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Cobianchi L, Dal Mas F, Verde JM, Garcia-Vazquez A, Martellucci J, Swanstrom L, Ansaloni L. Why non-technical skills matter in surgery. New paradigms for surgical leaders. DISCOVER HEALTH SYSTEMS 2022; 1:2. [PMID: 37521113 PMCID: PMC9466332 DOI: 10.1007/s44250-022-00002-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 08/24/2022] [Indexed: 01/12/2023]
Abstract
The surgical literature is paying more and more attention to the topic of soft or non-technical skills (NTS), defined as those cognitive and social skills that characterize high-performing individuals and teams. NTS are essential in supporting surgeons in dealing with unexpected situations. During the COVID-19 pandemic, NTS have been considered crucial in defining situation awareness, enhancing decision making, communicating among groups and teams, and fostering leadership. With a "looking back and planning forward" approach, the current perspective aims at deepening the contribution of NTS for surgeons to deal with the unexpected challenges posed by the COVID crisis, surgical emergencies, the introduction of new technologies in clinical practice, to understand how such skills may help shape the surgical leaders of the future.
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Affiliation(s)
- Lorenzo Cobianchi
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100 Pavia, Italy
- IRCCS Policlinico San Matteo Foundation, General Surgery, Pavia, Italy
| | - Francesca Dal Mas
- Department of Management, Ca’ Foscari University of Venice, Venice, Italy
| | | | | | | | - Lee Swanstrom
- Institut Hospitalo-Universitaire (IHU), Strasbourg, France
| | - Luca Ansaloni
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100 Pavia, Italy
- IRCCS Policlinico San Matteo Foundation, General Surgery, Pavia, Italy
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A novel multi-criteria decision-making approach for prioritization of elective surgeries through formulation of “weighted MeNTS scoring system”. Heliyon 2022; 8:e10339. [PMID: 36090224 PMCID: PMC9449563 DOI: 10.1016/j.heliyon.2022.e10339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 06/30/2022] [Accepted: 08/12/2022] [Indexed: 11/22/2022] Open
Abstract
Background Publicly funded healthcare system has long non-manageable elective surgery waiting lists due to the non-existence of systematic mathematical modelling that can assess the relative priority of patients on elective surgery waiting lists thus denying the provision of surgical support to the patients with higher urgency. Mostly the patients of general surgery are entertain with highly subjective “time-honoured” methods that are inadequate to measure and compare the urgency of surgical procedure. Objective A methodology of assigning priorities to patients on elective surgery waiting lists has been presented in this paper using weighted criteria objectives. The objectives hve been chosen and assigned weights based on hospital conditions, and in consultation with the surgeons in hospital in Pakistan. Methods The proposed methodology presents two working contributions; first, a scoring mechanism based on MeNTS scoring system with weighted criterion that objectively translate the condition of patient prior to the surgical procedure; and second, a patient prioritization methodology to select patients for surgeries according to the corresponding scores. Detailed simulation results from actual patient data have been presented to evaluate the effectiveness of the proposed methodology, and its applicability and ease of use has been tested in real-time by surgeons while providing consultations to their patients. Results The proposed methodology outperforms the traditional “first-come-first-serve” methodology as there was a 30% reduction in average waiting time in elective surgery waiting lists (from 4.246 to 2.956 days) with 103 (90%) of patients being entertained before or within the unprioritized surgeries time span, with 94 patients having surgery within 1 day of being on waiting list (an increase of 47 patients). Moreover, transparency and equity were also found in the adaptation of this strategy to prioritize the elective surgery patients. Conclusions Prioritizing patients on elective surgery waiting lists is an important concern in surgical field. In most of the methodologies presented in earlier research, prioritization of patients for surgery is carried out subjectively. This study shows that the proposed technique has the potential to decrease the waiting times for patients on elective surgery waiting lists, as well as be presented as an objective methodology for preparing the elective surgery waiting lists to increase the transparency in waiting list.
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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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104
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De Simone B, Di Saverio S. Invited Commentary: Artificial Intelligence in Surgical Care: We Must Overcome Ethical Boundaries. J Am Coll Surg 2022; 235:275-277. [PMID: 35839402 DOI: 10.1097/xcs.0000000000000227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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105
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Laios A, Kalampokis E, Johnson R, Munot S, Thangavelu A, Hutson R, Broadhead T, Theophilou G, Leach C, Nugent D, De Jong D. Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer. Cancers (Basel) 2022; 14:cancers14143447. [PMID: 35884506 PMCID: PMC9316555 DOI: 10.3390/cancers14143447] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
(1) Background: Surgical cytoreduction for epithelial ovarian cancer (EOC) is a complex procedure. Encompassed within the performance skills to achieve surgical precision, intra-operative surgical decision-making remains a core feature. The use of eXplainable Artificial Intelligence (XAI) could potentially interpret the influence of human factors on the surgical effort for the cytoreductive outcome in question; (2) Methods: The retrospective cohort study evaluated 560 consecutive EOC patients who underwent cytoreductive surgery between January 2014 and December 2019 in a single public institution. The eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) algorithms were employed to develop the predictive model, including patient- and operation-specific features, and novel features reflecting human factors in surgical heuristics. The precision, recall, F1 score, and area under curve (AUC) were compared between both training algorithms. The SHapley Additive exPlanations (SHAP) framework was used to provide global and local explainability for the predictive model; (3) Results: A surgical complexity score (SCS) cut-off value of five was calculated using a Receiver Operator Characteristic (ROC) curve, above which the probability of incomplete cytoreduction was more likely (area under the curve [AUC] = 0.644; 95% confidence interval [CI] = 0.598−0.69; sensitivity and specificity 34.1%, 86.5%, respectively; p = 0.000). The XGBoost outperformed the DNN assessment for the prediction of the above threshold surgical effort outcome (AUC = 0.77; 95% [CI] 0.69−0.85; p < 0.05 vs. AUC 0.739; 95% [CI] 0.655−0.823; p < 0.95). We identified “turning points” that demonstrated a clear preference towards above the given cut-off level of surgical effort; in consultant surgeons with <12 years of experience, age <53 years old, who, when attempting primary cytoreductive surgery, recorded the presence of ascites, an Intraoperative Mapping of Ovarian Cancer score >4, and a Peritoneal Carcinomatosis Index >7, in a surgical environment with the optimization of infrastructural support. (4) Conclusions: Using XAI, we explain how intra-operative decisions may consider human factors during EOC cytoreduction alongside factual knowledge, to maximize the magnitude of the selected trade-off in effort. XAI techniques are critical for a better understanding of Artificial Intelligence frameworks, and to enhance their incorporation in medical applications.
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Affiliation(s)
- Alexandros Laios
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
- Correspondence:
| | | | - Racheal Johnson
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Sarika Munot
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Amudha Thangavelu
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Richard Hutson
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Tim Broadhead
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Georgios Theophilou
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Chris Leach
- School of Human & Health Sciences, University of Huddersfield, Huddersfield HD1 3DH, UK;
- Department of Psychology Services, South West Yorkshire Mental Health NHS Foundation Trust, The Laura Mitchell Health & Wellbeing Centre, Halifax HX1 1YR, UK
| | - David Nugent
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Diederick De Jong
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
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106
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Cobianchi L, Dal Mas F, Ansaloni L. Editorial: New Frontiers for Artificial Intelligence in Surgical Decision Making and its Organizational Impacts. Front Surg 2022; 9:933673. [PMID: 35800112 PMCID: PMC9253456 DOI: 10.3389/fsurg.2022.933673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Affiliation(s)
- Lorenzo Cobianchi
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
- Department of General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, 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, Pavia, Italy
- Department of General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, Italy
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107
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Sharma M, Savage C, Nair M, Larsson I, Svedberg P, Nygren JM. Artificial Intelligence Applications in Health Care Practice: A Scoping Review (Preprint). J Med Internet Res 2022; 24:e40238. [PMID: 36197712 PMCID: PMC9582911 DOI: 10.2196/40238] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/19/2022] [Accepted: 08/30/2022] [Indexed: 11/25/2022] Open
Abstract
Background Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. Objective The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? Methods A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Results Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Conclusions Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.
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Affiliation(s)
- Malvika Sharma
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Medical Management Centre, Stockholm, Sweden
| | - Carl Savage
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Medical Management Centre, Stockholm, Sweden
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Monika Nair
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Ingrid Larsson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens M Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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108
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Greenberg JK, Otun A, Ghogawala Z, Yen PY, Molina CA, Limbrick DD, Foraker RE, Kelly MP, Ray WZ. Translating Data Analytics Into Improved Spine Surgery Outcomes: A Roadmap for Biomedical Informatics Research in 2021. Global Spine J 2022; 12:952-963. [PMID: 33973491 PMCID: PMC9344511 DOI: 10.1177/21925682211008424] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES There is growing interest in the use of biomedical informatics and data analytics tools in spine surgery. Yet despite the rapid growth in research on these topics, few analytic tools have been implemented in routine spine practice. The purpose of this review is to provide a health information technology (HIT) roadmap to help translate data assets and analytics tools into measurable advances in spine surgical care. METHODS We conducted a narrative review of PubMed and Google Scholar to identify publications discussing data assets, analytical approaches, and implementation strategies relevant to spine surgery practice. RESULTS A variety of data assets are available for spine research, ranging from commonly used datasets, such as administrative billing data, to emerging resources, such as mobile health and biobanks. Both regression and machine learning techniques are valuable for analyzing these assets, and researchers should recognize the particular strengths and weaknesses of each approach. Few studies have focused on the implementation of HIT, and a variety of methods exist to help translate analytic tools into clinically useful interventions. Finally, a number of HIT-related challenges must be recognized and addressed, including stakeholder acceptance, regulatory oversight, and ethical considerations. CONCLUSIONS Biomedical informatics has the potential to support the development of new HIT that can improve spine surgery quality and outcomes. By understanding the development life-cycle that includes identifying an appropriate data asset, selecting an analytic approach, and leveraging an effective implementation strategy, spine researchers can translate this potential into measurable advances in patient care.
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Affiliation(s)
- Jacob K. Greenberg
- Department of Neurological Surgery, Washington University School of Medicine,
St. Louis, MO, USA,Jacob K. Greenberg, Department of
Neurosurgery, Washington University School of Medicine, 660S. Euclid Ave., Box
8057, St. Louis, MO 63 110, USA.
| | - Ayodamola Otun
- Department of Neurological Surgery, Washington University School of Medicine,
St. Louis, MO, USA
| | - Zoher Ghogawala
- Department of Neurosurgery, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Po-Yin Yen
- Institute for Informatics, Washington University School of Medicine,
St. Louis, MO, USA
| | - Camilo A. Molina
- Department of Neurological Surgery, Washington University School of Medicine,
St. Louis, MO, USA
| | - David D. Limbrick
- Department of Neurological Surgery, Washington University School of Medicine,
St. Louis, MO, USA
| | - Randi E Foraker
- Institute for Informatics, Washington University School of Medicine,
St. Louis, MO, USA
| | - Michael P. Kelly
- Department of Orthopaedic Surgery, Washington University School of Medicine,
St. Louis, MO, USA
| | - Wilson Z. Ray
- Department of Neurological Surgery, Washington University School of Medicine,
St. Louis, MO, USA
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109
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Rasteau S, Ernenwein D, Savoldelli C, Bouletreau P. Artificial intelligence for oral and maxillo-facial surgery: A narrative review. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2022; 123:276-282. [PMID: 35091121 DOI: 10.1016/j.jormas.2022.01.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 01/23/2022] [Indexed: 12/24/2022]
Abstract
Artificial Intelligence (AI) is a set of technologies that simulate human cognition in order to address a specific problem. The improvement in computing speed, the exponential production and the routine collection of data have led to the rapid development of AI in the health sector. In this review, we propose to provide surgeons with the essential technical elements to help them understand the possibilities offered by AI and to review the current applications of AI for oral and maxillofacial surgery (OMFS). The review of the literature reveals a real research boom of AI in all fields in OMFS. The algorithms used are related to machine learning, with a strong representation of the convolutional neural networks specific to deep learning. The complex architecture of these networks gives them the capacity to extract and process the elementary characteristics of an image, and they are therefore particularly used for diagnostic purposes on medical imagery or facial photography. We identified representative articles dealing with AI algorithms providing assistance in diagnosis, therapeutic decision, preoperative planning, or prediction and evaluation of the outcomes. Thanks to their learning, classification, prediction and detection capabilities, AI algorithms complement human skills while limiting their imperfections. However, these algorithms should be subject to rigorous clinical evaluation, and ethical reflection on data protection should be systematically conducted.
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Affiliation(s)
- Simon Rasteau
- Maxillo-Facial Surgery, Facial Plastic Surgery, Stomatology and Oral Surgery, Hospices Civils de Lyon, Lyon-Sud Hospital - Claude-Bernard Lyon 1 University, 165 Chemin du Grand-Revoyet, Pierre-Bénite 69310, France.
| | - Didier Ernenwein
- Department of Pediatric Oral & Maxillofacial & Plastic Surgery, Children's Hospital Robert-Debré, Paris-Diderot University, Paris, France
| | - Charles Savoldelli
- University Institute of the Face and Neck, Côte d'Azur University, Nice University Hospital, 31 Avenue de Valombrose, Nice 06100, France
| | - Pierre Bouletreau
- Maxillo-Facial Surgery, Facial Plastic Surgery, Stomatology and Oral Surgery, Hospices Civils de Lyon, Lyon-Sud Hospital - Claude-Bernard Lyon 1 University, 165 Chemin du Grand-Revoyet, Pierre-Bénite 69310, France
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110
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Metabolomics in Bariatric and Metabolic Surgery Research and the Potential of Deep Learning in Bridging the Gap. Metabolites 2022; 12:metabo12050458. [PMID: 35629961 PMCID: PMC9143741 DOI: 10.3390/metabo12050458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 02/01/2023] Open
Abstract
During the past several years, there has been a shift in terminology from bariatric surgery alone to bariatric and metabolic surgery (BMS). More than a change in name, this signifies a paradigm shift that incorporates the metabolic effects of operations performed for weight loss and the amelioration of related medical problems. Metabolomics is a relatively novel concept in the field of bariatrics, with some consistent changes in metabolite concentrations before and after weight loss. However, the abundance of metabolites is not easy to handle. This is where artificial intelligence, and more specifically deep learning, would aid in revealing hidden relationships and would help the clinician in the decision-making process of patient selection in an individualized way.
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111
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Smits FJ, Henry AC, Besselink MG, Busch OR, van Eijck CH, Arntz M, Bollen TL, van Delden OM, van den Heuvel D, van der Leij C, van Lienden KP, Moelker A, Bonsing BA, Borel Rinkes IH, Bosscha K, van Dam RM, Derksen WJM, den Dulk M, Festen S, Groot Koerkamp B, de Haas RJ, Hagendoorn J, van der Harst E, de Hingh IH, Kazemier G, van der Kolk M, Liem M, Lips DJ, Luyer MD, de Meijer VE, Mieog JS, Nieuwenhuijs VB, Patijn GA, Te Riele WW, Roos D, Schreinemakers JM, Stommel MWJ, Wit F, Zonderhuis BA, Daamen LA, van Werkhoven CH, Molenaar IQ, van Santvoort HC. Algorithm-based care versus usual care for the early recognition and management of complications after pancreatic resection in the Netherlands: an open-label, nationwide, stepped-wedge cluster-randomised trial. Lancet 2022; 399:1867-1875. [PMID: 35490691 DOI: 10.1016/s0140-6736(22)00182-9] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 12/09/2021] [Accepted: 12/16/2021] [Indexed: 10/18/2022]
Abstract
BACKGROUND Early recognition and management of postoperative complications, before they become clinically relevant, can improve postoperative outcomes for patients, especially for high-risk procedures such as pancreatic resection. METHODS We did an open-label, nationwide, stepped-wedge cluster-randomised trial that included all patients having pancreatic resection during a 22-month period in the Netherlands. In this trial design, all 17 centres that did pancreatic surgery were randomly allocated for the timing of the crossover from usual care (the control group) to treatment given in accordance with a multimodal, multidisciplinary algorithm for the early recognition and minimally invasive management of postoperative complications (the intervention group). Randomisation was done by an independent statistician using a computer-generated scheme, stratified to ensure that low-medium-volume centres alternated with high-volume centres. Patients and investigators were not masked to treatment. A smartphone app was designed that incorporated the algorithm and included the daily evaluation of clinical and biochemical markers. The algorithm determined when to do abdominal CT, radiological drainage, start antibiotic treatment, and remove abdominal drains. After crossover, clinicians were trained in how to use the algorithm during a 4-week wash-in period; analyses comparing outcomes between the control group and the intervention group included all patients other than those having pancreatic resection during this wash-in period. The primary outcome was a composite of bleeding that required invasive intervention, organ failure, and 90-day mortality, and was assessed by a masked adjudication committee. This trial was registered in the Netherlands Trial Register, NL6671. FINDINGS From Jan 8, 2018, to Nov 9, 2019, all 1805 patients who had pancreatic resection in the Netherlands were eligible for and included in this study. 57 patients who underwent resection during the wash-in phase were excluded from the primary analysis. 1748 patients (885 receiving usual care and 863 receiving algorithm-centred care) were included. The primary outcome occurred in fewer patients in the algorithm-centred care group than in the usual care group (73 [8%] of 863 patients vs 124 [14%] of 885 patients; adjusted risk ratio [RR] 0·48, 95% CI 0·38-0·61; p<0·0001). Among patients treated according to the algorithm, compared with patients who received usual care there was a decrease in bleeding that required intervention (47 [5%] patients vs 51 [6%] patients; RR 0·65, 0·42-0·99; p=0·046), organ failure (39 [5%] patients vs 92 [10%] patients; 0·35, 0·20-0·60; p=0·0001), and 90-day mortality (23 [3%] patients vs 44 [5%] patients; 0·42, 0·19-0·92; p=0·029). INTERPRETATION The algorithm for the early recognition and minimally invasive management of complications after pancreatic resection considerably improved clinical outcomes compared with usual care. This difference included an approximate 50% reduction in mortality at 90 days. FUNDING The Dutch Cancer Society and UMC Utrecht.
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Affiliation(s)
- F Jasmijn Smits
- Department of Surgery, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands
| | - Anne Claire Henry
- Department of Surgery, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands
| | - Marc G Besselink
- Department of Surgery, Cancer Centre Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Olivier R Busch
- Department of Surgery, Cancer Centre Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Casper H van Eijck
- Department of Surgery, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Mark Arntz
- Department of Radiology, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Thomas L Bollen
- Department of Radiology, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands
| | - Otto M van Delden
- Department of Radiology, Cancer Centre Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Daniel van den Heuvel
- Department of Radiology, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands
| | | | - Krijn P van Lienden
- Department of Radiology, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands
| | - Adriaan Moelker
- Department of Radiology and Nuclear Medicine, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Bert A Bonsing
- Department of Surgery, Leiden University Medical Centre, Leiden, Netherlands
| | - Inne H Borel Rinkes
- Department of Surgery, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands
| | - Koop Bosscha
- Department of Surgery, Jeroen Bosch Hospital, Den Bosch, Netherlands
| | - Ronald M van Dam
- Department of Surgery, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Wouter J M Derksen
- Department of Surgery, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands
| | - Marcel den Dulk
- Department of Surgery, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Sebastiaan Festen
- Department of Surgery, Onze Lieve Vrouwe Gasthuis, Amsterdam, Netherlands
| | - Bas Groot Koerkamp
- Department of Surgery, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Robbert J de Haas
- Department of Radiology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Jeroen Hagendoorn
- Department of Surgery, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands
| | | | - Ignace H de Hingh
- Department of Surgery, Catharina Hospital, Eindhoven and GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Geert Kazemier
- Department of Surgery, Cancer Centre Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Marion van der Kolk
- Department of Surgery, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Mike Liem
- Department of Surgery, Medisch Spectrum Twente, Enschede, Netherlands
| | - Daan J Lips
- Department of Surgery, Medisch Spectrum Twente, Enschede, Netherlands
| | - Misha D Luyer
- Department of Surgery, Catharina Hospital, Eindhoven and GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Vincent E de Meijer
- Department of Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - J Sven Mieog
- Department of Surgery, Leiden University Medical Centre, Leiden, Netherlands
| | | | | | - Wouter W Te Riele
- Department of Surgery, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands
| | - Daphne Roos
- Department of Surgery, Reinier de Graaf Hospital, Delft, Netherlands
| | | | - Martijn W J Stommel
- Department of Surgery, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Fennie Wit
- Department of Surgery, Tjongerschans Hospital, Heerenveen, Netherlands
| | - Babs A Zonderhuis
- Department of Surgery, Cancer Centre Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Lois A Daamen
- Department of Surgery, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands
| | - C Henri van Werkhoven
- Julius Centre for Health Sciences and Primary Care, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands
| | - I Quintus Molenaar
- Department of Surgery, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands
| | - Hjalmar C van Santvoort
- Department of Surgery, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands.
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Loftus TJ, Vlaar APJ, Hung AJ, Bihorac A, Dennis BM, Juillard C, Hashimoto DA, Kaafarani HMA, Tighe PJ, Kuo PC, Miyashita S, Wexner SD, Behrns KE. Executive summary of the artificial intelligence in surgery series. Surgery 2022; 171:1435-1439. [PMID: 34815097 PMCID: PMC9379376 DOI: 10.1016/j.surg.2021.10.047] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/19/2021] [Accepted: 10/22/2021] [Indexed: 12/17/2022]
Abstract
As opportunities for artificial intelligence to augment surgical care expand, the accompanying surge in published literature has generated both substantial enthusiasm and grave concern regarding the safety and efficacy of artificial intelligence in surgery. For surgeons and surgical data scientists, it is increasingly important to understand the state-of-the-art, recognize knowledge and technology gaps, and critically evaluate the deluge of literature accordingly. This article summarizes the experiences and perspectives of a global, multi-disciplinary group of experts who have faced development and implementation challenges, overcome them, and produced incipient evidence thereof. Collectively, evidence suggests that artificial intelligence has the potential to augment surgeons via decision-support, technical skill assessment, and the semi-autonomous performance of tasks ranging from resource allocation to patching foregut defects. Most applications remain in preclinical phases. As technologies and their implementations improve and positive evidence accumulates, surgeons will face professional imperatives to lead the safe, effective clinical implementation of artificial intelligence in surgery. Substantial challenges remain; recent progress in using artificial intelligence to achieve performance advantages in surgery suggests that remaining challenges can and will be overcome.
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Affiliation(s)
- Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL.
| | - Alexander P J Vlaar
- Amsterdam UMC, location AMC, University of Amsterdam, Department of Intensive Care, Amsterdam, Netherlands
| | - Andrew J Hung
- Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, CA
| | - Azra Bihorac
- Department of Medicine, University of Florida Health, Gainesville, FL
| | - Bradley M Dennis
- Division of Trauma, Surgical Critical Care and Emergency General Surgery, Department of Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Catherine Juillard
- University of California, Los Angeles, Department of Surgery, Los Angeles, CA
| | - Daniel A Hashimoto
- Surgical Artificial Intelligence and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Haytham M A Kaafarani
- Division of Trauma, Emergency Surgery & Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - Patrick J Tighe
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL
| | - Paul C Kuo
- Department of General Surgery, University of South Florida Morsani College of Medicine, Tampa, FL
| | - Shuhei Miyashita
- Department of Automatic Control and Systems Engineering, University of Sheffield, UK
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113
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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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114
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Artificial intelligence-enabled decision support in nephrology. Nat Rev Nephrol 2022; 18:452-465. [PMID: 35459850 DOI: 10.1038/s41581-022-00562-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2022] [Indexed: 12/12/2022]
Abstract
Kidney pathophysiology is often complex, nonlinear and heterogeneous, which limits the utility of hypothetical-deductive reasoning and linear, statistical approaches to diagnosis and treatment. Emerging evidence suggests that artificial intelligence (AI)-enabled decision support systems - which use algorithms based on learned examples - may have an important role in nephrology. Contemporary AI applications can accurately predict the onset of acute kidney injury before notable biochemical changes occur; can identify modifiable risk factors for chronic kidney disease onset and progression; can match or exceed human accuracy in recognizing renal tumours on imaging studies; and may augment prognostication and decision-making following renal transplantation. Future AI applications have the potential to make real-time, continuous recommendations for discrete actions and yield the greatest probability of achieving optimal kidney health outcomes. Realizing the clinical integration of AI applications will require cooperative, multidisciplinary commitment to ensure algorithm fairness, overcome barriers to clinical implementation, and build an AI-competent workforce. AI-enabled decision support should preserve the pre-eminence of wisdom and augment rather than replace human decision-making. By anchoring intuition with objective predictions and classifications, this approach should favour clinician intuition when it is honed by experience.
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115
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Balch JA, Efron PA, Bihorac A, Loftus TJ. Gamification for Machine Learning in Surgical Patient Engagement. Front Surg 2022; 9:896351. [PMID: 35656082 PMCID: PMC9152738 DOI: 10.3389/fsurg.2022.896351] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
Patients and their surgeons face a complex and evolving set of choices in the process of shared decision making. The plan of care must be tailored to individual patient risk factors and values, though objective estimates of risk can be elusive, and these risk factors are often modifiable and can alter the plan of care. Machine learning can perform real-time predictions of outcomes, though these technologies are limited by usability and interpretability. Gamification, or the use of game elements in non-game contexts, may be able to incorporate machine learning technology to help patients optimize their pre-operative risks, reduce in-hospital complications, and hasten recovery. This article proposes a theoretical mobile application to help guide decision making and provide evidence-based, tangible goals for patients and surgeons with the goal of achieving the best possible operative outcome that aligns with patient values.
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Affiliation(s)
- Jeremy A. Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Philip A. Efron
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Azra Bihorac
- Department of Medicine, University of Florida Health, Gainesville, FL, United States
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States
| | - Tyler J. Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States
- Correspondence: Tyler J. Loftus
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Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14:765-793. [PMID: 35582107 PMCID: PMC9048537 DOI: 10.4251/wjgo.v14.i4.765] [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: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/27/2022] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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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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Pantelis AG, Panagopoulou PA, Lapatsanis DP. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms—A Scoping Review. Diagnostics (Basel) 2022; 12:diagnostics12040874. [PMID: 35453922 PMCID: PMC9027316 DOI: 10.3390/diagnostics12040874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 03/27/2022] [Accepted: 03/29/2022] [Indexed: 12/21/2022] Open
Abstract
Neuroendocrine neoplasms (NENs) and tumors (NETs) are rare neoplasms that may affect any part of the gastrointestinal system. In this scoping review, we attempt to map existing evidence on the role of artificial intelligence, machine learning and deep learning in the diagnosis and management of NENs of the gastrointestinal system. After implementation of inclusion and exclusion criteria, we retrieved 44 studies with 53 outcome analyses. We then classified the papers according to the type of studied NET (26 Pan-NETs, 59.1%; 3 metastatic liver NETs (6.8%), 2 small intestinal NETs, 4.5%; colorectal, rectal, non-specified gastroenteropancreatic and non-specified gastrointestinal NETs had from 1 study each, 2.3%). The most frequently used AI algorithms were Supporting Vector Classification/Machine (14 analyses, 29.8%), Convolutional Neural Network and Random Forest (10 analyses each, 21.3%), Random Forest (9 analyses, 19.1%), Logistic Regression (8 analyses, 17.0%), and Decision Tree (6 analyses, 12.8%). There was high heterogeneity on the description of the prediction model, structure of datasets, and performance metrics, whereas the majority of studies did not report any external validation set. Future studies should aim at incorporating a uniform structure in accordance with existing guidelines for purposes of reproducibility and research quality, which are prerequisites for integration into clinical practice.
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Affiliation(s)
- Athanasios G. Pantelis
- 4th Department of Surgery, Evaggelismos General Hospital of Athens, 10676 Athens, Greece;
- Correspondence:
| | | | - Dimitris P. Lapatsanis
- 4th Department of Surgery, Evaggelismos General Hospital of Athens, 10676 Athens, Greece;
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In Silico Finite Element Modeling of Stress Distribution in Osteosynthesis after Pertrochanteric Fractures. J Clin Med 2022; 11:jcm11071885. [PMID: 35407491 PMCID: PMC8999495 DOI: 10.3390/jcm11071885] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/15/2022] [Accepted: 03/25/2022] [Indexed: 12/13/2022] Open
Abstract
A stabilization method of pertrochanteric femur fractures is a contentious issue. Here, we assess the feasibility of rapid in silico 2D finite element modeling (FEM) to predict the distribution of stresses arising during the two most often used stabilization methods: gamma nail fixation (GNF) and dynamic hip screw (DHS). The modeling was based on standard pre-surgery radiographs of hip joints of 15 patients with pertrochanteric fractures of type A1, A2, and A3 according to the AO/OTA classification. The FEM showed that the stresses were similar for both GNF and DHS, with the medians ranging between 53-60 MPa and consistently lower for A1 than A3 fractures. Stresses also appeared in the fixation materials being about two-fold higher for GNF. Given similar bone stresses caused by both GNF and DHS but shorter surgery time, less extensive dissection, and faster patient mobilization, we submit that the GNF stabilization appears to be the most optimal system for pertrochanteric fractures. In silico FEM appears a viable perioperative method that helps predict the distribution of compressive stresses after osteosynthesis of pertrochanteric fractures. The promptness of modeling fits well into the rigid time framework of hip fracture surgery and may help optimize the fixation procedure for the best outcome. The study extends the use of FEM in complex orthopedic management. However, further datasets are required to firmly position the FEM in the treatment of pertrochanteric fractures.
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120
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Taha A, Ochs V, Kayhan LN, Enodien B, Frey DM, Krähenbühl L, Taha-Mehlitz S. Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58040459. [PMID: 35454298 PMCID: PMC9029673 DOI: 10.3390/medicina58040459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/14/2022] [Accepted: 03/18/2022] [Indexed: 02/06/2023]
Abstract
Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.
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Affiliation(s)
- Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, 4123 Allschwil, Switzerland
- Correspondence:
| | - Vincent Ochs
- Roche Innovation Center Basel, Department of Pharma Research & Early Development, 4070 Basel, Switzerland;
| | - Leos N. Kayhan
- Department of Surgery, Canntonal Hospital Luzern, 6004 Luzern, Switzerland;
| | - Bassey Enodien
- Department of Surgery, Wetzikon Hospital, 8620 Wetzikon, Switzerland; (B.E.); (D.M.F.)
| | - Daniel M. Frey
- Department of Surgery, Wetzikon Hospital, 8620 Wetzikon, Switzerland; (B.E.); (D.M.F.)
| | | | - Stephanie Taha-Mehlitz
- Clarunis, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, 4002 Basel, Switzerland;
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121
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De Simone B, Abu-Zidan FM, Gumbs AA, Chouillard E, Di Saverio S, Sartelli M, Coccolini F, Ansaloni L, Collins T, Kluger Y, Moore EE, Litvin A, Leppaniemi A, Mascagni P, Milone L, Piccoli M, Abu-Hilal M, Sugrue M, Biffl WL, Catena F. Knowledge, attitude, and practice of artificial intelligence in emergency and trauma surgery, the ARIES project: an international web-based survey. World J Emerg Surg 2022; 17:10. [PMID: 35144645 PMCID: PMC8832812 DOI: 10.1186/s13017-022-00413-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 01/24/2022] [Indexed: 12/12/2022] Open
Abstract
Aim We aimed to evaluate the knowledge, attitude, and practices in the application of AI in the emergency setting among international acute care and emergency surgeons. Methods An online questionnaire composed of 30 multiple choice and open-ended questions was sent to the members of the World Society of Emergency Surgery between 29th May and 28th August 2021. The questionnaire was developed by a panel of 11 international experts and approved by the WSES steering committee. Results 200 participants answered the survey, 32 were females (16%). 172 (86%) surgeons thought that AI will improve acute care surgery. Fifty surgeons (25%) were trained, robotic surgeons and can perform it. Only 19 (9.5%) were currently performing it. 126 (63%) surgeons do not have a robotic system in their institution, and for those who have it, it was mainly used for elective surgery. Only 100 surgeons (50%) were able to define different AI terminology. Participants thought that AI is useful to support training and education (61.5%), perioperative decision making (59.5%), and surgical vision (53%) in emergency surgery. There was no statistically significant difference between males and females in ability, interest in training or expectations of AI (p values 0.91, 0.82, and 0.28, respectively, Mann–Whitney U test). Ability was significantly correlated with interest and expectations (p < 0.0001 Pearson rank correlation, rho 0.42 and 0.47, respectively) but not with experience (p = 0.9, rho − 0.01). Conclusions The implementation of artificial intelligence in the emergency and trauma setting is still in an early phase. The support of emergency and trauma surgeons is essential for the progress of AI in their setting which can be augmented by proper research and training programs in this area. Supplementary Information The online version contains supplementary material available at 10.1186/s13017-022-00413-3.
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Affiliation(s)
- Belinda De Simone
- Department of Emergency and Metabolic Minimally Invasive Surgery, Centre Hospitalier Intercommunal de Poissy/Saint Germain en Laye, 10 Rue de Champ Gaillard, Poissy Cedex, France.
| | - Fikri M Abu-Zidan
- Department of Surgery, College of Medicine and Health Sciences, UAE University, Al-Ain, United Arab Emirates
| | - Andrew A Gumbs
- Department of Emergency and Metabolic Minimally Invasive Surgery, Centre Hospitalier Intercommunal de Poissy/Saint Germain en Laye, 10 Rue de Champ Gaillard, Poissy Cedex, France
| | - Elie Chouillard
- Department of Emergency and Metabolic Minimally Invasive Surgery, Centre Hospitalier Intercommunal de Poissy/Saint Germain en Laye, 10 Rue de Champ Gaillard, Poissy Cedex, France
| | - Salomone Di Saverio
- Department of General Surgery, Ospedale Civile "Madonna del Soccorso", San Benedetto del Tronto, AP, Italy
| | - Massimo Sartelli
- Department of General Surgery, Macerata Hospital, Macerata, Italy
| | | | - Luca Ansaloni
- Department of General Surgery, University Hospital of Pavia, Pavia, Italy
| | | | - Yoram Kluger
- Department of Emergency and Trauma Surgery, Rambam Health Campus, Haifa, Israel
| | - Ernest E Moore
- Department of Surgery, School of Medicine and the Ernest E. Moore Shock Trauma Center at Denver Health, University of Colorado, Denver, CO, USA
| | - Andrej Litvin
- Abdominal Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Ari Leppaniemi
- Department of Surgical Disciplines, Immanuel Kant Baltic Federal University, Regional Clinical Hospital, Kaliningrad, Russia
| | - Pietro Mascagni
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Luca Milone
- Department of General and Robotic Surgery, The Brooklyn Hospital Center, New York, USA
| | - Micaela Piccoli
- Division of General, Emergency Surgery and New Technologies, Ospedale Civile Di Baggiovara, Azienda Ospedaliero - Universitaria Di Modena, Modena, Italy
| | - Mohamed Abu-Hilal
- Hepato-Bilio-Pancreatic Minimally Invasive Surgery, Poliambulanza Foundation Hospital, Brescia, Italy
| | - Michael Sugrue
- Department of Surgery, Letterkenny University Hospital Ireland, Letterkenny, Ireland
| | - Walter L Biffl
- Department of Trauma and Acute Care Surgery, Scripps Memorial Hospital, La Jolla, CA, USA
| | - Fausto Catena
- Department of Emergency and Trauma Surgery, Bufalini Hospital, Cesena, Italy
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Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review. J Med Internet Res 2022; 24:e32215. [PMID: 35084349 PMCID: PMC8832266 DOI: 10.2196/32215] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/02/2021] [Accepted: 12/27/2021] [Indexed: 01/22/2023] Open
Abstract
Background Significant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice. Objective This paper aims to identify the implementation frameworks used to understand the application of AI in health care practice. Methods A scoping review was conducted using the Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases to identify publications that reported frameworks, models, and theories concerning AI implementation in health care. This review focused on studies published in English and investigating AI implementation in health care since 2000. A total of 2541 unique publications were retrieved from the databases and screened on titles and abstracts by 2 independent reviewers. Selected articles were thematically analyzed against the Nilsen taxonomy of implementation frameworks, and the Greenhalgh framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health care technologies. Results In total, 7 articles met all eligibility criteria for inclusion in the review, and 2 articles included formal frameworks that directly addressed AI implementation, whereas the other articles provided limited descriptions of elements influencing implementation. Collectively, the 7 articles identified elements that aligned with all the NASSS domains, but no single article comprehensively considered the factors known to influence technology implementation. New domains were identified, including dependency on data input and existing processes, shared decision-making, the role of human oversight, and ethics of population impact and inequality, suggesting that existing frameworks do not fully consider the unique needs of AI implementation. Conclusions This literature review demonstrates that understanding how to implement AI in health care practice is still in its early stages of development. Our findings suggest that further research is needed to provide the knowledge necessary to develop implementation frameworks to guide the future implementation of AI in clinical practice and highlight the opportunity to draw on existing knowledge from the field of implementation science.
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Affiliation(s)
- Fábio Gama
- School of Business, Innovation and Sustainability, Halmstad University, Halmstad, Sweden.,School of Administration and Economic Science, Santa Catarina State University, Florianópolis, Brazil
| | - Daniel Tyskbo
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - James Barlow
- Centre for Health Economics and Policy Innovation, Imperial College Business School, London, United Kingdom
| | - Julie Reed
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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Deyirmendjian C, Elterman D, Chughtai B, Zorn KC, Bhojani N. Surgical treatment options for benign prostatic obstruction: beyond prostate volume. Curr Opin Urol 2022; 32:102-108. [PMID: 34669611 DOI: 10.1097/mou.0000000000000937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Many surgical treatment options are available for patients who present with benign prostatic obstruction (BPO). This article reviews the current treatment options available and distinguishes them based on five clinical considerations: antithrombotic therapy, sexual function preservation, ambulatory procedures, anesthesia-related risks and duration of catheterization. RECENT FINDINGS A comprehensive review of the literature was performed on 10 BPO procedures. Laser enucleation of the prostate (LEP), bipolar plasma transurethral vaporization of the prostate and photoselective vaporization (PVP) of the prostate reduces the risk of bleeding, which is recommended for anticoagulated men. Ejaculatory function is more likely to be preserved following transurethral incision of the prostate, Rezūm, Aquablation, UroLift and iTind. Same-day discharge is possible for LEP, PVP and prostatic arterial embolization (PAE). For patients with high anesthesia-related risks, procedures compatible with local anesthesia (UroLift, Rezūm, iTind and PAE) should be favored. Catheterization duration is shorter with UroLift, PVP and LEP. SUMMARY BPO treatment options are growing rapidly. The optimal procedure for a given patient is based on factors such as associated risks, recovery and expected outcomes. Besides prostate volume, the clinical considerations in the present article can help elucidate the best surgical BPO treatment option for each patient based on their values, preferences, and risk tolerance.
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Affiliation(s)
| | - Dean Elterman
- Division of Urology, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Bilal Chughtai
- Department of Urology, Weill Cornell Medical College, New York, New York, USA
| | - Kevin C Zorn
- Division of Urology, Centre Hospitalier de l'Université de Montréal, University of Montreal, Montreal, Quebec, Canada
| | - Naeem Bhojani
- Division of Urology, Centre Hospitalier de l'Université de Montréal, University of Montreal, Montreal, Quebec, Canada
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Mlakar I, Smrke U, Flis V, Bergauer A, Kobilica N, Kampič T, Horvat S, Vidovič D, Musil B, Plohl N. A randomized controlled trial for evaluating the impact of integrating a computerized clinical decision support system and a socially assistive humanoid robot into grand rounds during pre/post-operative care. Digit Health 2022; 8:20552076221129068. [PMID: 36185391 PMCID: PMC9515524 DOI: 10.1177/20552076221129068] [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: 03/16/2022] [Accepted: 09/10/2022] [Indexed: 11/17/2022] Open
Abstract
Although clinical decision support systems (CDSSs) are increasingly emphasized as
one of the possible levers for improving care, they are still not widely used
due to different barriers, such as doubts about systems’ performance, their
complexity and poor design, practitioners’ lack of time to use them, poor
computer skills, reluctance to use them in front of patients, and deficient
integration into existing workflows. While several studies on CDSS exist, there
is a need for additional high-quality studies using large samples and examining
the differences between outcomes following a decision based on CDSS support and
those following decisions without this kind of information. Even less is known
about the effectiveness of a CDSS that is delivered during a grand round routine
and with the help of socially assistive humanoid robots (SAHRs). In this study,
200 patients will be randomized into a Control Group (i.e. standard care) and an
Intervention Group (i.e. standard care and novel CDSS delivered via a SAHR).
Health care quality and Quality of Life measures will be compared between the
two groups. Additionally, approximately 22 clinicians, who are also active
researchers at the University Clinical Center Maribor, will evaluate the
acceptability and clinical usability of the system. The results of the proposed
study will provide high-quality evidence on the effectiveness of CDSS systems
and SAHR in the grand round routine.
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Affiliation(s)
- Izidor Mlakar
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Urška Smrke
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Vojko Flis
- University Clinical Centre Maribor, Maribor, Slovenia
| | | | - Nina Kobilica
- University Clinical Centre Maribor, Maribor, Slovenia
| | - Tadej Kampič
- University Clinical Centre Maribor, Maribor, Slovenia
| | - Samo Horvat
- University Clinical Centre Maribor, Maribor, Slovenia
| | | | - Bojan Musil
- Faculty of Arts, Department of Psychology, University of Maribor, Maribor, Slovenia
| | - Nejc Plohl
- Faculty of Arts, Department of Psychology, University of Maribor, Maribor, Slovenia
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125
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Shi S, Tian Y, Ren Y, Li Q, Li L, Yu M, Wang J, Gao L, Xu S. A new machine learning-based prediction model for subtype diagnosis in primary aldosteronism. Front Endocrinol (Lausanne) 2022; 13:1005934. [PMID: 36506080 PMCID: PMC9728523 DOI: 10.3389/fendo.2022.1005934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/07/2022] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Unilateral primary aldosteronism (UPA) and bilateral primary aldosteronism (BPA) are the two subtypes of PA. Discriminating UPA from BPA is of great significance. Although adrenal venous sampling (AVS) is the gold standard for diagnosis, it has shortcomings. Thus, improved methods are needed. METHODS The original data were extracted from the public database "Dryad". Ten parameters were included to develop prediction models for PA subtype diagnosis using machine learning technology. Moreover, the optimal model was chose and validated in an external dataset. RESULTS In the modeling dataset, 165 patients (71 UPA, 94 BPA) were included, while in the external dataset, 43 consecutive patients (20 UPA, 23 BPA) were included. The ten parameters utilized in the prediction model include age, sex, systolic and diastolic blood pressure, aldosterone to renin ratio (ARR), serum potassium, ARR after 50 mg captopril challenge test (CCT), primary aldosterone concentration (PAC) after saline infusion test (SIT), PAC reduction rate after SIT, and number of types of antihypertensive agents at diagnosis. The accuracy, sensitivity, specificity, F1 score, and AUC for the optimal model using the random forest classifier were 90.0%, 81.8%, 96.4%, 0.878, and 0.938, respectively, in the testing dataset and 81.4%, 90.0%, 73.9%, 0.818 and 0.887, respectively, in the validating external dataset. The most important variables contributing to the prediction model were PAC after SIT, ARR, and ARR after CCT. DISCUSSION We developed a machine learning-based predictive model for PA subtype diagnosis based on ten clinical parameters without CT imaging. In the future, artificial intelligence-based prediction models might become a robust prediction tool for PA subtype diagnosis, thereby, might reducing at least some of the requests for CT or AVS and assisting clinical decision-making.
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Affiliation(s)
- Shaomin Shi
- Department of Endocrinology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Yuan Tian
- Department of Endocrinology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Yong Ren
- Department of Cardiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Qing’an Li
- Department of General Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Luhong Li
- Department of General Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Ming Yu
- Department of General Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Jingzhong Wang
- Department of Interventional Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Ling Gao
- Department of Endocrinology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
- *Correspondence: Shaoyong Xu, ; Ling Gao,
| | - Shaoyong Xu
- Department of Endocrinology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
- Center for Clinical Evidence-Based and Translational Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
- *Correspondence: Shaoyong Xu, ; Ling Gao,
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Affiliation(s)
- Andrew S Little
- 1Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona; and
| | - Sherry J Wu
- 2Anderson School of Management, Behavioral Decision Making and Management and Organizations, University of California, Los Angeles, California
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127
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Ozrazgat-Baslanti T, Loftus TJ, Ren Y, Ruppert MM, Bihorac A. Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury. Curr Opin Crit Care 2021; 27:560-572. [PMID: 34757993 PMCID: PMC8783984 DOI: 10.1097/mcc.0000000000000887] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
PURPOSE OF REVIEW Acute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory data produced in ICUs allow the application of artificial intelligence analytics. The purpose of this article is to assimilate and critically evaluate recently published literature regarding artificial intelligence applications for predicting, diagnosing and subphenotyping AKI among critically ill patients. RECENT FINDINGS Among recent studies regarding artificial intelligence implementations for predicting, diagnosing and subphenotyping AKI among critically ill patients, there are many promising models, but few had external validation, clinical interpretability and high predictive performance. Deep learning techniques leveraging multimodal clinical data show great potential to provide continuous, accurate, early predictions of AKI risk, which could be implemented clinically to optimize preventive and early therapeutic management strategies. SUMMARY Use of consensus criteria, standard definitions and common data models could facilitate access to machine learning-ready data sets for external validation. The lack of interpretability, explainability, fairness and transparency of artificial intelligence models hinder their entrustment and clinical implementation; compliance with standardized reporting guidelines can mitigate these challenges.
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Affiliation(s)
- Tezcan Ozrazgat-Baslanti
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Tyler J. Loftus
- Department of Surgery, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Yuanfang Ren
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Matthew M. Ruppert
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Azra Bihorac
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
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Barcellini A, Dal Mas F, Paoloni P, Loap P, Cobianchi L, Locati L, Rodríguez-Luna MR, Orlandi E. Please mind the gap-about equity and access to care in oncology. ESMO Open 2021; 6:100335. [PMID: 34902710 PMCID: PMC8671867 DOI: 10.1016/j.esmoop.2021.100335] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/26/2021] [Accepted: 11/11/2021] [Indexed: 12/25/2022] Open
Affiliation(s)
- A Barcellini
- Radiation Oncology Unit, Clinical Department, National Center for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - F Dal Mas
- Department of Management, Lincoln International Business School, University of Lincoln, Lincoln, UK; Ipazia Observatory on Gender Research, Rome, Italy; Interdepartmental Research Center "Organization and Governance of the Public Administration", University of Pavia, Pavia, Italy
| | - P Paoloni
- Ipazia Observatory on Gender Research, Rome, Italy; Department of Law and Economics of Productive Activities, Sapienza University of Rome, Rome, Italy
| | - P Loap
- Radiation Oncology Unit, Clinical Department, National Center for Oncological Hadrontherapy (CNAO), Pavia, Italy; Department of Radiation Oncology, Institut Curie, Paris, France
| | - L Cobianchi
- Department of General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, Italy; Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - L Locati
- Unit of Translational Oncology, IRCCS ICS Maugeri, University of Pavia, Pavia, Italy
| | - M R Rodríguez-Luna
- Institute for Research against Digestive Cancer (IRCAD), Strasbourg, France
| | - E Orlandi
- Radiation Oncology Unit, Clinical Department, National Center for Oncological Hadrontherapy (CNAO), Pavia, Italy.
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129
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Qian H, Dong B, Yuan JJ, Yin F, Wang Z, Wang HN, Wang HS, Tian D, Li WH, Zhang B, Zhao LB, Ning BT. Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics. Front Med (Lausanne) 2021; 8:695185. [PMID: 34820391 PMCID: PMC8606880 DOI: 10.3389/fmed.2021.695185] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 09/24/2021] [Indexed: 12/30/2022] Open
Abstract
Artificial intelligence (AI) has been deeply applied in the medical field and has shown broad application prospects. Pre-consultation system is an important supplement to the traditional face-to-face consultation. The combination of the AI and the pre-consultation system can help to raise the efficiency of the clinical work. However, it is still challenging for the AI to analyze and process the complicated electronic health record (EHR) data. Our pre-consultation system uses an automated natural language processing (NLP) system to communicate with the patients through the mobile terminals, applying the deep learning (DL) techniques to extract the symptomatic information, and finally outputs the structured electronic medical records. From November 2019 to May 2020, a total of 2,648 pediatric patients used our model to provide their medical history and get the primary diagnosis before visiting the physicians in the outpatient department of the Shanghai Children's Medical Center. Our task is to evaluate the ability of the AI and doctors to obtain the primary diagnosis and to analyze the effect of the consistency between the medical history described by our model and the physicians on the diagnostic performance. The results showed that if we do not consider whether the medical history recorded by the AI and doctors was consistent or not, our model performed worse compared to the physicians and had a lower average F1 score (0.825 vs. 0.912). However, when the chief complaint or the history of present illness described by the AI and doctors was consistent, our model had a higher average F1 score and was closer to the doctors. Finally, when the AI had the same diagnostic conditions with doctors, our model achieved a higher average F1 score (0.931) compared to the physicians (0.92). This study demonstrated that our model could obtain a more structured medical history and had a good diagnostic logic, which would help to improve the diagnostic accuracy of the outpatient doctors and reduce the misdiagnosis and missed diagnosis. But, our model still needs a good deal of training to obtain more accurate symptomatic information.
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Affiliation(s)
- Han Qian
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Department of Pediatric Intensive Care Unit, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bin Dong
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Pediatric AI Clinical Application and Research Center, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jia-Jun Yuan
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Pediatric AI Clinical Application and Research Center, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Fan Yin
- Department of Pediatric Intensive Care Unit, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhao Wang
- Product Department, Hangzhou YITU Healthcare Technology Company, Hangzhou, China
| | - Hai-Ning Wang
- Product Department, Hangzhou YITU Healthcare Technology Company, Hangzhou, China
| | - Han-Song Wang
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Pediatric AI Clinical Application and Research Center, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Dan Tian
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Pediatric AI Clinical Application and Research Center, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Clinic Office of Outpatient, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wei-Hua Li
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Pediatric AI Clinical Application and Research Center, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Clinic Office of Outpatient, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bin Zhang
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Pediatric AI Clinical Application and Research Center, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lie-Bin Zhao
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bo-Tao Ning
- Department of Pediatric Intensive Care Unit, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,Child Health Advocacy Institute, China Hospital Development Institute of Shanghai Jiao Tong University, Shanghai, China
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130
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Dyas AR, Colborn KL, Bronsert MR, Henderson WG, Mason NJ, Rozeboom PD, Pradhan N, Lambert-Kerzner A, Meguid RA. Comparison of Preoperative Surgical Risk Estimated by Thoracic Surgeons Versus a Standardized Surgical Risk Prediction Tool. Semin Thorac Cardiovasc Surg 2021; 34:1378-1385. [PMID: 34785355 DOI: 10.1053/j.semtcvs.2021.11.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 11/10/2021] [Indexed: 11/11/2022]
Abstract
Considerable variability exists between surgeons' assessments of a patient's individual pre-operative surgical risk. Surgical risk calculators are not routinely used despite their validation. We sought to compare thoracic surgeons' prediction of patients' risk of postoperative adverse outcomes versus a surgical risk calculator, the Surgical Risk Preoperative Assessment System (SURPAS). We developed vignettes from 30 randomly selected patients who underwent thoracic surgery in the American College of Surgeons' National Surgical Quality Improvement Program (NSQIP) database. Twelve thoracic surgeons estimated patients' preoperative risks of postoperative morbidity and mortality. These were compared to SURPAS estimates of the same vignettes. C-indices and Brier scores were calculated for the surgeons' and SURPAS estimates. Agreement between surgeon estimates was examined using intraclass correlation coefficients (ICCs). Surgeons estimated higher morbidity risk compared to SURPAS for low-risk patients (ASA classes 1-2, 11.5% vs. 5.1%, p=<0.001) and lower morbidity risk compared to SURPAS for high-risk patients (ASA class 5, 37.6% vs. 69.8%, p<0.001). This trend also occurred in high-risk patients for mortality (ASA 5, 11.1% vs. 44.3%, p<0.001). C-indices for SURPAS vs. surgeons were 0.84 vs. 0.76 (p=0.3) for morbidity and 0.98 vs. 0.85 (p=0.001) for mortality. Brier scores for SURPAS vs. surgeons were 0.1579 vs. 0.1986 for morbidity (p=0.03) and 0.0409 vs. 0.0543 for mortality (p=0.006). ICCs showed that surgeons had moderate risk agreement for morbidity (ICC=0.654) and mortality (ICC=0.507). Thoracic surgeons and patients could benefit from using a surgical risk calculator to better estimate patients' surgical risks during the informed consent process.
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Affiliation(s)
- Adam R Dyas
- Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
| | - Kathryn L Colborn
- Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
| | - Michael R Bronsert
- Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA; Adult and Child Center for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora, CO, USA
| | - William G Henderson
- Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA; Adult and Child Center for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora, CO, USA; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
| | - Nicholas J Mason
- Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
| | - Paul D Rozeboom
- Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
| | - Nisha Pradhan
- Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
| | - Anne Lambert-Kerzner
- Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA; Adult and Child Center for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora, CO, USA
| | - Robert A Meguid
- Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA; Adult and Child Center for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora, CO, USA.
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131
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Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition. J Pers Med 2021; 11:jpm11111172. [PMID: 34834524 PMCID: PMC8621146 DOI: 10.3390/jpm11111172] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 11/03/2021] [Accepted: 11/05/2021] [Indexed: 01/05/2023] Open
Abstract
Artificial intelligence (AI) technology is widely applied in different medical fields, including the diagnosis of various diseases on the basis of facial phenotypes, but there is no evaluation or quantitative synthesis regarding the performance of artificial intelligence. Here, for the first time, we summarized and quantitatively analyzed studies on the diagnosis of heterogeneous diseases on the basis on facial features. In pooled data from 20 systematically identified studies involving 7 single diseases and 12,557 subjects, quantitative random-effects models revealed a pooled sensitivity of 89% (95% CI 82% to 93%) and a pooled specificity of 92% (95% CI 87% to 95%). A new index, the facial recognition intensity (FRI), was established to describe the complexity of the association of diseases with facial phenotypes. Meta-regression revealed the important contribution of FRI to heterogeneous diagnostic accuracy (p = 0.021), and a similar result was found in subgroup analyses (p = 0.003). An appropriate increase in the training size and the use of deep learning models helped to improve the diagnostic accuracy for diseases with low FRI, although no statistically significant association was found between accuracy and photographic resolution, training size, AI architecture, and number of diseases. In addition, a novel hypothesis is proposed for universal rules in AI performance, providing a new idea that could be explored in other AI applications.
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D’Antoni F, Russo F, Ambrosio L, Vollero L, Vadalà G, Merone M, Papalia R, Denaro V. Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182010909. [PMID: 34682647 PMCID: PMC8535895 DOI: 10.3390/ijerph182010909] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/04/2021] [Accepted: 10/09/2021] [Indexed: 12/16/2022]
Abstract
Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords: "Artificial Intelligence", "Feature Extraction", "Segmentation", "Computer Vision", "Machine Learning", "Deep Learning", "Neural Network", "Low Back Pain", "Lumbar". Results: The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen-Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems' autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.
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Affiliation(s)
- Federico D’Antoni
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 21, 00128 Rome, Italy; (F.D.); (L.V.)
| | - Fabrizio Russo
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
- Correspondence: (F.R.); (M.M.)
| | - Luca Ambrosio
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Luca Vollero
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 21, 00128 Rome, Italy; (F.D.); (L.V.)
| | - Gianluca Vadalà
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Mario Merone
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 21, 00128 Rome, Italy; (F.D.); (L.V.)
- Correspondence: (F.R.); (M.M.)
| | - Rocco Papalia
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Vincenzo Denaro
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
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133
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Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27:6191-6223. [PMID: 34712027 PMCID: PMC8515803 DOI: 10.3748/wjg.v27.i37.6191] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/06/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.
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Affiliation(s)
- Chrysanthos D Christou
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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134
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Birkhoff DC, van Dalen ASH, Schijven MP. A Review on the Current Applications of Artificial Intelligence in the Operating Room. Surg Innov 2021; 28:611-619. [PMID: 33625307 PMCID: PMC8450995 DOI: 10.1177/1553350621996961] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background. Artificial intelligence (AI) is an era upcoming in medicine and, more recently, in the operating room (OR). Existing literature elaborates mainly on the future possibilities and expectations for AI in surgery. The aim of this study is to systematically provide an overview of the current actual AI applications used to support processes inside the OR. Methods. PubMed, Embase, Cochrane Library, and IEEE Xplore were searched using inclusion criteria for relevant articles up to August 25th, 2020. No study types were excluded beforehand. Articles describing current AI applications for surgical purposes inside the OR were reviewed. Results. Nine studies were included. An overview of the researched and described applications of AI in the OR is provided, including procedure duration prediction, gesture recognition, intraoperative cancer detection, intraoperative video analysis, workflow recognition, an endoscopic guidance system, knot-tying, and automatic registration and tracking of the bone in orthopedic surgery. These technologies are compared to their, often non-AI, baseline alternatives. Conclusions. Currently described applications of AI in the OR are limited to date. They may, however, have a promising future in improving surgical precision, reduce manpower, support intraoperative decision-making, and increase surgical safety. Nonetheless, the application and implementation of AI inside the OR still has several challenges to overcome. Clear regulatory, organizational, and clinical conditions are imperative for AI to redeem its promise. Future research on use of AI in the OR should therefore focus on clinical validation of AI applications, the legal and ethical considerations, and on evaluation of implementation trajectory.
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Affiliation(s)
- David C. Birkhoff
- Department of Surgery, Amsterdam UMC, University of Amsterdam, The Netherlands
| | | | - Marlies P. Schijven
- Department of Surgery, Amsterdam Gastroenterology and Metabolism, University of Amsterdam, The Netherlands
- institution-id-type="Ringgold" />Li Ka Shing Knowledge Institute, institution-id-type="Ringgold" />St Michaels Hospital, Toronto, Canada
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135
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Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department. Sci Rep 2021; 11:19472. [PMID: 34593930 PMCID: PMC8484275 DOI: 10.1038/s41598-021-98961-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 09/17/2021] [Indexed: 11/10/2022] Open
Abstract
Timely assessment to accurately prioritize patients is crucial for emergency department (ED) management. Urgent (i.e., level-3, on a 5-level emergency severity index system) patients have become a challenge since under-triage and over-triage often occur. This study was aimed to develop a computational model by artificial intelligence (AI) methodologies to accurately predict urgent patient outcomes using data that are readily available in most ED triage systems. We retrospectively collected data from the ED of a tertiary teaching hospital between January 1, 2015 and December 31, 2019. Eleven variables were used for data analysis and prediction model building, including 1 response, 2 demographic, and 8 clinical variables. A model to predict hospital admission was developed using neural networks and machine learning methodologies. A total of 282,971 samples of urgent (level-3) visits were included in the analysis. Our model achieved a validation area under the curve (AUC) of 0.8004 (95% CI 0.7963–0.8045). The optimal cutoff value identified by Youden's index for determining hospital admission was 0.5517. Using this cutoff value, the sensitivity was 0.6721 (95% CI 0.6624–0.6818), and the specificity was 0.7814 (95% CI 0.7777–0.7851), with a positive predictive value of 0.3660 (95% CI 0.3586–0.3733) and a negative predictive value of 0.9270 (95% CI 0.9244–0.9295). Subgroup analysis revealed that this model performed better in the nontraumatic adult subgroup and achieved a validation AUC of 0.8166 (95% CI 0.8199–0.8212). Our AI model accurately assessed the need for hospitalization for urgent patients, which constituted nearly 70% of ED visits. This model demonstrates the potential for streamlining ED operations using a very limited number of variables that are readily available in most ED triage systems. Subgroup analysis is an important topic for future investigation.
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136
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Litvin A, Korenev S, Rumovskaya S, Sartelli M, Baiocchi G, Biffl WL, Coccolini F, Di Saverio S, Kelly MD, Kluger Y, Leppäniemi A, Sugrue M, Catena F. WSES project on decision support systems based on artificial neural networks in emergency surgery. World J Emerg Surg 2021; 16:50. [PMID: 34565420 PMCID: PMC8474926 DOI: 10.1186/s13017-021-00394-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 09/13/2021] [Indexed: 12/11/2022] Open
Abstract
The article is a scoping review of the literature on the use of decision support systems based on artificial neural networks in emergency surgery. The authors present modern literature data on the effectiveness of artificial neural networks for predicting, diagnosing and treating abdominal emergency conditions: acute appendicitis, acute pancreatitis, acute cholecystitis, perforated gastric or duodenal ulcer, acute intestinal obstruction, and strangulated hernia. The intelligent systems developed at present allow a surgeon in an emergency setting, not only to check his own diagnostic and prognostic assumptions, but also to use artificial intelligence in complex urgent clinical cases. The authors summarize the main limitations for the implementation of artificial neural networks in surgery and medicine in general. These limitations are the lack of transparency in the decision-making process; insufficient quality educational medical data; lack of qualified personnel; high cost of projects; and the complexity of secure storage of medical information data. The development and implementation of decision support systems based on artificial neural networks is a promising direction for improving the forecasting, diagnosis and treatment of emergency surgical diseases and their complications.
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Affiliation(s)
- Andrey Litvin
- Department of Surgical Disciplines, Immanuel Kant Baltic Federal University, Kaliningrad, Russia.
| | - Sergey Korenev
- Department of Surgical Disciplines, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Sophiya Rumovskaya
- Kaliningrad Branch of Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Kaliningrad, Russia
| | | | - Gianluca Baiocchi
- Surgical Clinic, Department of Experimental and Clinical Sciences, University of Brescia, Brescia, Italy
| | - Walter L Biffl
- Division of Trauma and Acute Care Surgery, Scripps Memorial Hospital La Jolla, La Jolla, CA, USA
| | - Federico Coccolini
- General, Emergency and Trauma Surgery Department, Pisa University Hospital, Pisa, Italy
| | - Salomone Di Saverio
- Department of Surgery, Cambridge University Hospital, NHS Foundation Trust, Cambridge, UK
| | | | - Yoram Kluger
- Department of General Surgery, Rambam Healthcare Campus, Haifa, Israel
| | - Ari Leppäniemi
- Department of Gastrointestinal Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Michael Sugrue
- Donegal Clinical Research Academy, Letterkenny University Hospital, Donegal, Ireland
| | - Fausto Catena
- Department of Emergency and Trauma Surgery of the University Hospital of Parma, Parma, Italy
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137
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Sakamoto T, Goto T, Fujiogi M, Kawarai Lefor A. Machine learning in gastrointestinal surgery. Surg Today 2021; 52:995-1007. [PMID: 34559310 DOI: 10.1007/s00595-021-02380-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 07/03/2021] [Indexed: 12/11/2022]
Abstract
Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze "big data". In gastrointestinal surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surgery and predict patient prognosis. In the current "big data" era, the accelerating accumulation of a large amount of data drives studies using ML algorithms. Three subfields of ML are supervised learning, unsupervised learning, and reinforcement learning. In this review, we summarize applications of ML to surgical practice in the preoperative, intraoperative, and postoperative phases of care. Prediction and stratification using ML is promising; however, the current overarching concern is the availability of ML models. Information systems that can manage "big data" and integrate ML models into electronic health records are essential to incorporate ML into daily practice. ML is fundamental technology to meaningfully process data that exceeds the capacity of the human mind to comprehend. The accelerating accumulation of a large amount of data is changing the nature of surgical practice fundamentally. Artificial intelligence (AI), represented by ML, is being incorporated into daily surgical practice.
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Affiliation(s)
- Takashi Sakamoto
- Department of Gastroenterological Surgery, Gastroenterological Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto, Tokyo, 135-8550, Japan. .,Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Tadahiro Goto
- Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.,TXP Medical Co. Ltd, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 114-8485, Japan
| | - Michimasa Fujiogi
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.,Department of Pediatric Surgery, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Alan Kawarai Lefor
- Department of Surgery, Jichi Medical University, Shimotsuke, Tochigi, 3290498, Japan
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138
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Cobianchi L, Dal Mas F, Massaro M, Fugazzola P, Coccolini F, Kluger Y, Leppäniemi A, Moore EE, Sartelli M, Angelos P, Catena F, Ansaloni L. Team dynamics in emergency surgery teams: results from a first international survey. World J Emerg Surg 2021; 16:47. [PMID: 34530891 PMCID: PMC8443910 DOI: 10.1186/s13017-021-00389-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/20/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Emergency surgery represents a unique context. Trauma teams are often multidisciplinary and need to operate under extreme stress and time constraints, sometimes with no awareness of the trauma's causes or the patient's personal and clinical information. In this perspective, the dynamics of how trauma teams function is fundamental to ensuring the best performance and outcomes. METHODS An online survey was conducted among the World Society of Emergency Surgery members in early 2021. 402 fully filled questionnaires on the topics of knowledge translation dynamics and tools, non-technical skills, and difficulties in teamwork were collected. Data were analyzed using the software R, and reported following the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). RESULTS Findings highlight how several surgeons are still unsure about the meaning and potential of knowledge translation and its mechanisms. Tools like training, clinical guidelines, and non-technical skills are recognized and used in clinical practice. Others, like patients' and stakeholders' engagement, are hardly implemented, despite their increasing importance in the modern healthcare scenario. Several difficulties in working as a team are described, including the lack of time, communication, training, trust, and ego. DISCUSSION Scientific societies should take the lead in offering training and support about the abovementioned topics. Dedicated educational initiatives, practical cases and experiences, workshops and symposia may allow mitigating the difficulties highlighted by the survey's participants, boosting the performance of emergency teams. Additional investigation of the survey results and its characteristics may lead to more further specific suggestions and potential solutions.
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Affiliation(s)
- Lorenzo Cobianchi
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Polo Didattico "Cesare Brusotti" Viale Brambilla, 74, 27100, Pavia, Italy.
- IRCCS Policlinico San Matteo Foundation, General Surgery, Viale Camillo Golgi, 19, 27100, Pavia, Italy.
| | - Francesca Dal Mas
- Department of Management, Lincoln International Business School, University of Lincoln, Lincoln, UK
| | | | - Paola Fugazzola
- IRCCS Policlinico San Matteo Foundation, General Surgery, Viale Camillo Golgi, 19, 27100, Pavia, Italy
| | - Federico Coccolini
- Department of Surgery, University of Pisa, Pisa, Italy
- General, Emergency and Trauma Surgery, Pisa University Hospital, Pisa, Italy
| | - Yoram Kluger
- Department of General Surgery, Rambam Health Care Campus, Haifa, Israel
| | - Ari Leppäniemi
- Abdominal Center, University Hospital Meilahti, Helsinki, Finland
| | | | - Massimo Sartelli
- Department of General Surgery, Macerata's Hospital, Macerata, Italy
| | - Peter Angelos
- Department of Surgery and MacLean Center for Clinical Medical Ethics, The University of Chicago, Chicago, IL, USA
| | - Fausto Catena
- General and Emergency Surgery, Bufalini Hospital, Cesena, Italy
| | - Luca Ansaloni
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Polo Didattico "Cesare Brusotti" Viale Brambilla, 74, 27100, Pavia, Italy
- IRCCS Policlinico San Matteo Foundation, General Surgery, Viale Camillo Golgi, 19, 27100, Pavia, Italy
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139
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Abstract
PURPOSE OF REVIEW Artificial intelligence is the ability for machines to perform intelligent tasks. Artificial intelligence is already penetrating many aspects of medicine including cardiac surgery. Here, we offer a platform introduction to artificial intelligence for cardiac surgeons to understand the implementations of this transformative tool. RECENT FINDINGS Artificial intelligence has contributed greatly to the automation of cardiac imaging, including echocardiography, cardiac computed tomography, cardiac MRI and most recently, in radiomics. There are also several artificial intelligence based clinical prediction tools that predict complex outcomes after cardiac surgery. Waveform analysis, specifically, automated electrocardiogram analysis, has seen significant strides with promise in wearables and remote monitoring. Experimentally, artificial intelligence has also entered the operating room in the form of augmented reality and automated robotic surgery. SUMMARY Artificial intelligence has many potential exciting applications in cardiac surgery. It can streamline physician workload and help make medicine more human again by placing the physician back at the bedside. Here, we offer cardiac surgeons an introduction to this transformative tool so that they may actively participate in creating clinically relevant implementations to improve our practice.
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140
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Koh FH, Chua JMW, Tan JLJ, Foo FJ, Tan WJ, Sivarajah SS, Ho LML, Teh BT, Chew MH. Paradigm shift in gastrointestinal surgery − combating sarcopenia with prehabilitation: Multimodal review of clinical and scientific data. World J Gastrointest Surg 2021; 13:734-755. [PMID: 34512898 PMCID: PMC8394378 DOI: 10.4240/wjgs.v13.i8.734] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/08/2021] [Accepted: 07/12/2021] [Indexed: 02/06/2023] Open
Abstract
A growing body of evidence has demonstrated the prognostic significance of sarcopenia in surgical patients as an independent predictor of postoperative complications and outcomes. These included an increased risk of total complications, major complications, re-admissions, infections, severe infections, 30 d mortality, longer hospital stay and increased hospitalization expenditures. A program to enhance recovery after surgery was meant to address these complications; however, compliance to the program since its introduction has been less than ideal. Over the last decade, the concept of prehabilitation, or “pre-surgery rehabilitation”, has been discussed. The presurgical period represents a window of opportunity to boost and optimize the health of an individual, providing a compensatory “buffer” for the imminent reduction in physiological reserve post-surgery. Initial results have been promising. We review the literature to critically review the utility of prehabilitation, not just in the clinical realm, but also in the scientific realm, with a resource management point-of-view.
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Affiliation(s)
- Frederick H Koh
- Division of Surgery, Sengkang General Hospital, Singapore 544886, Singapore
| | - Jason MW Chua
- Institute of Molecular and Cell Biology, Agency for Science Technology and Research, Singapore 138673, Singapore
| | - Joselyn LJ Tan
- Institute of Molecular and Cell Biology, Agency for Science Technology and Research, Singapore 138673, Singapore
| | - Fung-Joon Foo
- Division of Surgery, Sengkang General Hospital, Singapore 544886, Singapore
| | - Winson J Tan
- Division of Surgery, Sengkang General Hospital, Singapore 544886, Singapore
| | | | - Leonard Ming Li Ho
- Division of Surgery, Sengkang General Hospital, Singapore 544886, Singapore
| | - Bin-Tean Teh
- Duke-NUS Graduate Medical School, National Cancer Centre Singapore, Singapore 169610, Singapore
| | - Min-Hoe Chew
- Division of Surgery, Sengkang General Hospital, Singapore 544886, Singapore
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141
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Ji GW, Fan Y, Sun DW, Wu MY, Wang K, Li XC, Wang XH. Machine Learning to Improve Prognosis Prediction of Early Hepatocellular Carcinoma After Surgical Resection. J Hepatocell Carcinoma 2021; 8:913-923. [PMID: 34414136 PMCID: PMC8370036 DOI: 10.2147/jhc.s320172] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 07/29/2021] [Indexed: 01/27/2023] Open
Abstract
Background Improved prognostic prediction is needed to stratify patients with early hepatocellular carcinoma (EHCC) to refine selection of adjuvant therapy. We aimed to develop a machine learning (ML)-based model to predict survival after liver resection for EHCC based on readily available clinical data. Methods We analyzed data of surgically resected EHCC (tumor≤5 cm without evidence of extrahepatic disease or major vascular invasion) patients from the Surveillance, Epidemiology, and End Results (SEER) Program to train and internally validate a gradient-boosting ML model to predict disease-specific survival (DSS). We externally tested the ML model using data from 2 Chinese institutions. Patients treated with resection were matched by propensity score to those treated with transplantation in the SEER-Medicare database. Results A total of 2778 EHCC patients treated with resection were enrolled, divided into 1899 for training/validation (SEER) and 879 for test (Chinese). The ML model consisted of 8 covariates (age, race, alpha-fetoprotein, tumor size, multifocality, vascular invasion, histological grade and fibrosis score) and predicted DSS with C-Statistics >0.72, better than proposed staging systems across study cohorts. The ML model could stratify 10-year DSS ranging from 70% in low-risk subset to 5% in high-risk subset. Compared with low-risk subset, no remarkable survival benefits were observed in EHCC patients receiving transplantation before and after propensity score matching. Conclusion An ML model trained on a large-scale dataset has good predictive performance at individual scale. Such a model is readily integrated into clinical practice and will be valuable in discussing treatment strategies.
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Affiliation(s)
- Gu-Wei Ji
- Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China.,Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, Nanjing, People's Republic of China.,NHC Key Laboratory of Living Donor Liver Transplantation (Nanjing Medical University), Nanjing, People's Republic of China
| | - Ye Fan
- Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China.,Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, Nanjing, People's Republic of China.,NHC Key Laboratory of Living Donor Liver Transplantation (Nanjing Medical University), Nanjing, People's Republic of China
| | - Dong-Wei Sun
- Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China.,Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, Nanjing, People's Republic of China.,NHC Key Laboratory of Living Donor Liver Transplantation (Nanjing Medical University), Nanjing, People's Republic of China
| | - Ming-Yu Wu
- Department of Hepatobiliary Surgery, Wuxi People's Hospital, Wuxi, People's Republic of China
| | - Ke Wang
- Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China.,Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, Nanjing, People's Republic of China.,NHC Key Laboratory of Living Donor Liver Transplantation (Nanjing Medical University), Nanjing, People's Republic of China
| | - Xiang-Cheng Li
- Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China.,Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, Nanjing, People's Republic of China.,NHC Key Laboratory of Living Donor Liver Transplantation (Nanjing Medical University), Nanjing, People's Republic of China
| | - Xue-Hao Wang
- Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China.,Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, Nanjing, People's Republic of China.,NHC Key Laboratory of Living Donor Liver Transplantation (Nanjing Medical University), Nanjing, People's Republic of China
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Misrai V, Pradere B, Herrmann T, Cornu JN. The Sound of Noise in Decision-making: An Illustration with Management of Male Lower Urinary Tract Symptoms. Eur Urol 2021; 80:529-530. [PMID: 34334222 DOI: 10.1016/j.eururo.2021.07.009] [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: 06/28/2021] [Accepted: 07/13/2021] [Indexed: 11/26/2022]
Abstract
High-quality patient care depends on the accuracy and efficacy of clinical decision-making, which can be affected by both cognitive bias and the risk of judgment variability, which is called noise. Deep learning algorithms, artificial intelligence, and robots could improve the reliability of decision-making, but until these become a reality, clinical practice guidelines are of great value in reducing this noise.
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Affiliation(s)
- Vincent Misrai
- Department of Urology, Clinique Pasteur, Toulouse, France.
| | - Benjamin Pradere
- Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Thomas Herrmann
- Department of Urology, Spital Thurgau AG, Frauenfeld, Switzerland
| | - Jean-Nicolas Cornu
- Department of Urology, Charles Nicolle University Hospital, Rouen, France
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143
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A Scoping Review of Artificial Intelligence and Machine Learning in Bariatric and Metabolic Surgery: Current Status and Future Perspectives. Obes Surg 2021; 31:4555-4563. [PMID: 34264433 DOI: 10.1007/s11695-021-05548-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/12/2021] [Accepted: 06/17/2021] [Indexed: 01/01/2023]
Abstract
Artificial intelligence (AI) is a revolution in data analysis with emerging roles in various specialties and with various applications. The objective of this scoping review was to retrieve current literature on the fields of AI that have been applied to metabolic bariatric surgery (MBS) and to investigate potential applications of AI as a decision-making tool of the bariatric surgeon. Initial search yielded 3260 studies published from January 2000 until March 2021. After screening, 49 unique articles were included in the final analysis. Studies were grouped into categories, and the frequency of appearing algorithms, dataset types, and metrics were documented. The heterogeneity of current studies showed that meticulous validation, strict reporting systems, and reliable benchmarking are mandatory for ensuring the clinical validity of future research.
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Elhage SA, Deerenberg EB, Ayuso SA, Murphy KJ, Shao JM, Kercher KW, Smart NJ, Fischer JP, Augenstein VA, Colavita PD, Heniford BT. Development and Validation of Image-Based Deep Learning Models to Predict Surgical Complexity and Complications in Abdominal Wall Reconstruction. JAMA Surg 2021; 156:933-940. [PMID: 34232255 DOI: 10.1001/jamasurg.2021.3012] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Importance Image-based deep learning models (DLMs) have been used in other disciplines, but this method has yet to be used to predict surgical outcomes. Objective To apply image-based deep learning to predict complexity, defined as need for component separation, and pulmonary and wound complications after abdominal wall reconstruction (AWR). Design, Setting, and Participants This quality improvement study was performed at an 874-bed hospital and tertiary hernia referral center from September 2019 to January 2020. A prospective database was queried for patients with ventral hernias who underwent open AWR by experienced surgeons and had preoperative computed tomography images containing the entire hernia defect. An 8-layer convolutional neural network was generated to analyze image characteristics. Images were batched into training (approximately 80%) or test sets (approximately 20%) to analyze model output. Test sets were blinded from the convolutional neural network until training was completed. For the surgical complexity model, a separate validation set of computed tomography images was evaluated by a blinded panel of 6 expert AWR surgeons and the surgical complexity DLM. Analysis started February 2020. Exposures Image-based DLM. Main Outcomes and Measures The primary outcome was model performance as measured by area under the curve in the receiver operating curve (ROC) calculated for each model; accuracy with accompanying sensitivity and specificity were also calculated. Measures were DLM prediction of surgical complexity using need for component separation techniques as a surrogate and prediction of postoperative surgical site infection and pulmonary failure. The DLM for predicting surgical complexity was compared against the prediction of 6 expert AWR surgeons. Results A total of 369 patients and 9303 computed tomography images were used. The mean (SD) age of patients was 57.9 (12.6) years, 232 (62.9%) were female, and 323 (87.5%) were White. The surgical complexity DLM performed well (ROC = 0.744; P < .001) and, when compared with surgeon prediction on the validation set, performed better with an accuracy of 81.3% compared with 65.0% (P < .001). Surgical site infection was predicted successfully with an ROC of 0.898 (P < .001). However, the DLM for predicting pulmonary failure was less effective with an ROC of 0.545 (P = .03). Conclusions and Relevance Image-based DLM using routine, preoperative computed tomography images was successful in predicting surgical complexity and more accurate than expert surgeon judgment. An additional DLM accurately predicted the development of surgical site infection.
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Affiliation(s)
- Sharbel Adib Elhage
- Department of Surgery, Franciscus Gasthuis en Vlietland, Rotterdam, the Netherlands
| | | | - Sullivan Armando Ayuso
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina
| | | | - Jenny Meng Shao
- Department of Surgery, University of Pennsylvania, Philadelphia
| | - Kent Williams Kercher
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina
| | - Neil James Smart
- Department of Colorectal Surgery, Royal Devon and Exeter NHS Foundation Trust, Royal Devon and Exeter Hospital, Exeter, United Kingdom
| | - John Patrick Fischer
- Division of Plastic Surgery, Department of Surgery, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Vedra Abdomerovic Augenstein
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina
| | - Paul Dominick Colavita
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina
| | - B Todd Heniford
- Division of Gastrointestinal and Minimally Invasive Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, North Carolina
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145
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Kazzazi F. The automation of doctors and machines: A classification for AI in medicine (ADAM framework). Future Healthc J 2021; 8:e257-e262. [PMID: 34286194 PMCID: PMC8285145 DOI: 10.7861/fhj.2020-0189] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
The advances in artificial intelligence (AI) provide an opportunity to expand the frontier of medicine to improve diagnosis, efficiency and management. By extension of being able to perform any task that a human could, a machine that meets the requirements of artificial general intelligence ('strong' AI; AGI) possesses the basic necessities to perform as, or at least qualify to become, a doctor. In this emerging field, this article explores the distinctions between doctors and AGI, and the prerequisites for AGI performing as clinicians. In doing so, it necessitates the requirement for a classification of medical AI and prepares for the development of AGI. With its imminent arrival, it is beneficial to create a framework from which leading institutions can define specific criteria for AGI.
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Affiliation(s)
- Fawz Kazzazi
- Mason Institute for Medicine, Life Sciences and Law, Edinburgh, UK
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146
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Parums DV. Editorial: Artificial Intelligence (AI) in Clinical Medicine and the 2020 CONSORT-AI Study Guidelines. Med Sci Monit 2021; 27:e933675. [PMID: 34176921 PMCID: PMC8252890 DOI: 10.12659/msm.933675] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 06/21/2021] [Indexed: 12/21/2022] Open
Abstract
Artificial intelligence (AI) in clinical medicine includes physical robotics and devices and virtual AI and machine learning. Concerns have been raised regarding ethical issues for the use of AI in surgery, including guidance for surgical decisions, patient confidentiality, and the need for support from controlled clinical trials to use these methods so that clinical guidelines can be developed. The most common applications for virtual AI include disease diagnosis, health monitoring and digital patient consultations, clinical training, patient data management, drug development, and personalized medicine. In September 2020, the CONSORT-A1 extension was developed with 14 additional items that should be reported for AI studies that include clear descriptions of the AI intervention, skills required, study setting, inputs and outputs of the AI intervention, analysis of errors, and the human and AI interactions. This Editorial aims to present current applications and challenges of AI in clinical medicine and the importance of the new 2020 CONSORT-AI study guidelines.
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Affiliation(s)
- Dinah V Parums
- Science Editor, Medical Science Monitor, International Scientific Information, Inc., Mellville, NY, USA
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147
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Binkley CE, Green BP. Does Intraoperative Artificial Intelligence Decision Support Pose Ethical Issues? JAMA Surg 2021; 156:2781032. [PMID: 34132749 DOI: 10.1001/jamasurg.2021.2055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Charles E Binkley
- Markkula Center for Applied Ethics at Santa Clara University, Santa Clara, California
| | - Brian P Green
- Markkula Center for Applied Ethics at Santa Clara University, Santa Clara, California
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148
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Solanki SL, Pandrowala S, Nayak A, Bhandare M, Ambulkar RP, Shrikhande SV. Artificial intelligence in perioperative management of major gastrointestinal surgeries. World J Gastroenterol 2021; 27:2758-2770. [PMID: 34135552 PMCID: PMC8173379 DOI: 10.3748/wjg.v27.i21.2758] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 04/06/2021] [Accepted: 04/28/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists, surgeons, and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management. AI uses available databases called “big data” to formulate an algorithm. Analysis of other data based on these algorithms can help in early diagnosis, accurate risk assessment, intraoperative management, automated drug delivery, predicting anesthesia and surgical complications and postoperative outcomes and can thus lead to effective perioperative management as well as to reduce the cost of treatment. Perioperative physicians, anesthesiologists, and surgeons are well-positioned to help integrate AI into modern surgical practice. We all need to partner and collaborate with data scientists to collect and analyze data across all phases of perioperative care to provide clinical scenarios and context. Careful implementation and use of AI along with real-time human interpretation will revolutionize perioperative care, and is the way forward in future perioperative management of major surgery.
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Affiliation(s)
- Sohan Lal Solanki
- Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Saneya Pandrowala
- Gastro-Intestinal Services, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Abhirup Nayak
- Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Manish Bhandare
- Gastro-Intestinal Services, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Reshma P Ambulkar
- Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
| | - Shailesh V Shrikhande
- Gastro-Intestinal Services, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai 400012, Maharashtra, India
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149
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Dagi TF, Barker FG, Glass J. Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges. Neurosurgery 2021; 89:133-142. [PMID: 34015816 DOI: 10.1093/neuros/nyab170] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- T Forcht Dagi
- Queen's University Belfast and The William J. Clinton Leadership Institute, Belfast, UK
- Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Fred G Barker
- Department of Neurosurgery, Harvard Medical School, Boston, Massachusetts, USA
- The Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jacob Glass
- Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Abdullah YI, Schuman JS, Shabsigh R, Caplan A, Al-Aswad LA. Ethics of Artificial Intelligence in Medicine and Ophthalmology. Asia Pac J Ophthalmol (Phila) 2021; 10:289-298. [PMID: 34383720 PMCID: PMC9167644 DOI: 10.1097/apo.0000000000000397] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND This review explores the bioethical implementation of artificial intelligence (AI) in medicine and in ophthalmology. AI, which was first introduced in the 1950s, is defined as "the machine simulation of human mental reasoning, decision making, and behavior". The increased power of computing, expansion of storage capacity, and compilation of medical big data helped the AI implementation surge in medical practice and research. Ophthalmology is a leading medical specialty in applying AI in screening, diagnosis, and treatment. The first Food and Drug Administration approved autonomous diagnostic system served to diagnose and classify diabetic retinopathy. Other ophthalmic conditions such as age-related macular degeneration, glaucoma, retinopathy of prematurity, and congenital cataract, among others, implemented AI too. PURPOSE To review the contemporary literature of the bioethical issues of AI in medicine and ophthalmology, classify ethical issues in medical AI, and suggest possible standardizations of ethical frameworks for AI implementation. METHODS Keywords were searched on Google Scholar and PubMed between October 2019 and April 2020. The results were reviewed, cross-referenced, and summarized. A total of 284 references including articles, books, book chapters, and regulatory reports and statements were reviewed, and those that were relevant were cited in the paper. RESULTS Most sources that studied the use of AI in medicine explored the ethical aspects. Bioethical challenges of AI implementation in medicine were categorized into 6 main categories. These include machine training ethics, machine accuracy ethics, patient-related ethics, physician-related ethics, shared ethics, and roles of regulators. CONCLUSIONS There are multiple stakeholders in the ethical issues surrounding AI in medicine and ophthalmology. Attention to the various aspects of ethics related to AI is important especially with the expanding use of AI. Solutions of ethical problems are envisioned to be multifactorial.
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Affiliation(s)
| | - Joel S Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY
- Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY
- Department of Physiology and Neuroscience, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
- Center for Neural Science, NYU College of Arts and Science, New York, NY
| | - Ridwan Shabsigh
- SBH Health System and Weill Cornell Medical College, New York, NY
| | - Arthur Caplan
- Department of Population Health, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
| | - Lama A Al-Aswad
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
- Department of Population Health, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
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