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Benhenneda R, Brouard T, Dordain F, Gadéa F, Charousset C, Berhouet J. Can artificial intelligence help decision-making in arthroscopy? Part 1: Use of a standardized analysis protocol improves inter-observer agreement of arthroscopic diagnostic assessments of the long head of biceps tendon in small rotator cuff tears. Orthop Traumatol Surg Res 2023; 109:103648. [PMID: 37356800 DOI: 10.1016/j.otsr.2023.103648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/09/2023] [Accepted: 05/17/2023] [Indexed: 06/27/2023]
Abstract
INTRODUCTION Injuries of the long head of biceps (LHB) tendon are common but difficult to diagnose clinically or using imaging. Arthroscopy is the preferred means of diagnostic assessment of the LHB, but it often proves challenging. Its reliability and reproducibility have not yet been assessed. Artificial intelligence (AI) could assist in the arthroscopic analysis of the LHB. The main objective of this study was to evaluate the inter-observer agreement for the specific LHB assessment, according to an analysis protocol based on images of interest. The secondary objective was to define a video database, called "ground truth", intended to create and train AI for the LHB assessment. HYPOTHESIS The hypothesis was that the inter-observer agreement analysis, on standardized images, was strong enough to allow the "ground truth" videos to be used as an input database for an AI solution to be used in making arthroscopic LHB diagnoses. MATERIALS AND METHOD One hundred and ninety-nine sets of standardized arthroscopic images of LHB exploration were evaluated by 3 independent observers. Each had to characterize the healthy or pathological state of the tendon, specifying the type of lesion: partial tear, hourglass hypertrophy, instability, fissure, superior labral anterior posterior lesion (SLAP 2), chondral print and pathological pulley without instability. Inter-observer agreement levels were measured using Cohen's Kappa (K) coefficient and Kappa Accuracy. RESULTS The strength of agreement was moderate to strong according to the observers (Kappa 0.54 to 0.7 and KappaAcc from 86 to 92%), when determining the healthy or pathological state of the LHB. When the tendon was pathological, the strength of agreement was moderate to strong when it came to a partial tear (Kappa 0.49 to 0.71 and KappaAcc from 85 to 92%), fissure (Kappa -0.5 to 0.7 and KappaAcc from 36 to 93%) or a SLAP tear (0.54 to 0.88 and KappaAcc from 90 to 97%). It was low for unstable lesion (Kappa 0.04 to 0.25 and KappaAcc from 36 to 88%). CONCLUSION The analysis of the LHB, from arthroscopic images, had a high level of agreement for the diagnosis of its healthy or pathological nature. However, the agreement rate decreased for the diagnosis of rare or dynamic tendon lesions. Thus, AI engineered from human analysis would have the same difficulties if it was limited only to an arthroscopic analysis. The integration of clinical and paraclinical data is necessary to improve the arthroscopic diagnosis of LHB injuries. It also seems to be an essential prerequisite for making a so-called "ground truth" database for building a high-performance AI solution. LEVEL OF EVIDENCE III; inter-observer prospective series.
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Affiliation(s)
- Rayane Benhenneda
- Service de Chirurgie Orthopédique, Hôpital Trousseau, Faculté de Médecine, Université de Tours Centre-Val de Loire, CHRU de Tours, Tours, France.
| | - Thierry Brouard
- LIFAT (EA6300), École Polytechnique Universitaire de Tours, 64, avenue Jean-Portalis, 37200 Tours, France
| | - Franck Dordain
- Hôpital Privé Saint-Martin, 18, rue des Roquemonts, 14000 Caen, France
| | - François Gadéa
- Centre Ortho-Globe, place du Globe, 83000 Toulon, France
| | | | - Julien Berhouet
- Service de Chirurgie Orthopédique, Hôpital Trousseau, Faculté de Médecine, Université de Tours Centre-Val de Loire, CHRU de Tours, Tours, France; LIFAT (EA6300), École Polytechnique Universitaire de Tours, 64, avenue Jean-Portalis, 37200 Tours, France
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Rogers MP, Janjua H, Read M, Pietrobon R, Kuo PC. Interpretable machine learning accurately reclassifies lobectomy surgical approaches by cost. Surgery 2023; 174:1422-1427. [PMID: 37833152 DOI: 10.1016/j.surg.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 06/27/2023] [Accepted: 09/05/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND The volume of robotic lung resection continues to increase despite its higher costs and unproven superiority to video-assisted thoracoscopic surgery. We evaluated whether machine learning can accurately identify factors influencing cost and reclassify high-cost operative approaches into lower-cost alternatives. METHODS The Florida Agency for Healthcare Administration and Centers for Medicare and Medicaid Services Hospital and Physician Compare datasets were queried for patients undergoing open, video-assisted thoracoscopic surgery and robotic lobectomy. K-means cluster analysis was used to identify robotic clusters based on total cost. Predictive models were built using artificial neural networks, Support Vector Machines, Classification and Regression Trees, and Gradient Boosted Machines algorithms. Models were applied to the high-volume robotic group to determine patients whose cost cluster changed if undergoing a video-assisted thoracoscopic surgery approach. A local interpretable model-agnostic explanation approach personalized cost per patient. RESULTS Of the 6,618 cases included in the analysis, we identified 4 cost clusters. Application of artificial neural networks to the robotic subgroup identified 1,642 (65%) cases with no re-assignment of cost cluster, 583 (23%) with reduced costs, and 300 (12%) with increased costs if they had undergone video-assisted thoracoscopic surgery approach. The 5 overall highest cost predictors were patient admission from the clinic, diagnosis of metastatic cancer, presence of cancer, urgent hospital admission, and dementia. CONCLUSION K-means cluster analysis and machine learning identify a patient population that may undergo video-assisted thoracoscopic surgery or robotic lobectomy without a significant difference in total cost. Local interpretable model-agnostic explanation identifies individual patient factors contributing to cost. Application of this modeling may reliably stratify high-cost patients into lower-cost approaches and provide a rationale for reducing expenditure.
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Affiliation(s)
- Michael P Rogers
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL
| | - Haroon Janjua
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL
| | - Meagan Read
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL
| | | | - Paul C Kuo
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL.
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Chen Z, Liang N, Zhang H, Li H, Yang Y, Zong X, Chen Y, Wang Y, Shi N. Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart 2023; 10:e002432. [PMID: 38016787 PMCID: PMC10685930 DOI: 10.1136/openhrt-2023-002432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/31/2023] [Indexed: 11/30/2023] Open
Abstract
Clinical decision support systems (CDSSs) are increasingly integrated into healthcare settings to improve patient outcomes, reduce medical errors and enhance clinical efficiency by providing clinicians with evidence-based recommendations at the point of care. However, the adoption and optimisation of these systems remain a challenge. This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions. We also explore the potential for enhancing their effectiveness and provide an outlook for future developments in this field. There are several challenges in CDSS implementation, including data privacy concerns, system integration and clinician acceptance. While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.
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Affiliation(s)
- Zhao Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ning Liang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haili Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Huizhen Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yijiu Yang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xingyu Zong
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yaxin Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanping Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Nannan Shi
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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Schulz PJ, Lwin MO, Kee KM, Goh WWB, Lam TYT, Sung JJY. Modeling the influence of attitudes, trust, and beliefs on endoscopists' acceptance of artificial intelligence applications in medical practice. Front Public Health 2023; 11:1301563. [PMID: 38089040 PMCID: PMC10715310 DOI: 10.3389/fpubh.2023.1301563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 11/03/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction The potential for deployment of Artificial Intelligence (AI) technologies in various fields of medicine is vast, yet acceptance of AI amongst clinicians has been patchy. This research therefore examines the role of antecedents, namely trust, attitude, and beliefs in driving AI acceptance in clinical practice. Methods We utilized online surveys to gather data from clinicians in the field of gastroenterology. Results A total of 164 participants responded to the survey. Participants had a mean age of 44.49 (SD = 9.65). Most participants were male (n = 116, 70.30%) and specialized in gastroenterology (n = 153, 92.73%). Based on the results collected, we proposed and tested a model of AI acceptance in medical practice. Our findings showed that while the proposed drivers had a positive impact on AI tools' acceptance, not all effects were direct. Trust and belief were found to fully mediate the effects of attitude on AI acceptance by clinicians. Discussion The role of trust and beliefs as primary mediators of the acceptance of AI in medical practice suggest that these should be areas of focus in AI education, engagement and training. This has implications for how AI systems can gain greater clinician acceptance to engender greater trust and adoption amongst public health systems and professional networks which in turn would impact how populations interface with AI. Implications for policy and practice, as well as future research in this nascent field, are discussed.
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Affiliation(s)
- Peter J. Schulz
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - May O. Lwin
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Kalya M. Kee
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Wilson W. B. Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
| | - Thomas Y. T Lam
- Faculty of Medicine, Institute of Digestive Diseases, The Chinese University of Hong Kong, Hong Kong, China
| | - Joseph J. Y. Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
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Qi C, Hu L, Zhang C, Wang K, Qiu B, Yi J, Shen Y. Role of surgery in T4N0-3M0 esophageal cancer. World J Surg Oncol 2023; 21:369. [PMID: 38008742 PMCID: PMC10680323 DOI: 10.1186/s12957-023-03239-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 11/13/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND This study aimed to investigate an unsettled issue that whether T4 esophageal cancer could benefit from surgery. METHODS Patients with T4N0-3M0 esophageal cancer from 2004 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. Kaplan-Meier method, Cox proportional hazard regression, and propensity score matching (PSM) were used to compare overall survival (OS) between the surgery and no-surgery group. RESULTS A total of 1822 patients were analyzed. The multivariable Cox regression showed the HR (95% CI) for surgery vs. no surgery was 0.492 (0.427-0.567) (P < 0.001) in T4N0-3M0 cohort, 0.471 (0.354-0.627) (P < 0.001) in T4aN0-3M0 cohort, and 0.480 (0.335-0.689) (P < 0.001) in T4bN0-3M0 cohort. The HR (95% CI) for neoadjuvant therapy plus surgery vs. no surgery and surgery without neoadjuvant therapy vs. no surgery were 0.548 (0.461-0.650) (P < 0.001) and 0.464 (0.375-0.574) (P < 0.001), respectively. No significant OS difference was observed between neoadjuvant therapy plus surgery and surgery without neoadjuvant therapy: 0.966 (0.686-1.360) (P = 0.843). Subgroup analyses and PSM-adjusted analyses showed consistent results. CONCLUSION Surgery might bring OS improvement for T4N0-3M0 esophageal cancer patients, no matter in T4a disease or in T4b disease. Surgery with and without neoadjuvant therapy might both achieve better OS than no surgery.
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Affiliation(s)
- Chen Qi
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Liwen Hu
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling Clinical Medical School, Nanjing Medical University, Nanjing, 210002, China
| | - Chi Zhang
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Kang Wang
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling Clinical Medical School, Nanjing Medical University, Nanjing, 210002, China
| | - Bingmei Qiu
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
- Department of Anesthesiology, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, 210004, China
| | - Jun Yi
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China.
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling Clinical Medical School, Nanjing Medical University, Nanjing, 210002, China.
| | - Yi Shen
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China.
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling Clinical Medical School, Nanjing Medical University, Nanjing, 210002, China.
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Giorgino R, Alessandri-Bonetti M, Luca A, Migliorini F, Rossi N, Peretti GM, Mangiavini L. ChatGPT in orthopedics: a narrative review exploring the potential of artificial intelligence in orthopedic practice. Front Surg 2023; 10:1284015. [PMID: 38026475 PMCID: PMC10654618 DOI: 10.3389/fsurg.2023.1284015] [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: 08/27/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
The field of orthopedics faces complex challenges requiring quick and intricate decisions, with patient education and compliance playing crucial roles in treatment outcomes. Technological advancements in artificial intelligence (AI) can potentially enhance orthopedic care. ChatGPT, a natural language processing technology developed by OpenAI, has shown promise in various sectors, including healthcare. ChatGPT can facilitate patient information exchange in orthopedics, provide clinical decision support, and improve patient communication and education. It can assist in differential diagnosis, suggest appropriate imaging modalities, and optimize treatment plans based on evidence-based guidelines. However, ChatGPT has limitations, such as insufficient expertise in specialized domains and a lack of contextual understanding. The application of ChatGPT in orthopedics is still evolving, with studies exploring its potential in clinical decision-making, patient education, workflow optimization, and scientific literature. The results indicate both the benefits and limitations of ChatGPT, emphasizing the need for caution, ethical considerations, and human oversight. Addressing training data quality, biases, data privacy, and accountability challenges is crucial for responsible implementation. While ChatGPT has the potential to transform orthopedic healthcare, further research and development are necessary to ensure its reliability, accuracy, and ethical use in patient care.
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Affiliation(s)
- Riccardo Giorgino
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Residency Program in Orthopedics and Traumatology, University of Milan, Milan, Italy
| | - Mario Alessandri-Bonetti
- Department of Plastic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Andrea Luca
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Filippo Migliorini
- Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Medical Centre, Aachen, Germany
- Department of Orthopedics and Trauma Surgery, Academic Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of the Paracelsus Medical University, Bolzano, Italy
| | - Nicolò Rossi
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Residency Program in Orthopedics and Traumatology, University of Milan, Milan, Italy
| | - Giuseppe M. Peretti
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
| | - Laura Mangiavini
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
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Vicente L, Matute H. Humans inherit artificial intelligence biases. Sci Rep 2023; 13:15737. [PMID: 37789032 PMCID: PMC10547752 DOI: 10.1038/s41598-023-42384-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/09/2023] [Indexed: 10/05/2023] Open
Abstract
Artificial intelligence recommendations are sometimes erroneous and biased. In our research, we hypothesized that people who perform a (simulated) medical diagnostic task assisted by a biased AI system will reproduce the model's bias in their own decisions, even when they move to a context without AI support. In three experiments, participants completed a medical-themed classification task with or without the help of a biased AI system. The biased recommendations by the AI influenced participants' decisions. Moreover, when those participants, assisted by the AI, moved on to perform the task without assistance, they made the same errors as the AI had made during the previous phase. Thus, participants' responses mimicked AI bias even when the AI was no longer making suggestions. These results provide evidence of human inheritance of AI bias.
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Affiliation(s)
- Lucía Vicente
- Department of Psychology, Deusto University, Avenida Universidades 24, 48007, Bilbao, Spain
| | - Helena Matute
- Department of Psychology, Deusto University, Avenida Universidades 24, 48007, Bilbao, Spain.
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58
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Lin KB, Wei Y, Liu Y, Hong FP, Yang YM, Lu P. An opponent model for agent-based shared decision-making via a genetic algorithm. Front Psychol 2023; 14:1124734. [PMID: 37854140 PMCID: PMC10580805 DOI: 10.3389/fpsyg.2023.1124734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 08/30/2023] [Indexed: 10/20/2023] Open
Abstract
Introduction Shared decision-making (SDM) has received a great deal of attention as an effective way to achieve patient-centered medical care. SDM aims to bring doctors and patients together to develop treatment plans through negotiation. However, time pressure and subjective factors such as medical illiteracy and inadequate communication skills prevent doctors and patients from accurately expressing and obtaining their opponent's preferences. This problem leads to SDM being in an incomplete information environment, which significantly reduces the efficiency of the negotiation and even leads to failure. Methods In this study, we integrated a negotiation strategy that predicts opponent preference using a genetic algorithm with an SDM auto-negotiation model constructed based on fuzzy constraints, thereby enhancing the effectiveness of SDM by addressing the problems posed by incomplete information environments and rapidly generating treatment plans with high mutual satisfaction. Results A variety of negotiation scenarios are simulated in experiments and the proposed model is compared with other excellent negotiation models. The results indicated that the proposed model better adapts to multivariate scenarios and maintains higher mutual satisfaction. Discussion The agent negotiation framework supports SDM participants in accessing treatment plans that fit individual preferences, thereby increasing treatment satisfaction. Adding GA opponent preference prediction to the SDM negotiation framework can effectively improve negotiation performance in incomplete information environments.
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Affiliation(s)
- Kai-Biao Lin
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Ying Wei
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Yong Liu
- School of Data Science and Intelligent Engineering, Xiamen Institute of Technology, Xiamen, China
| | - Fei-Ping Hong
- Department of Neonates, Xiamen Humanity Hospital, Xiamen, China
| | - Yi-Min Yang
- Department of Pediatrics, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, China
| | - Ping Lu
- School of Economics and Management, Xiamen University of Technology, Xiamen, China
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Wohlgemut JM, Pisirir E, Kyrimi E, Stoner RS, Marsh W, Perkins ZB, Tai NRM. Methods used to evaluate usability of mobile clinical decision support systems for healthcare emergencies: a systematic review and qualitative synthesis. JAMIA Open 2023; 6:ooad051. [PMID: 37449057 PMCID: PMC10336299 DOI: 10.1093/jamiaopen/ooad051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 06/15/2023] [Accepted: 06/30/2023] [Indexed: 07/18/2023] Open
Abstract
Objective The aim of this study was to determine the methods and metrics used to evaluate the usability of mobile application Clinical Decision Support Systems (CDSSs) used in healthcare emergencies. Secondary aims were to describe the characteristics and usability of evaluated CDSSs. Materials and Methods A systematic literature review was conducted using Pubmed/Medline, Embase, Scopus, and IEEE Xplore databases. Quantitative data were descriptively analyzed, and qualitative data were described and synthesized using inductive thematic analysis. Results Twenty-three studies were included in the analysis. The usability metrics most frequently evaluated were efficiency and usefulness, followed by user errors, satisfaction, learnability, effectiveness, and memorability. Methods used to assess usability included questionnaires in 20 (87%) studies, user trials in 17 (74%), interviews in 6 (26%), and heuristic evaluations in 3 (13%). Most CDSS inputs consisted of manual input (18, 78%) rather than automatic input (2, 9%). Most CDSS outputs comprised a recommendation (18, 78%), with a minority advising a specific treatment (6, 26%), or a score, risk level or likelihood of diagnosis (6, 26%). Interviews and heuristic evaluations identified more usability-related barriers and facilitators to adoption than did questionnaires and user testing studies. Discussion A wide range of metrics and methods are used to evaluate the usability of mobile CDSS in medical emergencies. Input of information into CDSS was predominantly manual, impeding usability. Studies employing both qualitative and quantitative methods to evaluate usability yielded more thorough results. Conclusion When planning CDSS projects, developers should consider multiple methods to comprehensively evaluate usability.
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Affiliation(s)
- Jared M Wohlgemut
- Corresponding Author: Jared M. Wohlgemut, MSc, Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, 4 Newark St, London E1 2AT, UK;
| | - Erhan Pisirir
- Department of Electrical Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Evangelia Kyrimi
- Department of Electrical Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Rebecca S Stoner
- Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK
- Trauma Service, Royal London Hospital, Barts NHS Health Trust, London, UK
| | - William Marsh
- Department of Electrical Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Zane B Perkins
- Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK
- Trauma Service, Royal London Hospital, Barts NHS Health Trust, London, UK
| | - Nigel R M Tai
- Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK
- Trauma Service, Royal London Hospital, Barts NHS Health Trust, London, UK
- Academic Department of Military Surgery and Trauma, Royal Centre of Defence Medicine, Birmingham, UK
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Cresswell K, Rigby M, Magrabi F, Scott P, Brender J, Craven CK, Wong ZSY, Kukhareva P, Ammenwerth E, Georgiou A, Medlock S, De Keizer NF, Nykänen P, Prgomet M, Williams R. The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision. Health Policy 2023; 136:104889. [PMID: 37579545 DOI: 10.1016/j.healthpol.2023.104889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 08/04/2023] [Indexed: 08/16/2023]
Abstract
Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology. We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes. Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.
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Affiliation(s)
- Kathrin Cresswell
- The University of Edinburgh, Usher Institute, Edinburgh, United Kingdom.
| | - Michael Rigby
- Keele University, School of Social, Political and Global Studies and School of Primary, Community and Social Care, Keele, United Kingdom
| | - Farah Magrabi
- Macquarie University, Australian Institute of Health Innovation, Sydney, Australia
| | - Philip Scott
- University of Wales Trinity Saint David, Swansea, United Kingdom
| | - Jytte Brender
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Catherine K Craven
- University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Zoie Shui-Yee Wong
- St. Luke's International University, Graduate School of Public Health, Tokyo, Japan
| | - Polina Kukhareva
- Department of Biomedical Informatics, University of Utah, United States of America
| | - Elske Ammenwerth
- UMIT TIROL, Private University for Health Sciences and Health Informatics, Institute of Medical Informatics, Hall in Tirol, Austria
| | - Andrew Georgiou
- Macquarie University, Australian Institute of Health Innovation, Sydney, Australia
| | - Stephanie Medlock
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health research institute, Digital Health and Quality of Care Amsterdam, the Netherlands
| | - Nicolette F De Keizer
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health research institute, Digital Health and Quality of Care Amsterdam, the Netherlands
| | - Pirkko Nykänen
- Tampere University, Faculty for Information Technology and Communication Sciences, Finland
| | - Mirela Prgomet
- Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Robin Williams
- The University of Edinburgh, Institute for the Study of Science, Technology and Innovation, Edinburgh, United Kingdom
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Stam WT, Ingwersen EW, Ali M, Spijkerman JT, Kazemier G, Bruns ERJ, Daams F. Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery. Surg Today 2023; 53:1209-1215. [PMID: 36840764 PMCID: PMC10520164 DOI: 10.1007/s00595-023-02662-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/07/2023] [Indexed: 02/26/2023]
Abstract
Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery.
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Affiliation(s)
- Wessel T Stam
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
- AGEM Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
| | - Erik W Ingwersen
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
- AGEM Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
| | - Mahsoem Ali
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Jorik T Spijkerman
- Independent Consultant in Computational Intelligence, Amsterdam, The Netherlands
| | - Geert Kazemier
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Emma R J Bruns
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Freek Daams
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands.
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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63
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Kovoor JG, Nann SD, Barot DD, Garg D, Hains L, Stretton B, Ovenden CD, Bacchi S, Chan E, Gupta AK, Hugh TJ. Prehabilitation for general surgery: a systematic review of randomized controlled trials. ANZ J Surg 2023; 93:2411-2425. [PMID: 37675939 DOI: 10.1111/ans.18684] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/23/2023] [Accepted: 08/27/2023] [Indexed: 09/08/2023]
Abstract
BACKGROUND Prehabilitation seeks to optimize patient health before surgery to improve outcomes. Randomized controlled trials (RCTs) have been conducted on prehabilitation, however an updated synthesis of this evidence is required across General Surgery to inform potential Supplementary discipline-level protocols. Accordingly, this systematic review of RCTs aimed to evaluate the use of prehabilitation interventions across the discipline of General Surgery. METHODS This study was registered with PROSPERO (CRD42023403289), and adhered to PRISMA 2020 and SWiM guidelines. PubMed/MEDLINE and Ovid Embase were searched to 4 March 2023 for RCTs evaluating prehabilitation interventions within the discipline of General Surgery. After data extraction, risk of bias was assessed using the Cochrane RoB 2 tool. Quantitative and qualitative data were synthesized and analysed. However, meta-analysis was precluded due to heterogeneity across included studies. RESULTS From 929 records, 36 RCTs of mostly low risk of bias were included. 17 (47.2%) were from Europe, and 14 (38.9%) North America. 30 (83.3%) investigated cancer populations. 31 (86.1%) investigated physical interventions, finding no significant difference in 16 (51.6%) and significant improvement in 14 (45.2%). Nine (25%) investigated psychological interventions: six (66.7%) found significant improvement, three (33.3%) found no significant difference. Five (13.9%) investigated nutritional interventions, finding no significant difference in three (60%), and significant improvement in two (40%). CONCLUSIONS Prehabilitation interventions showed mixed levels of effectiveness, and there is insufficient RCT evidence to suggest system-level delivery across General Surgery within standardized protocols. However, given potential benefits and non-inferiority to standard care, they should be considered on a case-by-case basis.
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Affiliation(s)
- Joshua G Kovoor
- University of Sydney, Sydney, New South Wales, Australia
- Queen Elizabeth Hospital, Adelaide, South Australia, Australia
- Health and Information, Adelaide, South Australia, Australia
| | - Silas D Nann
- Health and Information, Adelaide, South Australia, Australia
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Dwarkesh D Barot
- Health and Information, Adelaide, South Australia, Australia
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Devanshu Garg
- Health and Information, Adelaide, South Australia, Australia
- University of Adelaide, Adelaide, South Australia, Australia
| | - Lewis Hains
- Health and Information, Adelaide, South Australia, Australia
- University of Adelaide, Adelaide, South Australia, Australia
| | - Brandon Stretton
- Queen Elizabeth Hospital, Adelaide, South Australia, Australia
- Health and Information, Adelaide, South Australia, Australia
- University of Adelaide, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Christopher D Ovenden
- Health and Information, Adelaide, South Australia, Australia
- University of Adelaide, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Stephen Bacchi
- Queen Elizabeth Hospital, Adelaide, South Australia, Australia
- Health and Information, Adelaide, South Australia, Australia
- University of Adelaide, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Erick Chan
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Aashray K Gupta
- University of Sydney, Sydney, New South Wales, Australia
- Health and Information, Adelaide, South Australia, Australia
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
- University of Adelaide, Adelaide, South Australia, Australia
| | - Thomas J Hugh
- University of Sydney, Sydney, New South Wales, Australia
- Royal North Shore Hospital, Sydney, New South Wales, Australia
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Choi E, Leonard KW, Jassal JS, Levin AM, Ramachandra V, Jones LR. Artificial Intelligence in Facial Plastic Surgery: A Review of Current Applications, Future Applications, and Ethical Considerations. Facial Plast Surg 2023; 39:454-459. [PMID: 37353051 DOI: 10.1055/s-0043-1770160] [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: 06/25/2023] Open
Abstract
From virtual chat assistants to self-driving cars, artificial intelligence (AI) is often heralded as the technology that has and will continue to transform this generation. Among widely adopted applications in other industries, its potential use in medicine is being increasingly explored, where the vast amounts of data present in electronic health records and need for continuous improvements in patient care and workflow efficiency present many opportunities for AI implementation. Indeed, AI has already demonstrated capabilities for assisting in tasks such as documentation, image classification, and surgical outcome prediction. More specifically, this technology can be harnessed in facial plastic surgery, where the unique characteristics of the field lends itself well to specific applications. AI is not without its limitations, however, and the further adoption of AI in medicine and facial plastic surgery must necessarily be accompanied by discussion on the ethical implications and proper usage of AI in healthcare. In this article, we review current and potential uses of AI in facial plastic surgery, as well as its ethical ramifications.
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Affiliation(s)
- Elizabeth Choi
- Wayne State University School of Medicine, Detroit, Michigan
| | - Kyle W Leonard
- Department of Otolaryngology, Henry Ford Hospital, Detroit, Michigan
| | - Japnam S Jassal
- Department of Otolaryngology, Henry Ford Hospital, Detroit, Michigan
| | - Albert M Levin
- Department of Public Health Science, Henry Ford Health, Detroit, Michigan
- Center for Bioinformatics, Henry Ford Health, Detroit, Michigan
| | - Vikas Ramachandra
- Department of Public Health Science, Henry Ford Health, Detroit, Michigan
- Center for Bioinformatics, Henry Ford Health, Detroit, Michigan
| | - Lamont R Jones
- Department of Otolaryngology, Henry Ford Hospital, Detroit, Michigan
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Pedro AR, Dias MB, Laranjo L, Cunha AS, Cordeiro JV. Artificial intelligence in medicine: A comprehensive survey of medical doctor's perspectives in Portugal. PLoS One 2023; 18:e0290613. [PMID: 37676884 PMCID: PMC10484446 DOI: 10.1371/journal.pone.0290613] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 08/12/2023] [Indexed: 09/09/2023] Open
Abstract
Artificial Intelligence (AI) is increasingly influential across various sectors, including healthcare, with the potential to revolutionize clinical practice. However, risks associated with AI adoption in medicine have also been identified. Despite the general understanding that AI will impact healthcare, studies that assess the perceptions of medical doctors about AI use in medicine are still scarce. We set out to survey the medical doctors licensed to practice medicine in Portugal about the impact, advantages, and disadvantages of AI adoption in clinical practice. We designed an observational, descriptive, cross-sectional study with a quantitative approach and developed an online survey which addressed the following aspects: impact on healthcare quality of the extraction and processing of health data via AI; delegation of clinical procedures on AI tools; perception of the impact of AI in clinical practice; perceived advantages of using AI in clinical practice; perceived disadvantages of using AI in clinical practice and predisposition to adopt AI in professional activity. Our sample was also subject to demographic, professional and digital use and proficiency characterization. We obtained 1013 valid, fully answered questionnaires (sample representativeness of 99%, confidence level (p< 0.01), for the total universe of medical doctors licensed to practice in Portugal). Our results reveal that, in general terms, the medical community surveyed is optimistic about AI use in medicine and are predisposed to adopt it while still aware of some disadvantages and challenges to AI use in healthcare. Most medical doctors surveyed are also convinced that AI should be part of medical formation. These findings contribute to facilitating the professional integration of AI in medical practice in Portugal, aiding the seamless integration of AI into clinical workflows by leveraging its perceived strengths according to healthcare professionals. This study identifies challenges such as gaps in medical curricula, which hinder the adoption of AI applications due to inadequate digital health training. Due to high professional integration in the healthcare sector, particularly within the European Union, our results are also relevant for other jurisdictions and across diverse healthcare systems.
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Affiliation(s)
- Ana Rita Pedro
- NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, CHRC, NOVA University Lisbon, Lisbon, Portugal
| | - Michelle B. Dias
- NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Liliana Laranjo
- Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Australia
| | - Ana Soraia Cunha
- NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - João V. Cordeiro
- NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, CHRC, NOVA University Lisbon, Lisbon, Portugal
- CICS.NOVA Interdisciplinary Center of Social Sciences, Universidade NOVA de Lisboa, Lisbon, Portugal
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Pamporaki C, Berends AMA, Filippatos A, Prodanov T, Meuter L, Prejbisz A, Beuschlein F, Fassnacht M, Timmers HJLM, Nölting S, Abhyankar K, Constantinescu G, Kunath C, de Haas RJ, Wang K, Remde H, Bornstein SR, Januszewicz A, Robledo M, Lenders JWM, Kerstens MN, Pacak K, Eisenhofer G. Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort. Lancet Digit Health 2023; 5:e551-e559. [PMID: 37474439 PMCID: PMC10565306 DOI: 10.1016/s2589-7500(23)00094-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/20/2023] [Accepted: 05/03/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field. METHODS In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets. FINDINGS Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p<0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%. INTERPRETATION Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up. FUNDING Deutsche Forschungsgemeinschaft.
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Affiliation(s)
| | - Annika M A Berends
- Department of Endocrinology, Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Angelos Filippatos
- University Hospital Carl Gustav Carus, Institute of Lightweight Engineering and Polymer Technology, TU Dresden, Dresden, Germany; Machine Design Laboratory, Department of Mechanical Engineering & Aeronautics, University of Patras, Patras, Greece
| | - Tamara Prodanov
- Section on Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Leah Meuter
- Section on Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | | | - Felix Beuschlein
- Department of Internal Medicine IV, University Hospital LMU, Ludwig Maximilian University of Munich, Munich, Germany; Department of Endocrinology, Diabetology, and Clinical Nutrition, University Hospital, University of Zurich, Zurich, Switzerland
| | - Martin Fassnacht
- Department of Internal Medicine, Division of Endocrinology and Diabetes, University of Würzburg, Würzburg, Germany
| | - Henri J L M Timmers
- Department of Internal Medicine, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Svenja Nölting
- Department of Internal Medicine IV, University Hospital LMU, Ludwig Maximilian University of Munich, Munich, Germany; Department of Endocrinology, Diabetology, and Clinical Nutrition, University Hospital, University of Zurich, Zurich, Switzerland
| | - Kaushik Abhyankar
- University Hospital Carl Gustav Carus, Institute of Lightweight Engineering and Polymer Technology, TU Dresden, Dresden, Germany
| | | | - Carola Kunath
- Department of Medicine III, TU Dresden, Dresden, Germany
| | - Robbert J de Haas
- Department of Radiology, Medical Imaging Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Katharina Wang
- Department of Internal Medicine IV, University Hospital LMU, Ludwig Maximilian University of Munich, Munich, Germany
| | - Hanna Remde
- Department of Internal Medicine, Division of Endocrinology and Diabetes, University of Würzburg, Würzburg, Germany
| | | | | | - Mercedes Robledo
- Hereditary Endocrine Cancer Group, Human Cancer Genetics Programme, Spanish National Cancer Reserch Centre, Madrid, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
| | - Jacques W M Lenders
- Department of Medicine III, TU Dresden, Dresden, Germany; Department of Internal Medicine, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Michiel N Kerstens
- Department of Endocrinology, Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Karel Pacak
- Section on Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Graeme Eisenhofer
- Department of Medicine III, TU Dresden, Dresden, Germany; Institute of Clinical Chemistry and Laboratory Medicine, TU Dresden, Dresden, Germany
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67
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Hennocq Q, Bongibault T, Marlin S, Amiel J, Attie-Bitach T, Baujat G, Boutaud L, Carpentier G, Corre P, Denoyelle F, Djate Delbrah F, Douillet M, Galliani E, Kamolvisit W, Lyonnet S, Milea D, Pingault V, Porntaveetus T, Touzet-Roumazeille S, Willems M, Picard A, Rio M, Garcelon N, Khonsari RH. AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes. Front Pediatr 2023; 11:1171277. [PMID: 37664547 PMCID: PMC10469912 DOI: 10.3389/fped.2023.1171277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 07/26/2023] [Indexed: 09/05/2023] Open
Abstract
Introduction Mandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes. Methods The training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set. Results We trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838-0.999] (p < 0.001) and an AUC (Area Under the Curve) of 0.975 within controls (binary classification). Balanced accuracies were 0.811 [0.648-0.920] (p = 0.002) in a first multiclass design (MFDM vs. controls and differential diagnoses) and 0.813 [0.544-0.960] (p = 0.003) in a second multiclass design (MFDM vs. differential diagnoses). Conclusion This is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers.
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Affiliation(s)
- Quentin Hennocq
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
- Laboratoire ‘Forme et Croissance du Crâne’, Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
| | - Thomas Bongibault
- Imagine Institute, INSERM UMR1163, Paris, France
- Laboratoire ‘Forme et Croissance du Crâne’, Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
| | - Sandrine Marlin
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Jeanne Amiel
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Tania Attie-Bitach
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Geneviève Baujat
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Lucile Boutaud
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Georges Carpentier
- CHU Lille, Inserm, Service de Chirurgie Maxillo-Faciale et Stomatologie, U1008-Controlled Drug Delivery Systems and Biomaterial, Université de Lille, Lille, France
| | - Pierre Corre
- Department of Oral and Maxillofacial Surgery, INSERM U1229—Regenerative Medicine and Skeleton RMeS, Nantes, France
- Department of Oral and Maxillofacial Surgery, Nantes University, CHU Nantes, Nantes, France
| | - Françoise Denoyelle
- Department of Paediatric Otolaryngology, AP-HP, Hôpital Necker-Enfants Malades, Paris, France
| | | | | | - Eva Galliani
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Wuttichart Kamolvisit
- Center of Excellence for Medical Genomics, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Genomics and Precision Dentistry, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
| | - Stanislas Lyonnet
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Dan Milea
- Duke-NUS Medical School Singapore, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Véronique Pingault
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Thantrira Porntaveetus
- Center of Excellence for Medical Genomics, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Genomics and Precision Dentistry, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
| | - Sandrine Touzet-Roumazeille
- CHU Lille, Inserm, Service de Chirurgie Maxillo-Faciale et Stomatologie, U1008-Controlled Drug Delivery Systems and Biomaterial, Université de Lille, Lille, France
| | - Marjolaine Willems
- Département de Génétique Clinique, CHRU de Montpellier, Hôpital Arnaud de Villeneuve, Institute for Neurosciences of Montpellier, INSERM, Univ Montpellier, Montpellier, France
| | - Arnaud Picard
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Marlène Rio
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | | | - Roman H. Khonsari
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
- Laboratoire ‘Forme et Croissance du Crâne’, Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
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Igaki T, Kitaguchi D, Matsuzaki H, Nakajima K, Kojima S, Hasegawa H, Takeshita N, Kinugasa Y, Ito M. Automatic Surgical Skill Assessment System Based on Concordance of Standardized Surgical Field Development Using Artificial Intelligence. JAMA Surg 2023; 158:e231131. [PMID: 37285142 PMCID: PMC10248810 DOI: 10.1001/jamasurg.2023.1131] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 01/28/2023] [Indexed: 06/08/2023]
Abstract
Importance Automatic surgical skill assessment with artificial intelligence (AI) is more objective than manual video review-based skill assessment and can reduce human burden. Standardization of surgical field development is an important aspect of this skill assessment. Objective To develop a deep learning model that can recognize the standardized surgical fields in laparoscopic sigmoid colon resection and to evaluate the feasibility of automatic surgical skill assessment based on the concordance of the standardized surgical field development using the proposed deep learning model. Design, Setting, and Participants This retrospective diagnostic study used intraoperative videos of laparoscopic colorectal surgery submitted to the Japan Society for Endoscopic Surgery between August 2016 and November 2017. Data were analyzed from April 2020 to September 2022. Interventions Videos of surgery performed by expert surgeons with Endoscopic Surgical Skill Qualification System (ESSQS) scores higher than 75 were used to construct a deep learning model able to recognize a standardized surgical field and output its similarity to standardized surgical field development as an AI confidence score (AICS). Other videos were extracted as the validation set. Main Outcomes and Measures Videos with scores less than or greater than 2 SDs from the mean were defined as the low- and high-score groups, respectively. The correlation between AICS and ESSQS score and the screening performance using AICS for low- and high-score groups were analyzed. Results The sample included 650 intraoperative videos, 60 of which were used for model construction and 60 for validation. The Spearman rank correlation coefficient between the AICS and ESSQS score was 0.81. The receiver operating characteristic (ROC) curves for the screening of the low- and high-score groups were plotted, and the areas under the ROC curve for the low- and high-score group screening were 0.93 and 0.94, respectively. Conclusions and Relevance The AICS from the developed model strongly correlated with the ESSQS score, demonstrating the model's feasibility for use as a method of automatic surgical skill assessment. The findings also suggest the feasibility of the proposed model for creating an automated screening system for surgical skills and its potential application to other types of endoscopic procedures.
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Affiliation(s)
- Takahiro Igaki
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Gastrointestinal Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Yushima, Bunkyo-Ku, Tokyo, Japan
| | - Daichi Kitaguchi
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Hiroki Matsuzaki
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Kei Nakajima
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Shigehiro Kojima
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Hiro Hasegawa
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Nobuyoshi Takeshita
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
| | - Yusuke Kinugasa
- Department of Gastrointestinal Surgery, Tokyo Medical and Dental University Graduate School of Medicine, Yushima, Bunkyo-Ku, Tokyo, Japan
| | - Masaaki Ito
- Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Kashiwanoha, Kashiwa, Chiba, Japan
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Kaya Bicer E, Fangerau H, Sur H. Artifical intelligence use in orthopedics: an ethical point of view. EFORT Open Rev 2023; 8:592-596. [PMID: 37526254 PMCID: PMC10441251 DOI: 10.1530/eor-23-0083] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/02/2023] Open
Abstract
Artificial intelligence (AI) is increasingly being utilized in orthopedics practice. Ethical concerns have arisen alongside marked improvements and widespread utilization of AI. Patient privacy, consent, data protection, cybersecurity, data safety and monitoring, bias, and accountability are some of the ethical concerns.
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Affiliation(s)
- Elcil Kaya Bicer
- Department of Orthopedics and Traumatology, Ege University Faculty of Medicine, Izmir, Turkey
| | - Heiner Fangerau
- Department of the History, Philosophy and Ethics of Medicine, Heinrich-Heine-Universität Düsseldorf, Germany
| | - Hakki Sur
- Department of Orthopedics and Traumatology, Ege University Faculty of Medicine, Izmir, Turkey
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Alaimo L, Moazzam Z, Endo Y, Lima HA, Butey SP, Ruzzenente A, Guglielmi A, Aldrighetti L, Weiss M, Bauer TW, Alexandrescu S, Poultsides GA, Maithel SK, Marques HP, Martel G, Pulitano C, Shen F, Cauchy F, Koerkamp BG, Endo I, Kitago M, Kim A, Ejaz A, Beane J, Cloyd J, Pawlik TM. The Application of Artificial Intelligence to Investigate Long-Term Outcomes and Assess Optimal Margin Width in Hepatectomy for Intrahepatic Cholangiocarcinoma. Ann Surg Oncol 2023; 30:4292-4301. [PMID: 36952150 DOI: 10.1245/s10434-023-13349-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 01/29/2023] [Indexed: 03/24/2023]
Abstract
BACKGROUND Intrahepatic cholangiocarcinoma (ICC) is associated with poor long-term outcomes, and limited evidence exists on optimal resection margin width. This study used artificial intelligence to investigate long-term outcomes and optimal margin width in hepatectomy for ICC. METHODS The study enrolled patients who underwent curative-intent resection for ICC between 1990 and 2020. The optimal survival tree (OST) was used to investigate overall (OS) and recurrence-free survival (RFS). An optimal policy tree (OPT) assigned treatment recommendations based on random forest (RF) counterfactual survival probabilities associated with each possible margin width between 0 and 20 mm. RESULTS Among 600 patients, the median resection margin was 4 mm (interquartile range [IQR], 2-10). Overall, 379 (63.2 %) patients experienced recurrence with a 5-year RFS of 28.3 % and a 5-year OS of 38.7 %. The OST identified five subgroups of patients with different OS rates based on tumor size, a carbohydrate antigen 19-9 [CA19-9] level higher than 200 U/mL, nodal status, margin width, and age (area under the curve [AUC]: training, 0.81; testing, 0.69). The patients with tumors smaller than 4.8 cm and a margin width of 2.5 mm or greater had a relative increase in 5-year OS of 37 % compared with the entire cohort. The OST for RFS estimated a 46 % improvement in the 5-year RFS for the patients younger than 60 years who had small (<4.8 cm) well- or moderately differentiated tumors without microvascular invasion. The OPT suggested five optimal margin widths to maximize the 5-year OS for the subgroups of patients based on age, tumor size, extent of hepatectomy, and CA19-9 levels. CONCLUSIONS Artificial intelligence OST identified subgroups within ICC relative to long-term outcomes. Although tumor biology dictated prognosis, the OPT suggested that different margin widths based on patient and disease characteristics may optimize ICC long-term survival.
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Affiliation(s)
- Laura Alaimo
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
- Department of Surgery, University of Verona, Verona, Italy
| | - Zorays Moazzam
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Yutaka Endo
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Henrique A Lima
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Swatika P Butey
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | | | | | | | - Matthew Weiss
- Department of Surgery, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Todd W Bauer
- Department of Surgery, University of Virginia, Charlottesville, VA, USA
| | | | | | | | - Hugo P Marques
- Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal
| | | | - Carlo Pulitano
- Department of Surgery, Royal Prince Alfred Hospital, University of Sydney, Sydney, NSW, Australia
| | - Feng Shen
- Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - François Cauchy
- Department of Hepatobiliopancreatic Surgery and Liver Transplantation, AP-HP, Beaujon Hospital, Clichy, France
| | - Bas Groot Koerkamp
- Department of Surgery, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Itaru Endo
- Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan
| | - Minoru Kitago
- Department of Surgery, Keio University, Tokyo, Japan
| | - Alex Kim
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Aslam Ejaz
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Joal Beane
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Jordan Cloyd
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Timothy M Pawlik
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA.
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Kinoshita T, Komatsu M. Artificial Intelligence in Surgery and Its Potential for Gastric Cancer. J Gastric Cancer 2023; 23:400-409. [PMID: 37553128 PMCID: PMC10412972 DOI: 10.5230/jgc.2023.23.e27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 08/10/2023] Open
Abstract
Artificial intelligence (AI) has made significant progress in recent years, and many medical fields are attempting to introduce AI technology into clinical practice. Currently, much research is being conducted to evaluate that AI can be incorporated into surgical procedures to make them safer and more efficient, subsequently to obtain better outcomes for patients. In this paper, we review basic AI research regarding surgery and discuss the potential for implementing AI technology in gastric cancer surgery. At present, research and development is focused on AI technologies that assist the surgeon's understandings and judgment during surgery, such as anatomical navigation. AI systems are also being developed to recognize in which the surgical phase is ongoing. Such a surgical phase recognition systems is considered for effective storage of surgical videos and education, in the future, for use in systems to objectively evaluate the skill of surgeons. At this time, it is not considered practical to let AI make intraoperative decisions or move forceps automatically from an ethical standpoint, too. At present, AI research on surgery has various limitations, and it is desirable to develop practical systems that will truly benefit clinical practice in the future.
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Affiliation(s)
- Takahiro Kinoshita
- Gastric Surgery Division, National Cancer Center Hospital East, Kashiwa, Japan.
| | - Masaru Komatsu
- Gastric Surgery Division, National Cancer Center Hospital East, Kashiwa, Japan
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El Moheb M, Kaafarani H. Invited Commentary. J Am Coll Surg 2023; 236:1103-1104. [PMID: 36927802 DOI: 10.1097/xcs.0000000000000688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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Ramalho A, Petrica J. Knowledge in Motion: A Comprehensive Review of Evidence-Based Human Kinetics. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6020. [PMID: 37297624 PMCID: PMC10252659 DOI: 10.3390/ijerph20116020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/21/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023]
Abstract
This comprehensive review examines critical aspects of evidence-based human kinetics, focusing on bridging the gap between scientific evidence and practical implementation. To bridge this gap, the development of tailored education and training programs is essential, providing practitioners with the expertise and skills to effectively apply evidence-based programs and interventions. The effectiveness of these programs in improving physical fitness across all age groups has been widely demonstrated. In addition, integrating artificial intelligence and the principles of slow science into evidence-based practice promises to identify gaps in knowledge and stimulate further research in human kinetics. The purpose of this review is to provide researchers and practitioners with comprehensive information on the application of scientific principles in human kinetics. By highlighting the importance of evidence-based practice, this review is intended to promote the adoption of effective interventions to optimize physical health and enhance performance.
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Affiliation(s)
- André Ramalho
- Sport, Health & Exercise Research Unit (SHERU), Polytechnic Institute of Castelo Branco, 6000-266 Castelo Branco, Portugal
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Lewandrowski KU, Elfar JC, Li ZM, Burkhardt BW, Lorio MP, Winkler PA, Oertel JM, Telfeian AE, Dowling Á, Vargas RAA, Ramina R, Abraham I, Assefi M, Yang H, Zhang X, Ramírez León JF, Fiorelli RKA, Pereira MG, de Carvalho PST, Defino H, Moyano J, Lim KT, Kim HS, Montemurro N, Yeung A, Novellino P. The Changing Environment in Postgraduate Education in Orthopedic Surgery and Neurosurgery and Its Impact on Technology-Driven Targeted Interventional and Surgical Pain Management: Perspectives from Europe, Latin America, Asia, and The United States. J Pers Med 2023; 13:852. [PMID: 37241022 PMCID: PMC10221956 DOI: 10.3390/jpm13050852] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
Personalized care models are dominating modern medicine. These models are rooted in teaching future physicians the skill set to keep up with innovation. In orthopedic surgery and neurosurgery, education is increasingly influenced by augmented reality, simulation, navigation, robotics, and in some cases, artificial intelligence. The postpandemic learning environment has also changed, emphasizing online learning and skill- and competency-based teaching models incorporating clinical and bench-top research. Attempts to improve work-life balance and minimize physician burnout have led to work-hour restrictions in postgraduate training programs. These restrictions have made it particularly challenging for orthopedic and neurosurgery residents to acquire the knowledge and skill set to meet the requirements for certification. The fast-paced flow of information and the rapid implementation of innovation require higher efficiencies in the modern postgraduate training environment. However, what is taught typically lags several years behind. Examples include minimally invasive tissue-sparing techniques through tubular small-bladed retractor systems, robotic and navigation, endoscopic, patient-specific implants made possible by advances in imaging technology and 3D printing, and regenerative strategies. Currently, the traditional roles of mentee and mentor are being redefined. The future orthopedic surgeons and neurosurgeons involved in personalized surgical pain management will need to be versed in several disciplines ranging from bioengineering, basic research, computer, social and health sciences, clinical study, trial design, public health policy development, and economic accountability. Solutions to the fast-paced innovation cycle in orthopedic surgery and neurosurgery include adaptive learning skills to seize opportunities for innovation with execution and implementation by facilitating translational research and clinical program development across traditional boundaries between clinical and nonclinical specialties. Preparing the future generation of surgeons to have the aptitude to keep up with the rapid technological advances is challenging for postgraduate residency programs and accreditation agencies. However, implementing clinical protocol change when the entrepreneur-investigator surgeon substantiates it with high-grade clinical evidence is at the heart of personalized surgical pain management.
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Affiliation(s)
- Kai-Uwe Lewandrowski
- Center For Advanced Spine Care of Southern Arizona, 4787 E Camp Lowell Drive, Tucson, AZ 85719, USA
- Department of Orthopaedics, Fundación Universitaria Sanitas, Bogotá 111321, Colombia
| | - John C. Elfar
- Department of Orthopaedic Surgery, College of Medicine—Tucson Campus, Health Sciences Innovation Building (HSIB), University of Arizona, 1501 N. Campbell Avenue, Tower 4, 8th Floor, Suite 8401, Tucson, AZ 85721, USA;
| | - Zong-Ming Li
- Departments of Orthopaedic Surgery and Biomedical Engineering, College of Medicine—Tucson Campus, Health Sciences Innovation Building (HSIB), University of Arizona, 1501 N. Campbell Avenue, Tower 4, 8th Floor, Suite 8401, Tucson, AZ 85721, USA;
| | - Benedikt W. Burkhardt
- Wirbelsäulenzentrum/Spine Center—WSC, Hirslanden Klinik Zurich, Witellikerstrasse 40, 8032 Zurich, Switzerland;
| | - Morgan P. Lorio
- Advanced Orthopaedics, 499 E. Central Pkwy, Ste. 130, Altamonte Springs, FL 32701, USA;
| | - Peter A. Winkler
- Department of Neurosurgery, Charite Universitaetsmedizin Berlin, 13353 Berlin, Germany;
| | - Joachim M. Oertel
- Klinik für Neurochirurgie, Universitätsdes Saarlandes, Kirrberger Straße 100, 66421 Homburg, Germany;
| | - Albert E. Telfeian
- Department of Neurosurgery, Rhode Island Hospital, The Warren Alpert Medical School of Brown University, Providence, RI 02903, USA;
| | - Álvaro Dowling
- Orthopaedic Surgery, University of São Paulo, Brazilian Spine Society (SBC), Ribeirão Preto 14071-550, Brazil; (Á.D.); (H.D.)
| | - Roth A. A. Vargas
- Department of Neurosurgery, Foundation Hospital Centro Médico Campinas, Campinas 13083-210, Brazil;
| | - Ricardo Ramina
- Neurological Institute of Curitiba, Curitiba 80230-030, Brazil;
| | - Ivo Abraham
- Clinical Translational Sciences, University of Arizona, Roy P. Drachman Hall, Rm. B306H, Tucson, AZ 85721, USA;
| | - Marjan Assefi
- Department of Biology, Nano-Biology, University of North Carolina, Greensboro, NC 27413, USA;
| | - Huilin Yang
- Orthopaedic Department, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou 215031, China;
| | - Xifeng Zhang
- Department of Orthopaedics, First Medical Center, PLA General Hospital, Beijing 100853, China;
| | - Jorge Felipe Ramírez León
- Minimally Invasive Spine Center Bogotá D.C. Colombia, Reina Sofía Clinic Bogotá D.C. Colombia, Department of Orthopaedics Fundación Universitaria Sanitas, Bogotá 0819, Colombia;
| | - Rossano Kepler Alvim Fiorelli
- Department of General and Specialized Surgery, Gaffrée e Guinle University Hospital, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro 20270-004, Brazil;
| | - Mauricio G. Pereira
- Faculty of Medecine, University of Brasilia, Federal District, Brasilia 70919-900, Brazil;
| | | | - Helton Defino
- Orthopaedic Surgery, University of São Paulo, Brazilian Spine Society (SBC), Ribeirão Preto 14071-550, Brazil; (Á.D.); (H.D.)
| | - Jaime Moyano
- La Sociedad Iberolatinoamericana De Columna (SILACO), and the Spine Committee of the Ecuadorian Society of Orthopaedics and Traumatology (Comité de Columna de la Sociedad Ecuatoriana de Ortopedia y Traumatología), Quito 170521, Ecuador;
| | - Kang Taek Lim
- Good Doctor Teun Teun Spine Hospital, Anyang 14041, Republic of Korea;
| | - Hyeun-Sung Kim
- Department of Neurosurgery, Nanoori Hospital, Seoul 06048, Republic of Korea;
| | - Nicola Montemurro
- Department of Neurosurgery, Azienda Ospedaliero Universitaria Pisana, University of Pisa, 56124 Pisa, Italy;
| | - Anthony Yeung
- Desert Institute for Spine Care, Phoenix, AZ 85020, USA;
| | - Pietro Novellino
- Guinle and State Institute of Diabetes and Endocrinology, Rio de Janeiro 20270-004, Brazil;
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Wu S, Chen Z, Liu R, Li A, Cao Y, Wei A, Liu Q, Liu J, Wang Y, Jiang J, Ying Z, An J, Peng B, Wang X. SurgSmart: an artificial intelligent system for quality control in laparoscopic cholecystectomy: an observational study. Int J Surg 2023; 109:1105-1114. [PMID: 37039533 PMCID: PMC10389595 DOI: 10.1097/js9.0000000000000329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 02/22/2023] [Indexed: 04/12/2023]
Abstract
BACKGROUND The rate of bile duct injury in laparoscopic cholecystectomy (LC) continues to be high due to low critical view of safety (CVS) achievement and the absence of an effective quality control system. The development of an intelligent system enables the automatic quality control of LC surgery and, eventually, the mitigation of bile duct injury. This study aims to develop an intelligent surgical quality control system for LC and using the system to evaluate LC videos and investigate factors associated with CVS achievement. MATERIALS AND METHODS SurgSmart, an intelligent system capable of recognizing surgical phases, disease severity, critical division action, and CVS automatically, was developed using training datasets. SurgSmart was also applied in another multicenter dataset to validate its application and investigate factors associated with CVS achievement. RESULTS SurgSmart performed well in all models, with the critical division action model achieving the highest overall accuracy (98.49%), followed by the disease severity model (95.45%) and surgical phases model (88.61%). CVSI, CVSII, and CVSIII had an accuracy of 80.64, 97.62, and 78.87%, respectively. CVS was achieved in 4.33% in the system application dataset. In addition, the analysis indicated that surgeons at a higher hospital level had a higher CVS achievement rate. However, there was still considerable variation in CVS achievement among surgeons in the same hospital. CONCLUSIONS SurgSmart, the surgical quality control system, performed admirably in our study. In addition, the system's initial application demonstrated its broad potential for use in surgical quality control.
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Affiliation(s)
- Shangdi Wu
- Division of Pancreatic Surgery, Department of General Surgery
- West China School of Medicine
| | - Zixin Chen
- Division of Pancreatic Surgery, Department of General Surgery
- West China School of Medicine
| | - Runwen Liu
- ChengDu Withai Innovations Technology Company
| | - Ang Li
- Division of Pancreatic Surgery, Department of General Surgery
- Guang’an People’s Hospital, Guang’an, Sichuan Province, China
| | - Yu Cao
- Operating Room
- West China School of Nursing, Sichuan University
| | - Ailin Wei
- Guang’an People’s Hospital, Guang’an, Sichuan Province, China
| | | | - Jie Liu
- ChengDu Withai Innovations Technology Company
| | - Yuxian Wang
- ChengDu Withai Innovations Technology Company
| | - Jingwen Jiang
- West China Biomedical Big Data Center, West China Hospital of Sichuan University
- Med-X Center for Informatics, Sichuan University, Chengdu
| | - Zhiye Ying
- West China Biomedical Big Data Center, West China Hospital of Sichuan University
- Med-X Center for Informatics, Sichuan University, Chengdu
| | - Jingjing An
- Operating Room
- West China School of Nursing, Sichuan University
| | - Bing Peng
- Division of Pancreatic Surgery, Department of General Surgery
- West China School of Medicine
| | - Xin Wang
- Division of Pancreatic Surgery, Department of General Surgery
- West China School of Medicine
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Ma X, Lu T, Qin D, Cai H, Tang Z, Yang Y, Cui Y, Wang R. Analysis of pulmonary artery variation based on 3D reconstruction of CT angiography. Front Physiol 2023; 14:1156513. [PMID: 37234424 PMCID: PMC10206427 DOI: 10.3389/fphys.2023.1156513] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/12/2023] [Indexed: 05/28/2023] Open
Abstract
Objective: The aim of this study is to acquire pulmonary CT (Computed tomography) angiographic data for the purpose of creating a three-dimensional reconstruction. Additionally, we aim to analyze the features and deviations of the branches in both pulmonary lobes. This information is intended to serve as a more comprehensive and detailed reference for medical professionals when conducting preoperative evaluations and devising surgical plans. Method: Between August 2019 and December 2021, 420 patients were selected from the thoracic surgery department at the First Hospital of Jilin University, and underwent pulmonary 64 channel contrast enhanced CT examinations (Philips ICT 256). The images were acquired at a 1.5 mm slice thickness, and the DCM files that complied with DICOM (Digital Imaging and Communications in Medicine) standards were analysed for 3D (three dimensional) reconstruction using Mimics 22.0 software. The reconstructed pulmonary artery models were assessed by attending chest surgeons and radiologists with over 10 years of clinical experience. The two-dimensional image planes, as well as the coronary and sagittal planes, were utilized to evaluate the arteries. The study analyzed the characteristics and variations of the branches and courses of pulmonary arteries in each lobe of the lungs, with the exception of the subsegmental arterial system. Two chest surgeons and two radiologists with professional titles-all of whom had over a decade of clinical experience-jointly evaluated the 3D models of the pulmonary artery and similarly assessed the characteristics and variations of the branches and courses in each lobe of the lungs. Results: Significant variations were observed in the left superior pulmonary artery across the 420 subjects studied. In the left upper lobe, the blood supply of 4 arteries accounted for 50.5% (n = 212), while the blood supply of 2 arteries in the left lower lobe was the most common, accounting for 79.5% (n = 334). The greatest variation in the right pulmonary artery was observed in the branch supply of the right upper lobe mediastinal artery. In the majority of cases (77.9%), there were two arteries present, which was the most common configuration observed accounting for 64% (n = 269). In the right inferior lobe of the lung, there were typically 2-4 arteries, with 2 arteries being the most common configuration (observed in 79% of cases, n = 332). Conclusion: The three-dimensional reconstruction of pulmonary artery CT angiography enables clear observation of the branches and distribution of the pulmonary artery while also highlighting any variations. This technique holds significant clinical value for preoperative assessments regarding lesions and blood vessels.
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Affiliation(s)
- Xiaochao Ma
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Tianyu Lu
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Da Qin
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Hongfei Cai
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Ze Tang
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Yue Yang
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Youbin Cui
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Rui Wang
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, Jilin, China
- School of Public Health, Jilin University, Changchun, Jilin, China
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Pang J, Xiu W, Ma X. Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors. J Clin Med 2023; 12:jcm12082818. [PMID: 37109155 PMCID: PMC10144939 DOI: 10.3390/jcm12082818] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/01/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Artificial intelligence (AI), also known as machine intelligence, is widely utilized in the medical field, promoting medical advances. Malignant tumors are the critical focus of medical research and improvement of clinical diagnosis and treatment. Mediastinal malignancy is an important tumor that attracts increasing attention today due to the difficulties in treatment. Combined with artificial intelligence, challenges from drug discovery to survival improvement are constantly being overcome. This article reviews the progress of the use of AI in the diagnosis, treatment, and prognostic prospects of mediastinal malignant tumors based on current literature findings.
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Affiliation(s)
- Jiyun Pang
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- West China School of Medicine, Sichuan University, Chengdu 610041, China
| | - Weigang Xiu
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- West China School of Medicine, Sichuan University, Chengdu 610041, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
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Richburg CE, Dossett LA, Hughes TM. Cognitive Bias and Dissonance in Surgical Practice: A Narrative Review. Surg Clin North Am 2023; 103:271-285. [PMID: 36948718 DOI: 10.1016/j.suc.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
A cognitive bias describes "shortcuts" subconsciously applied to new scenarios to simplify decision-making. Unintentional introduction of cognitive bias in surgery may result in surgical diagnostic error that leads to delayed surgical care, unnecessary procedures, intraoperative complications, and delayed recognition of postoperative complications. Data suggest that surgical error secondary to the introduction of cognitive bias results in significant harm. Thus, debiasing is a growing area of research which urges practitioners to deliberately slow decision-making to reduce the effects of cognitive bias.
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Affiliation(s)
- Caroline E Richburg
- University of Michigan Medical School, 1500 East Medical Center Drive, Ann Arbor, MI, USA. https://twitter.com/cerichburg
| | - Lesly A Dossett
- Department of Surgery, Michigan Medicine, 2101 Taubman Center, 1500 East Medical Center Drive, Ann Arbor, MI, USA. https://twitter.com/leslydossett
| | - Tasha M Hughes
- Department of Surgery, Michigan Medicine, 2101 Taubman Center, 1500 East Medical Center Drive, Ann Arbor, MI, USA.
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Manuel Román-Belmonte J, De la Corte-Rodríguez H, Adriana Rodríguez-Damiani B, Carlos Rodríguez-Merchán E. Artificial Intelligence in Musculoskeletal Conditions. ARTIF INTELL 2023. [DOI: 10.5772/intechopen.110696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Abstract
Artificial intelligence (AI) refers to computer capabilities that resemble human intelligence. AI implies the ability to learn and perform tasks that have not been specifically programmed. Moreover, it is an iterative process involving the ability of computerized systems to capture information, transform it into knowledge, and process it to produce adaptive changes in the environment. A large labeled database is needed to train the AI system and generate a robust algorithm. Otherwise, the algorithm cannot be applied in a generalized way. AI can facilitate the interpretation and acquisition of radiological images. In addition, it can facilitate the detection of trauma injuries and assist in orthopedic and rehabilitative processes. The applications of AI in musculoskeletal conditions are promising and are likely to have a significant impact on the future management of these patients.
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80
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Tan HJ, Chung AE, Gotz D, Deal AM, Heiling HM, Teal R, Vu MB, Meeks WD, Fang R, Bennett AV, Nielsen ME, Basch E. Electronic Health Record Use and Perceptions among Urologic Surgeons. Appl Clin Inform 2023; 14:279-289. [PMID: 37044288 PMCID: PMC10097476 DOI: 10.1055/s-0043-1763513] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 01/19/2023] [Indexed: 04/14/2023] Open
Abstract
OBJECTIVE Electronic health records (EHRs) have become widely adopted with increasing emphasis on improving care delivery. Improvements in surgery may be limited by specialty-specific issues that impact EHR usability and engagement. Accordingly, we examined EHR use and perceptions in urology, a diverse surgical specialty. METHODS We conducted a national, sequential explanatory mixed methods study. Through the 2019 American Urological Association Census, we surveyed urologic surgeons on EHR use and perceptions and then identified associated characteristics through bivariable and multivariable analyses. Using purposeful sampling, we interviewed 25 urologists and applied coding-based thematic analysis, which was then integrated with survey findings. RESULTS Among 2,159 practicing urologic surgeons, 2,081 (96.4%) reported using an EHR. In the weighted sample (n = 12,366), over 90% used the EHR for charting, viewing results, and order entry with most using information exchange functions (59.0-79.6%). In contrast, only 35.8% felt the EHR increases clinical efficiency, whereas 43.1% agreed it improves patient care, which related thematically to information management, administrative burden, patient safety, and patient-surgeon interaction. Quantitatively and qualitatively, use and perceptions differed by years in practice and practice type with more use and better perceptions among more recent entrants into the urologic workforce and those in academic/multispecialty practices, who may have earlier EHR exposure, better infrastructure, and more support. CONCLUSION Despite wide and substantive usage, EHRs engender mixed feelings, especially among longer-practicing surgeons and those in lower-resourced settings (e.g., smaller and private practices). Beyond reducing administrative burden and simplifying information management, efforts to improve care delivery through the EHR should focus on surgeon engagement, particularly in the community, to boost implementation and user experience.
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Affiliation(s)
- Hung-Jui Tan
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States
| | - Arlene E. Chung
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, United States
| | - David Gotz
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States
- School of Information and Library Science, University of North Carolina, Chapel Hill, North Carolina, United States
| | - Allison M. Deal
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States
| | - Hillary M. Heiling
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States
| | - Randall Teal
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States
- Connected Health Applications and Interventions Core, University of North Carolina, Chapel Hill, North Carolina, United States
| | - Maihan B. Vu
- Connected Health Applications and Interventions Core, University of North Carolina, Chapel Hill, North Carolina, United States
- Center for Health Promotion and Disease Prevention, University of North Carolina, Chapel Hill, North Carolina, United States
| | - William D. Meeks
- Data Management and Statistical Analysis, American Urological Association, Linthicum, Maryland, United States
| | - Raymond Fang
- Data Management and Statistical Analysis, American Urological Association, Linthicum, Maryland, United States
| | - Antonia V. Bennett
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States
- Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States
| | - Matthew E. Nielsen
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States
- Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States
| | - Ethan Basch
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States
- Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States
- Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States
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Ingraham NE, Jones EK, King S, Dries J, Phillips M, Loftus T, Evans HL, Melton GB, Tignanelli CJ. Re-Aiming Equity Evaluation in Clinical Decision Support: A Scoping Review of Equity Assessments in Surgical Decision Support Systems. Ann Surg 2023; 277:359-364. [PMID: 35943199 PMCID: PMC9905217 DOI: 10.1097/sla.0000000000005661] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
OBJECTIVE We critically evaluated the surgical literature to explore the prevalence and describe how equity assessments occur when using clinical decision support systems. BACKGROUND Clinical decision support (CDS) systems are increasingly used to facilitate surgical care delivery. Despite formal recommendations to do so, equity evaluations are not routinely performed on CDS systems and underrepresented populations are at risk of harm and further health disparities. We explored surgical literature to determine frequency and rigor of CDS equity assessments and offer recommendations to improve CDS equity by appending existing frameworks. METHODS We performed a scoping review up to Augus 25, 2021 using PubMed and Google Scholar for the following search terms: clinical decision support, implementation, RE-AIM, Proctor, Proctor's framework, equity, trauma, surgery, surgical. We identified 1415 citations and 229 abstracts met criteria for review. A total of 84 underwent full review after 145 were excluded if they did not assess outcomes of an electronic CDS tool or have a surgical use case. RESULTS Only 6% (5/84) of surgical CDS systems reported equity analyses, suggesting that current methods for optimizing equity in surgical CDS are inadequate. We propose revising the RE-AIM framework to include an Equity element (RE 2 -AIM) specifying that CDS foundational analyses and algorithms are performed or trained on balanced datasets with sociodemographic characteristics that accurately represent the CDS target population and are assessed by sensitivity analyses focused on vulnerable subpopulations. CONCLUSION Current surgical CDS literature reports little with respect to equity. Revising the RE-AIM framework to include an Equity element (RE 2 -AIM) promotes the development and implementation of CDS systems that, at minimum, do not worsen healthcare disparities and possibly improve their generalizability.
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Affiliation(s)
- Nicholas E Ingraham
- Department of Medicine, University of Minnesota, Division of Pulmonary and Critical Care, Minneapolis, MN
| | - Emma K Jones
- Department of Surgery, University of Minnesota, Division of Acute Care Surgery, Minneapolis, MN
| | - Samantha King
- Department of Surgery, University of Minnesota, Division of Acute Care Surgery, Minneapolis, MN
| | - James Dries
- Department of Surgery, University of Minnesota, Division of Acute Care Surgery, Minneapolis, MN
| | - Michael Phillips
- Pediatric Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Tyler Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL
| | - Heather L Evans
- Department of Surgery, Medical University of South Carolina, Charleston, SC
| | - Genevieve B Melton
- Department of Surgery, University of Minnesota, Division of Acute Care Surgery, Minneapolis, MN
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN
| | - Christopher J Tignanelli
- Department of Surgery, University of Minnesota, Division of Acute Care Surgery, Minneapolis, MN
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN
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Vodanović M, Subašić M, Milošević D, Savić Pavičin I. Artificial Intelligence in Medicine and Dentistry. Acta Stomatol Croat 2023; 57:70-84. [PMID: 37288152 PMCID: PMC10243707 DOI: 10.15644/asc57/1/8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/01/2023] [Indexed: 09/14/2023] Open
Abstract
INTRODUCTION Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. OBJECTIVE Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. CONCLUSION The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.
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Affiliation(s)
- Marin Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
| | - Marko Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Denis Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Ivana Savić Pavičin
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
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83
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Artificial Intelligence in Surgical Learning. SURGERIES 2023. [DOI: 10.3390/surgeries4010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
(1) Background: Artificial Intelligence (AI) is transforming healthcare on all levels. While AI shows immense potential, the clinical implementation is lagging. We present a concise review of AI in surgical learning; (2) Methods: A non-systematic review of AI in surgical learning of the literature in English is provided; (3) Results: AI shows utility for all components of surgical competence within surgical learning. AI presents with great potential within robotic surgery specifically (4) Conclusions: Technology will evolve in ways currently unimaginable, presenting us with novel applications of AI and derivatives thereof. Surgeons must be open to new modes of learning to be able to implement all evidence-based applications of AI in the future. Systematic analyses of AI in surgical learning are needed.
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84
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Leung T, Janssen DM, van der Steen MC, Delvaux EJLG, Hendriks JGE, Janssen RPA. Digital Health Applications to Establish a Remote Diagnosis of Orthopedic Knee Disorders: Scoping Review. J Med Internet Res 2023; 25:e40504. [PMID: 36566450 PMCID: PMC9951077 DOI: 10.2196/40504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 10/04/2022] [Accepted: 12/23/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Knee pain is highly prevalent worldwide, and this number is expected to rise in the future. The COVID-19 outbreak, in combination with the aging population, rising health care costs, and the need to make health care more accessible worldwide, has led to an increasing demand for digital health care applications to deliver care for patients with musculoskeletal conditions. Digital health and other forms of telemedicine can add value in optimizing health care for patients and health care providers. This might reduce health care costs and make health care more accessible while maintaining a high level of quality. Although expectations are high, there is currently no overview comparing digital health applications with face-to-face contact in clinical trials to establish a primary knee diagnosis in orthopedic surgery. OBJECTIVE This study aimed to investigate the currently available digital health and telemedicine applications to establish a primary knee diagnosis in orthopedic surgery in the general population in comparison with imaging or face-to-face contact between patients and physicians. METHODS A scoping review was conducted using the PubMed and Embase databases according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement. The inclusion criteria were studies reporting methods to determine a primary knee diagnosis in orthopedic surgery using digital health or telemedicine. On April 28 and 29, 2021, searches were conducted in PubMed (MEDLINE) and Embase. Data charting was conducted using a predefined form and included details on general study information, study population, type of application, comparator, analyses, and key findings. A risk-of-bias analysis was not deemed relevant considering the scoping review design of the study. RESULTS After screening 5639 articles, 7 (0.12%) were included. In total, 2 categories to determine a primary diagnosis were identified: screening studies (4/7, 57%) and decision support studies (3/7, 43%). There was great heterogeneity in the included studies in algorithms used, disorders, input parameters, and outcome measurements. No more than 25 knee disorders were included in the studies. The included studies showed a relatively high sensitivity (67%-91%). The accuracy of the different studies was generally lower, with a specificity of 27% to 48% for decision support studies and 73% to 96% for screening studies. CONCLUSIONS This scoping review shows that there are a limited number of available applications to establish a remote diagnosis of knee disorders in orthopedic surgery. To date, there is limited evidence that digital health applications can assist patients or orthopedic surgeons in establishing the primary diagnosis of knee disorders. Future research should aim to integrate multiple sources of information and a standardized study design with close collaboration among clinicians, data scientists, data managers, lawyers, and service users to create reliable and secure databases.
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Affiliation(s)
| | - Daan M Janssen
- Department of Orthopedic Surgery & Trauma, Máxima Medical Center, Veldhoven, Netherlands
| | - Maria C van der Steen
- Department of Orthopedic Surgery & Trauma, Máxima Medical Center, Veldhoven, Netherlands.,Department of Orthopedic Surgery & Trauma, Catharina Hospital, Eindhoven, Netherlands
| | | | - Johannes G E Hendriks
- Department of Orthopedic Surgery & Trauma, Máxima Medical Center, Veldhoven, Netherlands
| | - Rob P A Janssen
- Department of Orthopedic Surgery & Trauma, Máxima Medical Center, Veldhoven, Netherlands.,Orthopedic Biomechanics, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Value-Based Health Care, Department of Paramedical Sciences, Fontys University of Applied Sciences, Eindhoven, Netherlands
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85
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Bai BL, Wu ZY, Weng SJ, Yang Q. Application of interpretable machine learning algorithms to predict distant metastasis in osteosarcoma. Cancer Med 2023; 12:5025-5034. [PMID: 36082478 PMCID: PMC9972029 DOI: 10.1002/cam4.5225] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/21/2022] [Accepted: 08/23/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Osteosarcoma is well-established as the most common bone cancer in children and adolescents. Patients with localized disease have different prognoses and management than those with metastasis at the time of diagnosis. The purpose of this study was to explore potential risk factors for metastatic disease. METHODS The Surveillance, Epidemiology, and End Results (SEER) Program database was used to identify patients diagnosed with osteosarcoma between 2004 and 2015. We developed prediction models for distant metastasis using six machine learning (ML) techniques, including logistic regression (LR), support vector machine (SVM), Gaussian Naive Bayes (GaussianNB), Extreme Gradient Boosting (XGBoost), random forest (RF), and k-nearest neighbor algorithm (kNN). The adaptive synthetic (ADASYN) technique was used to deal with imbalanced data. The Shapley Additive Explanation (SHAP) analysis generated visualized explanations for each patient. Finally, the average precision (AP), sensitivity, specificity, accuracy, F1 score, precision-recall curves, calibration plots, and decision curve analysis (DCA) were conducted to evaluate the models' effectiveness. RESULTS The six machine learning algorithms achieved AP of 0.661-0.781 for predicting distant metastasis. The RF model yielded the best performance with an accuracy of 71.8 percent and an AP of 0.781 and was highly dependent on tumor size, primary surgery, and age. SHAP analysis provided model-independent interpretation, highlighting significant clinical factors associated with the risk of metastasis in osteosarcoma patients. CONCLUSIONS An accurate machine learning-based prediction model was established for metastasis in osteosarcoma patients to help clinicians during clinical decision-making.
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Affiliation(s)
- Bing-Li Bai
- Department of Orthopedics Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zong-Yi Wu
- Department of Orthopedics Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - She-Ji Weng
- Department of Orthopedics Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qing Yang
- Department of Breast Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
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86
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Hennocq Q, Bongibault T, Bizière M, Delassus O, Douillet M, Cormier-Daire V, Amiel J, Lyonnet S, Marlin S, Rio M, Picard A, Khonsari RH, Garcelon N. An automatic facial landmarking for children with rare diseases. Am J Med Genet A 2023; 191:1210-1221. [PMID: 36714960 DOI: 10.1002/ajmg.a.63126] [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: 11/02/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/31/2023]
Abstract
Two to three thousand syndromes modify facial features: their screening requires the eye of an expert in dysmorphology. A widely used tool in shape characterization is geometric morphometrics based on landmarks, which are precise and reproducible anatomical points. Landmark positioning is user dependent and time consuming. Many automatic landmarking tools are currently available but do not work for children, because they have mainly been trained using photographic databases of healthy adults. Here, we developed a method for building an automatic landmarking pipeline for frontal and lateral facial photographs as well as photographs of external ears. We evaluated the algorithm on patients diagnosed with Treacher Collins (TC) syndrome as it is the most frequent mandibulofacial dysostosis in humans and is clinically recognizable although highly variable in severity. We extracted photographs from the photographic database of the maxillofacial surgery and plastic surgery department of Hôpital Necker-Enfants Malades in Paris, France with the diagnosis of TC syndrome. The control group was built from children admitted for craniofacial trauma or skin lesions. After testing two methods of object detection by bounding boxes, a Haar Cascade-based tool and a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based tool, we evaluated three different automatic annotation algorithms: the patch-based active appearance model (AAM), the holistic AAM, and the constrained local model (CLM). The final error corresponding to the distance between the points placed by automatic annotation and those placed by manual annotation was reported. We included, respectively, 1664, 2044, and 1375 manually annotated frontal, profile, and ear photographs. Object recognition was optimized with the Faster R-CNN-based detector. The best annotation model was the patch-based AAM (p < 0.001 for frontal faces, p = 0.082 for profile faces and p < 0.001 for ears). This automatic annotation model resulted in the same classification performance as manually annotated data. Pretraining on public photographs did not improve the performance of the model. We defined a pipeline to create automatic annotation models adapted to faces with congenital anomalies, an essential prerequisite for research in dysmorphology.
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Affiliation(s)
- Quentin Hennocq
- Imagine Institute, INSERM UMR 1163, Paris, France.,Département de chirurgie maxillo-faciale et chirurgie plastique pédiatrique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
| | | | | | | | | | - Valérie Cormier-Daire
- Fédération de médecine génomique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Jeanne Amiel
- Fédération de médecine génomique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Stanislas Lyonnet
- Fédération de médecine génomique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Sandrine Marlin
- Fédération de médecine génomique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Marlène Rio
- Fédération de médecine génomique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Arnaud Picard
- Département de chirurgie maxillo-faciale et chirurgie plastique pédiatrique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Roman Hossein Khonsari
- Département de chirurgie maxillo-faciale et chirurgie plastique pédiatrique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
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87
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Cobianchi L, Piccolo D, Dal Mas F, Agnoletti V, Ansaloni L, Balch J, Biffl W, Butturini G, Catena F, Coccolini F, Denicolai S, De Simone B, Frigerio I, Fugazzola P, Marseglia G, Marseglia GR, Martellucci J, Modenese M, Previtali P, Ruta F, Venturi A, Kaafarani HM, Loftus TJ. Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey. World J Emerg Surg 2023; 18:1. [PMID: 36597105 PMCID: PMC9811693 DOI: 10.1186/s13017-022-00467-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 11/28/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons' knowledge and perception of using AI-based tools in clinical decision-making processes. METHODS An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society's website and Twitter profile. RESULTS 650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust. DISCUSSION The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI.
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Affiliation(s)
- Lorenzo Cobianchi
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy.
- General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, Italy.
- ITIR - Institute for Transformative Innovation Research, University of Pavia, Pavia, Italy.
| | - Daniele Piccolo
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy
- Department of Neurosurgery, ASUFC Santa Maria Della Misericordia, Udine, Italy
| | - Francesca Dal Mas
- Department of Management, Ca' Foscari University of Venice, Venice, Italy
| | | | - Luca Ansaloni
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy
- General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, Italy
| | - Jeremy Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, USA
| | - Walter Biffl
- Division of Trauma and Acute Care Surgery, Scripps Memorial Hospital La Jolla, La Jolla, CA, USA
| | - Giovanni Butturini
- Department of HPB Surgery, Pederzoli Hospital, Peschiera del Garda, Italy
| | | | - Federico Coccolini
- General, Emergency and Trauma Surgery Department, Pisa University Hospital Pisa, Pisa, Italy
| | - Stefano Denicolai
- ITIR - Institute for Transformative Innovation Research, University of Pavia, Pavia, Italy
- Department of Economics and Management, University of Pavia, Pavia, Italy
| | - Belinda De Simone
- Department of Emergency, Digestive and Metabolic Minimally Invasive Surgery, Poissy and Saint Germain en Laye Hospitals, Poissy, France
| | - Isabella Frigerio
- Department of HPB Surgery, Pederzoli Hospital, Peschiera del Garda, Italy
| | - Paola Fugazzola
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy
- General Surgery, IRCCS Policlinico San Matteo Foundation, Pavia, Italy
| | - Gianluigi Marseglia
- Department of Clinical, Diagnostic and Pediatric Sciences, University of Pavia, Via Alessandro Brambilla, 74, 27100, Pavia, PV, Italy
- IRCCS Policlinico San Matteo Foundation, Pediatric Clinic., Pavia, Italy
| | | | | | | | - Pietro Previtali
- ITIR - Institute for Transformative Innovation Research, University of Pavia, Pavia, Italy
- Department of Economics and Management, University of Pavia, Pavia, Italy
| | - Federico Ruta
- General Direction, ASL BAT (Health Agency), Andria, Italy
| | - Alessandro Venturi
- ITIR - Institute for Transformative Innovation Research, University of Pavia, Pavia, Italy
- Department of Political and Social Sciences, University of Pavia, Pavia, Italy
- Bureau of the Presidency, IRCCS Policlinico San Matteo Foundation, Pavia, Italy
| | - Haytham M Kaafarani
- Harvard Medical School, Boston, MA, USA
- Division of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA, USA
| | - Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, USA
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88
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Morris MX, Song EY, Rajesh A, Asaad M, Phillips BT. Ethical, Legal, and Financial Considerations of Artificial Intelligence in Surgery. Am Surg 2023; 89:55-60. [PMID: 35978473 DOI: 10.1177/00031348221117042] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Machine learning systems have become integrated into some of the most vital decision-making aspects of humanity, including hiring decisions, loan applications, and automobile safety, to name just a few. As applications increase in both gravity and complexity, the data quality and algorithmic interpretability of the systems must rise to meet those challenges. This is especially vital for navigating the nuances of health care, particularly among the high stakes of surgical operations. In addition to inherent ethical challenges of enabling a "black box" system to influence decision-making in patient care, the creation of biased datasets leads to biased algorithms with the power to perpetuate discrimination and reinforce disparities. Transparency and responsibility are paramount to the implementation of artificial intelligence in surgical decision-making and autonomous robotic surgery. Machine learning has been permeating health care across diverse clinical and surgical contexts but continues to face sizable obstacles, including apprehension from patients and providers alike. To integrate the technology fully while upholding standard of care and patient-provider trust, one must acknowledge and address the ethical, financial, and legal implications of using artificial intelligence for patient care.
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Affiliation(s)
- Miranda X Morris
- 12277Duke University School of Medicine, Durham, NC, USA.,22957Duke Pratt School of Engineering, Durham, NC, USA
| | - Ethan Y Song
- Division of Plastic, Maxillofacial, and Oral Surgery, Department of Surgery, 14742Duke University Hospital, Durham, NC, USA
| | - Aashish Rajesh
- Department of Surgery, 571198University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Malke Asaad
- Department of Plastic Surgery, 22957University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Brett T Phillips
- Division of Plastic, Maxillofacial, and Oral Surgery, Department of Surgery, 14742Duke University Hospital, Durham, NC, USA
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89
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Wu R, Smith A, Brown T, Hunt JP, Greiffenstein P, Taghavi S, Tatum D, Jackson-Weaver O, Duchesne J. Deterioration Index in Critically Injured Patients: A Feasibility Analysis. J Surg Res 2023; 281:45-51. [PMID: 36115148 DOI: 10.1016/j.jss.2022.08.019] [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: 02/08/2022] [Revised: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Continuous prediction surveillance modeling is an emerging tool giving dynamic insight into conditions with potential mitigation of adverse events (AEs) and failure to rescue. The Epic electronic medical record contains a Deterioration Index (DI) algorithm that generates a prediction score every 15 min using objective data. Previous validation studies show rapid increases in DI score (≥14) predict a worse prognosis. The aim of this study was to demonstrate the utility of DI scores in the trauma intensive care unit (ICU) population. METHODS A prospective, single-center study of trauma ICU patients in a Level 1 trauma center was conducted during a 3-mo period. Charts were reviewed every 24 h for minimum and maximum DI score, largest score change (Δ), and AE. Patients were grouped as low risk (ΔDI <14) or high risk (ΔDI ≥14). RESULTS A total of 224 patients were evaluated. High-risk patients were more likely to experience AEs (69.0% versus 47.6%, P = 0.002). No patients with DI scores <30 were readmitted to the ICU after being stepped down to the floor. Patients that were readmitted and subsequently died all had DI scores of ≥60 when first stepped down from the ICU. CONCLUSIONS This study demonstrates DI scores predict decompensation risk in the surgical ICU population, which may otherwise go unnoticed in real time. This can identify patients at risk of AE when transferred to the floor. Using the DI model could alert providers to increase surveillance in high-risk patients to mitigate unplanned returns to the ICU and failure to rescue.
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Affiliation(s)
- Rebecca Wu
- Department of Surgery, Houston Methodist Hospital, Houston, Texas.
| | - Alison Smith
- Department of Surgery, Louisiana State University, New Orleans, Louisiana
| | - Tommy Brown
- Department of Surgery, Louisiana State University, New Orleans, Louisiana
| | - John P Hunt
- Department of Surgery, Louisiana State University, New Orleans, Louisiana
| | | | - Sharven Taghavi
- Department of Surgery, Tulane University, New Orleans, Louisiana
| | - Danielle Tatum
- Department of Surgery, Tulane University, New Orleans, Louisiana
| | | | - Juan Duchesne
- Department of Surgery, Tulane University, New Orleans, Louisiana
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90
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van Varsseveld OC, Ten Broeke A, Chorus CG, Heyning N, Kooi EMW, Hulscher JBF. Surgery or comfort care for neonates with surgical necrotizing enterocolitis: Lessons learned from behavioral artificial intelligence technology. Front Pediatr 2023; 11:1122188. [PMID: 36925670 PMCID: PMC10011167 DOI: 10.3389/fped.2023.1122188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/31/2023] [Indexed: 03/08/2023] Open
Abstract
Background Critical decision making in surgical necrotizing enterocolitis (NEC) is highly complex and hard to capture in decision rules due to case-specificity and high mortality risk. In this choice experiment, we aimed to identify the implicit weight of decision factors towards future decision support, and to assess potential differences between specialties or centers. Methods Thirty-five hypothetical surgical NEC scenarios with different factor levels were evaluated by neonatal care experts of all Dutch neonatal care centers in an online environment, where a recommendation for surgery or comfort care was requested. We conducted choice analysis by constructing a binary logistic regression model according to behavioral artificial intelligence technology (BAIT). Results Out of 109 invited neonatal care experts, 62 (57%) participated, including 45 neonatologists, 16 pediatric surgeons and one neonatology physician assistant. Cerebral ultrasound (Relative importance = 20%, OR = 4.06, 95% CI = 3.39-4.86) was the most important factor in the decision surgery versus comfort care in surgical NEC, nationwide and for all specialties and centers. Pediatric surgeons more often recommended surgery compared to neonatologists (62% vs. 57%, p = 0.03). For all centers, cerebral ultrasound, congenital comorbidity, hemodynamics and parental preferences were significant decision factors (p < 0.05). Sex (p = 0.14), growth since birth (p = 0.25), and estimated parental capacities (p = 0.06) had no significance in nationwide nor subgroup analyses. Conclusion We demonstrated how BAIT can analyze the implicit weight of factors in the complex and critical decision for surgery or comfort care for (surgical) NEC. The findings reflect Dutch expertise, but the technique can be expanded internationally. After validation, our choice model/BAIT may function as decision aid.
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Affiliation(s)
- Otis C van Varsseveld
- Department of Surgery, Division of Pediatric Surgery, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | | | - Caspar G Chorus
- Councyl, Delft, Netherlands.,Department of Engineering Systems and Services, Faculty Technology Policy and Management, Delft University of Technology, Delft, Netherlands
| | | | - Elisabeth M W Kooi
- Department of Neonatology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Jan B F Hulscher
- Department of Surgery, Division of Pediatric Surgery, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
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91
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Djulbegovic B, Hozo I. Medical Decision-Making and Artificial Intelligence. Cancer Treat Res 2023; 189:101-108. [PMID: 37789165 DOI: 10.1007/978-3-031-37993-2_9] [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: 10/05/2023]
Abstract
In this chapter, we discuss the potential role that artificial intelligence (AI) may have in medical decision-making, the pros and cons, and the limitations and biases that might be introduced when using these novel techniques. As computing becomes more powerful and models continue to grow increasingly more complex, the potential of AI to improve decision-making is increasingly promising. Within many medical fields, however, at the time of this writing (September 2023), the promise of AI is yet to translate into everyday reality. Here, we summarize the role of AI in medical decision-making (diagnosis, prognosis, and treatment).
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Affiliation(s)
- Benjamin Djulbegovic
- Hematology Stewardship Program, Division of Hematology/Oncology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA.
| | - Iztok Hozo
- Department of Mathematics, Indiana University Northwest, Gary, IN, USA
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92
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Rajesh A, Chartier C, Asaad M, Butler CE. A Synopsis of Artificial Intelligence and its Applications in Surgery. Am Surg 2023; 89:20-24. [PMID: 35713389 DOI: 10.1177/00031348221109450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) has made steady in-roads into the healthcare scenario over the last decade. While widespread adoption into clinical practice remains elusive, the outreach of this discipline has progressed beyond the physician scientist, and different facets of this technology have been incorporated into the care of surgical patients. New AI applications are developing at rapid pace, and it is imperative that the general surgeon be aware of the broad utility of AI as applicable in his or her day-to-day practice, so that healthcare continues to remain up-to-date and evidence based. This review provides a broad account of the tip of the AI iceberg and highlights it potential for positively impacting surgical care.
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Affiliation(s)
- Aashish Rajesh
- Department of Surgery, 14742University of Texas Health Science Center, San Antonio, TX, USA
| | | | - Malke Asaad
- Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Charles E Butler
- Department of Plastic & Reconstructive Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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93
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Ruiz de Angulo Martín D. El arte de tomar decisiones en cirugía oncológica. Cir Esp 2023. [DOI: 10.1016/j.ciresp.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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94
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Kostick-Quenet KM, Gerke S. AI in the hands of imperfect users. NPJ Digit Med 2022; 5:197. [PMID: 36577851 PMCID: PMC9795935 DOI: 10.1038/s41746-022-00737-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 11/29/2022] [Indexed: 12/29/2022] Open
Abstract
As the use of artificial intelligence and machine learning (AI/ML) continues to expand in healthcare, much attention has been given to mitigating bias in algorithms to ensure they are employed fairly and transparently. Less attention has fallen to addressing potential bias among AI/ML's human users or factors that influence user reliance. We argue for a systematic approach to identifying the existence and impacts of user biases while using AI/ML tools and call for the development of embedded interface design features, drawing on insights from decision science and behavioral economics, to nudge users towards more critical and reflective decision making using AI/ML.
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Affiliation(s)
| | - Sara Gerke
- Penn State Dickinson Law, Carlisle, PA, USA
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95
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Predicting graft failure in pediatric liver transplantation based on early biomarkers using machine learning models. Sci Rep 2022; 12:22411. [PMID: 36575218 PMCID: PMC9794703 DOI: 10.1038/s41598-022-25900-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 12/06/2022] [Indexed: 12/28/2022] Open
Abstract
The early detection of graft failure in pediatric liver transplantation is crucial for appropriate intervention. Graft failure is associated with numerous perioperative risk factors. This study aimed to develop an individualized predictive model for 90-days graft failure in pediatric liver transplantation using machine learning methods. We conducted a single-center retrospective cohort study. A total of 87 liver transplantation cases performed in patients aged < 12 years at the Severance Hospital between January 2010 and September 2020 were included as data samples. Preoperative conditions of recipients and donors, intraoperative care, postoperative serial laboratory parameters, and events observed within seven days of surgery were collected as features. A least absolute shrinkage and selection operator (LASSO) -based method was used for feature selection to overcome the high dimensionality and collinearity of variables. Among 146 features, four variables were selected as the resultant features, namely, preoperative hepatic encephalopathy, sodium level at the end of surgery, hepatic artery thrombosis, and total bilirubin level on postoperative day 7. These features were selected from different times and represent distinct clinical aspects. The model with logistic regression demonstrated the best prediction performance among various machine learning methods tested (area under the receiver operating characteristic curve (AUROC) = 0.898 and area under the precision-recall curve (AUPR) = 0.882). The risk scoring system developed based on the logistic regression model showed an AUROC of 0.910 and an AUPR of 0.830. Together, the prediction of graft failure in pediatric liver transplantation using the proposed machine learning model exhibited superior discrimination power and, therefore, can provide valuable information to clinicians for their decision making during the postoperative management of the patients.
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96
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Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: retrospective and prospective validation study. EBioMedicine 2022; 87:104422. [PMID: 36565503 PMCID: PMC9798171 DOI: 10.1016/j.ebiom.2022.104422] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/04/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Anthropomorphic phantoms are used in surgical planning and intervention. Ideal accuracy and high efficiency are prerequisites for its clinical application. We aimed to develop a fully automated artificial intelligence-based three-dimensional (3D) reconstruction system (AI system) to assist thoracic surgery and to determine its accuracy, efficiency, and safety for clinical use. METHODS This AI system was developed based on a 3D convolutional neural network (CNN) and optimized by gradient descent after training with 500 cases, achieving a Dice coefficient of 89.2%. Accuracy was verified by comparing virtual structures predicted by the AI system with anatomical structures of patients in retrospective (n = 113) and prospective cohorts (n = 139) who underwent lobectomy or segmentectomy at the Peking University Cancer Hospital. Operation time and blood loss were compared between the retrospective cohort (without AI assistance) and prospective cohort (with AI assistance) for safety evaluation. The time consumption for reconstruction and the quality score were compared between the AI system and manual reconstruction software (Mimics®) for efficiency validation. This study was registered at https://www.chictr.org.cn as ChiCTR2100050985. FINDINGS The AI system reconstructed 13,608 pulmonary segmental branches from retrospective and prospective cohorts, and 1573 branches of interest corresponding to phantoms were detectable during the operation for verification, achieving 100% and 97% accuracy for segmental bronchi, 97.2% and 99.1% for segmental arteries, and 93.2% and 98.8% for segmental veins, respectively. With the assistance of the AI system, the operation time was shortened by 24.5 min for lobectomy (p < 0.001) and 20 min for segmentectomy (p = 0.007). Compared to Mimics®, the AI system reduced the model reconstruction time by 14.2 min (p < 0.001), and it also outperformed Mimics® in model quality scores (p < 0.001). INTERPRETATION The AI system can accurately predict thoracic anatomical structures with higher efficiency than manual reconstruction software. Constant optimization and larger population validation are required. FUNDING This study was funded by the Beijing Natural Science Foundation (No. L222020) and other sources.
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97
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Ray A, Das J, Wenzel SE. Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning. Cell Rep Med 2022; 3:100857. [PMID: 36543110 PMCID: PMC9798025 DOI: 10.1016/j.xcrm.2022.100857] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/24/2022] [Accepted: 11/18/2022] [Indexed: 12/24/2022]
Abstract
There is unprecedented opportunity to use machine learning to integrate high-dimensional molecular data with clinical characteristics to accurately diagnose and manage disease. Asthma is a complex and heterogeneous disease and cannot be solely explained by an aberrant type 2 (T2) immune response. Available and emerging multi-omics datasets of asthma show dysregulation of different biological pathways including those linked to T2 mechanisms. While T2-directed biologics have been life changing for many patients, they have not proven effective for many others despite similar biomarker profiles. Thus, there is a great need to close this gap to understand asthma heterogeneity, which can be achieved by harnessing and integrating the rich multi-omics asthma datasets and the corresponding clinical data. This article presents a compendium of machine learning approaches that can be utilized to bridge the gap between predictive biomarkers and actual causal signatures that are validated in clinical trials to ultimately establish true asthma endotypes.
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Affiliation(s)
- Anuradha Ray
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, 3459 Fifth Avenue, MUH 628 NW, Pittsburgh, PA 15213, USA; Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
| | - Jishnu Das
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Center for Systems Immunology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Sally E Wenzel
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, 3459 Fifth Avenue, MUH 628 NW, Pittsburgh, PA 15213, USA; Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Environmental Medicine and Occupational Health, School of Public Health, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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Farhadi F, Barnes MR, Sugito HR, Sin JM, Henderson ER, Levy JJ. Applications of artificial intelligence in orthopaedic surgery. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:995526. [PMID: 36590152 PMCID: PMC9797865 DOI: 10.3389/fmedt.2022.995526] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
The practice of medicine is rapidly transforming as a result of technological breakthroughs. Artificial intelligence (AI) systems are becoming more and more relevant in medicine and orthopaedic surgery as a result of the nearly exponential growth in computer processing power, cloud based computing, and development, and refining of medical-task specific software algorithms. Because of the extensive role of technologies such as medical imaging that bring high sensitivity, specificity, and positive/negative prognostic value to management of orthopaedic disorders, the field is particularly ripe for the application of machine-based integration of imaging studies, among other applications. Through this review, we seek to promote awareness in the orthopaedics community of the current accomplishments and projected uses of AI and ML as described in the literature. We summarize the current state of the art in the use of ML and AI in five key orthopaedic disciplines: joint reconstruction, spine, orthopaedic oncology, trauma, and sports medicine.
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Affiliation(s)
- Faraz Farhadi
- Geisel School of Medicine, Dartmouth College, Hanover, NH, United States,Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, United States,Correspondence: Faraz Farhadi Joshua J. Levy
| | - Matthew R. Barnes
- Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
| | - Harun R. Sugito
- Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
| | - Jessica M. Sin
- Department of Radiology, Dartmouth Health, Lebanon, United States
| | - Eric R. Henderson
- Department of Orthopaedics, Dartmouth Health, Lebanon, United States
| | - Joshua J. Levy
- Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, NH, United States,Correspondence: Faraz Farhadi Joshua J. Levy
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99
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Familiari F, Galasso O, Massazza F, Mercurio M, Fox H, Srikumaran U, Gasparini G. Artificial Intelligence in the Management of Rotator Cuff Tears. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16779. [PMID: 36554660 PMCID: PMC9779744 DOI: 10.3390/ijerph192416779] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Technological innovation is a key component of orthopedic surgery. Artificial intelligence (AI), which describes the ability of computers to process massive data and "learn" from it to produce outputs that mirror human cognition and problem solving, may become an important tool for orthopedic surgeons in the future. AI may be able to improve decision making, both clinically and surgically, via integrating additional data-driven problem solving into practice. The aim of this article will be to review the current applications of AI in the management of rotator cuff tears. The article will discuss various stages of the clinical course: predictive models and prognosis, diagnosis, intraoperative applications, and postoperative care and rehabilitation. Throughout the article, which is a review in terms of study design, we will introduce the concept of AI in rotator cuff tears and provide examples of how these tools can impact clinical practice and patient care. Though many advancements in AI have been made regarding evaluating rotator cuff tears-particularly in the realm of diagnostic imaging-further advancements are required before they become a regular facet of daily clinical practice.
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Affiliation(s)
- Filippo Familiari
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| | - Olimpio Galasso
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| | - Federica Massazza
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| | - Michele Mercurio
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| | - Henry Fox
- Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Uma Srikumaran
- Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Giorgio Gasparini
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
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De Simone B, Chouillard E, Gumbs AA, Loftus TJ, Kaafarani H, Catena F. Artificial intelligence in surgery: the emergency surgeon's perspective (the ARIES project). DISCOVER HEALTH SYSTEMS 2022; 1:9. [PMID: 37521114 PMCID: PMC9734362 DOI: 10.1007/s44250-022-00014-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022]
Abstract
Artificial Intelligence (AI) has been developed and implemented in healthcare with the valuable potential to reduce health, social, and economic inequities, help actualize universal health coverage, and improve health outcomes on a global scale. The application of AI in emergency surgery settings could improve clinical practice and operating rooms management by promoting consistent, high-quality decision making while preserving the importance of bedside assessment and human intuition as well as respect for human rights and equitable surgical care, but ethical and legal issues are slowing down surgeons' enthusiasm. Emergency surgeons are aware that prioritizing education, increasing the availability of high AI technologies for emergency and trauma surgery, and funding to support research projects that use AI to provide decision support in the operating room are crucial to create an emergency "intelligent" surgery.
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Affiliation(s)
- Belinda De Simone
- Department of Emergency, Digestive and Metabolic Minimally Invasive Surgery, Poissy and St Germain en Laye Hospitals, Poissy, France
| | - Elie Chouillard
- Department of Emergency, Digestive and Metabolic Minimally Invasive Surgery, Poissy and St Germain en Laye Hospitals, Poissy, France
| | - Andrew A. Gumbs
- Department of Emergency, Digestive and Metabolic Minimally Invasive Surgery, Poissy and St Germain en Laye Hospitals, Poissy, France
| | - Tyler J. Loftus
- Department of Surgery, University of Florida Health, Gainesville, USA
| | - Haytham Kaafarani
- Division of Trauma, Emergency Surgery and Surgical Critical Care, Massachusetts General Hospital, Boston, USA
| | - Fausto Catena
- Department of Emergency and General Surgery, Level I Trauma Center, Bufalini Hospital, Cesena, Italy
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