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Nhat PTH, Van Hao N, Tho PV, Kerdegari H, Pisani L, Thu LNM, Phuong LT, Duong HTH, Thuy DB, McBride A, Xochicale M, Schultz MJ, Razavi R, King AP, Thwaites L, Van Vinh Chau N, Yacoub S, Gomez A. Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit. Crit Care 2023; 27:257. [PMID: 37393330 PMCID: PMC10314555 DOI: 10.1186/s13054-023-04548-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/24/2023] [Indexed: 07/03/2023] Open
Abstract
BACKGROUND Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and middle- income countries (LMICs) where there is limited training available. Despite recent advances in the use of Artificial Intelligence (AI) to automate many ultrasound imaging analysis tasks, no AI-enabled LUS solutions have been proven to be clinically useful in ICUs, and specifically in LMICs. Therefore, we developed an AI solution that assists LUS practitioners and assessed its usefulness in a low resource ICU. METHODS This was a three-phase prospective study. In the first phase, the performance of four different clinical user groups in interpreting LUS clips was assessed. In the second phase, the performance of 57 non-expert clinicians with and without the aid of a bespoke AI tool for LUS interpretation was assessed in retrospective offline clips. In the third phase, we conducted a prospective study in the ICU where 14 clinicians were asked to carry out LUS examinations in 7 patients with and without our AI tool and we interviewed the clinicians regarding the usability of the AI tool. RESULTS The average accuracy of beginners' LUS interpretation was 68.7% [95% CI 66.8-70.7%] compared to 72.2% [95% CI 70.0-75.6%] in intermediate, and 73.4% [95% CI 62.2-87.8%] in advanced users. Experts had an average accuracy of 95.0% [95% CI 88.2-100.0%], which was significantly better than beginners, intermediate and advanced users (p < 0.001). When supported by our AI tool for interpreting retrospectively acquired clips, the non-expert clinicians improved their performance from an average of 68.9% [95% CI 65.6-73.9%] to 82.9% [95% CI 79.1-86.7%], (p < 0.001). In prospective real-time testing, non-expert clinicians improved their baseline performance from 68.1% [95% CI 57.9-78.2%] to 93.4% [95% CI 89.0-97.8%], (p < 0.001) when using our AI tool. The time-to-interpret clips improved from a median of 12.1 s (IQR 8.5-20.6) to 5.0 s (IQR 3.5-8.8), (p < 0.001) and clinicians' median confidence level improved from 3 out of 4 to 4 out of 4 when using our AI tool. CONCLUSIONS AI-assisted LUS can help non-expert clinicians in an LMIC ICU improve their performance in interpreting LUS features more accurately, more quickly and more confidently.
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Affiliation(s)
- Phung Tran Huy Nhat
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK.
| | - Nguyen Van Hao
- Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
- Hospital of Tropical Diseases, Ho Chi Minh City, Vietnam
- University of Medicine and Pharmacy, Ho Chi Minh City, Vietnam
| | - Phan Vinh Tho
- Hospital of Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Hamideh Kerdegari
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
| | - Luigi Pisani
- Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
| | | | - Le Thanh Phuong
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | | | | | - Angela McBride
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Miguel Xochicale
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
| | - Marcus J Schultz
- Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
| | - Reza Razavi
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
| | - Andrew P King
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
| | - Louise Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | | | - Sophie Yacoub
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Alberto Gomez
- School of Biomedical Engineering Imaging Sciences, King's College London, London, UK
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Baker C, Xochicale M, Lin FY, Mathews S, Joubert F, Shakir DI, Miles R, Mosse CA, Zhao T, Liang W, Kunpalin Y, Dromey B, Mistry T, Sebire NJ, Zhang E, Ourselin S, Beard PC, David AL, Desjardins AE, Vercauteren T, Xia W. Intraoperative Needle Tip Tracking with an Integrated Fibre-Optic Ultrasound Sensor. Sensors (Basel) 2022; 22:9035. [PMID: 36501738 PMCID: PMC9739176 DOI: 10.3390/s22239035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/15/2022] [Accepted: 11/16/2022] [Indexed: 06/17/2023]
Abstract
Ultrasound is an essential tool for guidance of many minimally-invasive surgical and interventional procedures, where accurate placement of the interventional device is critical to avoid adverse events. Needle insertion procedures for anaesthesia, fetal medicine and tumour biopsy are commonly ultrasound-guided, and misplacement of the needle may lead to complications such as nerve damage, organ injury or pregnancy loss. Clear visibility of the needle tip is therefore critical, but visibility is often precluded by tissue heterogeneities or specular reflections from the needle shaft. This paper presents the in vitro and ex vivo accuracy of a new, real-time, ultrasound needle tip tracking system for guidance of fetal interventions. A fibre-optic, Fabry-Pérot interferometer hydrophone is integrated into an intraoperative needle and used to localise the needle tip within a handheld ultrasound field. While previous, related work has been based on research ultrasound systems with bespoke transmission sequences, the new system-developed under the ISO 13485 Medical Devices quality standard-operates as an adjunct to a commercial ultrasound imaging system and therefore provides the image quality expected in the clinic, superimposing a cross-hair onto the ultrasound image at the needle tip position. Tracking accuracy was determined by translating the needle tip to 356 known positions in the ultrasound field of view in a tank of water, and by comparison to manual labelling of the the position of the needle in B-mode US images during an insertion into an ex vivo phantom. In water, the mean distance between tracked and true positions was 0.7 ± 0.4 mm with a mean repeatability of 0.3 ± 0.2 mm. In the tissue phantom, the mean distance between tracked and labelled positions was 1.1 ± 0.7 mm. Tracking performance was found to be independent of needle angle. The study demonstrates the performance and clinical compatibility of ultrasound needle tracking, an essential step towards a first-in-human study.
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Affiliation(s)
- Christian Baker
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 4th Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, UK
| | - Miguel Xochicale
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 4th Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, UK
| | - Fang-Yu Lin
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 4th Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, UK
| | - Sunish Mathews
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - Francois Joubert
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 4th Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, UK
| | - Dzhoshkun I. Shakir
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 4th Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, UK
| | - Richard Miles
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 4th Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, UK
| | - Charles A. Mosse
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - Tianrui Zhao
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 4th Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, UK
| | - Weidong Liang
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 4th Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, UK
| | - Yada Kunpalin
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
- Elizabeth Garrett Anderson Institute for Women’s Health, University College London, 74 Huntley Street, London WC1E 6AU, UK
| | - Brian Dromey
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
- Elizabeth Garrett Anderson Institute for Women’s Health, University College London, 74 Huntley Street, London WC1E 6AU, UK
| | - Talisa Mistry
- NIHR Great Ormond Street BRC and Institute of Child Health, University College London, 30 Guilford Street, London WC1N 1EH, UK
| | - Neil J. Sebire
- NIHR Great Ormond Street BRC and Institute of Child Health, University College London, 30 Guilford Street, London WC1N 1EH, UK
| | - Edward Zhang
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 4th Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, UK
| | - Paul C. Beard
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - Anna L. David
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
- Elizabeth Garrett Anderson Institute for Women’s Health, University College London, 74 Huntley Street, London WC1E 6AU, UK
| | - Adrien E. Desjardins
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London WC1E 6BT, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 4th Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, UK
| | - Wenfeng Xia
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 4th Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, UK
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Beale R, Rosendo JB, Bergeles C, Beverly A, Camporota L, Castrejón-Pita AA, Crockett DC, Cronin JN, Denison T, East S, Edwardes C, Farmery AD, Fele F, Fisk J, Fuenteslópez CV, Garstka M, Goulart P, Heaysman C, Hussain A, Jha P, Kempf I, Kumar AS, Möslein A, Orr ACJ, Ourselin S, Salisbury D, Seneci C, Staruch R, Steel H, Thompson M, Tran MC, Vitiello V, Xochicale M, Zhou F, Formenti F, Kirk T. OxVent: Design and evaluation of a rapidly-manufactured Covid-19 ventilator. EBioMedicine 2022; 76:103868. [PMID: 35172957 PMCID: PMC8842095 DOI: 10.1016/j.ebiom.2022.103868] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/08/2021] [Accepted: 01/21/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The manufacturing of any standard mechanical ventilator cannot rapidly be upscaled to several thousand units per week, largely due to supply chain limitations. The aim of this study was to design, verify and perform a pre-clinical evaluation of a mechanical ventilator based on components not required for standard ventilators, and that met the specifications provided by the Medicines and Healthcare Products Regulatory Agency (MHRA) for rapidly-manufactured ventilator systems (RMVS). METHODS The design utilises closed-loop negative feedback control, with real-time monitoring and alarms. Using a standard test lung, we determined the difference between delivered and target tidal volume (VT) at respiratory rates between 20 and 29 breaths per minute, and the ventilator's ability to deliver consistent VT during continuous operation for >14 days (RMVS specification). Additionally, four anaesthetised domestic pigs (3 male-1 female) were studied before and after lung injury to provide evidence of the ventilator's functionality, and ability to support spontaneous breathing. FINDINGS Continuous operation lasted 23 days, when the greatest difference between delivered and target VT was 10% at inspiratory flow rates >825 mL/s. In the pre-clinical evaluation, the VT difference was -1 (-90 to 88) mL [mean (LoA)], and positive end-expiratory pressure (PEEP) difference was -2 (-8 to 4) cmH2O. VT delivery being triggered by pressures below PEEP demonstrated spontaneous ventilation support. INTERPRETATION The mechanical ventilator presented meets the MHRA therapy standards for RMVS and, being based on largely available components, can be manufactured at scale. FUNDING Work supported by Wellcome/EPSRC Centre for Medical Engineering,King's Together Fund and Oxford University.
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Affiliation(s)
- Richard Beale
- Centre for Human and Applied Physiological Sciences, King's College London, UK; Intensive Care Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Christos Bergeles
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Anair Beverly
- Department of Engineering Science, University of Oxford, UK
| | - Luigi Camporota
- Centre for Human and Applied Physiological Sciences, King's College London, UK; Intensive Care Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Douglas C Crockett
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, UK
| | - John N Cronin
- Centre for Human and Applied Physiological Sciences, King's College London, UK; Department of Anaesthesia, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Timothy Denison
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | - Sebastian East
- Department of Engineering Science, University of Oxford, UK
| | | | - Andrew D Farmery
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Filiberto Fele
- Department of Engineering Science, University of Oxford, UK
| | - James Fisk
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | - Carla V Fuenteslópez
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | | | - Paul Goulart
- Department of Engineering Science, University of Oxford, UK
| | - Clare Heaysman
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | | | - Prashant Jha
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Idris Kempf
- Department of Engineering Science, University of Oxford, UK
| | | | - Annika Möslein
- Department of Engineering Science, University of Oxford, UK
| | - Andrew C J Orr
- Department of Engineering Science, University of Oxford, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - David Salisbury
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | - Carlo Seneci
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Robert Staruch
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK; Nuffield Department of Orthopaedic, Rheumatology and Musculoskeletal Sciences, University of Oxford, UK; The Academic Department of Military Surgery and Trauma, Birmingham, UK
| | - Harrison Steel
- Department of Engineering Science, University of Oxford, UK
| | - Mark Thompson
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | - Minh C Tran
- Department of Engineering Science, University of Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Valentina Vitiello
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Miguel Xochicale
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Feibiao Zhou
- Department of Engineering Science, University of Oxford, UK
| | - Federico Formenti
- Centre for Human and Applied Physiological Sciences, King's College London, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Department of Biomechanics, University of Nebraska Omaha, Omaha, NE, USA.
| | - Thomas Kirk
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK.
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