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Özgür EG, Ulgen A, Uzun S, Bekiroğlu GN. Evaluation of risk factors and survival rates of patients with early-stage breast cancer with machine learning and traditional methods. Int J Med Inform 2024; 190:105548. [PMID: 39003789 DOI: 10.1016/j.ijmedinf.2024.105548] [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: 05/29/2024] [Revised: 07/04/2024] [Accepted: 07/09/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND This article is aimed to make predictions in terms of prognostic factors and compare prediction methods by using Cox proportional hazards regression analysis (CPH), some machine learning techniques and Accelerated Failure Time (AFT) model for post-treatment survival probabilities according to clinical presentations and pathological information of early-stage breast cancer patients. MATERIAL AND METHODS The study was carried out in three stages. In the first stage, the CPH method was applied. In the second stage, the AFT model and in the last stage, machine learning methods were applied. The data set consists of 697 breast cancer patients who applied to Marmara University Hospital oncology clinic between 01.01.1994 and 31.12.2009. The models obtained by using various parameters of the patients were compared according to the C index, 5-year survival rate and 10-year survival rate. RESULTS AND CONCLUSION According to the models obtained as a result of the analyses applied, MetLN and age were obtained as a significant risk factor as a result of CPH method and AFT methods, while MetLN, age, tumor size, LV1 and extracapsular involvement were obtained as risk factors in machine learning methods. In addition, when the c-index values of the handheld models are examined, it is obtained as 69.8 for the CPH model, 70.36 for the AFT model, 72.1 for the random survival forest and 72.8 for the gradient boosting machine. In conclusion, the study highlights the potential of comparing conventional statistical methods and machine-learning algorithms to improve the precision of risk factor determination in early-stage breast cancer prognosis. Additionally, efforts should be made to enhance the interpretability of machine-learning models, ensuring that the results obtained can be effectively communicated and utilized by clinical practitioners. This would enable more informed decision-making and personalized care in the treatment and follow-up processes for early-stage breast cancer patients.
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Affiliation(s)
- Emrah Gökay Özgür
- Marmara University, School of Medicine, Department of Biostatistics, Turkiye.
| | - Ayse Ulgen
- Department of Mathematics and Physics. School of Science and Technology. Nottingham Trent University. United Kingdom. Girne American University, Faculty of Medicine, Department of Biostatistics, Cyprus
| | - Sinan Uzun
- Marmara University, Institute of Health Sciences, Department of Biostatistics, Turkiye
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Zhang H, Zhao J, Farzan R, Alizadeh Otaghvar H. Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review. Int Wound J 2024; 21:e14665. [PMID: 38272811 PMCID: PMC10805538 DOI: 10.1111/iwj.14665] [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: 12/09/2023] [Revised: 12/24/2023] [Accepted: 12/29/2023] [Indexed: 01/27/2024] Open
Abstract
Surgical wounds may arise due to harm inflicted upon soft tissue during surgical intervention, and many complications and injuries may accompany them. These complications can lead to prolonged hospitalization and poorer clinical outcomes. Also, Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in medical care and is increasingly used for diagnosis, complications, prognosis and recurrence prediction. This study aims to investigate surgical wound risk predictions and management using a ML algorithm by R programming language analysis. The systematic review, following PRISMA guidelines, spanned electronic databases using search terms like 'machine learning', 'surgical' and 'wound'. Inclusion criteria covered experimental studies from 1990 to the present on ML's application in surgical wound evaluation. Exclusion criteria included studies lacking full text, focusing on ML in all surgeries, neglecting wound assessment and duplications. Two authors rigorously assessed titles, abstracts and full texts, excluding reviews and guidelines. Ultimately, relevant articles were then analysed. The present study identified nine articles employing ML for surgical wound management. The analysis encompassed various surgical procedures, including Cardiothoracic, Caesarean total abdominal colectomy, Burn plastic surgery, facial plastic surgery, laparotomy, minimal invasive surgery, hernia repair and unspecified surgeries. ML was skillful in evaluating surgical site infections (SSI) in seven studies, while two extended its use to burn-grade diagnosis and wound classification. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) were the most utilized algorithms. ANN achieved a 96% accuracy in facial plastic surgery wound management. CNN demonstrated commendable accuracies in various surgeries, and SVM exhibited high accuracy in multiple surgeries and burn plastic surgery. In sum, these findings underscore ML's potential for significant improvements in postoperative management and the development of enhanced care techniques, particularly in surgical wound management.
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Affiliation(s)
- Hui Zhang
- The Second Clinical Medical SchoolLanzhou UniversityLanzhouChina
| | - Junde Zhao
- Department of Clinical Medicine, Health Science CenterLanzhou UniversityLanzhouChina
| | - Ramyar Farzan
- Department of Plastic & Reconstructive Surgery, School of MedicineGuilan University of Medical SciencesRashtIran
| | - Hamidreza Alizadeh Otaghvar
- Associate Professor of Plastic Surgery, Trauma and Injury Research CenterIran University of Medical SciencesTehranIran
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Tiribelli S, Monnot A, Shah SFH, Arora A, Toong PJ, Kong S. Ethics Principles for Artificial Intelligence-Based Telemedicine for Public Health. Am J Public Health 2023; 113:577-584. [PMID: 36893365 PMCID: PMC10088937 DOI: 10.2105/ajph.2023.307225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
The use of artificial intelligence (AI) in the field of telemedicine has grown exponentially over the past decade, along with the adoption of AI-based telemedicine to support public health systems. Although AI-based telemedicine can open up novel opportunities for the delivery of clinical health and care and become a strong aid to public health systems worldwide, it also comes with ethical risks that should be detected, prevented, or mitigated for the responsible use of AI-based telemedicine in and for public health. However, despite the current proliferation of AI ethics frameworks, thus far, none have been developed for the design of AI-based telemedicine, especially for the adoption of AI-based telemedicine in and for public health. We aimed to fill this gap by mapping the most relevant AI ethics principles for AI-based telemedicine for public health and by showing the need to revise them via major ethical themes emerging from bioethics, medical ethics, and public health ethics toward the definition of a unified set of 6 AI ethics principles for the implementation of AI-based telemedicine. (Am J Public Health. Published online ahead of print March 9, 2023:e1-e8. https://doi.org/10.2105/AJPH.2022.307225).
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Affiliation(s)
- Simona Tiribelli
- Simona Tiribelli is with the Department of Political Sciences, Communication, and International Relations, University of Macerata, Macerata, Italy, and the Institute for Technology and Global Health, Cambridge, MA. Annabelle Monnot is with Polygeia, Global Health Think Tank, Cambridge, UK. Syed F. H. Shah and Anmol Arora are with the School of Clinical Medicine, University of Cambridge, Cambridge. Ping J. Toong is with the Department of Pathology, University of Cambridge. Sokanha Kong is with the Department of Medical Genetics, University of Cambridge
| | - Annabelle Monnot
- Simona Tiribelli is with the Department of Political Sciences, Communication, and International Relations, University of Macerata, Macerata, Italy, and the Institute for Technology and Global Health, Cambridge, MA. Annabelle Monnot is with Polygeia, Global Health Think Tank, Cambridge, UK. Syed F. H. Shah and Anmol Arora are with the School of Clinical Medicine, University of Cambridge, Cambridge. Ping J. Toong is with the Department of Pathology, University of Cambridge. Sokanha Kong is with the Department of Medical Genetics, University of Cambridge
| | - Syed F H Shah
- Simona Tiribelli is with the Department of Political Sciences, Communication, and International Relations, University of Macerata, Macerata, Italy, and the Institute for Technology and Global Health, Cambridge, MA. Annabelle Monnot is with Polygeia, Global Health Think Tank, Cambridge, UK. Syed F. H. Shah and Anmol Arora are with the School of Clinical Medicine, University of Cambridge, Cambridge. Ping J. Toong is with the Department of Pathology, University of Cambridge. Sokanha Kong is with the Department of Medical Genetics, University of Cambridge
| | - Anmol Arora
- Simona Tiribelli is with the Department of Political Sciences, Communication, and International Relations, University of Macerata, Macerata, Italy, and the Institute for Technology and Global Health, Cambridge, MA. Annabelle Monnot is with Polygeia, Global Health Think Tank, Cambridge, UK. Syed F. H. Shah and Anmol Arora are with the School of Clinical Medicine, University of Cambridge, Cambridge. Ping J. Toong is with the Department of Pathology, University of Cambridge. Sokanha Kong is with the Department of Medical Genetics, University of Cambridge
| | - Ping J Toong
- Simona Tiribelli is with the Department of Political Sciences, Communication, and International Relations, University of Macerata, Macerata, Italy, and the Institute for Technology and Global Health, Cambridge, MA. Annabelle Monnot is with Polygeia, Global Health Think Tank, Cambridge, UK. Syed F. H. Shah and Anmol Arora are with the School of Clinical Medicine, University of Cambridge, Cambridge. Ping J. Toong is with the Department of Pathology, University of Cambridge. Sokanha Kong is with the Department of Medical Genetics, University of Cambridge
| | - Sokanha Kong
- Simona Tiribelli is with the Department of Political Sciences, Communication, and International Relations, University of Macerata, Macerata, Italy, and the Institute for Technology and Global Health, Cambridge, MA. Annabelle Monnot is with Polygeia, Global Health Think Tank, Cambridge, UK. Syed F. H. Shah and Anmol Arora are with the School of Clinical Medicine, University of Cambridge, Cambridge. Ping J. Toong is with the Department of Pathology, University of Cambridge. Sokanha Kong is with the Department of Medical Genetics, University of Cambridge
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Irgang L, Barth H, Holmén M. Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections: a Systematic Review. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:1-41. [PMID: 36910913 PMCID: PMC9995622 DOI: 10.1007/s41666-023-00129-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 01/16/2023] [Accepted: 02/03/2023] [Indexed: 03/02/2023]
Abstract
Despite the advances in modern medicine, the use of data-driven technologies (DDTs) to prevent surgical site infections (SSIs) remains a major challenge. Scholars recognise that data management is the next frontier in infection prevention, but many aspects related to the benefits and advantages of using DDTs to mitigate SSI risk factors remain unclear and underexplored in the literature. This study explores how DDTs enable value creation in the prevention of SSIs. This study follows a systematic literature review approach and the PRISMA statement to analyse peer-reviewed articles from seven databases. Fifty-nine articles were included in the review and were analysed through a descriptive and a thematic analysis. The findings suggest a growing interest in DDTs in SSI prevention in the last 5 years, and that machine learning and smartphone applications are widely used in SSI prevention. DDTs are mainly applied to prevent SSIs in clean and clean-contaminated surgeries and often used to manage patient-related data in the postoperative stage. DDTs enable the creation of nine categories of value that are classified in four dimensions: cost/sacrifice, functional/instrumental, experiential/hedonic, and symbolic/expressive. This study offers a unique and systematic overview of the value creation aspects enabled by DDT applications in SSI prevention and suggests that additional research is needed in four areas: value co-creation and product-service systems, DDTs in contaminated and dirty surgeries, data legitimation and explainability, and data-driven interventions. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-023-00129-2.
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Affiliation(s)
- Luís Irgang
- School of Business, Innovation and Sustainability - Department of Engineering and Innovation, Halmstad University, Halmstad, Sweden
| | - Henrik Barth
- School of Business, Innovation and Sustainability - Department of Engineering and Innovation, Halmstad University, Halmstad, Sweden
| | - Magnus Holmén
- School of Business, Innovation and Sustainability - Department of Engineering and Innovation, Halmstad University, Halmstad, Sweden
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Cobianchi L, Piccolo D, Dal Mas F, Agnoletti V, Ansaloni L, Balch J, Biffl W, Butturini G, Catena F, Coccolini F, Denicolai S, De Simone B, Frigerio I, Fugazzola P, Marseglia G, Marseglia GR, Martellucci J, Modenese M, Previtali P, Ruta F, Venturi A, Kaafarani HM, Loftus TJ. Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey. World J Emerg Surg 2023; 18:1. [PMID: 36597105 PMCID: PMC9811693 DOI: 10.1186/s13017-022-00467-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 11/28/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons' knowledge and perception of using AI-based tools in clinical decision-making processes. METHODS An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society's website and Twitter profile. RESULTS 650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust. DISCUSSION The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI.
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Affiliation(s)
- Lorenzo Cobianchi
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy.
- General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, Italy.
- ITIR - Institute for Transformative Innovation Research, University of Pavia, Pavia, Italy.
| | - Daniele Piccolo
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy
- Department of Neurosurgery, ASUFC Santa Maria Della Misericordia, Udine, Italy
| | - Francesca Dal Mas
- Department of Management, Ca' Foscari University of Venice, Venice, Italy
| | | | - Luca Ansaloni
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy
- General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, Italy
| | - Jeremy Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, USA
| | - Walter Biffl
- Division of Trauma and Acute Care Surgery, Scripps Memorial Hospital La Jolla, La Jolla, CA, USA
| | - Giovanni Butturini
- Department of HPB Surgery, Pederzoli Hospital, Peschiera del Garda, Italy
| | | | - Federico Coccolini
- General, Emergency and Trauma Surgery Department, Pisa University Hospital Pisa, Pisa, Italy
| | - Stefano Denicolai
- ITIR - Institute for Transformative Innovation Research, University of Pavia, Pavia, Italy
- Department of Economics and Management, University of Pavia, Pavia, Italy
| | - Belinda De Simone
- Department of Emergency, Digestive and Metabolic Minimally Invasive Surgery, Poissy and Saint Germain en Laye Hospitals, Poissy, France
| | - Isabella Frigerio
- Department of HPB Surgery, Pederzoli Hospital, Peschiera del Garda, Italy
| | - Paola Fugazzola
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy
- General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, Italy
| | - Gianluigi Marseglia
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy
- IRCCS Policlinico San Matteo Foundation, Pediatric Clinic., Pavia, Italy
| | | | | | | | - Pietro Previtali
- ITIR - Institute for Transformative Innovation Research, University of Pavia, Pavia, Italy
- Department of Economics and Management, University of Pavia, Pavia, Italy
| | - Federico Ruta
- General Direction, ASL BAT (Health Agency), Andria, Italy
| | - Alessandro Venturi
- ITIR - Institute for Transformative Innovation Research, University of Pavia, Pavia, Italy
- Department of Political and Social Sciences, University of Pavia, Pavia, Italy
- Bureau of the Presidency, IRCCS Policlinico San Matteo Foundation, Pavia, Italy
| | - Haytham M Kaafarani
- Harvard Medical School, Boston, MA, USA
- Division of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA, USA
| | - Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, USA
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Abstract
BACKGROUND Artificial intelligence (AI) applications aiming to support surgical decision-making processes are generating novel threats to ethical surgical care. To understand and address these threats, we summarize the main ethical issues that may arise from applying AI to surgery, starting from the Ethics Guidelines for Trustworthy Artificial Intelligence framework recently promoted by the European Commission. STUDY DESIGN A modified Delphi process has been employed to achieve expert consensus. RESULTS The main ethical issues that arise from applying AI to surgery, described in detail here, relate to human agency, accountability for errors, technical robustness, privacy and data governance, transparency, diversity, non-discrimination, and fairness. It may be possible to address many of these ethical issues by expanding the breadth of surgical AI research to focus on implementation science. The potential for AI to disrupt surgical practice suggests that formal digital health education is becoming increasingly important for surgeons and surgical trainees. CONCLUSIONS A multidisciplinary focus on implementation science and digital health education is desirable to balance opportunities offered by emerging AI technologies and respect for the ethical principles of a patient-centric philosophy.
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Gadot R, Anand A, Lovin BD, Sweeney AD, Patel AJ. Predicting surgical decision-making in vestibular schwannoma using tree-based machine learning. Neurosurg Focus 2022; 52:E8. [DOI: 10.3171/2022.1.focus21708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/19/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE
Vestibular schwannomas (VSs) are the most common neoplasm of the cerebellopontine angle in adults. Though these lesions are generally slow growing, their growth patterns and associated symptoms can be unpredictable, which may complicate the decision to pursue conservative management versus active intervention. Additionally, surgical decision-making can be controversial because of limited high-quality evidence and multiple quality-of-life considerations. Machine learning (ML) is a powerful tool that utilizes data sets to essentialize multidimensional clinical processes. In this study, the authors trained multiple tree-based ML algorithms to predict the decision for active treatment versus MRI surveillance of VS in a single institutional cohort. In doing so, they sought to assess which preoperative variables carried the most weight in driving the decision for intervention and could be used to guide future surgical decision-making through an evidence-based approach.
METHODS
The authors reviewed the records of patients who had undergone evaluation by neurosurgery and otolaryngology with subsequent active treatment (resection or radiation) for unilateral VS in the period from 2009 to 2021, as well as those of patients who had been evaluated for VS and were managed conservatively throughout 2021. Clinical presentation, radiographic data, and management plans were abstracted from each patient record from the time of first evaluation until the last follow-up or surgery. Each encounter with the patient was treated as an instance involving a management decision that depended on demographics, symptoms, and tumor profile. Decision tree and random forest classifiers were trained and tested to predict the decision for treatment versus imaging surveillance on the basis of unseen data using an 80/20 pseudorandom split. Predictor variables were tuned to maximize performance based on lowest Gini impurity indices. Model performance was optimized using fivefold cross-validation.
RESULTS
One hundred twenty-four patients with 198 rendered decisions concerning management were included in the study. In the decision tree analysis, only a maximum tumor dimension threshold of 1.6 cm and progressive symptoms were required to predict the decision for treatment with 85% accuracy. Optimizing maximum dimension thresholds and including age at presentation boosted accuracy to 88%. Random forest analysis (n = 500 trees) predicted the decision for treatment with 80% accuracy. Factors with the highest variable importance based on multiple measures of importance, including mean minimal conditional depth and largest Gini impurity reduction, were maximum tumor dimension, age at presentation, Koos grade, and progressive symptoms at presentation.
CONCLUSIONS
Tree-based ML was used to predict which factors drive the decision for active treatment of VS with 80%–88% accuracy. The most important factors were maximum tumor dimension, age at presentation, Koos grade, and progressive symptoms. These results can assist in surgical decision-making and patient counseling. They also demonstrate the power of ML algorithms in extracting useful insights from limited data sets.
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Affiliation(s)
- Ron Gadot
- Department of Neurosurgery, Baylor College of Medicine
| | - Adrish Anand
- Department of Neurosurgery, Baylor College of Medicine
| | - Benjamin D. Lovin
- Department of Otolaryngology-Head and Neck Surgery, Baylor College of Medicine, Houston; and
| | - Alex D. Sweeney
- Department of Otolaryngology-Head and Neck Surgery, Baylor College of Medicine, Houston; and
| | - Akash J. Patel
- Department of Neurosurgery, Baylor College of Medicine
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, Texas
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