Grande do Sul, Brazil
- Department of Informatics, King's College London, London, United Kingdom
- Data Science Institute, London School of Economics and Political Science, London, United Kingdom
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13
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Ascencio-Cabral A, Reyes-Aldasoro CC. Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography. J Imaging 2022; 8:jimaging8090237. [PMID: 36135403 PMCID: PMC9500990 DOI: 10.3390/jimaging8090237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/12/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
In this work, the performance of five deep learning architectures in classifying COVID-19 in a multi-class set-up is evaluated. The classifiers were built on pretrained ResNet-50, ResNet-50r (with kernel size 5×5 in the first convolutional layer), DenseNet-121, MobileNet-v3 and the state-of-the-art CaiT-24-XXS-224 (CaiT) transformer. The cross entropy and weighted cross entropy were minimised with Adam and AdamW. In total, 20 experiments were conducted with 10 repetitions and obtained the following metrics: accuracy (Acc), balanced accuracy (BA), F1 and F2 from the general Fβ macro score, Matthew’s Correlation Coefficient (MCC), sensitivity (Sens) and specificity (Spec) followed by bootstrapping. The performance of the classifiers was compared by using the Friedman–Nemenyi test. The results show that less complex architectures such as ResNet-50, ResNet-50r and DenseNet-121 were able to achieve better generalization with rankings of 1.53, 1.71 and 3.05 for the Matthew Correlation Coefficient, respectively, while MobileNet-v3 and CaiT obtained rankings of 3.72 and 5.0, respectively.
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14
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Ali A, Nettey-Oppong EE, Effah E, Yu CY, Muhammad R, Soomro TA, Byun KM, Choi SH. Miniaturized Raman Instruments for SERS-Based Point-of-Care Testing on Respiratory Viruses. BIOSENSORS 2022; 12:bios12080590. [PMID: 36004986 PMCID: PMC9405795 DOI: 10.3390/bios12080590] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/24/2022] [Accepted: 07/26/2022] [Indexed: 06/12/2023]
Abstract
As surface-enhanced Raman scattering (SERS) has been used to diagnose several respiratory viruses (e.g., influenza A virus subtypes such as H1N1 and the new coronavirus SARS-CoV-2), SERS is gaining popularity as a method for diagnosing viruses at the point-of-care. Although the prior and quick diagnosis of respiratory viruses is critical in the outbreak of infectious disease, ELISA, PCR, and RT-PCR have been used to detect respiratory viruses for pandemic control that are limited for point-of-care testing. SERS provides quantitative data with high specificity and sensitivity in a real-time, label-free, and multiplex manner recognizing molecular fingerprints. Recently, the design of Raman spectroscopy system was simplified from a complicated design to a small and easily accessible form that enables point-of-care testing. We review the optical design (e.g., laser wavelength/power and detectors) of commercialized and customized handheld Raman instruments. As respiratory viruses have prominent risk on the pandemic, we review the applications of handheld Raman devices for detecting respiratory viruses. By instrumentation and commercialization advancements, the advent of the portable SERS device creates a fast, accurate, practical, and cost-effective analytical method for virus detection, and would continue to attract more attention in point-of-care testing.
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Affiliation(s)
- Ahmed Ali
- Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan;
| | - Ezekiel Edward Nettey-Oppong
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea; (E.E.N.-O.); (E.E.); (C.Y.Y.); (R.M.)
| | - Elijah Effah
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea; (E.E.N.-O.); (E.E.); (C.Y.Y.); (R.M.)
| | - Chan Yeong Yu
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea; (E.E.N.-O.); (E.E.); (C.Y.Y.); (R.M.)
| | - Riaz Muhammad
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea; (E.E.N.-O.); (E.E.); (C.Y.Y.); (R.M.)
| | - Toufique Ahmed Soomro
- Department of Electronic Engineering, Quid-e-Awam University of Engineering, Science and Technology, Larkana 77150, Pakistan;
| | - Kyung Min Byun
- Department of Biomedical Engineering, Kyung Hee University, Yongin 17104, Korea
- Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Korea
| | - Seung Ho Choi
- Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea; (E.E.N.-O.); (E.E.); (C.Y.Y.); (R.M.)
- Department of Integrative Medicine, Major in Digital Healthcare, Yonsei University College of Medicine, Seoul 06229, Korea
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15
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Heo S, Ha J, Jung W, Yoo S, Song Y, Kim T, Cha WC. Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury. Sci Rep 2022; 12:12454. [PMID: 35864281 PMCID: PMC9304372 DOI: 10.1038/s41598-022-16313-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/07/2022] [Indexed: 11/09/2022] Open
Abstract
The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for predicting traumatic intracranial hemorrhages (DEEPTICH) using a national trauma registry with 1.8 million cases. For simulation, 24 cases were selected from previous emergency department cases. For each case, physicians made decisions on ordering a head CT twice: initially without the DEEPTICH assistance, and subsequently with the DEEPTICH assistance. Of the 528 responses from 22 participants, 201 initial decisions were different from the DEEPTICH recommendations. Of these 201 initial decisions, 94 were changed after DEEPTICH assistance (46.8%). For the cases in which CT was initially not ordered, 71.4% of the decisions were changed (p < 0.001), and for the cases in which CT was initially ordered, 37.2% (p < 0.001) of the decisions were changed after DEEPTICH assistance. When using DEEPTICH, 46 (11.6%) unnecessary CTs were avoided (p < 0.001) and 10 (11.4%) traumatic intracranial hemorrhages (ICHs) that would have been otherwise missed were found (p = 0.039). We found that emergency physicians were likely to accept AI based on how they perceived its safety.
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Affiliation(s)
- Sejin Heo
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Juhyung Ha
- Department of Computer Science, Indiana University Bloomington, Bloomington, IN, USA
| | - Weon Jung
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Suyoung Yoo
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Yeejun Song
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Taerim Kim
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Won Chul Cha
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.
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16
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Mannepalli DP, Namdeo V. An effective detection of COVID-19 using adaptive dual-stage horse herd bidirectional long short-term memory framework. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022; 32:1049-1067. [PMID: 35937036 PMCID: PMC9347606 DOI: 10.1002/ima.22747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 04/05/2022] [Accepted: 04/22/2022] [Indexed: 05/08/2023]
Abstract
COVID-19 is a quickly increasing severe viral disease that affects the human beings as well as animals. The increasing amount of infection and death due to COVID-19 needs timely detection. This work presented an innovative deep learning methodology for the prediction of COVID-19 patients with chest x-ray images. Chest x-ray is the most effective imaging technique for predicting the lung associated diseases. An effective approach with adaptive dual-stage horse herd bidirectional LSTM model is presented for the classification of images into normal, lung opacity, viral pneumonia, and COVID-19. Initially, the input images are preprocessed using modified histogram equalization approach. This is utilized to improve the contrast of the images by changing low-resolution images into high-resolution images. Subsequently, an extended dual tree complex wavelet with trigonometric transform is introduced to extract the high-density features to decrease the complexity of features. Moreover, the dimensionality of the features reduced by adaptive beetle antennae search optimization is utilized. This approach enhances the performance of disease classification by reducing the computational complexity. Finally, an adaptive dual-stage horse herd bidirectional LSTM model is utilized for the classification of images into normal, viral pneumonia, lung opacity, and COVID-19. The implementation platform used in the work is PYTHON. The performance of the presented approach is proved by comparing with the existing approaches in accuracy (99.07%), sensitivity (97.6%), F-measure (97.1%), specificity (99.36%), kappa coefficient (97.7%), precision (98.56%), and area under the receiver operating characteristic curve (99%) for COVID-19 chest x-ray database.
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Affiliation(s)
- Durga Prasad Mannepalli
- Department of Computer Science and EngineeringSarvepalli Radhakrishna UniversityBhopalMadhya PradeshIndia
| | - Varsha Namdeo
- Department of Computer Science and EngineeringSarvepalli Radhakrishna UniversityBhopalMadhya PradeshIndia
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17
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Wang H, Jia S, Li Z, Duan Y, Tao G, Zhao Z. A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic. Front Genet 2022; 13:845305. [PMID: 35559010 PMCID: PMC9086537 DOI: 10.3389/fgene.2022.845305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 03/30/2022] [Indexed: 11/13/2022] Open
Abstract
The unprecedented outbreak of the Corona Virus Disease 2019 (COVID-19) pandemic has seriously affected numerous countries in the world from various aspects such as education, economy, social security, public health, etc. Most governments have made great efforts to control the spread of COVID-19, e.g., locking down hard-hit cities and advocating masks for the population. However, some countries and regions have relatively poor medical conditions in terms of insufficient medical equipment, hospital capacity overload, personnel shortage, and other problems, resulting in the large-scale spread of the epidemic. With the unique advantages of Artificial Intelligence (AI), it plays an extremely important role in medical imaging, clinical data, drug development, epidemic prediction, and telemedicine. Therefore, AI is a powerful tool that can help humans solve complex problems, especially in the fight against COVID-19. This study aims to analyze past research results and interpret the role of Artificial Intelligence in the prevention and treatment of COVID-19 from five aspects. In this paper, we also discuss the future development directions in different fields and prove the validity of the models through experiments, which will help researchers develop more efficient models to control the spread of COVID-19.
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Affiliation(s)
- Haishuai Wang
- College of Computer Science, Zhejiang University, Hangzhou, China
| | - Shangru Jia
- Department of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
| | - Zhao Li
- Alibaba-ZJU Joint Research Institute of Frontier Technologies, Zhejiang University, Hangzhou, China
| | - Yucong Duan
- College of Computer Science and Technology, Hainan University, Haikou, China
| | - Guangyu Tao
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Ziping Zhao
- Department of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
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18
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Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:7377502. [PMID: 35280708 PMCID: PMC8896964 DOI: 10.1155/2022/7377502] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 01/24/2022] [Indexed: 12/17/2022]
Abstract
Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers have utilized deep learning models for the automated screening of COVID-19 suspected cases. An ensemble deep learning and Internet of Things (IoT) based framework is proposed for screening of COVID-19 suspected cases. Three well-known pretrained deep learning models are ensembled. The medical IoT devices are utilized to collect the CT scans, and automated diagnoses are performed on IoT servers. The proposed framework is compared with thirteen competitive models over a four-class dataset. Experimental results reveal that the proposed ensembled deep learning model yielded 98.98% accuracy. Moreover, the model outperforms all competitive models in terms of other performance metrics achieving 98.56% precision, 98.58% recall, 98.75% F-score, and 98.57% AUC. Therefore, the proposed framework can improve the acceleration of COVID-19 diagnosis.
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Agarwal P, Swami S, Malhotra SK. Artificial Intelligence Adoption in the Post COVID-19 New-Normal and Role of Smart Technologies in Transforming Business: a Review. JOURNAL OF SCIENCE AND TECHNOLOGY POLICY MANAGEMENT 2022. [DOI: 10.1108/jstpm-08-2021-0122] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.
Design/methodology/approach
The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.
Findings
The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.
Research limitations/implications
Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.
Practical implications
First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.
Originality/value
As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.
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20
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Kufel J, Bargieł K, Koźlik M, Czogalik Ł, Dudek P, Jaworski A, Cebula M, Gruszczyńska K. Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review. Int J Med Sci 2022; 19:1743-1752. [PMID: 36313227 PMCID: PMC9608047 DOI: 10.7150/ijms.76515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/07/2022] [Indexed: 11/06/2022] Open
Abstract
This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images. Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study. Results: The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%. Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.
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Affiliation(s)
- Jakub Kufel
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland
| | - Katarzyna Bargieł
- Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-752 Katowice, Poland
| | - Maciej Koźlik
- Division of Cardiology and Structural Heart Disease, Medical University of Silesia, 40-635 Katowice, Poland
| | - Łukasz Czogalik
- Professor Zbigniew Religa Student Scientific Association at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland
| | - Piotr Dudek
- Professor Zbigniew Religa Student Scientific Association at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland
| | - Aleksander Jaworski
- Professor Zbigniew Religa Student Scientific Association at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland
| | - Maciej Cebula
- Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-754 Katowice, Poland
| | - Katarzyna Gruszczyńska
- Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-754 Katowice, Poland
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21
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Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments. Symmetry (Basel) 2021. [DOI: 10.3390/sym14010016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.
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22
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López-Cabrera JD, Orozco-Morales R, Portal-Díaz JA, Lovelle-Enríquez O, Pérez-Díaz M. Current limitations to identify covid-19 using artificial intelligence with chest x-ray imaging (part ii). The shortcut learning problem. HEALTH AND TECHNOLOGY 2021; 11:1331-1345. [PMID: 34660166 PMCID: PMC8502237 DOI: 10.1007/s12553-021-00609-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/05/2021] [Indexed: 12/12/2022]
Abstract
Since the outbreak of the COVID-19 pandemic, computer vision researchers have been working on automatic identification of this disease using radiological images. The results achieved by automatic classification methods far exceed those of human specialists, with sensitivity as high as 100% being reported. However, prestigious radiology societies have stated that the use of this type of imaging alone is not recommended as a diagnostic method. According to some experts the patterns presented in these images are unspecific and subtle, overlapping with other viral pneumonias. This report seeks to evaluate the analysis the robustness and generalizability of different approaches using artificial intelligence, deep learning and computer vision to identify COVID-19 using chest X-rays images. We also seek to alert researchers and reviewers to the issue of "shortcut learning". Recommendations are presented to identify whether COVID-19 automatic classification models are being affected by shortcut learning. Firstly, papers using explainable artificial intelligence methods are reviewed. The results of applying external validation sets are evaluated to determine the generalizability of these methods. Finally, studies that apply traditional computer vision methods to perform the same task are considered. It is evident that using the whole chest X-Ray image or the bounding box of the lungs, the image regions that contribute most to the classification appear outside of the lung region, something that is not likely possible. In addition, although the investigations that evaluated their models on data sets external to the training set, the effectiveness of these models decreased significantly, it may provide a more realistic representation as how the model will perform in the clinic. The results indicate that, so far, the existing models often involve shortcut learning, which makes their use less appropriate in the clinical setting.
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Affiliation(s)
- José Daniel López-Cabrera
- Centro de Investigaciones de La Informática, Facultad de Matemática, Física y Computación, Universidad Central “Marta Abreu” de Las Villas, Villa Clara, Santa Clara, Cuba
| | - Rubén Orozco-Morales
- Departamento de Control Automático, Facultad de Ingeniería Eléctrica, Universidad Central “Marta Abreu” de Las Villas, Villa Clara, Santa Clara, Cuba
| | - Jorge Armando Portal-Díaz
- Departamento de Control Automático, Facultad de Ingeniería Eléctrica, Universidad Central “Marta Abreu” de Las Villas, Villa Clara, Santa Clara, Cuba
| | - Orlando Lovelle-Enríquez
- Departamento de Imagenología, Hospital Comandante Manuel Fajardo Rivero, Villa Clara, Santa Clara, Cuba
| | - Marlén Pérez-Díaz
- Departamento de Control Automático, Facultad de Ingeniería Eléctrica, Universidad Central “Marta Abreu” de Las Villas, Villa Clara, Santa Clara, Cuba
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23
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El-Rashidy N, Abdelrazik S, Abuhmed T, Amer E, Ali F, Hu JW, El-Sappagh S. Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic. Diagnostics (Basel) 2021; 11:1155. [PMID: 34202587 PMCID: PMC8303306 DOI: 10.3390/diagnostics11071155] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 12/11/2022] Open
Abstract
Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.
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Affiliation(s)
- Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt
| | - Samir Abdelrazik
- Information System Department, Faculty of Computer Science and Information Systems, Mansoura University, Mansoura 13518, Egypt;
| | - Tamer Abuhmed
- College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Korea
| | - Eslam Amer
- Faculty of Computer Science, Misr International University, Cairo 11828, Egypt;
| | - Farman Ali
- Department of Software, Sejong University, Seoul 05006, Korea;
| | - Jong-Wan Hu
- Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
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