|
1
|
Chu Y, Luo X, Zhang J, Shen L, Zhu L, Wu C, Wang H, Yao Y. An 8-point scale lung ultrasound scoring network fusing local detail and global features. Sci Rep 2025; 15:5687. [PMID: 39956844 PMCID: PMC11830796 DOI: 10.1038/s41598-025-90018-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: 10/21/2024] [Accepted: 02/10/2025] [Indexed: 02/18/2025] Open
Abstract
Manual lung ultrasound (LUS) scoring is influenced by clinicians' subjective interpretation, leading to potential inconsistencies and misdiagnoses due to varying levels of experience. To improve monitoring of pulmonary ventilation and support early diagnosis, we propose an automated LUS scoring network based on an 8-point scale, named the detailed-global fusion residual network (DGF-ResNet). This network combines local and global features using the hybrid feature fusion Block, which includes the detail feature extraction (DFE) and global feature extraction (GFE) Modules. The DFE module employs a local channel and spatial attention mechanism to capture fine details, while the GFE Module utilizes a three-order recursive gated convolution and a global channel and spatial attention mechanism to extract global features. Experimental results on the FCSPF-13324 dataset from the Second Affiliated Hospital of Zhejiang University show that DGF-ResNet outperforms VGG16, ResNet50, and Vision Transformer in accuracy, precision, recall, and F1-score. Specifically, DGF-ResNet improves over Vision Transformer by 7.05, 4.52, and 5.89 percentage points, over VGG16 by 3.06, 4.37, and 3.8 points, and over ResNet50 by 2.05, 4.26, and 3.34 points, respectively.
Collapse
Affiliation(s)
- Yonghua Chu
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, 310009, China
| | - Xiang Luo
- College of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Jucheng Zhang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, 310009, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China.
| | - Lei Shen
- College of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Lihang Zhu
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, 310009, China
| | - Chunshuang Wu
- Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
- Key Laboratory of The Diagnosis and Treatment of Severe Trauma and Burn of Zhejiang Province, Zhejiang Province Clinical Research Center for Emergency and Critical Care Medicine, Hangzhou, 310009, China
| | - Huaxia Wang
- Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, 08028, USA
| | - Yudong Yao
- Department of Electrical and Computer Engineering, The Stevens Institute of Technology, Hoboken, NJ, 07030, USA
| |
Collapse
|
|
2
|
Mento F, Perpenti M, Barcellona G, Perrone T, Demi L. Lung Ultrasound Spectroscopy Applied to the Differential Diagnosis of Pulmonary Diseases: An In Vivo Multicenter Clinical Study. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2024; 71:1217-1232. [PMID: 39236134 DOI: 10.1109/tuffc.2024.3454956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
Lung ultrasound (LUS) is an important imaging modality to assess the state of the lung surface. However, current LUS approaches are based on subjective interpretation of imaging artifacts, which results in poor specificity as quantitative evaluation lacks. The latter could be improved by adopting LUS spectroscopy of vertical artifacts. Indeed, parameterizing these artifacts with native frequency, bandwidth, and total intensity ( [Formula: see text]) already showed potentials in differentiating pulmonary fibrosis (PF). In this study, we acquired radio frequency (RF) data from 114 patients. These data (representing the largest LUS RF dataset worldwide) were acquired by utilizing a multifrequency approach, implemented with an ULtrasound Advanced Open Platform (ULA-OP). Convex (CA631) and linear (LA533) probes (Esaote, Florence, Italy) were utilized to acquire RF data at three (2, 3, and 4 MHz), and four (3, 4, 5, and 6 MHz) imaging frequencies. A multifrequency analysis was conducted on vertical artifacts detected in patients having cardiogenic pulmonary edema (CPE), pneumonia, or PF. These artifacts were characterized by the three abovementioned parameters, and their mean values were used to project each patient into a feature space having up to three dimensions. Binary classifiers were used to evaluate the performance of these three mean features in differentiating patients affected by CPE, pneumonia, and PF. Acquisitions of multifrequency data performed with linear probe lead to accuracies up to 85.43% in the differential diagnosis of these diseases (convex probes' maximum accuracy was 74.51%). Moreover, the results showed high potentials of mean [Formula: see text] (by itself or combined with other features) in improving LUS specificity.
Collapse
|
|
3
|
Li Z, Yang X, Lan H, Wang M, Huang L, Wei X, Xie G, Wang R, Yu J, He Q, Zhang Y, Luo J. Knowledge fused latent representation from lung ultrasound examination for COVID-19 pneumonia severity assessment. ULTRASONICS 2024; 143:107409. [PMID: 39053242 DOI: 10.1016/j.ultras.2024.107409] [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: 03/19/2024] [Revised: 06/19/2024] [Accepted: 07/17/2024] [Indexed: 07/27/2024]
Abstract
COVID-19 pneumonia severity assessment is of great clinical importance, and lung ultrasound (LUS) plays a crucial role in aiding the severity assessment of COVID-19 pneumonia due to its safety and portability. However, its reliance on qualitative and subjective observations by clinicians is a limitation. Moreover, LUS images often exhibit significant heterogeneity, emphasizing the need for more quantitative assessment methods. In this paper, we propose a knowledge fused latent representation framework tailored for COVID-19 pneumonia severity assessment using LUS examinations. The framework transforms the LUS examination into latent representation and extracts knowledge from regions labeled by clinicians to improve accuracy. To fuse the knowledge into the latent representation, we employ a knowledge fusion with latent representation (KFLR) model. This model significantly reduces errors compared to approaches that lack prior knowledge integration. Experimental results demonstrate the effectiveness of our method, achieving high accuracy of 96.4 % and 87.4 % for binary-level and four-level COVID-19 pneumonia severity assessments, respectively. It is worth noting that only a limited number of studies have reported accuracy for clinically valuable exam level assessments, and our method surpass existing methods in this context. These findings highlight the potential of the proposed framework for monitoring disease progression and patient stratification in COVID-19 pneumonia cases.
Collapse
Affiliation(s)
- Zhiqiang Li
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Xueping Yang
- Department of Ultrasound, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Hengrong Lan
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Mixue Wang
- Department of Ultrasound, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Lijie Huang
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Xingyue Wei
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Gangqiao Xie
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Rui Wang
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Jing Yu
- Department of Ultrasound, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Qiong He
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Yao Zhang
- Department of Ultrasound, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China.
| | - Jianwen Luo
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
| |
Collapse
|
|
4
|
Piliuk K, Tomforde S. Artificial intelligence in emergency medicine. A systematic literature review. Int J Med Inform 2023; 180:105274. [PMID: 37944275 DOI: 10.1016/j.ijmedinf.2023.105274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/21/2023] [Accepted: 10/26/2023] [Indexed: 11/12/2023]
Abstract
Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies. METHODS The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms. FINDINGS AND DISCUSSION The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction. CONCLUSION Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.
Collapse
Affiliation(s)
| | - Sven Tomforde
- Christian-Albrechts-Universität zu Kiel, 24118 Kiel, Germany
| |
Collapse
|
|
5
|
Fu B, Zhang P, Zhang J. Diagnosis and Prognosis Evaluation of Severe Pneumonia by Lung Ultrasound Score Combined with Serum Inflammatory Markers. Mediterr J Hematol Infect Dis 2023; 15:e2023057. [PMID: 38028392 PMCID: PMC10631708 DOI: 10.4084/mjhid.2023.057] [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: 02/08/2023] [Accepted: 10/01/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction To analyze the significance of lung ultrasound score (LUS) combined with serum inflammatory indexes in different severities of severe pneumonia and its clinical value on prognosis. Methods 100 patients with severe pneumonia treated in the Gansu Provincial Hospital from June 2017 to June 2021 were selected as the research objects. According to the acute physiology and chronic health (APACHE II) score, they were divided into a low-risk group (28 cases), a medium-risk group (39 cases) and a high-risk group (33 cases). The general clinical data of the patients (age, gender, smoking history, and underlying diseases) were collected, the lung ultrasound score (LUS) of the patients was measured, and the serum inflammatory indicators (IL-6, IL-10, TNF-α, CRP and NLR) levels; Pearson correlation analysis to evaluate the correlation between LUS score, serum inflammatory index levels and disease severity; receiver operating characteristic (ROC) curve analysis to evaluate the prognostic value of the combined diagnosis of LUS score and serum inflammatory index for the severity of severe pneumonia. Results With the increase in the severity of severe pneumonia, the LUS score and the level of inflammation in the body continued to increase, and LUS combined with serum inflammatory indexes could distinguish the severity of low-risk, medium-risk and high-risk severe pneumonia and had high diagnostic value. In addition, the combined diagnosis of LUS and serum inflammatory markers is also closely related to the prognosis of patients with severe pneumonia, which can distinguish the prognosis. Conclusion LUS combined with serum inflammatory indicators (IL-6, IL-10, TNF-α, CRP and NLR) can differentiate the severity and prognosis of severe pneumonia, which may be a new direction for diagnosing severe pneumonia and guide early clinical intervention.
Collapse
Affiliation(s)
- Bo Fu
- Department of Ultrasound Diagnosis, Gansu Provincial Hospital, Lanzhou City, Gansu Province, 730000, China
| | - Peng Zhang
- Department of Intensive Care Medicine, Gansu Gem Flower Hospital, Lanzhou City, 730060, Gansu Province, China
| | - JunHua Zhang
- Department of Intensive Care Medicine, Gansu Gem Flower Hospital, Lanzhou City, 730060, Gansu Province, China
| |
Collapse
|
|
6
|
Gürsoy E, Kaya Y. An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works. MULTIMEDIA SYSTEMS 2023; 29:1603-1627. [PMID: 37261262 PMCID: PMC10039775 DOI: 10.1007/s00530-023-01083-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 03/20/2023] [Indexed: 06/02/2023]
Abstract
The World Health Organization (WHO) declared a pandemic in response to the coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the disease appears to have lost its impact, millions of people have been affected by this virus, and new infections still occur. Identifying COVID-19 requires a reverse transcription-polymerase chain reaction test (RT-PCR) or analysis of medical data. Due to the high cost and time required to scan and analyze medical data, researchers are focusing on using automated computer-aided methods. This review examines the applications of deep learning (DL) and machine learning (ML) in detecting COVID-19 using medical data such as CT scans, X-rays, cough sounds, MRIs, ultrasound, and clinical markers. First, the data preprocessing, the features used, and the current COVID-19 detection methods are divided into two subsections, and the studies are discussed. Second, the reported publicly available datasets, their characteristics, and the potential comparison materials mentioned in the literature are presented. Third, a comprehensive comparison is made by contrasting the similar and different aspects of the studies. Finally, the results, gaps, and limitations are summarized to stimulate the improvement of COVID-19 detection methods, and the study concludes by listing some future research directions for COVID-19 classification.
Collapse
Affiliation(s)
- Ercan Gürsoy
- Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Turkey
| | - Yasin Kaya
- Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Turkey
| |
Collapse
|
|
7
|
Chen J, Shen M, Hou S, Duan X, Yang M, Cao Y, Qin W, Niu Q, Li Q, Zhang Y, Wang Y. Intelligent interpretation of four lung ultrasonographic features with split attention based deep learning model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
|
8
|
Rao Y, Lv Q, Zeng S, Yi Y, Huang C, Gao Y, Cheng Z, Sun J. COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold. Biomed Signal Process Control 2023; 81:104486. [PMID: 36505089 PMCID: PMC9721288 DOI: 10.1016/j.bspc.2022.104486] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/23/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
The ground glass opacity (GGO) of the lung is one of the essential features of COVID-19. The GGO in computed tomography (CT) images has various features and low-intensity contrast between the GGO and edge structures. These problems pose significant challenges for segmenting the GGO. To tackle these problems, we propose a new threshold method for accurate segmentation of GGO. Specifically, we offer a framework for adjusting the threshold parameters according to the image contrast. Three functions include Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL). The lung is divided into three areas using the attention mechanism threshold. Further, the segmentation parameters of the attention mechanism thresholds of the three parts are adaptively adjusted according to the image contrast. Only the segmentation regions restricted by the lung segmentation results are retained. Extensive experiments on four COVID datasets show that ACL can segment GGO images at low contrast well. Compared with the state-of-the-art methods, the similarity Dice of the ACL segmentation results is improved by 8.9%, the average symmetry surface distance ASD is reduced by 23%, and the required computational power F L O P s are only 0.09% of those of deep learning models. For GGO segmentation, ACL is more lightweight, and the accuracy is higher. Code will be released at https://github.com/Lqs-github/ACL.
Collapse
Affiliation(s)
- Yunbo Rao
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Qingsong Lv
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Shaoning Zeng
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313000, China
| | - Yuling Yi
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Cheng Huang
- Fifth Clinical College of Chongqing Medical University, Chongqing, 402177, China
| | - Yun Gao
- Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Zhanglin Cheng
- Advanced Technology Chinese Academy of Sciences, Shenzhen, 610042, China
| | - Jihong Sun
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310014, China
| |
Collapse
|
|
9
|
Demi L, Wolfram F, Klersy C, De Silvestri A, Ferretti VV, Muller M, Miller D, Feletti F, Wełnicki M, Buda N, Skoczylas A, Pomiecko A, Damjanovic D, Olszewski R, Kirkpatrick AW, Breitkreutz R, Mathis G, Soldati G, Smargiassi A, Inchingolo R, Perrone T. New International Guidelines and Consensus on the Use of Lung Ultrasound. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:309-344. [PMID: 35993596 PMCID: PMC10086956 DOI: 10.1002/jum.16088] [Citation(s) in RCA: 135] [Impact Index Per Article: 67.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/28/2022] [Accepted: 07/31/2022] [Indexed: 05/02/2023]
Abstract
Following the innovations and new discoveries of the last 10 years in the field of lung ultrasound (LUS), a multidisciplinary panel of international LUS experts from six countries and from different fields (clinical and technical) reviewed and updated the original international consensus for point-of-care LUS, dated 2012. As a result, a total of 20 statements have been produced. Each statement is complemented by guidelines and future developments proposals. The statements are furthermore classified based on their nature as technical (5), clinical (11), educational (3), and safety (1) statements.
Collapse
Affiliation(s)
- Libertario Demi
- Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly
| | - Frank Wolfram
- Department of Thoracic and Vascular SurgerySRH Wald‐Klinikum GeraGeraGermany
| | - Catherine Klersy
- Unit of Clinical Epidemiology and BiostatisticsFondazione IRCCS Policlinico S. MatteoPaviaItaly
| | - Annalisa De Silvestri
- Unit of Clinical Epidemiology and BiostatisticsFondazione IRCCS Policlinico S. MatteoPaviaItaly
| | | | - Marie Muller
- Department of Mechanical and Aerospace EngineeringNorth Carolina State UniversityRaleighNorth CarolinaUSA
| | - Douglas Miller
- Department of RadiologyMichigan MedicineAnn ArborMichiganUSA
| | - Francesco Feletti
- Department of Diagnostic ImagingUnit of Radiology of the Hospital of Ravenna, Ausl RomagnaRavennaItaly
- Department of Translational Medicine and for RomagnaUniversità Degli Studi di FerraraFerraraItaly
| | - Marcin Wełnicki
- 3rd Department of Internal Medicine and CardiologyMedical University of WarsawWarsawPoland
| | - Natalia Buda
- Department of Internal Medicine, Connective Tissue Disease and GeriatricsMedical University of GdanskGdanskPoland
| | - Agnieszka Skoczylas
- Geriatrics DepartmentNational Institute of Geriatrics, Rheumatology and RehabilitationWarsawPoland
| | - Andrzej Pomiecko
- Clinic of Pediatrics, Hematology and OncologyUniversity Clinical CenterGdańskPoland
| | - Domagoj Damjanovic
- Heart Center Freiburg University, Department of Cardiovascular Surgery, Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Robert Olszewski
- Department of Gerontology, Public Health and DidacticsNational Institute of Geriatrics, Rheumatology and RehabilitationWarsawPoland
| | - Andrew W. Kirkpatrick
- Departments of Critical Care Medicine and SurgeryUniversity of Calgary and the TeleMentored Ultrasound Supported Medical Interventions Research GroupCalgaryCanada
| | - Raoul Breitkreutz
- FOM Hochschule für Oekonomie & Management gGmbHDepartment of Health and SocialEssenGermany
| | - Gebhart Mathis
- Emergency UltrasoundAustrian Society for Ultrasound in Medicine and BiologyViennaAustria
| | - Gino Soldati
- Diagnostic and Interventional Ultrasound UnitValledel Serchio General HospitalLuccaItaly
| | - Andrea Smargiassi
- Pulmonary Medicine Unit, Department of Medical and Surgical SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Department of Internal Medicine, IRCCS San Matteo Hospital FoundationUniversity of PaviaPaviaItaly
| | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Department of Medical and Surgical SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Department of Internal Medicine, IRCCS San Matteo Hospital FoundationUniversity of PaviaPaviaItaly
| | | |
Collapse
|
|
10
|
Xing W, Li G, He C, Huang Q, Cui X, Li Q, Li W, Chen J, Ta D. Automatic detection of A-line in lung ultrasound images using deep learning and image processing. Med Phys 2023; 50:330-343. [PMID: 35950481 DOI: 10.1002/mp.15908] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 06/29/2022] [Accepted: 07/30/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Auxiliary diagnosis and monitoring of lung diseases based on lung ultrasound (LUS) images is important clinical research. A-line is one of the most common indicators of LUS that can offer support for the assessment of lung diseases. A traditional A-line detection method mainly relies on experienced clinicians, which is inefficient and cannot meet the needs of these areas with backward medical level. Therefore, how to realize the automatic detection of A-line in LUS image is important. PURPOSE In order to solve the disadvantages of traditional A-line detection methods, realize automatic and accurate detection, and provide theoretical support for clinical application, we proposed a novel A-line detection method for LUS images with different probe types in this paper. METHODS First, the improved Faster R-CNN model with a selection strategy of localization box was designed to accurately locate the pleural line. Then, the LUS image below the pleural line was segmented for independent analysis excluding the influence of other similar structures. Next, image-processing methods based on total variation, matched filter, and gray difference were applied to achieve the automatic A-line detection. Finally, the "depth" index was designed to verify the accuracy by judging whether the automatic measurement results belong to corresponding manual results (±5%). In experiments, 3000 convex array LUS images were used for training and validating the improved pleural line localization model by five-fold cross validation. 850 convex array LUS images and 1080 linear array LUS images were used for testing the trained pleural line localization model and the proposed image-processing-based A-line detection method. The accuracy analysis, error statistics, and Harsdorff distance were employed to evaluate the experimental results. RESULTS After 100 epochs, the mean loss value of training and validation set of improved Faster R-CNN model reached 0.6540 and 0.7882, with the validation accuracy of 98.70%. The trained pleural line localization model was applied in the testing set of convex and linear probes and reached the accuracy of 97.88% and 97.11%, respectively, which were 3.83% and 8.70% higher than the original Faster R-CNN model. The accuracy, sensitivity, and specificity of A-line detection reached 95.41%, 0.9244%, 0.9875%, and 94.63%, 0.9230%, and 0.9766% for convex and linear probes, respectively. Compared to the experienced clinicians' results, the mean value and p value of depth error were 1.5342 ± 1.2097 and 0.9021, respectively, and the Harsdorff distance was 5.7305 ± 1.8311. In addition, the accumulated accuracy of the two-stage experiment (pleural line localization and A-line detection) was calculated as the final accuracy of the whole A-line detection system. They were 93.39% and 91.90% for convex and linear probes, respectively, which were higher than these previous methods. CONCLUSIONS The proposed method combining image processing and deep learning can automatically and accurately detect A-line in LUS images with different probe types, which has important application value for clinical diagnosis.
Collapse
Affiliation(s)
- Wenyu Xing
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
| | - Guannan Li
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
| | - Chao He
- Department of Emergency and Critical Care, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Qiming Huang
- School of Advanced Computing and Artificial Intelligence, Xi'an Jiaotong-liverpool University, Suzhou, China
| | - Xulei Cui
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Qingli Li
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
| | - Wenfang Li
- Department of Emergency and Critical Care, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Jiangang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China.,Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Dean Ta
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, China.,Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
|
11
|
Yang T, Karakus O, Anantrasirichai N, Achim A. Current Advances in Computational Lung Ultrasound Imaging: A Review. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2023; 70:2-15. [PMID: 36355735 DOI: 10.1109/tuffc.2022.3221682] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
In the field of biomedical imaging, ultrasonography has become common practice, and used as an important auxiliary diagnostic tool with unique advantages, such as being non-ionizing and often portable. This article reviews the state-of-the-art in medical ultrasound (US) image processing and in particular its applications in the examination of the lungs. First, we briefly introduce the basis of lung US (LUS) examination. We focus on (i) the characteristics of lung ultrasonography and (ii) its ability to detect a variety of diseases through the identification of various artifacts exhibiting on LUS images. We group medical US image computing methods into two categories: 1) model-based methods and 2) data-driven methods. We particularly discuss inverse problem-based methods exploited in US image despeckling, deconvolution, and line artifacts detection for the former, while we exemplify various works based on deep/machine learning (ML), which exploit various network architectures through supervised, weakly supervised, and unsupervised learning for the data-driven approaches.
Collapse
|
|
12
|
Mento F, Khan U, Faita F, Smargiassi A, Inchingolo R, Perrone T, Demi L. State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:2398-2416. [PMID: 36155147 PMCID: PMC9499741 DOI: 10.1016/j.ultrasmedbio.2022.07.007] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 05/27/2023]
Abstract
Lung ultrasound (LUS) has been increasingly expanding since the 1990s, when the clinical relevance of vertical artifacts was first reported. However, the massive spread of LUS is only recent and is associated with the coronavirus disease 2019 (COVID-19) pandemic, during which semi-quantitative computer-aided techniques were proposed to automatically classify LUS data. In this review, we discuss the state of the art in LUS, from semi-quantitative image analysis approaches to quantitative techniques involving the analysis of radiofrequency data. We also discuss recent in vitro and in silico studies, as well as research on LUS safety. Finally, conclusions are drawn highlighting the potential future of LUS.
Collapse
Affiliation(s)
- Federico Mento
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Umair Khan
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Francesco Faita
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Andrea Smargiassi
- Department of Cardiovascular and Thoracic Sciences, Pulmonary Medicine Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Riccardo Inchingolo
- Department of Cardiovascular and Thoracic Sciences, Pulmonary Medicine Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | - Libertario Demi
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
| |
Collapse
|
|
13
|
Durrani N, Vukovic D, van der Burgt J, Antico M, van Sloun RJG, Canty D, Steffens M, Wang A, Royse A, Royse C, Haji K, Dowling J, Chetty G, Fontanarosa D. Automatic deep learning-based consolidation/collapse classification in lung ultrasound images for COVID-19 induced pneumonia. Sci Rep 2022; 12:17581. [PMID: 36266463 PMCID: PMC9584232 DOI: 10.1038/s41598-022-22196-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 10/11/2022] [Indexed: 01/13/2023] Open
Abstract
Our automated deep learning-based approach identifies consolidation/collapse in LUS images to aid in the identification of late stages of COVID-19 induced pneumonia, where consolidation/collapse is one of the possible associated pathologies. A common challenge in training such models is that annotating each frame of an ultrasound video requires high labelling effort. This effort in practice becomes prohibitive for large ultrasound datasets. To understand the impact of various degrees of labelling precision, we compare labelling strategies to train fully supervised models (frame-based method, higher labelling effort) and inaccurately supervised models (video-based methods, lower labelling effort), both of which yield binary predictions for LUS videos on a frame-by-frame level. We moreover introduce a novel sampled quaternary method which randomly samples only 10% of the LUS video frames and subsequently assigns (ordinal) categorical labels to all frames in the video based on the fraction of positively annotated samples. This method outperformed the inaccurately supervised video-based method and more surprisingly, the supervised frame-based approach with respect to metrics such as precision-recall area under curve (PR-AUC) and F1 score, despite being a form of inaccurate learning. We argue that our video-based method is more robust with respect to label noise and mitigates overfitting in a manner similar to label smoothing. The algorithm was trained using a ten-fold cross validation, which resulted in a PR-AUC score of 73% and an accuracy of 89%. While the efficacy of our classifier using the sampled quaternary method significantly lowers the labelling effort, it must be verified on a larger consolidation/collapse dataset, our proposed classifier using the sampled quaternary video-based method is clinically comparable with trained experts' performance.
Collapse
Affiliation(s)
- Nabeel Durrani
- Faculty of Engineering, Queensland University of Technology, Gardens Point Campus, 2 George St, Brisbane, QLD, 4000, Australia
| | - Damjan Vukovic
- School of Clinical Sciences, Queensland University of Technology, Gardens Point Campus, 2 George St, Brisbane, QLD, 4000, Australia.
- Centre for Biomedical Technologies (CBT), Queensland University of Technology, Brisbane, QLD, 4000, Australia.
| | - Jeroen van der Burgt
- School of Clinical Sciences, Queensland University of Technology, Gardens Point Campus, 2 George St, Brisbane, QLD, 4000, Australia
| | - Maria Antico
- Faculty of Engineering, Queensland University of Technology, Gardens Point Campus, 2 George St, Brisbane, QLD, 4000, Australia
- Centre for Biomedical Technologies (CBT), Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - Ruud J G van Sloun
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands
| | - David Canty
- Department of Surgery (Royal Melbourne Hospital), University of Melbourne, Royal Parade, Parkville, VIC, 3050, Australia
- Department of Medicine and Nursing, Monash University, Wellington Road, Clayton, VIC, 3800, Australia
| | - Marian Steffens
- School of Clinical Sciences, Queensland University of Technology, Gardens Point Campus, 2 George St, Brisbane, QLD, 4000, Australia
| | - Andrew Wang
- Department of Surgery (Royal Melbourne Hospital), University of Melbourne, Royal Parade, Parkville, VIC, 3050, Australia
| | - Alistair Royse
- Department of Surgery (Royal Melbourne Hospital), University of Melbourne, Royal Parade, Parkville, VIC, 3050, Australia
| | - Colin Royse
- Department of Surgery (Royal Melbourne Hospital), University of Melbourne, Royal Parade, Parkville, VIC, 3050, Australia
- Outcomes Research Consortium, Cleveland Clinic, Cleveland, OH, USA
| | - Kavi Haji
- Department of Surgery (Royal Melbourne Hospital), University of Melbourne, Royal Parade, Parkville, VIC, 3050, Australia
| | - Jason Dowling
- CSIRO Health and Biosecurity, The Australian eHealth Research Centre, Herston, QLD, 4029, Australia
| | - Girija Chetty
- School of IT & Systems, Faculty of Science and Technology, University of Canberra, 11 Kirinari Street, Bruce, ACT, 2617, Australia
| | - Davide Fontanarosa
- School of Clinical Sciences, Queensland University of Technology, Gardens Point Campus, 2 George St, Brisbane, QLD, 4000, Australia.
- Centre for Biomedical Technologies (CBT), Queensland University of Technology, Brisbane, QLD, 4000, Australia.
| |
Collapse
|
|
14
|
Maximino J, Coimbra M, Pedrosa J. Detection of COVID-19 in Point of Care Lung Ultrasound. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1527-1530. [PMID: 36086665 DOI: 10.1109/embc48229.2022.9871235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The coronavirus disease 2019 (COVID-19) evolved into a global pandemic, responsible for a significant number of infections and deaths. In this scenario, point-of-care ultrasound (POCUS) has emerged as a viable and safe imaging modality. Computer vision (CV) solutions have been proposed to aid clinicians in POCUS image interpretation, namely detection/segmentation of structures and image/patient classification but relevant challenges still remain. As such, the aim of this study is to develop CV algorithms, using Deep Learning techniques, to create tools that can aid doctors in the diagnosis of viral and bacterial pneumonia (VP and BP) through POCUS exams. To do so, convolutional neural networks were designed to perform in classification tasks. The architectures chosen to build these models were the VGG16, ResNet50, DenseNet169 e MobileNetV2. Patients images were divided in three classes: healthy (HE), BP and VP (which includes COVID-19). Through a comparative study, which was based on several performance metrics, the model based on the DenseNet169 architecture was designated as the best performing model, achieving 78% average accuracy value of the five iterations of 5- Fold Cross-Validation. Given that the currently available POCUS datasets for COVID-19 are still limited, the training of the models was negatively affected by such and the models were not tested in an independent dataset. Furthermore, it was also not possible to perform lesion detection tasks. Nonetheless, in order to provide explainability and understanding of the models, Gradient-weighted Class Activation Mapping (GradCAM) were used as a tool to highlight the most relevant classification regions. Clinical relevance - Reveals the potential of POCUS to support COVID-19 screening. The results are very promising although the dataset is limite.
Collapse
|
|
15
|
Filchakova O, Dossym D, Ilyas A, Kuanysheva T, Abdizhamil A, Bukasov R. Review of COVID-19 testing and diagnostic methods. Talanta 2022; 244:123409. [PMID: 35390680 PMCID: PMC8970625 DOI: 10.1016/j.talanta.2022.123409] [Citation(s) in RCA: 126] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 01/09/2023]
Abstract
More than six billion tests for COVID-19 has been already performed in the world. The testing for SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) virus and corresponding human antibodies is essential not only for diagnostics and treatment of the infection by medical institutions, but also as a pre-requisite for major semi-normal economic and social activities such as international flights, off line work and study in offices, access to malls, sport and social events. Accuracy, sensitivity, specificity, time to results and cost per test are essential parameters of those tests and even minimal improvement in any of them may have noticeable impact on life in the many countries of the world. We described, analyzed and compared methods of COVID-19 detection, while representing their parameters in 22 tables. Also, we compared test performance of some FDA approved test kits with clinical performance of some non-FDA approved methods just described in scientific literature. RT-PCR still remains a golden standard in detection of the virus, but a pressing need for alternative less expensive, more rapid, point of care methods is evident. Those methods that may eventually get developed to satisfy this need are explained, discussed, quantitatively compared. The review has a bioanalytical chemistry prospective, but it may be interesting for a broader circle of readers who are interested in understanding and improvement of COVID-19 testing, helping eventually to leave COVID-19 pandemic in the past.
Collapse
Affiliation(s)
- Olena Filchakova
- Biology Department, SSH, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
| | - Dina Dossym
- Chemistry Department, SSH, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
| | - Aisha Ilyas
- Chemistry Department, SSH, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
| | - Tamila Kuanysheva
- Chemistry Department, SSH, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
| | - Altynay Abdizhamil
- Chemistry Department, SSH, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
| | - Rostislav Bukasov
- Chemistry Department, SSH, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan.
| |
Collapse
|
|
16
|
Huang Q, Lei Y, Xing W, He C, Wei G, Miao Z, Hao Y, Li G, Wang Y, Li Q, Li X, Li W, Chen J. Evaluation of Pulmonary Edema Using Ultrasound Imaging in Patients With COVID-19 Pneumonia Based on a Non-local Channel Attention ResNet. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:945-953. [PMID: 35277285 PMCID: PMC8818339 DOI: 10.1016/j.ultrasmedbio.2022.01.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 01/10/2022] [Accepted: 01/27/2022] [Indexed: 05/16/2023]
Abstract
Recent research has revealed that COVID-19 pneumonia is often accompanied by pulmonary edema. Pulmonary edema is a manifestation of acute lung injury (ALI), and may progress to hypoxemia and potentially acute respiratory distress syndrome (ARDS), which have higher mortality. Precise classification of the degree of pulmonary edema in patients is of great significance in choosing a treatment plan and improving the chance of survival. Here we propose a deep learning neural network named Non-local Channel Attention ResNet to analyze the lung ultrasound images and automatically score the degree of pulmonary edema of patients with COVID-19 pneumonia. The proposed method was designed by combining the ResNet with the non-local module and the channel attention mechanism. The non-local module was used to extract the information on characteristics of A-lines and B-lines, on the basis of which the degree of pulmonary edema could be defined. The channel attention mechanism was used to assign weights to decisive channels. The data set contains 2220 lung ultrasound images provided by Huoshenshan Hospital, Wuhan, China, of which 2062 effective images with accurate scores assigned by two experienced clinicians were used in the experiment. The experimental results indicated that our method achieved high accuracy in classifying the degree of pulmonary edema in patients with COVID-19 pneumonia by comparison with previous deep learning methods, indicating its potential to monitor patients with COVID-19 pneumonia.
Collapse
Affiliation(s)
- Qinghua Huang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China; School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University, Xi'an, China
| | - Ye Lei
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China; School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University, Xi'an, China
| | - Wenyu Xing
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, China
| | - Chao He
- Department of Emergency and Critical Care, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Gaofeng Wei
- Naval Medical Department, Naval Medical University, Shanghai, China
| | - Zhaoji Miao
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China; School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University, Xi'an, China
| | - Yifan Hao
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China; School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University, Xi'an, China
| | - Guannan Li
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai, China
| | - Yan Wang
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai, China
| | - Qingli Li
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai, China
| | - Xuelong Li
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China; School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University, Xi'an, China
| | - Wenfang Li
- Department of Emergency and Critical Care, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Jiangang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China.
| |
Collapse
|
|
17
|
Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model. Biomed Signal Process Control 2022; 75:103561. [PMID: 35154355 PMCID: PMC8818345 DOI: 10.1016/j.bspc.2022.103561] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 01/20/2022] [Accepted: 02/02/2022] [Indexed: 02/02/2023]
Abstract
Coronavirus disease 2019 (COVID-19) pneumonia has erupted worldwide, causing massive population deaths and huge economic losses. In clinic, lung ultrasound (LUS) plays an important role in the auxiliary diagnosis of COVID-19 pneumonia. However, the lack of medical resources leads to the low using efficiency of the LUS, to address this problem, a novel automated LUS scoring system for evaluating COVID-19 pneumonia based on the two-stage cascaded deep learning model was proposed in this paper. 18,330 LUS images collected from 26 COVID-19 pneumonia patients were successfully assigned scores by two experienced doctors according to the designed four-level scoring standard for training the model. At the first stage, we made a secondary selection of these scored images through five ResNet-50 models and five-fold cross validation to obtain the available 12,949 LUS images which were highly relevant to the initial scoring results. At the second stage, three deep learning models including ResNet-50, Vgg-19, and GoogLeNet were formed the cascaded scored model and trained using the new dataset, whose predictive result was obtained by the voting mechanism. In addition, 1000 LUS images collected another 5 COVID-19 pneumonia patients were employed to test the model. Experiments results showed that the automated LUS scoring model was evaluated in terms of accuracy, sensitivity, specificity, and F1-score, being 96.1%, 96.3%, 98.8%, and 96.1%, respectively. They proved the proposed two-stage cascaded deep learning model could automatically score an LUS image, which has great potential for application to the clinics on various occasions.
Collapse
|
|
18
|
De Rosa L, L'Abbate S, Kusmic C, Faita F. Applications of artificial intelligence in lung ultrasound: Review of deep learning methods for COVID-19 fighting. Artif Intell Med Imaging 2022; 3:42-54. [DOI: 10.35711/aimi.v3.i2.42] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/22/2022] [Accepted: 04/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The pandemic outbreak of the novel coronavirus disease (COVID-19) has highlighted the need to combine rapid, non-invasive and widely accessible techniques with the least risk of patient’s cross-infection to achieve a successful early detection and surveillance of the disease. In this regard, the lung ultrasound (LUS) technique has been proved invaluable in both the differential diagnosis and the follow-up of COVID-19 patients, and its potential may be destined to evolve. Recently, indeed, LUS has been empowered through the development of automated image processing techniques.
AIM To provide a systematic review of the application of artificial intelligence (AI) technology in medical LUS analysis of COVID-19 patients using the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines.
METHODS A literature search was performed for relevant studies published from March 2020 - outbreak of the pandemic - to 30 September 2021. Seventeen articles were included in the result synthesis of this paper.
RESULTS As part of the review, we presented the main characteristics related to AI techniques, in particular deep learning (DL), adopted in the selected articles. A survey was carried out on the type of architectures used, availability of the source code, network weights and open access datasets, use of data augmentation, use of the transfer learning strategy, type of input data and training/test datasets, and explainability.
CONCLUSION Finally, this review highlighted the existing challenges, including the lack of large datasets of reliable COVID-19-based LUS images to test the effectiveness of DL methods and the ethical/regulatory issues associated with the adoption of automated systems in real clinical scenarios.
Collapse
Affiliation(s)
- Laura De Rosa
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
| | - Serena L'Abbate
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
- Institute of Life Sciences, Scuola Superiore Sant’Anna, Pisa 56124, Italy
| | - Claudia Kusmic
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
| | - Francesco Faita
- Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
| |
Collapse
|
|
19
|
Wang Y, Zhang Y, He Q, Liao H, Luo J. Quantitative Analysis of Pleural Line and B-Lines in Lung Ultrasound Images for Severity Assessment of COVID-19 Pneumonia. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2022; 69:73-83. [PMID: 34428140 PMCID: PMC8905613 DOI: 10.1109/tuffc.2021.3107598] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 08/21/2021] [Indexed: 06/12/2023]
Abstract
Specific patterns of lung ultrasound (LUS) images are used to assess the severity of coronavirus disease 2019 (COVID-19) pneumonia, while such assessment is mainly based on clinicians' qualitative and subjective observations. In this study, we quantitatively analyze the LUS images to assess the severity of COVID-19 pneumonia by characterizing the patterns related to the pleural line (PL) and B-lines (BLs). Twenty-seven patients with COVID-19 pneumonia, including 13 moderate cases, seven severe cases, and seven critical cases, are enrolled. Features related to the PL, including the thickness (TPL) and roughness of the PL (RPL), and the mean (MPLI) and standard deviation (SDPLI) of the PL intensities are extracted from the LUS images. Features related to the BLs, including the number (NBL), accumulated width (AWBL), attenuation coefficient (ACBL), and accumulated intensity (AIBL) of BLs, are also extracted. The correlations of these features with the disease severity are evaluated. The performances of the binary severe/non-severe classification are assessed for each feature and support vector machine (SVM) classifiers with various combinations of features as input. Several features, including the RPL, NBL, AWBL, and AIBL, show significant correlations with disease severity (all ). The classification performance is optimal using the SVM classifier using all the features as input (area under the receiver operating characteristic (ROC) curve = 0.96, sensitivity = 0.93, and specificity = 1). These findings demonstrate that the proposed method may be a promising tool for automatic grading diagnosis and follow-up of patients with COVID-19 pneumonia.
Collapse
Affiliation(s)
- Yuanyuan Wang
- Department of Biomedical EngineeringSchool of MedicineTsinghua UniversityBeijing100084China
| | - Yao Zhang
- Department of UltrasoundBeijing Ditan HospitalCapital Medical UniversityBeijing100015China
| | - Qiong He
- Department of Biomedical EngineeringSchool of MedicineTsinghua UniversityBeijing100084China
| | - Hongen Liao
- Department of Biomedical EngineeringSchool of MedicineTsinghua UniversityBeijing100084China
| | - Jianwen Luo
- Department of Biomedical EngineeringSchool of MedicineTsinghua UniversityBeijing100084China
| |
Collapse
|