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Chang JY, Makary MS. Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging. Diagnostics (Basel) 2024; 14:1456. [PMID: 39001346 PMCID: PMC11240935 DOI: 10.3390/diagnostics14131456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 07/01/2024] [Accepted: 07/06/2024] [Indexed: 07/16/2024] Open
Abstract
The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.
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Affiliation(s)
- Jin Y Chang
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Mina S Makary
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
- Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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2
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Cusumano G, D'Arrigo S, Terminella A, Lococo F. Artificial Intelligence Applications for Thoracic Surgeons: "The Phenomenal Cosmic Powers of the Magic Lamp". J Clin Med 2024; 13:3750. [PMID: 38999317 PMCID: PMC11242691 DOI: 10.3390/jcm13133750] [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: 05/12/2024] [Revised: 06/17/2024] [Accepted: 06/21/2024] [Indexed: 07/14/2024] Open
Abstract
In the digital age, artificial intelligence (AI) is emerging as a transformative force in various sectors, including medicine. This article explores the potential of AI, which is akin to the magical genie of Aladdin's lamp, particularly within thoracic surgery and lung cancer management. It examines AI applications like machine learning and deep learning in achieving more precise diagnoses, preoperative risk assessment, and improved surgical outcomes. The challenges and advancements in AI integration, especially in computer vision and multi-modal models, are discussed alongside their impact on robotic surgery and operating room management. Despite its transformative potential, implementing AI in medicine faces challenges regarding data scarcity, interpretability issues, and ethical concerns. Collaboration between AI and medical communities is essential to address these challenges and unlock the full potential of AI in revolutionizing clinical practice. This article underscores the importance of further research and interdisciplinary collaboration to ensure the safe and effective deployment of AI in real-world clinical settings.
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Affiliation(s)
- Giacomo Cusumano
- General Thoracic Surgery Unit, Azienda Ospedaliero Universitaria Policlinico "G. Rodolico-San Marco", Via Santa Sofia 78, 95100 Catania, Italy
- Department of Surgery and Medical-Surgical Specialties, University of Catania, Via Santa Sofia 78, 95100 Catania, Italy
| | - Stefano D'Arrigo
- Department of Computer, Control and Management Engineering, Università La Sapienza, 00185 Rome, Italy
| | - Alberto Terminella
- General Thoracic Surgery Unit, Azienda Ospedaliero Universitaria Policlinico "G. Rodolico-San Marco", Via Santa Sofia 78, 95100 Catania, Italy
| | - Filippo Lococo
- General Thoracic Surgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Department of Thoracic Surgery, "Sacro Cuore"-Catholic University, 00168 Rome, Italy
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Bassi M, Vaz Sousa R, Zacchini B, Centofanti A, Ferrante F, Poggi C, Carillo C, Pecoraro Y, Amore D, Diso D, Anile M, De Giacomo T, Venuta F, Vannucci J. Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment. Healthcare (Basel) 2024; 12:803. [PMID: 38610225 PMCID: PMC11011722 DOI: 10.3390/healthcare12070803] [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: 01/07/2024] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
The general world population is aging and patients are often diagnosed with early-stage lung cancer at an advanced age. Several studies have shown that age is not itself a contraindication for lung cancer surgery, and therefore, more and more octogenarians with early-stage lung cancer are undergoing surgery with curative intent. However, octogenarians present some peculiarities that make surgical treatment more challenging, so an accurate preoperative selection is mandatory. In recent years, new artificial intelligence techniques have spread worldwide in the diagnosis, treatment, and therapy of lung cancer, with increasing clinical applications. However, there is still no evidence coming out from trials specifically designed to assess the potential of artificial intelligence in the preoperative evaluation of octogenarian patients. The aim of this narrative review is to investigate, through the analysis of the available international literature, the advantages and implications that these tools may have in the preoperative assessment of this particular category of frail patients. In fact, these tools could represent an important support in the decision-making process, especially in octogenarian patients in whom the diagnostic and therapeutic options are often questionable. However, these technologies are still developing, and a strict human-led process is mandatory.
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Affiliation(s)
- Massimiliano Bassi
- Division of Thoracic Surgery, Department of General Surgery and Surgical Specialties “Paride Stefanini”, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy
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Arina P, Kaczorek MR, Hofmaenner DA, Pisciotta W, Refinetti P, Singer M, Mazomenos EB, Whittle J. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology 2024; 140:85-101. [PMID: 37944114 PMCID: PMC11146190 DOI: 10.1097/aln.0000000000004764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. METHODS A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. RESULTS A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. CONCLUSIONS The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Pietro Arina
- Bloomsbury Institute of Intensive Care Medicine and Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Maciej R. Kaczorek
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Daniel A. Hofmaenner
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom; and Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Walter Pisciotta
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Patricia Refinetti
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Mervyn Singer
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Evangelos B. Mazomenos
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - John Whittle
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
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Vesovic R, Milosavljevic M, Punt M, Radomirovic J, Bascarevic S, Savic M, Milenkovic V, Popovic M, Ercegovac M. The role of the diaphragm in prediction of respiratory function in the immediate postoperative period in lung cancer patients using a machine learning model. World J Surg Oncol 2023; 21:393. [PMID: 38135875 PMCID: PMC10740273 DOI: 10.1186/s12957-023-03278-1] [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: 09/29/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND The prediction of postoperative respiratory function is necessary in identifying patients that are at greater risk of complications. There are not enough studies on the effect of the diaphragm on postoperative respiratory function prediction in lung cancer surgical patients. The objective of this study is to estimate the precision of machine learning methods in the prediction of respiratory function in the immediate postoperative period and how diaphragm function contributes to that prediction. MATERIALS AND METHODS Our prospective study included 79 patients who underwent lung cancer surgery. Diaphragm function was estimated by its mobility measured both ultrasonographically and radiographically and by noninvasive muscle strength tests. We present a new machine learning multilayer regression metamodel, which predicts FEV1 for each patient based on preoperative measurements. RESULTS The proposed regression models are specifically trained to predict FEV1 in the immediate postoperative period and were proved to be highly accurate (mean absolute error in the range from 8 to 11%). Predictive models based on resected segments give two to three times less precise results. Measured FEV1 was 44.68% ± 14.07%, 50.95% ± 15.80%, and 58.0%1 ± 14.78%, and predicted postoperative (ppo) FEV1 was 43.85% ± 8.80%, 50.62% ± 9.28%, and 57.85% ± 10.58% on the first, fourth, and seventh day, respectively. By interpreting the obtained model, the diaphragm contributes to ppoFEV1 13.62% on the first day, 10.52% on the fourth, and 9.06% on the seventh day. CONCLUSION The machine learning metamodel gives more accurate predictions of postoperative lung function than traditional calculations. The diaphragm plays a notable role in the postoperative FEV1 prediction.
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Affiliation(s)
- Radomir Vesovic
- Faculty of Medicine, University of Belgrade, Dr Subotica 8, 11000, Belgrade, Serbia.
- Clinic for Thoracic Surgery, University Clinical Center of Serbia, Koste Todorovica 26, 11000, Belgrade, Serbia.
| | - Milan Milosavljevic
- Vlatacom Institute of High Technology, Bulevar Milutina Milankovica 5, 11000, Belgrade, Serbia
| | - Marija Punt
- School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11000, Belgrade, Serbia
| | - Jelica Radomirovic
- Vlatacom Institute of High Technology, Bulevar Milutina Milankovica 5, 11000, Belgrade, Serbia
- School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11000, Belgrade, Serbia
| | - Slavisa Bascarevic
- Faculty of Medicine, University of Belgrade, Dr Subotica 8, 11000, Belgrade, Serbia
- Clinic for Thoracic Surgery, University Clinical Center of Serbia, Koste Todorovica 26, 11000, Belgrade, Serbia
| | - Milan Savic
- Faculty of Medicine, University of Belgrade, Dr Subotica 8, 11000, Belgrade, Serbia
- Clinic for Thoracic Surgery, University Clinical Center of Serbia, Koste Todorovica 26, 11000, Belgrade, Serbia
| | - Vladimir Milenkovic
- Faculty of Medicine, University of Belgrade, Dr Subotica 8, 11000, Belgrade, Serbia
- Clinic for Thoracic Surgery, University Clinical Center of Serbia, Koste Todorovica 26, 11000, Belgrade, Serbia
| | - Marko Popovic
- Clinic for Thoracic Surgery, University Clinical Center of Serbia, Koste Todorovica 26, 11000, Belgrade, Serbia
| | - Maja Ercegovac
- Faculty of Medicine, University of Belgrade, Dr Subotica 8, 11000, Belgrade, Serbia
- Clinic for Thoracic Surgery, University Clinical Center of Serbia, Koste Todorovica 26, 11000, Belgrade, Serbia
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Zhang G, Luo L, Zhang L, Liu Z. Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence. Diagnostics (Basel) 2023; 13:diagnostics13030357. [PMID: 36766460 PMCID: PMC9914063 DOI: 10.3390/diagnostics13030357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/06/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models.
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Affiliation(s)
- Gerui Zhang
- Department of Critical Care Unit, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
| | - Lin Luo
- Department of Critical Care Unit, The Second Hospital of Dalian Medical University, 467 Zhongshan Road, Shahekou District, Dalian 116023, China
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
| | - Zhuo Liu
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
- Correspondence:
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Huang G, Liu L, Wang L, Li S. Prediction of postoperative cardiopulmonary complications after lung resection in a Chinese population: A machine learning-based study. Front Oncol 2022; 12:1003722. [PMID: 36212485 PMCID: PMC9539671 DOI: 10.3389/fonc.2022.1003722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/12/2022] [Indexed: 11/30/2022] Open
Abstract
Background Approximately 20% of patients with lung cancer would experience postoperative cardiopulmonary complications after anatomic lung resection. Current prediction models for postoperative complications were not suitable for Chinese patients. This study aimed to develop and validate novel prediction models based on machine learning algorithms in a Chinese population. Methods Patients with lung cancer receiving anatomic lung resection and no neoadjuvant therapies from September 1, 2018 to August 31, 2019 were enrolled. The dataset was split into two cohorts at a 7:3 ratio. The logistic regression, random forest, and extreme gradient boosting were applied to construct models in the derivation cohort with 5-fold cross validation. The validation cohort accessed the model performance. The area under the curves measured the model discrimination, while the Spiegelhalter z test evaluated the model calibration. Results A total of 1085 patients were included, and 760 were assigned to the derivation cohort. 8.4% and 8.0% of patients experienced postoperative cardiopulmonary complications in the two cohorts. All baseline characteristics were balanced. The values of the area under the curve were 0.728, 0.721, and 0.767 for the logistic, random forest and extreme gradient boosting models, respectively. No significant differences existed among them. They all showed good calibration (p > 0.05). The logistic model consisted of male, arrhythmia, cerebrovascular disease, the percentage of predicted postoperative forced expiratory volume in one second, and the ratio of forced expiratory volume in one second to forced vital capacity. The last two variables, the percentage of forced vital capacity and age ranked in the top five important variables for novel machine learning models. A nomogram was plotted for the logistic model. Conclusion Three models were developed and validated for predicting postoperative cardiopulmonary complications among Chinese patients with lung cancer. They all exerted good discrimination and calibration. The percentage of predicted postoperative forced expiratory volume in one second and the ratio of forced expiratory volume in one second to forced vital capacity might be the most important variables. Further validation in different scenarios is still warranted.
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Verma A, Tran Z, Sakowitz S, Hadaya J, Lee C, Madrigal J, Revels S, Benharash P. Hospital variation in the development of respiratory failure after pulmonary lobectomy: A national analysis. Surgery 2022; 172:379-384. [DOI: 10.1016/j.surg.2022.03.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/23/2022] [Accepted: 03/15/2022] [Indexed: 11/27/2022]
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Yoon HK, Yang HL, Jung CW, Lee HC. Artificial intelligence in perioperative medicine - a narrative review. Korean J Anesthesiol 2022; 75:202-215. [PMID: 35345305 PMCID: PMC9171545 DOI: 10.4097/kja.22157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/15/2022] [Indexed: 11/23/2022] Open
Abstract
Recent advancements in artificial intelligence (AI) techniques have enabled the development of accurate prediction models using clinical big data. AI models for perioperative risk stratification, intraoperative event prediction, biosignal analyses, and intensive care medicine have been developed in the field of perioperative medicine. Some of these models have been validated using external datasets and randomized controlled trials. Once these models are implemented in electronic health record systems or software medical devices, they could help anesthesiologists improve clinical outcomes by accurately predicting complications and suggesting optimal treatment strategies in real-time. This review provides an overview of the AI techniques used in perioperative medicine and a summary of the studies that have been published using these techniques. Understanding these techniques will aid in their appropriate application in clinical practice.
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Affiliation(s)
- Hyun-Kyu Yoon
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyun-Lim Yang
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Chul-Woo Jung
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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Bellini V, Valente M, Del Rio P, Bignami E. Artificial intelligence in thoracic surgery: a narrative review. J Thorac Dis 2022; 13:6963-6975. [PMID: 35070380 PMCID: PMC8743413 DOI: 10.21037/jtd-21-761] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022]
Abstract
Objective The aim of this article is to review the current applications of artificial intelligence in thoracic surgery, from diagnosis and pulmonary disease management, to preoperative risk-assessment, surgical planning, and outcomes prediction. Background Artificial intelligence implementation in healthcare settings is rapidly growing, though its widespread use in clinical practice is still limited. The employment of machine learning algorithms in thoracic surgery is wide-ranging, including all steps of the clinical pathway. Methods We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases, including all the relevant studies published in the last ten years, until March 2021. Conclusion Machine learning methods are promising encouraging results throughout the key issues of thoracic surgery, both clinical, organizational, and educational. Artificial intelligence-based technologies showed remarkable efficacy to improve the perioperative evaluation of the patient, to assist the decision-making process, to enhance the surgical performance, and to optimize the operating room scheduling. Still, some concern remains about data supply, protection, and transparency, thus further studies and specific consensus guidelines are needed to validate these technologies for daily common practice. Keywords Artificial intelligence (AI); thoracic surgery; machine learning; lung resection; perioperative medicine
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Paolo Del Rio
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
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Seastedt KP, Moukheiber D, Mahindre SA, Thammineni C, Rosen DT, Watkins AA, Hashimoto DA, Hoang CD, Kpodonu J, Celi LA. A scoping review of artificial intelligence applications in thoracic surgery. Eur J Cardiothorac Surg 2021; 61:239-248. [PMID: 34601587 DOI: 10.1093/ejcts/ezab422] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 09/16/2021] [Accepted: 09/16/2021] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVES Machine learning (ML) has great potential, but there are few examples of its implementation improving outcomes. The thoracic surgeon must be aware of pertinent ML literature and how to evaluate this field for the safe translation to patient care. This scoping review provides an introduction to ML applications specific to the thoracic surgeon. We review current applications, limitations and future directions. METHODS A search of the PubMed database was conducted with inclusion requirements being the use of an ML algorithm to analyse patient information relevant to a thoracic surgeon and contain sufficient details on the data used, ML methods and results. Twenty-two papers met the criteria and were reviewed using a methodological quality rubric. RESULTS ML demonstrated enhanced preoperative test accuracy, earlier pathological diagnosis, therapies to maximize survival and predictions of adverse events and survival after surgery. However, only 4 performed external validation. One demonstrated improved patient outcomes, nearly all failed to perform model calibration and one addressed fairness and bias with most not generalizable to different populations. There was a considerable variation to allow for reproducibility. CONCLUSIONS There is promise but also challenges for ML in thoracic surgery. The transparency of data and algorithm design and the systemic bias on which models are dependent remain issues to be addressed. Although there has yet to be widespread use in thoracic surgery, it is essential thoracic surgeons be at the forefront of the eventual safe introduction of ML to the clinic and operating room.
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Affiliation(s)
- Kenneth P Seastedt
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Dana Moukheiber
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Saurabh A Mahindre
- Institute for Computational and Data Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Chaitanya Thammineni
- HILS Laboratory, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Darin T Rosen
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Ammara A Watkins
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Daniel A Hashimoto
- Surgical AI & Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chuong D Hoang
- Thoracic Surgery Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jacques Kpodonu
- Division of Cardiac Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Leo A Celi
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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Chang W, Ji X, Wang L, Liu H, Zhang Y, Chen B, Zhou S. A Machine-Learning Method of Predicting Vital Capacity Plateau Value for Ventilatory Pump Failure Based on Data Mining. Healthcare (Basel) 2021; 9:1306. [PMID: 34682985 PMCID: PMC8544367 DOI: 10.3390/healthcare9101306] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/22/2021] [Accepted: 09/26/2021] [Indexed: 11/23/2022] Open
Abstract
Ventilatory pump failure is a common cause of death for patients with neuromuscular diseases. The vital capacity plateau value (VCPLAT) is an important indicator to judge the status of ventilatory pump failure for patients with congenital myopathy, Duchenne muscular dystrophy and spinal muscular atrophy. Due to the complex relationship between VCPLAT and the patient's own condition, it is difficult to predict the VCPLAT for pediatric disease from a medical perspective. We established a VCPLAT prediction model based on data mining and machine learning. We first performed the correlation analysis and recursive feature elimination with cross-validation (RFECV) to provide high-quality feature combinations. Based on this, the Light Gradient Boosting Machine (LightGBM) algorithm was to establish a prediction model with powerful performance. Finally, we verified the validity and superiority of the proposed method via comparison with other prediction models in similar works. After 10-fold cross-validation, the proposed prediction method had the best performance and its explained variance score (EVS), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), median absolute error (MedAE) and R2 were 0.949, 0.028, 0.002, 0.045, 0.015 and 0.948, respectively. It also performed well on test datasets. Therefore, it can accurately and effectively predict the VCPLAT, thereby determining the severity of the condition to provide auxiliary decision-making for doctors in clinical diagnosis and treatment.
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Affiliation(s)
- Wenbing Chang
- School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China; (W.C.); (X.J.); (H.L.); (Y.Z.); (B.C.)
| | - Xinpeng Ji
- School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China; (W.C.); (X.J.); (H.L.); (Y.Z.); (B.C.)
| | - Liping Wang
- Department of Neurology, Peking University Third Hospital, Beijing 100191, China;
| | - Houxiang Liu
- School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China; (W.C.); (X.J.); (H.L.); (Y.Z.); (B.C.)
| | - Yue Zhang
- School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China; (W.C.); (X.J.); (H.L.); (Y.Z.); (B.C.)
| | - Bang Chen
- School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China; (W.C.); (X.J.); (H.L.); (Y.Z.); (B.C.)
| | - Shenghan Zhou
- School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China; (W.C.); (X.J.); (H.L.); (Y.Z.); (B.C.)
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Prevent deterioration and long-term ventilation: intensive care following thoracic surgery. Curr Opin Anaesthesiol 2021; 34:20-24. [PMID: 33315639 DOI: 10.1097/aco.0000000000000944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Patients with indication for lung surgery besides the pulmonary pathology often suffer from independent comorbidities affecting several other organ systems. Preventing patients from harmful complications due to decompensation of underlying organ insufficiencies perioperatively is pivotal. This review draws attention to the peri- and postoperative responsibility of the anaesthetist and intensivist to prevent patients undergoing lung surgery deterioration. RECENT FINDINGS During the last decades we had to accept that 'traditional' intensive care medicine implying deep sedation, controlled ventilation, liberal fluid therapy, and broad-spectrum antimicrobial therapy because of several side-effects resulted in prolongation of hospital length of stay and a decline in quality of life. Modern therapy therefore should focus on the convalescence of the patient and earliest possible reintegration in the 'life-before.' Avoidance of sedative and anticholinergic drugs, early extubation, prophylactic noninvasive ventilation and high-flow nasal oxygen therapy, early mobilization, well-adjusted fluid balance and reasonable use of antibiotics are the keystones of success. SUMMARY A perioperative interprofessional approach and a change in paradigms are the prerequisites to improve outcome and provide treatment for elder and comorbid patients with an indication for thoracic surgery.
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Bolourani S, Wang P, Patel VM, Manetta F, Lee PC. Corrigendum to "Predicting respiratory failure after pulmonary lobectomy using machine learning techniques". Surgery 2020; 169:1001. [PMID: 33288213 DOI: 10.1016/j.surg.2020.10.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Siavash Bolourani
- The Feinstein Institute for Medical Research, Manhasset, NY; Elmezzi Graduate School of Molecular Medicine, Manhasset, NY; Department of Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New Hyde Park, NY; Department of Cardiovascular and Thoracic Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY
| | - Ping Wang
- The Feinstein Institute for Medical Research, Manhasset, NY; Elmezzi Graduate School of Molecular Medicine, Manhasset, NY; Department of Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New Hyde Park, NY
| | - Vihas M Patel
- Department of Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New Hyde Park, NY
| | - Frank Manetta
- Department of Cardiovascular and Thoracic Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY
| | - Paul C Lee
- Department of Cardiovascular and Thoracic Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY
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Douthit BJ, Musser RC, Lytle KS, Richesson RL. A Closer Look at the "Right" Format for Clinical Decision Support: Methods for Evaluating a Storyboard BestPractice Advisory. J Pers Med 2020; 10:jpm10040142. [PMID: 32977564 PMCID: PMC7712422 DOI: 10.3390/jpm10040142] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/11/2020] [Accepted: 09/18/2020] [Indexed: 01/17/2023] Open
Abstract
(1) Background: The five rights of clinical decision support (CDS) are a well-known framework for planning the nuances of CDS, but recent advancements have given us more options to modify the format of the alert. One-size-fits-all assessments fail to capture the nuance of different BestPractice Advisory (BPA) formats. To demonstrate a tailored evaluation methodology, we assessed a BPA after implementation of Storyboard for changes in alert fatigue, behavior influence, and task completion; (2) Methods: Data from 19 weeks before and after implementation were used to evaluate differences in each domain. Individual clinics were evaluated for task completion and compared for changes pre- and post-redesign; (3) Results: The change in format was correlated with an increase in alert fatigue, a decrease in erroneous free text answers, and worsened task completion at a system level. At a local level, however, 14% of clinics had improved task completion; (4) Conclusions: While the change in BPA format was correlated with decreased performance, the changes may have been driven primarily by the COVID-19 pandemic. The framework and metrics proposed can be used in future studies to assess the impact of new CDS formats. Although the changes in this study seemed undesirable in aggregate, some positive changes were observed at the level of individual clinics. Personalized implementations of CDS tools based on local need should be considered.
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Affiliation(s)
- Brian J. Douthit
- School of Nursing, Duke University, Durham, NC 27710, USA;
- Correspondence:
| | - R. Clayton Musser
- School of Medicine, Duke University, Durham, NC 27710, USA;
- Duke Health, Duke School of Medicine, Durham, NC 27710 USA;
| | - Kay S. Lytle
- Duke Health, Duke School of Medicine, Durham, NC 27710 USA;
| | - Rachel L. Richesson
- School of Nursing, Duke University, Durham, NC 27710, USA;
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA
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