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Choudhary N, Gupta A, Gupta N. Artificial intelligence and robotics in regional anesthesia. World J Methodol 2024; 14:95762. [DOI: 10.5662/wjm.v14.i4.95762] [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: 04/17/2024] [Revised: 06/03/2024] [Accepted: 06/13/2024] [Indexed: 07/26/2024] Open
Abstract
Artificial intelligence (AI) technology is vital for practitioners to incorporate AI and robotics in day-to-day regional anesthesia practice. Recent literature is encouraging on its applications in regional anesthesia, but the data are limited. AI can help us identify and guide the needle tip precisely to the location. This may help us reduce the time, improve precision, and reduce the associated side effects of improper distribution of drugs. In this article, we discuss the potential roles of AI and robotics in regional anesthesia.
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Affiliation(s)
- Nitin Choudhary
- Department of Anesthesiology, Pain Medicine and Critical Care, All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
| | - Anju Gupta
- Department of Anesthesiology, Pain Medicine and Critical Care, All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
| | - Nishkarsh Gupta
- Department of Onco-Anesthesiology and Palliative Medicine, All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
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Laterza V, Marchegiani F, Aisoni F, Ammendola M, Schena CA, Lavazza L, Ravaioli C, Carra MC, Costa V, De Franceschi A, De Simone B, de’Angelis N. Smart Operating Room in Digestive Surgery: A Narrative Review. Healthcare (Basel) 2024; 12:1530. [PMID: 39120233 PMCID: PMC11311806 DOI: 10.3390/healthcare12151530] [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: 06/30/2024] [Revised: 07/24/2024] [Accepted: 07/29/2024] [Indexed: 08/10/2024] Open
Abstract
The introduction of new technologies in current digestive surgical practice is progressively reshaping the operating room, defining the fourth surgical revolution. The implementation of black boxes and control towers aims at streamlining workflow and reducing surgical error by early identification and analysis, while augmented reality and artificial intelligence augment surgeons' perceptual and technical skills by superimposing three-dimensional models to real-time surgical images. Moreover, the operating room architecture is transitioning toward an integrated digital environment to improve efficiency and, ultimately, patients' outcomes. This narrative review describes the most recent evidence regarding the role of these technologies in transforming the current digestive surgical practice, underlining their potential benefits and drawbacks in terms of efficiency and patients' outcomes, as an attempt to foresee the digestive surgical practice of tomorrow.
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Affiliation(s)
- Vito Laterza
- Department of Digestive Surgical Oncology and Liver Transplantation, University Hospital of Besançon, 3 Boulevard Alexandre Fleming, 25000 Besancon, France;
| | - Francesco Marchegiani
- Unit of Colorectal and Digestive Surgery, DIGEST Department, Beaujon University Hospital, AP-HP, University of Paris Cité, Clichy, 92110 Paris, France
| | - Filippo Aisoni
- Unit of Emergency Surgery, Department of Surgery, Ferrara University Hospital, 44124 Ferrara, Italy;
| | - Michele Ammendola
- Digestive Surgery Unit, Health of Science Department, University Hospital “R.Dulbecco”, 88100 Catanzaro, Italy;
| | - Carlo Alberto Schena
- Unit of Robotic and Minimally Invasive Surgery, Department of Surgery, Ferrara University Hospital, 44124 Ferrara, Italy; (C.A.S.); (N.d.)
| | - Luca Lavazza
- Hospital Network Coordinator of Azienda Ospedaliero, Universitaria and Azienda USL di Ferrara, 44121 Ferrara, Italy;
| | - Cinzia Ravaioli
- Azienda Ospedaliero, Universitaria di Ferrara, 44121 Ferrara, Italy;
| | - Maria Clotilde Carra
- Rothschild Hospital (AP-HP), 75012 Paris, France;
- INSERM-Sorbonne Paris Cité, Epidemiology and Statistics Research Centre, 75004 Paris, France
| | - Vittore Costa
- Unit of Orthopedics, Humanitas Hospital, 24125 Bergamo, Italy;
| | | | - Belinda De Simone
- Department of Emergency Surgery, Academic Hospital of Villeneuve St Georges, 91560 Villeneuve St. Georges, France;
| | - Nicola de’Angelis
- Unit of Robotic and Minimally Invasive Surgery, Department of Surgery, Ferrara University Hospital, 44124 Ferrara, Italy; (C.A.S.); (N.d.)
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy
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Savage M, Spence A, Turbitt L. The educational impact of technology-enhanced learning in regional anaesthesia: a scoping review. Br J Anaesth 2024; 133:400-415. [PMID: 38824073 DOI: 10.1016/j.bja.2024.04.045] [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: 12/31/2023] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 06/03/2024] Open
Abstract
BACKGROUND Effective training in regional anaesthesia (RA) is paramount to ensuring widespread competence. Technology-based learning has assisted other specialties in achieving more rapid procedural skill acquisition. If applicable to RA, technology-enhanced training has the potential to provide an effective learning experience and to overcome barriers to RA training. We review the current evidence base for use of innovative technologies in assisting learning of RA. METHODS Using scoping review methodology, three databases (MEDLINE, Embase, and Web of Science) were searched, identifying 158 relevant citations. Citations were screened against defined eligibility criteria with 27 studies selected for inclusion. Data relating to study details, technological learning interventions, and impact on learner experience were extracted and analysed. RESULTS Seven different technologies were used to train learners in RA: artificial intelligence, immersive virtual reality, desktop virtual reality, needle guidance technology, robotics, augmented reality, and haptic feedback devices. Of 27 studies, 26 reported a positive impact of technology-enhanced RA training, with different technologies offering benefits for differing components of RA training. Artificial intelligence improved sonoanatomical knowledge and ultrasound skills for RA, whereas needle guidance technologies enhanced confidence and improved needling performance, particularly in novices. Immersive virtual reality allowed more rapid acquisition of needling skills, but its functionality was limited when combined with haptic feedback technology. User friendly technologies enhanced participant experience and improved confidence in RA; however, limitations in technology-assisted RA training restrict its widespread use. CONCLUSIONS Technology-enhanced RA training can provide a positive and effective learning experience, with potential to reduce the steep learning curve associated with gaining RA proficiency. A combined approach to RA education, using both technological and traditional approaches, should be maintained as no single method has been shown to provide comprehensive RA training.
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Affiliation(s)
- Mairead Savage
- Department of Anaesthesia, Belfast Health and Social Care Trust, Belfast, UK.
| | - Andrew Spence
- Department of Gastroenterology, Belfast Health and Social Care Trust, Belfast, UK; School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Lloyd Turbitt
- Department of Anaesthesia, Belfast Health and Social Care Trust, Belfast, UK
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Jacobs E, Wainman B, Bowness J. Applying artificial intelligence to the use of ultrasound as an educational tool: A focus on ultrasound-guided regional anesthesia. ANATOMICAL SCIENCES EDUCATION 2024; 17:919-925. [PMID: 36880869 DOI: 10.1002/ase.2266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/10/2023] [Accepted: 02/25/2023] [Indexed: 06/18/2023]
Affiliation(s)
- Emma Jacobs
- Department of Anaesthesia, Royal Gwent Hospital, Aneurin Bevan University Health Board, Newport, UK
| | - Bruce Wainman
- Education Program in Anatomy, McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Science, McMaster University, Hamilton, Ontario, Canada
| | - James Bowness
- Department of Anaesthesia, Royal Gwent Hospital, Aneurin Bevan University Health Board, Newport, UK
- OxSTaR Center, Nuffield Division of Anaesthetics, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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Yang L, Zhang Y, Cai W, Tan J, Hansen H, Wang H, Chen Y, Zhu M, Mu J. Electrochemically-driven actuators: from materials to mechanisms and from performance to applications. Chem Soc Rev 2024; 53:5956-6010. [PMID: 38721851 DOI: 10.1039/d3cs00906h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Soft actuators, pivotal for converting external energy into mechanical motion, have become increasingly vital in a wide range of applications, from the subtle engineering of soft robotics to the demanding environments of aerospace exploration. Among these, electrochemically-driven actuators (EC actuators), are particularly distinguished by their operation through ion diffusion or intercalation-induced volume changes. These actuators feature notable advantages, including precise deformation control under electrical stimuli, freedom from Carnot efficiency limitations, and the ability to maintain their actuated state with minimal energy use, akin to the latching state in skeletal muscles. This review extensively examines EC actuators, emphasizing their classification based on diverse material types, driving mechanisms, actuator configurations, and potential applications. It aims to illuminate the complicated driving mechanisms of different categories, uncover their underlying connections, and reveal the interdependencies among materials, mechanisms, and performances. We conduct an in-depth analysis of both conventional and emerging EC actuator materials, casting a forward-looking lens on their trajectories and pinpointing areas ready for innovation and performance enhancement strategies. We also navigate through the challenges and opportunities within the field, including optimizing current materials, exploring new materials, and scaling up production processes. Overall, this review aims to provide a scientifically robust narrative that captures the current state of EC actuators and sets a trajectory for future innovation in this rapidly advancing field.
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Affiliation(s)
- Lixue Yang
- School of Mechanical Engineering, Tianjin University, 135 Yaguan Road, Tianjin 300350, China.
| | - Yiyao Zhang
- School of Mechanical Engineering, Tianjin University, 135 Yaguan Road, Tianjin 300350, China.
| | - Wenting Cai
- School of Chemistry, Xi'an Jiaotong University, 28 Xianning West Road, Xi'an, 710049, China
| | - Junlong Tan
- School of Mechanical Engineering, Tianjin University, 135 Yaguan Road, Tianjin 300350, China.
| | - Heather Hansen
- Department of Biochemistry and Molecular Medicine, West Virginia University, Morgantown, WV, 26506, USA
| | - Hongzhi Wang
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China.
- Shanghai Dianji University, 201306, Shanghai, China
| | - Yan Chen
- School of Mechanical Engineering, Tianjin University, 135 Yaguan Road, Tianjin 300350, China.
- Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, 135 Yaguan Road, Tianjin 300350, China.
| | - Meifang Zhu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China.
| | - Jiuke Mu
- School of Mechanical Engineering, Tianjin University, 135 Yaguan Road, Tianjin 300350, China.
- Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, 135 Yaguan Road, Tianjin 300350, China.
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Li Y, Wang M, Wang L, Cao Y, Liu Y, Zhao Y, Yuan R, Yang M, Lu S, Sun Z, Zhou F, Qian Z, Kang H. Advances in the Application of AI Robots in Critical Care: Scoping Review. J Med Internet Res 2024; 26:e54095. [PMID: 38801765 PMCID: PMC11165292 DOI: 10.2196/54095] [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: 10/29/2023] [Revised: 03/07/2024] [Accepted: 04/22/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND In recent epochs, the field of critical medicine has experienced significant advancements due to the integration of artificial intelligence (AI). Specifically, AI robots have evolved from theoretical concepts to being actively implemented in clinical trials and applications. The intensive care unit (ICU), known for its reliance on a vast amount of medical information, presents a promising avenue for the deployment of robotic AI, anticipated to bring substantial improvements to patient care. OBJECTIVE This review aims to comprehensively summarize the current state of AI robots in the field of critical care by searching for previous studies, developments, and applications of AI robots related to ICU wards. In addition, it seeks to address the ethical challenges arising from their use, including concerns related to safety, patient privacy, responsibility delineation, and cost-benefit analysis. METHODS Following the scoping review framework proposed by Arksey and O'Malley and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a scoping review to delineate the breadth of research in this field of AI robots in ICU and reported the findings. The literature search was carried out on May 1, 2023, across 3 databases: PubMed, Embase, and the IEEE Xplore Digital Library. Eligible publications were initially screened based on their titles and abstracts. Publications that passed the preliminary screening underwent a comprehensive review. Various research characteristics were extracted, summarized, and analyzed from the final publications. RESULTS Of the 5908 publications screened, 77 (1.3%) underwent a full review. These studies collectively spanned 21 ICU robotics projects, encompassing their system development and testing, clinical trials, and approval processes. Upon an expert-reviewed classification framework, these were categorized into 5 main types: therapeutic assistance robots, nursing assistance robots, rehabilitation assistance robots, telepresence robots, and logistics and disinfection robots. Most of these are already widely deployed and commercialized in ICUs, although a select few remain under testing. All robotic systems and tools are engineered to deliver more personalized, convenient, and intelligent medical services to patients in the ICU, concurrently aiming to reduce the substantial workload on ICU medical staff and promote therapeutic and care procedures. This review further explored the prevailing challenges, particularly focusing on ethical and safety concerns, proposing viable solutions or methodologies, and illustrating the prospective capabilities and potential of AI-driven robotic technologies in the ICU environment. Ultimately, we foresee a pivotal role for robots in a future scenario of a fully automated continuum from admission to discharge within the ICU. CONCLUSIONS This review highlights the potential of AI robots to transform ICU care by improving patient treatment, support, and rehabilitation processes. However, it also recognizes the ethical complexities and operational challenges that come with their implementation, offering possible solutions for future development and optimization.
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Affiliation(s)
- Yun Li
- Medical School of Chinese PLA, Beijing, China
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Min Wang
- Medical School of Chinese PLA, Beijing, China
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Lu Wang
- Medical School of Chinese PLA, Beijing, China
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Yuan Cao
- The Second Hospital, Hebei Medical University, Hebei, China
| | - Yuyan Liu
- Medical School of Chinese PLA, Beijing, China
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Yan Zhao
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Rui Yuan
- Medical School of Chinese PLA, Beijing, China
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Mengmeng Yang
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Siqian Lu
- Beidou Academic & Research Center, Beidou Life Science, Guangzhou, China
| | - Zhichao Sun
- Beidou Academic & Research Center, Beidou Life Science, Guangzhou, China
| | - Feihu Zhou
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Zhirong Qian
- Beidou Academic & Research Center, Beidou Life Science, Guangzhou, China
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Fujian, China
- The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Hongjun Kang
- The First Medical Centre, Chinese PLA General Hospital, Beijing, China
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Bowness JS, Liu X, Keane PA. Leading in the development, standardised evaluation, and adoption of artificial intelligence in clinical practice: regional anaesthesia as an example. Br J Anaesth 2024; 132:1016-1021. [PMID: 38302346 DOI: 10.1016/j.bja.2023.12.024] [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: 11/19/2023] [Revised: 12/19/2023] [Accepted: 12/27/2023] [Indexed: 02/03/2024] Open
Abstract
A recent study by Suissa and colleagues explored the clinical relevance of a medical image segmentation metric (Dice metric) commonly used in the field of artificial intelligence (AI). They showed that pixel-wise agreement for physician identification of structures on ultrasound images is variable, and a relatively low Dice metric (0.34) correlated to a substantial agreement on subjective clinical assessment. We highlight the need to bring structure and clinical perspective to the evaluation of medical AI, which clinicians are best placed to direct.
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Affiliation(s)
- James S Bowness
- Nuffield Department of Clinical Anaesthesia, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK.
| | - Xiaoxuan Liu
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Pearse A Keane
- Institute of Ophthalmology, Faculty of Brain Sciences, University College London, London, UK; NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
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Dougan J, Patel N, Bardarov S. A Comparison of Diagnostic and Immunohistochemical Workup and Literature Review Capabilities of Online Artificial Intelligence Assistance Models in Pathology. Cureus 2024; 16:e61075. [PMID: 38915984 PMCID: PMC11196119 DOI: 10.7759/cureus.61075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/23/2024] [Indexed: 06/26/2024] Open
Abstract
Artificial intelligence (AI) is a suite of technologies that enables computers to learn and interpret information like human cognition. It has found applications across various fields, including healthcare, agriculture, astronomy, navigation, and robotics. Within healthcare, AI has the potential to enhance diagnostic accuracy, facilitate drug research, and automate patient experiences. This comparative study focuses on the proficiency of AI in generating accurate differential diagnoses in the field of pathology. Six medical vignettes were crafted, and each scenario was then input into three different AI platforms. The pathologist reviewed and determined the most accurate AI model.
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Affiliation(s)
- Johnika Dougan
- Pathology and Laboratory Medicine, St. George's University School of Medicine, St. George's, GRD
| | - Netra Patel
- Pathology and Laboratory Medicine, American University of Antigua, Antigua, USA
| | - Svetoslav Bardarov
- Pathology and Laboratory Medicine, Richmond University Medical Center, Staten Island, USA
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Ferry J, Lewis O, Lloyd J, El-Boghdadly K, Kearns R, Albrecht E, Altermatt F, Ashokka B, Ayad AE, Aziz ES, Aziz L, Jagannathan B, Bouarroudj N, Chin KJ, Delbos A, de Gracia A, Ip VHY, Kwofie K, Layera S, Lobo CA, Mohammed M, Moka E, Moreno M, Morgan B, Polela A, Rahimzadeh P, Tangwiwat S, Uppal V, Vaz Perez M, Volk T, Wong PBY, Bowness JS, Macfarlane AJR. Research priorities in regional anaesthesia: an international Delphi study. Br J Anaesth 2024; 132:1041-1048. [PMID: 38448274 PMCID: PMC11103078 DOI: 10.1016/j.bja.2024.01.033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 01/05/2024] [Accepted: 01/24/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Regional anaesthesia use is growing worldwide, and there is an increasing emphasis on research in regional anaesthesia to improve patient outcomes. However, priorities for future study remain unclear. We therefore conducted an international research prioritisation exercise, setting the agenda for future investigators and funding bodies. METHODS We invited members of specialist regional anaesthesia societies from six continents to propose research questions that they felt were unanswered. These were consolidated into representative indicative questions, and a literature review was undertaken to determine if any indicative questions were already answered by published work. Unanswered indicative questions entered a three-round modified Delphi process, whereby 29 experts in regional anaesthesia (representing all participating specialist societies) rated each indicative question for inclusion on a final high priority shortlist. If ≥75% of participants rated an indicative question as 'definitely' include in any round, it was accepted. Indicative questions rated as 'definitely' or 'probably' by <50% of participants in any round were excluded. Retained indicative questions were further ranked based on the rating score in the final Delphi round. The final research priorities were ratified by the Delphi expert group. RESULTS There were 1318 responses from 516 people in the initial survey, from which 71 indicative questions were formed, of which 68 entered the modified Delphi process. Eleven 'highest priority' research questions were short listed, covering themes of pain management; training and assessment; clinical practice and efficacy; technology and equipment. CONCLUSIONS We prioritised unanswered research questions in regional anaesthesia. These will inform a coordinated global research strategy for regional anaesthesia and direct investigators to address high-priority areas.
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Affiliation(s)
- Jenny Ferry
- Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, South Wales, UK
| | - Owen Lewis
- Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, South Wales, UK
| | - James Lloyd
- Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, South Wales, UK
| | - Kariem El-Boghdadly
- Department of Anaesthesia & Perioperative Medicine, Guy's and St Thomas' NHS Foundation Trust, London, UK; Centre for Human and Applied Physiological Sciences, King's College London, London, UK
| | - Rachel Kearns
- Department of Anaesthesia, Glasgow Royal Infirmary, Glasgow, UK; School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, UK
| | - Eric Albrecht
- University Hospital of Lausanne, Lausanne, Switzerland; Department of Anaesthesia, University of Lausanne, Lausanne, Switzerland
| | - Fernando Altermatt
- Department of Anesthesiology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | - Amany E Ayad
- Department of Anesthesia, ICU and Pain, Cairo University, Cairo, Egypt
| | - Ezzat S Aziz
- Department of Anesthesia, ICU and Pain, Cairo University, Cairo, Egypt
| | - Lutful Aziz
- Department of Anaesthesia and Pain Medicine, Evercare Hospital, Dhaka, Bangladesh
| | | | | | - Ki Jinn Chin
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, ON, Canada; Department of Anesthesiology and Pain Medicine, Toronto Western Hospital, Toronto, ON, Canada
| | - Alain Delbos
- Department of Anesthesia, Medipole Garonne, Toulouse, France
| | - Alex de Gracia
- Hospital Rafael Estevez, Caja de Seguro Social, Aguadulce, Panama
| | - Vivian H Y Ip
- Department of Anesthesia and Pain Medicine, University of Alberta Hospital, Edmonton, AB, Canada
| | - Kwesi Kwofie
- Department of Anesthesia, Pain Management and Perioperative Medicine, Dalhousie University, Halifax, NS, Canada
| | - Sebastian Layera
- Department of Anesthesiology and Perioperative Medicine, University of Chile, Santiago, Chile
| | | | | | - Eleni Moka
- Creta InterClinic Hospital, Hellenic Healthcare Group (HHG), Heraklion, Crete, Greece
| | - Milena Moreno
- Department of Anaesthesiology, Pontifical Xavierian University, Bogotá, Colombia; Hospital Universitario San Ignacio, Bogotá, Columbia
| | - Bethan Morgan
- Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - Arthur Polela
- Department of Anaesthesia and Critical Care, Levy Mwanawasa University Teaching Hospital, Lusaka, Zambia
| | - Poupak Rahimzadeh
- Pain Research Center, Department of Anesthesiology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Suwimon Tangwiwat
- Department of Anesthesiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Vishal Uppal
- Department of Anesthesia, Pain Management and Perioperative Medicine, Dalhousie University, Halifax, NS, Canada
| | - Marcelo Vaz Perez
- Departament of Anesthesiology and Pain Therapy of Faculdade de Ciências Médicas da Santa Casa de São Paulo, São Paulo, Brazil
| | - Thomas Volk
- Department of Anaesthesiology, Intensive Care and Pain Therapy, Saarland University Medical Centre, Homburg, Germany; Faculty of Medicine, Saarland University, Homburg, Germany
| | - Patrick B Y Wong
- Department of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, ON, Canada
| | - James S Bowness
- Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, South Wales, UK; Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK.
| | - Alan J R Macfarlane
- Department of Anaesthesia, Glasgow Royal Infirmary, Glasgow, UK; School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, UK
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Bowness JS, Metcalfe D, El-Boghdadly K, Thurley N, Morecroft M, Hartley T, Krawczyk J, Noble JA, Higham H. Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines. Br J Anaesth 2024; 132:1049-1062. [PMID: 38448269 PMCID: PMC11103083 DOI: 10.1016/j.bja.2024.01.036] [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: 11/28/2023] [Revised: 01/09/2024] [Accepted: 01/24/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) for ultrasound scanning in regional anaesthesia is a rapidly developing interdisciplinary field. There is a risk that work could be undertaken in parallel by different elements of the community but with a lack of knowledge transfer between disciplines, leading to repetition and diverging methodologies. This scoping review aimed to identify and map the available literature on the accuracy and utility of AI systems for ultrasound scanning in regional anaesthesia. METHODS A literature search was conducted using Medline, Embase, CINAHL, IEEE Xplore, and ACM Digital Library. Clinical trial registries, a registry of doctoral theses, regulatory authority databases, and websites of learned societies in the field were searched. Online commercial sources were also reviewed. RESULTS In total, 13,014 sources were identified; 116 were included for full-text review. A marked change in AI techniques was noted in 2016-17, from which point on the predominant technique used was deep learning. Methods of evaluating accuracy are variable, meaning it is impossible to compare the performance of one model with another. Evaluations of utility are more comparable, but predominantly gained from the simulation setting with limited clinical data on efficacy or safety. Study methodology and reporting lack standardisation. CONCLUSIONS There is a lack of structure to the evaluation of accuracy and utility of AI for ultrasound scanning in regional anaesthesia, which hinders rigorous appraisal and clinical uptake. A framework for consistent evaluation is needed to inform model evaluation, allow comparison between approaches/models, and facilitate appropriate clinical adoption.
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Affiliation(s)
- James S Bowness
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK.
| | - David Metcalfe
- Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK; Emergency Medicine Research in Oxford (EMROx), Oxford University Hospitals NHS Foundation Trust, Oxford, UK. https://twitter.com/@TraumaDataDoc
| | - Kariem El-Boghdadly
- Department of Anaesthesia and Peri-operative Medicine, Guy's & St Thomas's NHS Foundation Trust, London, UK; Centre for Human and Applied Physiological Sciences, King's College London, London, UK. https://twitter.com/@elboghdadly
| | - Neal Thurley
- Bodleian Health Care Libraries, University of Oxford, Oxford, UK
| | - Megan Morecroft
- Faculty of Medicine, Health & Life Sciences, University of Swansea, Swansea, UK
| | - Thomas Hartley
- Intelligent Ultrasound, Cardiff, UK. https://twitter.com/@tomhartley84
| | - Joanna Krawczyk
- Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK
| | - J Alison Noble
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK. https://twitter.com/@AlisonNoble_OU
| | - Helen Higham
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Anaesthesia, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. https://twitter.com/@HelenEHigham
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Fallon F, Moorthy A, Skerritt C, Crowe GG, Buggy DJ. Latest Advances in Regional Anaesthesia. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:735. [PMID: 38792918 PMCID: PMC11123025 DOI: 10.3390/medicina60050735] [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: 03/28/2024] [Revised: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024]
Abstract
Training and expertise in regional anaesthesia have increased significantly in tandem with increased interest over the past two decades. This review outlines the most recent advances in regional anaesthesia and focuses on novel areas of interest including fascial plane blocks. Pharmacological advances in the form of the prolongation of drug duration with liposomal bupivacaine are considered. Neuromodulation in the context of regional anaesthesia is outlined as a potential future direction. The growing use of regional anaesthesia outside of the theatre environment and current thinking on managing the rebound plane after regional block regression are also discussed. Recent relevant evidence is summarised, unanswered questions are outlined, and priorities for ongoing investigation are suggested.
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Affiliation(s)
- Frances Fallon
- Department of Anaesthesia, Mater Misericordiae University Hospital, Eccles St, D07 WKW8 Dublin, Ireland;
| | - Aneurin Moorthy
- Department of Anaesthesia, National Orthopaedic Hospital Cappagh/Mater Misericordiae University Hospital, Eccles St, D07 WKW8 Dublin, Ireland; (A.M.)
- School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Conor Skerritt
- Department of Anaesthesia, National Orthopaedic Hospital Cappagh/Mater Misericordiae University Hospital, Eccles St, D07 WKW8 Dublin, Ireland; (A.M.)
| | - Gillian G. Crowe
- Department of Anaesthesia, Cork University Hospital, Wilton, T12 DC4A Cork, Ireland
| | - Donal J. Buggy
- Department of Anaesthesia, Mater Misericordiae University Hospital, Eccles St, D07 WKW8 Dublin, Ireland;
- School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
- The ESA-IC Oncoanaesthesiology Research Group and Outcomes Research, Cleveland, OH 44195, USA
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12
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Singla N, Kundu R, Dey P. Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology. Cytopathology 2024; 35:226-234. [PMID: 37970960 DOI: 10.1111/cyt.13336] [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: 08/20/2023] [Revised: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023]
Abstract
Artificial Intelligence (AI) is an emerging, transforming and revolutionary technology that has captured attention worldwide. It is translating research into precision oncology treatments. AI can analyse large or big data sets requiring high-speed specialized computing solutions. The data are big in terms of volume and multimodal with the amalgamation of images, text and structure. Machine learning has identified antifungal drug targets, and taxonomic and phylogenetic classification of fungi based on sequence analysis is now available. Real-time identification tools and user-friendly mobile applications for identifying fungi have been discovered. Akin to histopathology, AI can be applied to fungal cytology. AI has been fruitful in cytopathology of the thyroid gland, breast, urine and uterine cervical lesions. AI has a huge scope in fungal cytology and would certainly bear fruit with its accuracy, reproducibility and capacity for handling big data. The purpose of this systematic review was to highlight the AI's utility in detecting fungus and its typing with a special focus on future application in fungal cytology. We also touch upon the basics of AI in brief.
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Affiliation(s)
- Nidhi Singla
- Department of Microbiology, Government Medical College and Hospital, Chandigarh, India
| | - Reetu Kundu
- Department of Cytology and Gynaecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Pranab Dey
- Department of Cytology and Gynaecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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13
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Cascella M, Cascella A, Monaco F, Shariff MN. Envisioning gamification in anesthesia, pain management, and critical care: basic principles, integration of artificial intelligence, and simulation strategies. JOURNAL OF ANESTHESIA, ANALGESIA AND CRITICAL CARE 2023; 3:33. [PMID: 37697415 PMCID: PMC10494447 DOI: 10.1186/s44158-023-00118-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 08/29/2023] [Indexed: 09/13/2023]
Abstract
Unlike traditional video games developed solely for entertainment purposes, game-based learning employs intentionally crafted approaches that seamlessly merge entertainment and educational content, resulting in captivating and effective learning encounters. These pedagogical methods include serious video games and gamification. Serious games are video games utilized as tools for acquiring crucial (serious) knowledge and skills. On the other hand, gamification requires integrating gaming elements (game mechanics) such as points, leaderboards, missions, levels, rewards, and more, into a context that may not be associated with video gaming activities. They can be dynamically (game dynamics) combined developing various strategic approaches. Operatively, gamification adopts simulation elements and leverages the interactive nature of gaming to teach players specific skills, convey knowledge, or address real-world issues. External incentives stimulate internal motivation. Therefore, these techniques place the learners in the central role, allowing them to actively construct knowledge through firsthand experiences.Anesthesia, pain medicine, and critical care demand a delicate interplay of technical competence and non-technical proficiencies. Gamification techniques can offer advantages to both domains. Game-based modalities provide a dynamic, interactive, and highly effective opportunity to learn, practice, and improve both technical and non-technical skills, enriching the overall proficiency of anesthesia professionals. These properties are crucial in a discipline where personal skills, human factors, and the influence of stressors significantly impact daily work activities. Furthermore, gamification can also be embraced for patient education to enhance comfort and compliance, particularly within pediatric settings (game-based distraction), and in pain medicine through stress management techniques. On these bases, the creation of effective gamification tools for anesthesiologists can present a formidable opportunity for users and developers.This narrative review comprehensively examines the intricate aspects of gamification and its potentially transformative influence on the fields of anesthesiology. It delves into theoretical frameworks, potential advantages in education and training, integration with artificial intelligence systems and immersive techniques, and also addresses the challenges that could arise within these contexts.
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Affiliation(s)
- Marco Cascella
- Department of Anesthesia and Critical Care, Istituto Nazionale Tumori-IRCCS, Fondazione Pascale, Via Mariano Semmola, 53, 80131, Naples, Italy.
| | | | | | - Mohammed Naveed Shariff
- Department of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Chennai, India
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14
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Machado TM, Berssaneti FT. Literature review of digital twin in healthcare. Heliyon 2023; 9:e19390. [PMID: 37809792 PMCID: PMC10558347 DOI: 10.1016/j.heliyon.2023.e19390] [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: 09/29/2022] [Revised: 05/26/2023] [Accepted: 08/21/2023] [Indexed: 10/10/2023] Open
Abstract
This article aims to make a bibliometric literature review using systematic scientific mapping and content analysis of digital twins in healthcare to know the evolution, domain, keywords, content type, and kind and purpose of digital twin's implementation in healthcare, so a consolidation and future improvement of existing knowledge can be made and gaps for new studies can be identified. The increase in publications of digital twins in healthcare is quite recent and it is still concentrated in the domain of technology sources. The subject is majorly concentrated in patient's digital twin group and in precision medicine and aspects, issues and/or policies subgroups, although the publications keywords mirror it only at the group side. Digital twins in healthcare are probably stepping out of the infancy phase. On the other hand, digital twins in hospital group and the device and facilities management subgroups are more mature with all knowledge gathered from the manufacturing sector. There is an absence of some publication's types in general, device and care subgroup and no whole body or hospital digital twin was reported. Based on the presented arguments, guidelines for future research were presented: advance in the creation of general frameworks, in subgroups not as much explored, and in groups and subgroups already explored, but that need more advancement to achieve the main goals of a whole human or hospital digital twin with the main issues resolved.
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Affiliation(s)
- Tatiana Mallet Machado
- Production Engineering Department, Polytechnic School University of São Paulo, Av. Prof. Almeida Prado, Brazil
| | - Fernando Tobal Berssaneti
- Production Engineering Department, Polytechnic School University of São Paulo, Av. Prof. Almeida Prado, Brazil
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15
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Debs P, Fayad LM. The promise and limitations of artificial intelligence in musculoskeletal imaging. FRONTIERS IN RADIOLOGY 2023; 3:1242902. [PMID: 37609456 PMCID: PMC10440743 DOI: 10.3389/fradi.2023.1242902] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 07/26/2023] [Indexed: 08/24/2023]
Abstract
With the recent developments in deep learning and the rapid growth of convolutional neural networks, artificial intelligence has shown promise as a tool that can transform several aspects of the musculoskeletal imaging cycle. Its applications can involve both interpretive and non-interpretive tasks such as the ordering of imaging, scheduling, protocoling, image acquisition, report generation and communication of findings. However, artificial intelligence tools still face a number of challenges that can hinder effective implementation into clinical practice. The purpose of this review is to explore both the successes and limitations of artificial intelligence applications throughout the muscuskeletal imaging cycle and to highlight how these applications can help enhance the service radiologists deliver to their patients, resulting in increased efficiency as well as improved patient and provider satisfaction.
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Affiliation(s)
- Patrick Debs
- The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, MD, United States
| | - Laura M. Fayad
- The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, MD, United States
- Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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16
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Karmakar A, Khan MJ, Abdul-Rahman MEF, Shahid U. The Advances and Utility of Artificial Intelligence and Robotics in Regional Anesthesia: An Overview of Recent Developments. Cureus 2023; 15:e44306. [PMID: 37779803 PMCID: PMC10535025 DOI: 10.7759/cureus.44306] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/29/2023] [Indexed: 10/03/2023] Open
Abstract
The integration of artificial intelligence (AI) and robotics in regional anesthesia has brought about transformative changes in acute pain management for surgical procedures. This review explores the evolving landscape of AI and robotics applications in regional anesthesia, outlining their potential benefits, challenges, and ethical considerations. AI-driven pain assessment, real-time guidance for needle placement during nerve blocks, and predictive modeling solutions for nerve blocks have the potential to enhance procedural precision and improve patient outcomes. Robotic technology aids in accurate needle insertion, reducing complications and improving pain relief. This review also highlights the ethical and safety considerations surrounding AI implementation, emphasizing data security and professional training. While challenges such as costs and regulatory hurdles exist, ongoing research and clinical trials demonstrate the practical utility of these technologies. In conclusion, AI and robotics have the potential to reshape regional anesthesia practice, ultimately improving patient care and procedural accuracy in pain management.
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Affiliation(s)
- Arunabha Karmakar
- Anesthesiology and Critical Care, Hamad Medical Corporation, Doha, QAT
| | | | | | - Umair Shahid
- Anesthesiology and Critical Care, Hamad Medical Corporation, Doha, QAT
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17
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D'Angelo T, Caudo D, Blandino A, Albrecht MH, Vogl TJ, Gruenewald LD, Gaeta M, Yel I, Koch V, Martin SS, Lenga L, Muscogiuri G, Sironi S, Mazziotti S, Booz C. Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications. JOURNAL OF CLINICAL ULTRASOUND : JCU 2022; 50:1414-1431. [PMID: 36069404 DOI: 10.1002/jcu.23321] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/18/2022] [Accepted: 08/20/2022] [Indexed: 06/15/2023]
Abstract
Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest developmental potential. Artificial intelligence aims at human intelligence simulation through the management of complex problems. This review describes the technical background of artificial intelligence, machine learning, and deep learning. The first section illustrates the general potential of artificial intelligence applications in the context of request management, data acquisition, image reconstruction, archiving, and communication systems. In the second section, the prospective of dedicated tools for segmentation, lesion detection, automatic diagnosis, and classification of musculoskeletal disorders is discussed.
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Affiliation(s)
- Tommaso D'Angelo
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy
- Department of Radiology and Nuclear Medicine, Rotterdam, Netherlands
| | - Danilo Caudo
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy
- Department or Radiology, IRRCS Centro Neurolesi "Bonino Pulejo", Messina, Italy
| | - Alfredo Blandino
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy
| | - Moritz H Albrecht
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Thomas J Vogl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Leon D Gruenewald
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Michele Gaeta
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy
| | - Ibrahim Yel
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Vitali Koch
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Simon S Martin
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Lukas Lenga
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Giuseppe Muscogiuri
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
- Department of Radiology, IRCCS Istituto Auxologico Italiano, San Luca Hospital, Milan, Italy
| | - Sandro Sironi
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
- Department of Radiology, ASST Papa Giovanni XXIII Hospital, Bergamo, Italy
| | - Silvio Mazziotti
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy
| | - Christian Booz
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
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18
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Lockwood H, McLeod GA. A paired comparison of nerve dimensions using B-Mode ultrasound and shear wave elastography during regional anaesthesia. ULTRASOUND 2022; 30:346-354. [PMID: 36969534 PMCID: PMC10034658 DOI: 10.1177/1742271x221091726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 02/26/2022] [Indexed: 11/15/2022]
Abstract
Introduction: Shear wave elastography (SWE) presents nerves in colour, but the dimensions of its colour maps have not been validated with paired B-Mode nerve images. Our primary objective was to define the bias and limits of agreement of SWE with B-Mode nerve diameter. Our secondary objectives were to compare nerve area and shape, and provide a clinical standard for future application of new colour imaging technologies such as artificial intelligence. Materials and Methods: Eleven combined ultrasound-guided regional nerve blocks were conducted using a dual-mode transducer. Two raters outlined nerve margins on 110 paired B-Mode and SWE images every second for 20 s before and during injection. Bias and limits of agreement were plotted on Bland-Altman plots. We hypothesized that the bias of nerve diameter would be <2.5% and that the percent limits of agreement would lie ±0.67% (2 SD) of the bias. Results: There was no difference in the bias (95% confidence interval (CI) limits of agreement) of nerve diameter measurement, 0.01 (−0.14 to 0.16) cm, P = 0.85, equivalent to a 1.4% (−56.6% to 59.5) % difference. The bias and limits of agreement were 0.03 (−0.08 to 0.15) cm2, P = 0.54 for cross-sectional nerve area; and 0.02 (−0.03 to 0.07), P = 0.45 for shape. Reliability (ICC) between raters was 0.96 (0.94–0.98) for B-Mode nerve area and 0.91 (0.83–0.95) for SWE nerve area. Conclusions: Nerve diameter measurement from B-Mode and SWE images fell within a priori measures of bias and limits of agreement.
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Affiliation(s)
| | - Graeme A McLeod
- Institute of Academic Anesthesia,
School of Medicine, University of Dundee, Dundee, UK
- Graeme A McLeod, Institute of Academic
Anesthesia, School of Medicine, University of Dundee, Dundee DD1 9SY, UK.
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19
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Modern Machine Learning Practices in Colorectal Surgery: A Scoping Review. J Clin Med 2022; 11:jcm11092431. [PMID: 35566555 PMCID: PMC9100508 DOI: 10.3390/jcm11092431] [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: 02/02/2022] [Revised: 03/12/2022] [Accepted: 03/29/2022] [Indexed: 12/09/2022] Open
Abstract
Objective: The use of machine learning (ML) has revolutionized every domain of medicine. Surgeons are now using ML models for disease detection and outcome prediction with high precision. ML-guided colorectal surgeries are more efficient than conventional surgical procedures. The primary aim of this paper is to provide an overview of the latest research on “ML in colorectal surgery”, with its viable applications. Methods: PubMed, Google Scholar, Medline, and Cochrane library were searched. Results: After screening, 27 articles out of 172 were eventually included. Among all of the reviewed articles, those found to fit the criteria for inclusion had exclusively focused on ML in colorectal surgery, with justified applications. We identified existing applications of ML in colorectal surgery. Additionally, we discuss the benefits, risks, and safety issues. Conclusions: A better, more sustainable, and more efficient method, with useful applications, for ML in surgery is possible if we and data scientists work together to address the drawbacks of the current approach. Potential problems related to patients’ perspectives also need to be resolved. The development of accurate technologies alone will not solve the problem of perceived unreliability from the patients’ end. Confidence can only be developed within society if more research with precise results is carried out.
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A Study on the Role of Intelligent Medical Simulation Systems in Teaching First Aid Competence in Anesthesiology. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8163546. [PMID: 35494522 PMCID: PMC9050259 DOI: 10.1155/2022/8163546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/04/2022] [Indexed: 11/17/2022]
Abstract
Anesthesiology is a subject with strong practicality and application. Undergraduate anesthesiology teaching needs to strike a balance between theoretical knowledge, clinical skill training, and clinical thinking development. Clinical probation and practice are an important part of undergraduate anesthesia teaching. Traditional clinical teaching uses real patients for demonstration and training, but as patients become more self-protective and less cooperative, there are not enough patients for clinical skill training. Simulation is to teach medical scenes in real life under the control of standardized technical guidelines and parameters. Since then, with the rapid development of computer technology, simulation technology and simulation teaching have been greatly developed and are more and more used in clinical teaching, skill evaluation, and scientific research. This study explores the effective methods of clinical teaching in anesthesiology by comparing the effectiveness of traditional teaching methods and simulation teaching methods in undergraduate clinical teaching. It is difficult to combine theory and practice in first aid, which does not allow them to directly receive and deal with emergency medical treatment and resuscitation. In China's current medical environment and patients' high demand for medical services, it is imperative to vigorously carry out simulated medical education. In the eastern part of Inner Mongolia, according to the advantages of teaching hospitals, our hospital took the lead in carrying out the simulation education project, which is still in the exploratory stage and not systematic enough. This study will help us to better carry out simulation teaching and improve the clinical skills of medical students in the future. Methods. The student group and class took the advanced simulator training as the experimental group, applied the advanced integrated simulator and other systems of the Norwegian company, referred to the international guidelines for cardiopulmonary resuscitation and cardiovascular first aid in 2005, and practiced in the emergency department during the clinical internship and “emergency clinical simulation training” course. The course includes basic life support, advanced life support, and comprehensive training of CPR (cardiopulmonary resuscitation) and endotracheal intubation. Results. The passing rate of simulated first aid practice was 94.4%; 100% of the students think it is necessary to set up the course, 91% of the students think it is practical, 91% of the students think the course content is reasonable and perfect, and 77%–100% of the students think the course has improved their first aid operation ability, comprehensive application of knowledge, and clinical thinking ability. Conclusion. Carrying out the course of “clinical simulated first aid training” through the advanced simulator system can effectively improve the interns' clinical first aid operation ability, teamwork ability, and self-confidence, improve the students' clinical thinking and judgment ability, and improve the service level to patients.
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21
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Kearns RJ, Womack J, Macfarlane AJR. Regional anaesthesia research – where to now? Br J Pain 2022; 16:132-135. [PMID: 35419194 PMCID: PMC8998529 DOI: 10.1177/20494637221091139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- Rachel J Kearns
- Department of Anaesthesia, Glasgow Royal Infirmary, NHS Greater Glasgow and Clyde, Glasgow, UK
- School of Medicine, University of Glasgow, Glasgow, UK
| | - Jonathan Womack
- Department of Anaesthesia, Royal Victoria, Infirmary, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Alan JR Macfarlane
- Department of Anaesthesia, Glasgow Royal Infirmary, NHS Greater Glasgow and Clyde, Glasgow, UK
- School of Medicine, University of Glasgow, Glasgow, UK
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22
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Mi J, Han X, Wang R, Ma R, Zhao D. Diagnostic Accuracy of Wireless Capsule Endoscopy in Polyp Recognition Using Deep Learning: A Meta-Analysis. Int J Clin Pract 2022; 2022:9338139. [PMID: 35685533 PMCID: PMC9159236 DOI: 10.1155/2022/9338139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/18/2022] [Accepted: 02/25/2022] [Indexed: 12/24/2022] Open
Abstract
AIM As the completed studies have small sample sizes and different algorithms, a meta-analysis was conducted to assess the accuracy of WCE in identifying polyps using deep learning. METHOD Two independent reviewers searched PubMed, Embase, the Web of Science, and the Cochrane Library for potentially eligible studies published up to December 8, 2021, which were analysed on a per-image basis. STATA RevMan and Meta-DiSc were used to conduct this meta-analysis. A random effects model was used, and a subgroup and regression analysis was performed to explore sources of heterogeneity. RESULTS Eight studies published between 2017 and 2021 included 819 patients, and 18,414 frames were eventually included in the meta-analysis. The summary estimates for the WCE in identifying polyps by deep learning were sensitivity 0.97 (95% confidence interval (CI), 0.95-0.98); specificity 0.97 (95% CI, 0.94-0.98); positive likelihood ratio 27.19 (95% CI, 15.32-50.42); negative likelihood ratio 0.03 (95% CI 0.02-0.05); diagnostic odds ratio 873.69 (95% CI, 387.34-1970.74); and the area under the sROC curve 0.99. CONCLUSION WCE uses deep learning to identify polyps with high accuracy, but multicentre prospective randomized controlled studies are needed in the future.
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Affiliation(s)
- Junjie Mi
- Digestive Endoscopy Center, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Xiaofang Han
- Reproductive Medicine, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Rong Wang
- Digestive Endoscopy Center, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Ruijun Ma
- Digestive Endoscopy Center, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Danyu Zhao
- Digestive Endoscopy Center, Shanxi Provincial People's Hospital, Taiyuan, China
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23
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Abstract
PURPOSE OF REVIEW To explore the role and impact of e-learning technologies on regional anesthesia. RECENT FINDINGS 21st century technologies, such as 'smart' medical appliances, personal computers, sophisticated apps, the ubiquitous Internet, and online 'e-learning' curricula, are having a powerful impact on anesthesia training: when we learn, what we learn, and how we learn. But is 'new' necessarily 'better'? The answer will result from the application of developments in IT technology through the current vision of architects of future anesthesia training programs. This narrative review aims to summarize the recent developments in anesthesia e-learning, and to forecast trends using regional anesthesia as an example. SUMMARY The review offers some recommendations to ensure that the blessings promised to human learning by this 'Brave New Cyberworld' do not become its nemesis.
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Affiliation(s)
- Stavros Prineas
- Department of Anaesthesia Cuyx - Assistent in Anesthesiologie, Blue Mountains and Springwood Hospitals, Springwood, New South Wales, Australia
| | - Lotte Cuyx
- Katholieke Universiteit Leuven, Leuven, Belgium
| | - Jeroen Smet
- Katholieke Universiteit Leuven, Leuven, Belgium
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24
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Chin KJ, Mariano ER, El-Boghdadly K. Advancing towards the next frontier in regional anaesthesia. Anaesthesia 2021; 76 Suppl 1:3-7. [PMID: 33426654 DOI: 10.1111/anae.15321] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/22/2020] [Indexed: 12/14/2022]
Affiliation(s)
- K J Chin
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, ON, Canada
| | - E R Mariano
- Department of Anesthesiology, Peri-operative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.,Anesthesiology and Peri-operative Care Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - K El-Boghdadly
- Department of Anaesthesia and Peri-operative Medicine, Guy's and St Thomas' NHS Foundation Trust, London, UK.,King's College London, London, UK
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