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Ochs V, Tobler A, Wolleb J, Bieder F, Saad B, Enodien B, Fischer LE, Honaker MD, Drews S, Rosenblum I, Stoll R, Probst P, Müller MK, Lavanchy JL, Taha-Mehlitz S, Müller BP, Rosenberg R, Frey DM, Cattin PC, Taha A. Development of predictive model for predicting postoperative BMI and optimize bariatric surgery: a single center pilot study. Surg Obes Relat Dis 2024:S1550-7289(24)00680-4. [PMID: 39117560 DOI: 10.1016/j.soard.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 06/04/2024] [Accepted: 06/30/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND The pilot study addresses the challenge of predicting postoperative outcomes, particularly body mass index (BMI) trajectories, following bariatric surgery. The complexity of this task makes preoperative personalized obesity treatment challenging. OBJECTIVES To develop and validate sophisticated machine learning (ML) algorithms capable of accurately forecasting BMI reductions up to 5 years following bariatric surgery aiming to enhance planning and postoperative care. The secondary goal involves the creation of an accessible web-based calculator for healthcare professionals. This is the first article that compares these methods in BMI prediction. SETTING The study was carried out from January 2012 to December 2021 at GZOAdipositas Surgery Center, Switzerland. Preoperatively, data for 1004 patients were available. Six months postoperatively, data for 1098 patients were available. For the time points 12 months, 18 months, 2 years, 3 years, 4 years, and 5 years the following number of follow-ups were available: 971, 898, 829, 693, 589, and 453. METHODS We conducted a comprehensive retrospective review of adult patients who underwent bariatric surgery (Roux-en-Y gastric bypass or sleeve gastrectomy), focusing on individuals with preoperative and postoperative data. Patients with certain preoperative conditions and those lacking complete data sets were excluded. Additional exclusion criteria were patients with incomplete data or follow-up, pregnancy during the follow-up period, or preoperative BMI ≤30 kg/m2. RESULTS This study analyzed 1104 patients, with 883 used for model training and 221 for final evaluation, the study achieved reliable predictive capabilities, as measured by root mean square error (RMSE). The RMSE values for three tasks were 2.17 (predicting next BMI value), 1.71 (predicting BMI at any future time point), and 3.49 (predicting the 5-year postoperative BMI curve). These results were showcased through a web application, enhancing clinical accessibility and decision-making. CONCLUSION This study highlights the potential of ML to significantly improve bariatric surgical outcomes and overall healthcare efficiency through precise BMI predictions and personalized intervention strategies.
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Affiliation(s)
- Vincent Ochs
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Anja Tobler
- Department of Surgery, GZO-Hospital, Wetzikon, Switzerland
| | - Julia Wolleb
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Florentin Bieder
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Baraa Saad
- Faculty of Medicine, St. George's University of London, London, UK
| | - Bassey Enodien
- Department of Surgery, GZO-Hospital, Wetzikon, Switzerland
| | - Laura E Fischer
- Department of Surgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Michael D Honaker
- Department of Surgery, East Carolina University, Brody School of Medicine, Greenville, North Carolina
| | - Susanne Drews
- Department of Surgery, Centre for Gastrointestinal Diseases, Cantonal Hospital Basel-Landschaft, Liestal, Switzerland
| | - Ilan Rosenblum
- Department of Surgery, Centre for Gastrointestinal Diseases, Cantonal Hospital Basel-Landschaft, Liestal, Switzerland
| | - Reinhard Stoll
- Department of Surgery, Centre for Gastrointestinal Diseases, Cantonal Hospital Basel-Landschaft, Liestal, Switzerland
| | - Pascal Probst
- Department of Surgery, Cantonal Hospital Thurgau, Frauenfeld, Switzerland
| | - Markus K Müller
- Department of Surgery, Cantonal Hospital Thurgau, Frauenfeld, Switzerland
| | - Joël L Lavanchy
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland; Clarunis, Department of Visceral Surgery, University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Stephanie Taha-Mehlitz
- Clarunis, Department of Visceral Surgery, University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Beat P Müller
- Clarunis, Department of Visceral Surgery, University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Robert Rosenberg
- Department of Surgery, Centre for Gastrointestinal Diseases, Cantonal Hospital Basel-Landschaft, Liestal, Switzerland
| | - Daniel M Frey
- Department of Surgery, GZO-Hospital, Wetzikon, Switzerland; Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Philippe C Cattin
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland; Department of Surgery, East Carolina University, Brody School of Medicine, Greenville, North Carolina; Department of Surgery, Centre for Gastrointestinal Diseases, Cantonal Hospital Basel-Landschaft, Liestal, Switzerland.
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Greene ME, Grieco A, Evans-Labok K, Ko CY, Hutter MM. First report of outcomes from the patient-reported outcome measures program in the Metabolic and Bariatric Surgery Accreditation Quality Improvement Program. Surg Obes Relat Dis 2024; 20:173-183. [PMID: 37949691 DOI: 10.1016/j.soard.2023.09.010] [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: 03/08/2023] [Revised: 07/14/2023] [Accepted: 09/04/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Health-related quality-of-life (HRQoL) is one of the most important outcomes to metabolic and bariatric surgery (MBS) patients but was not measured by the Metabolic and Bariatric Surgery Accreditation Quality Improvement Program (MBSAQIP). A patient-reported outcome measures (PROMs) program pilot started in 2016 with MBSAQIP implementation in 2019. OBJECTIVES To measure how MBS impacts patient HRQoL 1 and 2 years after primary laparoscopic Roux-en-Y gastric bypass (bypass) or laparoscopic sleeve gastrectomy (sleeve). SETTING The 82 centers in the United States participating in the MBSAQIP PROMs program. METHODS Preoperative HRQoL scores and satisfaction were compared with postoperative scores 1 and 2 years after surgery with univariate comparisons and adjusted regression models. RESULTS There were 13,901 PROMs responses from 11,146 patients. Patient satisfaction with their MBS decision was 97%. On average, patients had significant improvement in Obesity-related Problem (OP) scores (65.8 preoperatively, 23.0 at 1 yr, and 26.3 at 2 yr; P <.05), Obesity and Weight-Loss Quality-of-Life (OWLQOL) scores (36.7 preoperatively, 77.2 at 1 yr, and 74.6 at 2 yr; P < .05), their physical health (39.2 preoperatively versus 51.7 at 1 yr and 50.0 at 2 yr), and mental health (45.6 preoperatively versus 53.3 at 1 yr and 51.4 at 2 yr). Compared with bypass patients, sleeve patients had significantly lower odds of having low OP scores postoperatively (odds ratio [95% CI) ] .67 [.53, .83]) and lower odds of high OWLQOL (.61 [.48, .77]) at 1 year. CONCLUSION All patients regardless of procedure on average report significant improvement in their scores for OP, OWLQOL, and physical and mental health after MBS. At 1 and 2 years, bypass patients reported greater improvement in their obesity-related PROMs than sleeve patients.
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Affiliation(s)
- Meridith E Greene
- Department of Surgery, Codman Center for Clinical Effectiveness in Surgery, Massachusetts General Hospital, Boston, Massachusetts
| | | | | | - Clifford Y Ko
- American College of Surgeons, Chicago, Illinois; Department of Surgery, University of California Los Angeles David Geffen School of Medicine, VA Greater Los Angeles Healthcare System, Los Angeles, California
| | - Matthew M Hutter
- Department of Surgery, Codman Center for Clinical Effectiveness in Surgery, Massachusetts General Hospital, Boston, Massachusetts.
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Guillen-Grima F, Guillen-Aguinaga S, Guillen-Aguinaga L, Alas-Brun R, Onambele L, Ortega W, Montejo R, Aguinaga-Ontoso E, Barach P, Aguinaga-Ontoso I. Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine. Clin Pract 2023; 13:1460-1487. [PMID: 37987431 PMCID: PMC10660543 DOI: 10.3390/clinpract13060130] [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: 09/26/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 11/22/2023] Open
Abstract
The rapid progress in artificial intelligence, machine learning, and natural language processing has led to increasingly sophisticated large language models (LLMs) for use in healthcare. This study assesses the performance of two LLMs, the GPT-3.5 and GPT-4 models, in passing the MIR medical examination for access to medical specialist training in Spain. Our objectives included gauging the model's overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician. MATERIAL AND METHODS We studied the 2022 Spanish MIR examination results after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM GPT-4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length, sequence, and performance. We also analyzed the 23 questions with images, using GPT-4's new image analysis capability. RESULTS GPT-4 outperformed GPT-3.5, scoring 86.81% in Spanish (p < 0.001). English translations had a slightly enhanced performance. GPT-4 scored 26.1% of the questions with images in English. The results were worse when the questions were in Spanish, 13.0%, although the differences were not statistically significant (p = 0.250). Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, and the Pharmacology, Critical Care, and Infectious Diseases specialties showed lower performance. The error analysis revealed that while a 13.2% error rate existed, the gravest categories, such as "error requiring intervention to sustain life" and "error resulting in death", had a 0% rate. CONCLUSIONS GPT-4 performs robustly on the Spanish MIR examination, with varying capabilities to discriminate knowledge across specialties. While the model's high success rate is commendable, understanding the error severity is critical, especially when considering AI's potential role in real-world medical practice and its implications for patient safety.
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Affiliation(s)
- Francisco Guillen-Grima
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
- Healthcare Research Institute of Navarra (IdiSNA), 31008 Pamplona, Spain
- Department of Preventive Medicine, Clinica Universidad de Navarra, 31008 Pamplona, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Institute of Health Carlos III, 46980 Madrid, Spain
| | - Sara Guillen-Aguinaga
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
| | - Laura Guillen-Aguinaga
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
- Department of Nursing, Kystad Helse-og Velferdssenter, 7026 Trondheim, Norway
| | - Rosa Alas-Brun
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
| | - Luc Onambele
- School of Health Sciences, Catholic University of Central Africa, Yaoundé 1100, Cameroon;
| | - Wilfrido Ortega
- Department of Surgery, Medical and Social Sciences, University of Alcala de Henares, 28871 Alcalá de Henares, Spain;
| | - Rocio Montejo
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, University of Gothenburg, 413 46 Gothenburg, Sweden;
- Department of Obstetrics and Gynecology, Sahlgrenska University Hospital, 413 46 Gothenburg, Sweden
| | | | - Paul Barach
- Jefferson College of Population Health, Philadelphia, PA 19107, USA;
- School of Medicine, Thomas Jefferson University, Philadelphia, PA 19107, USA
- Interdisciplinary Research Institute for Health Law and Science, Sigmund Freud University, 1020 Vienna, Austria
- Department of Surgery, Imperial College, London SW7 2AZ, UK
| | - Ines Aguinaga-Ontoso
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
- Healthcare Research Institute of Navarra (IdiSNA), 31008 Pamplona, Spain
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Wang X, Ren J, Ren H, Song W, Qiao Y, Zhao Y, Linghu L, Cui Y, Zhao Z, Chen L, Qiu L. Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta. Sci Rep 2023; 13:12718. [PMID: 37543637 PMCID: PMC10404250 DOI: 10.1038/s41598-023-40036-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 08/03/2023] [Indexed: 08/07/2023] Open
Abstract
Diabetes mellitus (DM) has become the third chronic non-infectious disease affecting patients after tumor, cardiovascular and cerebrovascular diseases, becoming one of the major public health issues worldwide. Detection of early warning risk factors for DM is key to the prevention of DM, which has been the focus of some previous studies. Therefore, from the perspective of residents' self-management and prevention, this study constructed Bayesian networks (BNs) combining feature screening and multiple resampling techniques for DM monitoring data with a class imbalance in Shanxi Province, China, to detect risk factors in chronic disease monitoring programs and predict the risk of DM. First, univariate analysis and Boruta feature selection algorithm were employed to conduct the preliminary screening of all included risk factors. Then, three resampling techniques, SMOTE, Borderline-SMOTE (BL-SMOTE) and SMOTE-ENN, were adopted to deal with data imbalance. Finally, BNs developed by three algorithms (Tabu, Hill-climbing and MMHC) were constructed using the processed data to find the warning factors that strongly correlate with DM. The results showed that the accuracy of DM classification is significantly improved by the BNs constructed by processed data. In particular, the BNs combined with the SMOTE-ENN resampling improved the most, and the BNs constructed by the Tabu algorithm obtained the best classification performance compared with the hill-climbing and MMHC algorithms. The best-performing joint Boruta-SMOTE-ENN-Tabu model showed that the risk factors of DM included family history, age, central obesity, hyperlipidemia, salt reduction, occupation, heart rate, and BMI.
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Affiliation(s)
- Xuchun Wang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jiahui Ren
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Hao Ren
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Wenzhu Song
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yuchao Qiao
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Ying Zhao
- Shanxi Centre for Disease Control and Prevention, Taiyuan, 030012, Shanxi, China
| | - Liqin Linghu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
- Shanxi Centre for Disease Control and Prevention, Taiyuan, 030012, Shanxi, China
| | - Yu Cui
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Zhiyang Zhao
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Limin Chen
- Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China.
| | - Lixia Qiu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
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Sun S, Stenberg E, Lindholm L, Salén KG, Franklin KA, Luo N, Cao Y. Prediction of quality-adjusted life years (QALYs) after bariatric surgery using regularized linear regression models: results from a Swedish nationwide quality register. Obes Surg 2023; 33:2452-2462. [PMID: 37322243 PMCID: PMC10345068 DOI: 10.1007/s11695-023-06685-1] [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: 03/27/2023] [Revised: 05/25/2023] [Accepted: 06/08/2023] [Indexed: 06/17/2023]
Abstract
PURPOSE To investigate whether the quality-adjusted life years (QALYs) of the patients who underwent bariatric surgery could be predicted using their baseline information. MATERIALS AND METHODS All patients who received bariatric surgery in Sweden between January 1, 2011 and March 31, 2019 were obtained from the Scandinavian Obesity Surgery Registry (SOReg). Baseline information included patients' sociodemographic characteristics, details regarding the procedure, and postsurgical conditions. QALYs were assessed by the SF-6D at follow-up years 1 and 2. The general and regularized linear regression models were used to predict postoperative QALYs. RESULTS All regression models demonstrated satisfactory and comparable performance in predicting QALYs at follow-up year 1, with R2 and relative root mean squared error (RRMSE) values of about 0.57 and 9.6%, respectively. The performance of the general linear regression model increased with the number of variables; however, the improvement was ignorable when the number of variables was more than 30 and 50 for follow-up years 1 and 2, respectively. Although minor L1 and L2 regularization provided better prediction ability, the improvement was negligible when the number of variables was more than 20. All the models showed poorer performance for predicting QALYs at follow-up year 2. CONCLUSIONS Patient characteristics before bariatric surgery including health related quality of life, age, sex, BMI, postoperative complications within six weeks, and smoking status, may be adequate in predicting their postoperative QALYs after one year. Understanding these factors can help identify individuals who require more personalized and intensive support before, during, and after surgery.
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Affiliation(s)
- Sun Sun
- Department of Epidemiology and Global Health, Umeå University, 901 87, Umeå, Sweden.
| | - Erik Stenberg
- Department of Surgery, Faculty of Medicine and Health, Örebro University, 701 85, Örebro, Sweden
| | - Lars Lindholm
- Department of Epidemiology and Global Health, Umeå University, 901 87, Umeå, Sweden
| | - Klas-Göran Salén
- Department of Epidemiology and Global Health, Umeå University, 901 87, Umeå, Sweden
| | - Karl A Franklin
- Department of Surgical and Perioperative Sciences, Surgery, Umeå University, Umeå, Sweden
| | - Nan Luo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Yang Cao
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 701 82, Örebro, Sweden.
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, 171 77, Stockholm, Sweden.
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van der Ploeg T, Gobbens RJJ, Salem BE. Bayesian Techniques in Predicting Frailty among Community-Dwelling Older Adults in the Netherlands. Arch Gerontol Geriatr 2023; 105:104836. [PMID: 36343439 DOI: 10.1016/j.archger.2022.104836] [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: 09/10/2022] [Revised: 10/03/2022] [Accepted: 10/10/2022] [Indexed: 12/13/2022]
Abstract
Background Frailty is a syndrome that is defined as an accumulation of deficits in physical, psychological, and social domains. On a global scale, there is an urgent need to create frailty-ready healthcare systems due to the healthcare burden that frailty confers on systems and the increased risk of falls, healthcare utilization, disability, and premature mortality. Several studies have been conducted to develop prediction models for predicting frailty. Most studies used logistic regression as a technique to develop a prediction model. One area that has experienced significant growth is the application of Bayesian techniques, partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. Objective We compared ten different Bayesian networks as proposed by ten experts in the field of frail elderly people to predict frailty with a choice from ten dichotomized determinants for frailty. Methods We used the opinion of ten experts who could indicate, using an empty Bayesian network graph, the important predictors for frailty and the interactions between the different predictors. The candidate predictors were age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. The ten Bayesian network models were evaluated in terms of their ability to predict frailty. For the evaluation, we used the data of 479 participants that filled in the Tilburg Frailty indicator (TFI) questionnaire for assessing frailty among community-dwelling older people. The data set contained the aforementioned variables and the outcome "frail". The model fit of each model was measured using the Akaike information criterion (AIC) and the predictive performance of the models was measured using the area under the curve (AUC) of the receiver operator characteristic (ROC). The AUCs of the models were validated using bootstrapping with 100 repetitions. The relative importance of the predictors in the models was calculated using the permutation feature importance algorithm (PFI). Results The ten Bayesian networks of the ten experts differed considerably regarding the predictors and the connections between the predictors and the outcome. However, all ten networks had corrected AUCs >0.700. Evaluating the importance of the predictors in each model, "diseases or chronic disorders" was the most important predictor in all models (10 times). The predictors "lifestyle" and "monthly income" were also often present in the models (both 6 times). One or more diseases or chronic disorders, an unhealthy lifestyle, and a monthly income below 1800 euro increased the likelihood of frailty. Conclusions Although the ten experts all made different graphs, the predictive performance was always satisfying (AUCs >0.700). While it is true that the predictor importance varied all the time, the top three of the predictor importance consisted of "diseases or chronic disorders", "lifestyle" and "monthly income". All in all, asking for the opinion of experts in the field of frail elderly to predict frailty with Bayesian networks may be more rewarding than a data-driven forecast with Bayesian networks because they have expert knowledge regarding interactions between the different predictors.
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Affiliation(s)
- Tjeerd van der Ploeg
- Faculty of Health, Sports and Social Work, Inholland University of Applied Sciences, Amsterdam, the Netherlands.
| | - Robbert J J Gobbens
- Faculty of Health, Sports and Social Work, Inholland University of Applied Sciences, Amsterdam, the Netherlands; Zonnehuisgroep Amstelland, Amstelveen, the Netherlands; Department Family Medicine and Population Health, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Tranzo, Tilburg University, Tilburg, the Netherlands
| | - Benissa E Salem
- School of Nursing, University of California, Los Angeles, USA
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Enodien B, Taha-Mehlitz S, Saad B, Nasser M, Frey DM, Taha A. The development of machine learning in bariatric surgery. Front Surg 2023; 10:1102711. [PMID: 36911599 PMCID: PMC9998495 DOI: 10.3389/fsurg.2023.1102711] [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: 11/19/2022] [Accepted: 02/08/2023] [Indexed: 03/14/2023] Open
Abstract
Background Machine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the medical domain. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery. Methods The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR). A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. Eligible studies included journals published from 2016 to the current date. The PRESS checklist was used to evaluate the consistency demonstrated during the process. Results A total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML algorithms in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation. Conclusions This study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. The evidence suggests that bariatric surgeons can benefit from ML algorithms since they will facilitate the prediction and evaluation of patient outcomes. Also, ML approaches to enhance work processes by making data categorization and analysis easier. However, further large multicenter studies are required to validate results internally and externally as well as explore and address limitations of ML application in bariatric surgery.
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Affiliation(s)
- Bassey Enodien
- Department of Surgery, GZO-Hospital, Wetzikon, Switzerland
| | - Stephanie Taha-Mehlitz
- Clarunis, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Baraa Saad
- School of Medicine, St George's University of London, London, United Kingdom
| | - Maya Nasser
- School of Medicine, St George's University of London, London, United Kingdom
| | - Daniel M Frey
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Anas Taha
- Clarunis, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland.,Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
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Chen Y, Zhu Y, Zhong K, Yang Z, Li Y, Shu X, Wang D, Deng P, Bai X, Gu J, Lu K, Zhang J, Zhao L, Zhu T, Wei K, Yi B. Optimization of anesthetic decision-making in ERAS using Bayesian network. Front Med (Lausanne) 2022; 9:1005901. [PMID: 36186765 PMCID: PMC9519180 DOI: 10.3389/fmed.2022.1005901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
Enhanced recovery after surgery (ERAS) can accelerate patient recovery. However, little research has been done on optimizing the ERAS-related measures and how the measures interact with each other. The Bayesian network (BN) is a graphical model that describes the dependencies between variables and is also a model for uncertainty reasoning. In this study, we aimed to develop a method for optimizing anesthetic decisions in ERAS and then investigate the relationship between anesthetic decisions and outcomes. First, assuming that the indicators used were independent, the effects of combinations of single indicators were analyzed based on BN. Additionally, the impact indicators for outcomes were selected with statistical tests. Then, based on the previously selected indicators, the Bayesian network was constructed using the proposed structure learning method based on Strongly Connected Components (SCC) Local Structure determination by Hill Climbing Twice (LSHCT) and adjusted according to the expert’s knowledge. Finally, the relationship is analyzed. The proposed method is validated by the real clinical data of patients with benign gynecological tumors from 3 hospitals in China. Postoperative length of stay (LOS) and total cost (TC) were chosen as the outcomes. Experimental results show that the ERAS protocol has some pivotal indicators influencing LOS and TC. Identifying the relationship between these indicators can help anesthesiologists optimize the ERAS protocol and make individualized decisions.
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Affiliation(s)
- Yuwen Chen
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (CAS), Chongqing, China
| | - Yiziting Zhu
- Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Kunhua Zhong
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (CAS), Chongqing, China
| | - Zhiyong Yang
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Yujie Li
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Xin Shu
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Dandan Wang
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Peng Deng
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Xuehong Bai
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Jianteng Gu
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Kaizhi Lu
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Ju Zhang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (CAS), Chongqing, China
| | - Lei Zhao
- Department of Anesthesiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tao Zhu
- Department of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
| | - Ke Wei
- Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Ke Wei,
| | - Bin Yi
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
- Bin Yi,
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9
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Bellini V, Valente M, Turetti M, Del Rio P, Saturno F, Maffezzoni M, Bignami E. Current Applications of Artificial Intelligence in Bariatric Surgery. Obes Surg 2022; 32:2717-2733. [PMID: 35616768 PMCID: PMC9273529 DOI: 10.1007/s11695-022-06100-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 11/27/2022]
Abstract
The application of artificial intelligence technologies is growing in several fields of healthcare settings. The aim of this article is to review the current applications of artificial intelligence in bariatric surgery. We performed a review of the literature on Scopus, PubMed and Cochrane databases, screening all relevant studies published until September 2021, and finally including 36 articles. The use of machine learning algorithms in bariatric surgery is explored in all steps of the clinical pathway, from presurgical risk-assessment and intraoperative management to complications and outcomes prediction. The models showed remarkable results helping physicians in the decision-making process, thus improving the quality of care, and contributing to precision medicine. Several legal and ethical hurdles should be overcome before these methods can be used in common practice.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Melania Turetti
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Paolo Del Rio
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Francesco Saturno
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Massimo Maffezzoni
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
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10
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Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med 2022; 11:jcm11082265. [PMID: 35456357 PMCID: PMC9031863 DOI: 10.3390/jcm11082265] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.
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Affiliation(s)
- Ștefan Busnatu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
| | - Alexandra Bolocan
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - George E. D. Petrescu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Dan Nicolae Păduraru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Iulian Năstasă
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Mircea Lupușoru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Marius Geantă
- Centre for Innovation in Medicine, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Octavian Andronic
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 50044 Bucharest, Romania
- Correspondence:
| | - Henrique Martins
- Faculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilha, Portugal;
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11
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A Scoping Review of Artificial Intelligence and Machine Learning in Bariatric and Metabolic Surgery: Current Status and Future Perspectives. Obes Surg 2021; 31:4555-4563. [PMID: 34264433 DOI: 10.1007/s11695-021-05548-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/12/2021] [Accepted: 06/17/2021] [Indexed: 01/01/2023]
Abstract
Artificial intelligence (AI) is a revolution in data analysis with emerging roles in various specialties and with various applications. The objective of this scoping review was to retrieve current literature on the fields of AI that have been applied to metabolic bariatric surgery (MBS) and to investigate potential applications of AI as a decision-making tool of the bariatric surgeon. Initial search yielded 3260 studies published from January 2000 until March 2021. After screening, 49 unique articles were included in the final analysis. Studies were grouped into categories, and the frequency of appearing algorithms, dataset types, and metrics were documented. The heterogeneity of current studies showed that meticulous validation, strict reporting systems, and reliable benchmarking are mandatory for ensuring the clinical validity of future research.
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