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Clau Terré F, Vicho Pereira R, Ayuela Azcárate JM, Ruiz Bailén M. New ultrasound techniques. Present and future. Med Intensiva 2024:S2173-5727(24)00236-4. [PMID: 39368887 DOI: 10.1016/j.medine.2024.09.010] [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: 04/25/2024] [Revised: 07/11/2024] [Accepted: 07/16/2024] [Indexed: 10/07/2024]
Abstract
The present study highlights the advances in ultrasound, especially regarding its clinical applications to critically ill patients. Artificial intelligence (AI) is crucial in automating image interpretation, improving accuracy and efficiency. Software has been developed to make it easier to perform accurate bedside ultrasound examinations, even by professionals lacking prior experience, with automatic image optimization. In addition, some applications identify cardiac structures, perform planimetry of the Doppler wave, and measure the size of vessels, which is especially useful in hemodynamic monitoring and continuous recording. The "strain" and "strain rate" parameters evaluate ventricular function, while "auto strain" automates its calculation from bedside images. These advances, and the automatic determination of ventricular volume, make ultrasound monitoring more precise and faster. The next step is continuous monitoring using gel devices attached to the skin.
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Affiliation(s)
- Fernando Clau Terré
- Servicio de Anestesia y Reanimación, Hospital Universitari Vall d'Hebron; Steering Committe Acreditación Avanzada Ecocardiografía en Críticos (EDEC-ESICM), Barcelona, Spain.
| | - Raul Vicho Pereira
- Servicio de Medicina Intensiva, Hospital Quirónsalud Palmaplanas, Supervisor Acreditación Avanzada Ecocardiografía en Críticos (EDEC-ESICM), Palma, Balearic Islands, Spain
| | - Jose Maria Ayuela Azcárate
- Servicio de Medicina Intensiva, Hospital Universitario de Burgos (Retirado), Supervisor Acreditación Avanzada Ecocardiografía en Críticos (EDEC-ESICM), Burgos, Spain
| | - Manuel Ruiz Bailén
- Servicio de Medicina Intensiva, Hospital Universitario de Jaén, Supervisor Acreditación Avanzada Ecocardiografía en Críticos (EDEC-ESICM). Profesor Asociado, Universidad de Jaén, Jaén, Spain
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Segev G, Cortellini S, Foster JD, Francey T, Langston C, Londoño L, Schweighauser A, Jepson RE. International Renal Interest Society best practice consensus guidelines for the diagnosis and management of acute kidney injury in cats and dogs. Vet J 2024; 305:106068. [PMID: 38325516 DOI: 10.1016/j.tvjl.2024.106068] [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: 06/11/2023] [Revised: 12/10/2023] [Accepted: 01/19/2024] [Indexed: 02/09/2024]
Abstract
Acute kidney injury (AKI) is defined as an injury to the renal parenchyma, with or without a decrease in kidney function, as reflected by accumulation of uremic toxins or altered urine production (i.e., increased or decreased). AKI might result from any of several factors, including ischemia, inflammation, nephrotoxins, and infectious diseases. AKI can be community- or hospital-acquired. The latter was not previously considered a common cause for AKI in animals; however, recent evidence suggests that the prevalence of hospital-acquired AKI is increasing in veterinary medicine. This is likely due to a combination of increased recognition and awareness of AKI, as well as increased treatment intensity (e.g., ventilation and prolonged hospitalization) in some veterinary patients and increased management of geriatric veterinary patients with multiple comorbidities. Advancements in the management of AKI, including the increased availability of renal replacement therapies, have been made; however, the overall mortality of animals with AKI remains high. Despite the high prevalence of AKI and the high mortality rate, the body of evidence regarding the diagnosis and the management of AKI in veterinary medicine is very limited. Consequently, the International Renal Interest Society (IRIS) constructed a working group to provide guidelines for animals with AKI. Recommendations are based on the available literature and the clinical experience of the members of the working group and reflect consensus of opinion. Fifty statements were generated and were voted on in all aspects of AKI and explanatory text can be found either before or after each statement.
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Affiliation(s)
- Gilad Segev
- Koret School of Veterinary Medicine, The Robert H. Smith Faculty of Agriculture, Food and Environment, Hebrew University of Jerusalem, Israel.
| | - Stefano Cortellini
- Department of Clinical Science and Services, Royal Veterinary College, Hawkshead Lane, North Mymms, Hertfordshire, UK
| | - Jonathan D Foster
- Department of Nephrology and Urology, Friendship Hospital for Animals, Washington DC, USA
| | - Thierry Francey
- Department of Clinical Veterinary Medicine, Vetsuisse Faculty University of Bern, Bern, Switzerland
| | - Catherine Langston
- Veterinary Clinical Science, The Ohio State University, Columbus, OH, USA
| | - Leonel Londoño
- Department of Critical Care, Capital Veterinary Specialists, Jacksonville, FL, USA
| | - Ariane Schweighauser
- Department of Clinical Veterinary Medicine, Vetsuisse Faculty University of Bern, Bern, Switzerland
| | - Rosanne E Jepson
- Department of Clinical Science and Services, Royal Veterinary College, Hawkshead Lane, North Mymms, Hertfordshire, UK
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Dadon Z, Rav Acha M, Orlev A, Carasso S, Glikson M, Gottlieb S, Alpert EA. Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction. Diagnostics (Basel) 2024; 14:767. [PMID: 38611680 PMCID: PMC11011323 DOI: 10.3390/diagnostics14070767] [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: 03/11/2024] [Revised: 03/28/2024] [Accepted: 03/31/2024] [Indexed: 04/14/2024] Open
Abstract
INTRODUCTION Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making. AIM To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes. METHODS Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020. RESULTS The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083-6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure. CONCLUSION AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.
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Affiliation(s)
- Ziv Dadon
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Moshe Rav Acha
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Amir Orlev
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Shemy Carasso
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Michael Glikson
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Shmuel Gottlieb
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Evan Avraham Alpert
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
- Department of Emergency Medicine, Hadassah Medical Center—Ein Kerem, Jerusalem 9112001, Israel
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Collins PD, Giosa L, Camporota L, Barrett NA. State of the art: Monitoring of the respiratory system during veno-venous extracorporeal membrane oxygenation. Perfusion 2024; 39:7-30. [PMID: 38131204 DOI: 10.1177/02676591231210461] [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] [Indexed: 12/23/2023]
Abstract
Monitoring the patient receiving veno-venous extracorporeal membrane oxygenation (VV ECMO) is challenging due to the complex physiological interplay between native and membrane lung. Understanding these interactions is essential to understand the utility and limitations of different approaches to respiratory monitoring during ECMO. We present a summary of the underlying physiology of native and membrane lung gas exchange and describe different tools for titrating and monitoring gas exchange during ECMO. However, the most important role of VV ECMO in severe respiratory failure is as a means of avoiding further ergotrauma. Although optimal respiratory management during ECMO has not been defined, over the last decade there have been advances in multimodal respiratory assessment which have the potential to guide care. We describe a combination of imaging, ventilator-derived or invasive lung mechanic assessments as a means to individualise management during ECMO.
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Affiliation(s)
- Patrick Duncan Collins
- Department of Critical Care Medicine, Guy's and St Thomas' National Health Service Foundation Trust, London, UK
- Centre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Lorenzo Giosa
- Department of Critical Care Medicine, Guy's and St Thomas' National Health Service Foundation Trust, London, UK
| | - Luigi Camporota
- Department of Critical Care Medicine, Guy's and St Thomas' National Health Service Foundation Trust, London, UK
- Centre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Nicholas A Barrett
- Department of Critical Care Medicine, Guy's and St Thomas' National Health Service Foundation Trust, London, UK
- Centre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London, London, UK
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Policastro P, Mesin L. Processing Ultrasound Scans of the Inferior Vena Cava: Techniques and Applications. Bioengineering (Basel) 2023; 10:1076. [PMID: 37760178 PMCID: PMC10525913 DOI: 10.3390/bioengineering10091076] [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: 07/29/2023] [Revised: 09/08/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
The inferior vena cava (IVC) is the largest vein in the body. It returns deoxygenated blood to the heart from the tissues placed under the diaphragm. The size and dynamics of the IVC depend on the blood volume and right atrial pressure, which are important indicators of a patient's hydration and reflect possible pathological conditions. Ultrasound (US) assessment of the IVC is a promising technique for evaluating these conditions, because it is fast, non-invasive, inexpensive, and without side effects. However, the standard M-mode approach for measuring IVC diameter is prone to errors due to the vein movements during respiration. B-mode US produces two-dimensional images that better capture the IVC shape and size. In this review, we discuss the pros and cons of current IVC segmentation techniques for B-mode longitudinal and transverse views. We also explored several scenarios where automated IVC segmentation could improve medical diagnosis and prognosis.
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Affiliation(s)
| | - Luca Mesin
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy;
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Lucassen RT, Jafari MH, Duggan NM, Jowkar N, Mehrtash A, Fischetti C, Bernier D, Prentice K, Duhaime EP, Jin M, Abolmaesumi P, Heslinga FG, Veta M, Duran-Mendicuti MA, Frisken S, Shyn PB, Golby AJ, Boyer E, Wells WM, Goldsmith AJ, Kapur T. Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound. IEEE J Biomed Health Inform 2023; 27:4352-4361. [PMID: 37276107 PMCID: PMC10540221 DOI: 10.1109/jbhi.2023.3282596] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F 1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility.
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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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