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Schneider E, Maimon N, Hasidim A, Shnaider A, Migliozzi G, Haviv YS, Halpern D, Abu Ganem B, Fuchs L. Can Dialysis Patients Identify and Diagnose Pulmonary Congestion Using Self-Lung Ultrasound? J Clin Med 2023; 12:jcm12113829. [PMID: 37298024 DOI: 10.3390/jcm12113829] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/22/2023] [Accepted: 05/31/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND With the recent developments in automated tools, smaller and cheaper machines for lung ultrasound (LUS) are leading us toward the potential to conduct POCUS tele-guidance for the early detection of pulmonary congestion. This study aims to evaluate the feasibility and accuracy of a self-lung ultrasound study conducted by hemodialysis (HD) patients to detect pulmonary congestion, with and without artificial intelligence (AI)-based automatic tools. METHODS This prospective pilot study was conducted between November 2020 and September 2021. Nineteen chronic HD patients were enrolled in the Soroka University Medical Center (SUMC) Dialysis Clinic. First, we examined the patient's ability to obtain a self-lung US. Then, we used interrater reliability (IRR) to compare the self-detection results reported by the patients to the observation of POCUS experts and an ultrasound (US) machine with an AI-based automatic B-line counting tool. All the videos were reviewed by a specialist blinded to the performer. We examined their agreement degree using the weighted Cohen's kappa (Kw) index. RESULTS A total of 19 patients were included in our analysis. We found moderate to substantial agreement between the POCUS expert review and the automatic counting both when the patient performed the LUS (Kw = 0.49 [95% CI: 0.05-0.93]) and when the researcher performed it (Kw = 0.67 [95% CI: 0.67-0.67]). Patients were able to place the probe in the correct position and present a lung image well even weeks from the teaching session, but did not show good abilities in correctly saving or counting B-lines compared to an expert or an automatic counting tool. CONCLUSIONS Our results suggest that LUS self-monitoring for pulmonary congestion can be a reliable option if the patient's count is combined with an AI application for the B-line count. This study provides insight into the possibility of utilizing home US devices to detect pulmonary congestion, enabling patients to have a more active role in their health care.
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Affiliation(s)
- Eyal Schneider
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 7747629, Israel
| | - Netta Maimon
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 7747629, Israel
| | - Ariel Hasidim
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 7747629, Israel
| | - Alla Shnaider
- Department of Nephrology, Soroka University Medical Center, Beer-Sheva 8457108, Israel
| | - Gabrielle Migliozzi
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 7747629, Israel
| | - Yosef S Haviv
- Department of Nephrology, Soroka University Medical Center, Beer-Sheva 8457108, Israel
| | - Dor Halpern
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 7747629, Israel
| | - Basel Abu Ganem
- Department of Health Policy and Management, Ben-Gurion University of the Negev, Beer-Sheva 7747629, Israel
- Emergency Room, Joseftal Hospital, Eilat 8808024, Israel
| | - Lior Fuchs
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 7747629, Israel
- Medical Intensive Care Unit and Clinical Research Center, Soroka University Medical Center, Beer-Sheva 8457108, Israel
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Vinod DN, Prabaharan SRS. COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:2667-2682. [PMID: 36685135 PMCID: PMC9843670 DOI: 10.1007/s11831-023-09882-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 01/05/2023] [Indexed: 05/29/2023]
Abstract
The absolute previously infected novel coronavirus (COVID-19) was found in Wuhan, China, in December 2019. The COVID-19 epidemic has spread to more than 220 nations and territories globally and has altogether influenced each part of our day-to-day lives. As of 9th March 2022, a total aggregate of 44,78,82,185 (60,07,317) contaminated (dead) COVID-19 cases were accounted for all over the world. The quantities of contaminated cases passing despite everything increment essentially and do not indicate a controlled circumstance. The scope of this paper is to address this issue by presenting a comprehensive and comparative analysis of the existing Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) based approaches used in significance in reacting to the COVID-19 epidemic and diagnosing the severe impacts. The paper provides, firstly, an overview of COVID-19 infection and highlights of this article; Secondly, an overview of exploring various executive innovations by utilizing different resources to stop the spread of COVID-19; Thirdly, a comparison of existing predicting methods of COVID-19 in the literature, with focus on ML, DL and AI-driven techniques with performance metrics; and finally, a discussion on the results of the work as well as future scope.
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Affiliation(s)
- Dasari Naga Vinod
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu 600062 India
| | - S. R. S. Prabaharan
- Sathyabama Centre for Advanced Studies, Sathyabama Institute of Science and Technology, Rajiv Gandhi Salai, Chennai, Tamil Nadu 600119 India
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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: 0] [Impact Index Per Article: 0] [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.
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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
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Wang J, Yang X, Zhou B, Sohn JJ, Zhou J, Jacob JT, Higgins KA, Bradley JD, Liu T. Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic. J Imaging 2022; 8:65. [PMID: 35324620 PMCID: PMC8952297 DOI: 10.3390/jimaging8030065] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 12/25/2022] Open
Abstract
Ultrasound imaging of the lung has played an important role in managing patients with COVID-19-associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.
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Affiliation(s)
- Jing Wang
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Boran Zhou
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - James J. Sohn
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23219, USA;
| | - Jun Zhou
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Jesse T. Jacob
- Division of Infectious Diseases, Department of Medicine, Emory University, Atlanta, GA 30322, USA;
| | - Kristin A. Higgins
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Jeffrey D. Bradley
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Tian Liu
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
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Zhao L, Lediju Bell MA. A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients. BME FRONTIERS 2022; 2022:9780173. [PMID: 36714302 PMCID: PMC9880989 DOI: 10.34133/2022/9780173] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
The massive and continuous spread of COVID-19 has motivated researchers around the world to intensely explore, understand, and develop new techniques for diagnosis and treatment. Although lung ultrasound imaging is a less established approach when compared to other medical imaging modalities such as X-ray and CT, multiple studies have demonstrated its promise to diagnose COVID-19 patients. At the same time, many deep learning models have been built to improve the diagnostic efficiency of medical imaging. The integration of these initially parallel efforts has led multiple researchers to report deep learning applications in medical imaging of COVID-19 patients, most of which demonstrate the outstanding potential of deep learning to aid in the diagnosis of COVID-19. This invited review is focused on deep learning applications in lung ultrasound imaging of COVID-19 and provides a comprehensive overview of ultrasound systems utilized for data acquisition, associated datasets, deep learning models, and comparative performance.
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Affiliation(s)
- Lingyi Zhao
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Muyinatu A. Lediju Bell
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA,Department of Computer Science, Johns Hopkins University, Baltimore, USA,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, USA
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Sosa FA, Matarrese A, Saavedra S, Osatnik J, Roberti J, Oribe BT, Ivulich D, Durán AL, Caputo C, Benay C. Lung ultrasound as a predictor of mortality of patients with COVID-19. J Bras Pneumol 2021; 47:e20210092. [PMID: 34495211 PMCID: PMC8647154 DOI: 10.36416/1806-3756/e20210092] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 06/04/2021] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE To evaluate the performance of lung ultrasound to determine short-term outcomes of patients with COVID-19 admitted to the intensive care unit. METHODS This is a Prospective, observational study. Between July and November 2020, 59 patients were included and underwent at least two LUS assessments using LUS score (range 0-42) on day of admission, day 5th, and 10th of admission. RESULTS Age was 66.5±15 years, APACHE II was 8.3±3.9, 12 (20%) patients had malignancy, 46 (78%) patients had a non-invasive ventilation/high-flow nasal cannula and 38 (64%) patients required mechanical ventilation. The median stay in ICU was 12 days (IQR 8.5-20.5 days). ICU or hospital mortality was 54%. On admission, the LUS score was 20.8±6.1; on day 5th and day 10th of admission, scores were 27.6±5.5 and 29.4±5.3, respectively (P=0.007). As clinical condition deteriorated the LUS score increased, with a positive correlation of 0.52, P <0.001. Patients with worse LUS on day 5th versus better score had a mortality of 76% versus 33% (OR 6.29, 95%CI 2.01-19.65, p. 0.003); a similar difference was observed on day 10. LUS score of 5th day of admission had an area under the curve of 0.80, best cut-point of 27, sensitivity and specificity of 0.75 and 0.78 respectively. CONCLUSION These findings position LUS as a simple and reproducible method to predict the course of COVID-19 patients.
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Affiliation(s)
| | | | | | | | - Javier Roberti
- Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
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