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Summers KL, Kerut EK, To F, Sheahan CM, Sheahan MG. Machine learning-based prediction of abdominal aortic aneurysms for individualized patient care. J Vasc Surg 2024; 79:1057-1067.e2. [PMID: 38185212 DOI: 10.1016/j.jvs.2023.12.046] [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: 08/11/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 01/09/2024]
Abstract
OBJECTIVE The United States Preventative Services Task Force guidelines for screening for abdominal aortic aneurysms (AAA) are broad and exclude many at risk groups. We analyzed a large AAA screening database to examine the utility of a novel machine learning (ML) model for predicting individual risk of AAA. METHODS We created a ML model to predict the presence of AAAs (>3 cm) from the database of a national nonprofit screening organization (AAAneurysm Outreach). Participants self-reported demographics and comorbidities. The model is a two-layered feed-forward shallow network. The ML model then generated AAA probability based on patient characteristics. We evaluated graphs to determine significant factors, and then compared those graphs with a traditional logistic regression model. RESULTS We analyzed a cohort of 10,033 patients with an AAA prevalence of 2.74%. Consistent with logistic regression analysis, the ML model identified the following predictors of AAA: Caucasian race, male gender, advancing age, and recent or past smoker with recent smoker having a more profound affect (P < .05). Interestingly, the ML model showed body mass index (BMI) was associated with likelihood of AAAs, especially for younger females. The ML model also identified a higher than predicted risk of AAA in several groups, including female nonsmokers with cardiac disease, female diabetics, those with a family history of AAA, and those with hypertension or hyperlipidemia at older ages. An elevated BMI conveyed a higher than expected risk in male smokers and all females. The ML model also identified a complex relationship of both diabetes mellitus and hyperlipidemia with gender. Family history of AAA was a more important risk factor in the ML model for both men and women too. CONCLUSIONS We successfully developed an ML model based on an AAA screening database that unveils a complex relationship between AAA prevalence and many risk factors, including BMI. The model also highlights the need to expand AAA screening efforts in women. Using ML models in the clinical setting has the potential to deliver precise, individualized screening recommendations.
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Affiliation(s)
- Kelli L Summers
- Division of Vascular Surgery, Department of Surgery, LSU Health Sciences Center, School of Medicine, New Orleans, LA.
| | - Edmund K Kerut
- Division of Cardiovascular Diseases, Department of Medicine, LSU Health Sciences Center, New Orleans, LA; Heart Clinic of Louisiana, Marrero, LA
| | - Filip To
- Department of Agricultural and Biological Engineering, Bagley College of Engineering, Mississippi State University, Mississippi State, MS
| | - Claudie M Sheahan
- Division of Vascular Surgery, Department of Surgery, LSU Health Sciences Center, School of Medicine, New Orleans, LA
| | - Malachi G Sheahan
- Division of Vascular Surgery, Department of Surgery, LSU Health Sciences Center, School of Medicine, New Orleans, LA
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2
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Ji S. SSC: The novel self-stack ensemble model for thyroid disease prediction. PLoS One 2024; 19:e0295501. [PMID: 38170718 PMCID: PMC10763970 DOI: 10.1371/journal.pone.0295501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 11/22/2023] [Indexed: 01/05/2024] Open
Abstract
Thyroid disease presents a significant health risk, lowering the quality of life and increasing treatment costs. The diagnosis of thyroid disease can be challenging, especially for inexperienced practitioners. Machine learning has been established as one of the methods for disease diagnosis based on previous studies. This research introduces a novel and more effective technique for predicting thyroid disease by utilizing machine learning methodologies, surpassing the performance of previous studies in this field. This study utilizes the UCI thyroid disease dataset, which consists of 9172 samples and 30 features, and exhibits a highly imbalanced target class distribution. However, machine learning algorithms trained on imbalanced thyroid disease data face challenges in reliably detecting minority data and disease. To address this issue, re-sampling is employed, which modifies the ratio between target classes to balance the data. In this study, the down-sampling approach is utilized to achieve a balanced distribution of target classes. A novel RF-based self-stacking classifier is presented in this research for efficient thyroid disease detection. The proposed approach demonstrates the ability to diagnose primary hypothyroidism, increased binding protein, compensated hypothyroidism, and concurrent non-thyroidal illness with an accuracy of 99.5%. The recommended model exhibits state-of-the-art performance, achieving 100% macro precision, 100% macro recall, and 100% macro F1-score. A thorough comparative assessment is conducted to demonstrate the viability of the proposed approach, including several machine learning classifiers, deep neural networks, and ensemble voting classifiers. The results of K-fold cross-validation provide further support for the efficacy of the proposed self-stacking classifier.
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Affiliation(s)
- Shengjun Ji
- School of information, Xi’an University of Finance and Economics, Xi’an, China
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3
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Long B, Cremat DL, Serpa E, Qian S, Blebea J. Applying Artificial Intelligence to Predict Complications After Endovascular Aneurysm Repair. Vasc Endovascular Surg 2024; 58:65-75. [PMID: 37429299 DOI: 10.1177/15385744231189024] [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] [Indexed: 07/12/2023]
Abstract
Objective: Complications after Endovascular Aneurysm Repair (EVAR) can be fatal. Patient follow-up for surveillance imaging is becoming more challenging as fewer patients are seen, particularly after the first year. The aim of this study was to develop an artificial intelligence model to predict the complication probability of individual patients to better identify those needing more intensive post-operative surveillance. Methods: Pre-operative CTA 3D reconstruction images of AAA from 273 patients who underwent EVAR from 2011-2020 were collected. Of these, 48 patients had post-operative complications including endoleak, AAA rupture, graft limb occlusion, renal artery occlusion, and neck dilation. A deep convolutional neural network model (VascAI©) was developed which utilized pre-operative 3D CT images to predict risk of complications after EVAR. The model was built with TensorFlow software and run on the Google Colab Platform. An initial training subset of 40 randomly selected patients with complications and 189 without were used to train the AI model while the remaining 8 positive and 36 negative cases tested its performance and prediction accuracy. Data down-sampling was used to alleviate data imbalance and data augmentation methodology to further boost model performance. Results: Successful training was completed on the 229 cases in the training set and then applied to predict the complication probability of each individual in the held-out performance testing cases. The model provided a complication sensitivity of 100% and identified all the patients who later developed complications after EVAR. Of 36 patients without complications, 16 (44%) were falsely predicted to develop complications. The results therefore demonstrated excellent sensitivity for identifying patients who would benefit from more stringent surveillance and decrease the frequency of surveillance in 56% of patients unlike to develop complications. Conclusion: AI models can be developed to predict the risk of post-operative complications with high accuracy. Compared to existing methods, the model developed in this study did not require any expert-annotated data but only the AAA CTA images as inputs. This model can play an assistive role in identifying patients at high risk for post-EVAR complications and the need for greater compliance in surveillance.
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Affiliation(s)
- Becky Long
- Department of Surgery, College of Medicine, Central Michigan University, Saginaw, MI, USA
| | - Danielle L Cremat
- Department of Surgery, College of Medicine, Central Michigan University, Saginaw, MI, USA
| | - Eduardo Serpa
- Department of Surgery, College of Medicine, Central Michigan University, Saginaw, MI, USA
| | - Sinong Qian
- Department of Surgery, College of Medicine, Central Michigan University, Saginaw, MI, USA
| | - John Blebea
- Department of Surgery, College of Medicine, Central Michigan University, Saginaw, MI, USA
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Amin A, Cardoso SA, Suyambu J, Abdus Saboor H, Cardoso RP, Husnain A, Isaac NV, Backing H, Mehmood D, Mehmood M, Maslamani ANJ. Future of Artificial Intelligence in Surgery: A Narrative Review. Cureus 2024; 16:e51631. [PMID: 38318552 PMCID: PMC10839429 DOI: 10.7759/cureus.51631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/03/2024] [Indexed: 02/07/2024] Open
Abstract
Artificial intelligence (AI) is the capability of a machine to execute cognitive processes that are typically considered to be functions of the human brain. It is the study of algorithms that enable machines to reason and perform mental tasks, including problem-solving, object and word recognition, and decision-making. Once considered science fiction, AI today is a fact and an increasingly prevalent subject in both academic and popular literature. It is expected to reshape medicine, benefiting both healthcare professionals and patients. Machine learning (ML) is a subset of AI that allows machines to learn and make predictions by recognizing patterns, thus empowering the medical team to deliver better care to patients through accurate diagnosis and treatment. ML is expanding its footprint in a variety of surgical specialties, including general surgery, ophthalmology, cardiothoracic surgery, and vascular surgery, to name a few. In recent years, we have seen AI make its way into the operating theatres. Though it has not yet been able to replace the surgeon, it has the potential to become a highly valuable surgical tool. Rest assured that the day is not far off when AI shall play a significant intraoperative role, a projection that is currently marred by safety concerns. This review aims to explore the present application of AI in various surgical disciplines and how it benefits both patients and physicians, as well as the current obstacles and limitations facing its seemingly unstoppable rise.
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Affiliation(s)
- Aamir Amin
- Cardiothoracic Surgery, Harefield Hospital, Guy's and St Thomas' NHS Foundation Trust, London, GBR
| | - Swizel Ann Cardoso
- Major Trauma Services, University Hospital Birmingham NHS Foundation Trust DC, Birmingham, GBR
| | - Jenisha Suyambu
- Medicine, University of Perpetual Help System Data - Jonelta Foundation School of Medicine, Las Piñas, PHL
| | | | - Rayner P Cardoso
- Medicine and Surgery, All India Institute of Medical Sciences, Jodhpur, Jodhpur, IND
| | - Ali Husnain
- Radiology, Northwestern University, Lahore, PAK
| | - Natasha Varghese Isaac
- Medicine and Surgery, St John's Medical College Hospital, Rajiv Gandhi University of Health Sciences, Bengaluru, IND
| | - Haydee Backing
- Medicine, Universidad de San Martin de Porres, Lima, PER
| | - Dalia Mehmood
- Community Medicine, Fatima Jinnah Medical University, Lahore, PAK
| | - Maria Mehmood
- Internal Medicine, Shalamar Medical and Dental College, Lahore, PAK
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Li B, Aljabri B, Verma R, Beaton D, Eisenberg N, Lee DS, Wijeysundera DN, Forbes TL, Rotstein OD, de Mestral C, Mamdani M, Roche-Nagle G, Al-Omran M. Using machine learning to predict outcomes following open abdominal aortic aneurysm repair. J Vasc Surg 2023; 78:1426-1438.e6. [PMID: 37634621 DOI: 10.1016/j.jvs.2023.08.121] [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: 07/12/2023] [Revised: 08/15/2023] [Accepted: 08/19/2023] [Indexed: 08/29/2023]
Abstract
OBJECTIVE Prediction of outcomes following open abdominal aortic aneurysm (AAA) repair remains challenging with a lack of widely used tools to guide perioperative management. We developed machine learning (ML) algorithms that predict outcomes following open AAA repair. METHODS The Vascular Quality Initiative (VQI) database was used to identify patients who underwent elective open AAA repair between 2003 and 2023. Input features included 52 preoperative demographic/clinical variables. All available preoperative variables from VQI were used to maximize predictive performance. The primary outcome was in-hospital major adverse cardiovascular event (MACE; composite of myocardial infarction, stroke, or death). Secondary outcomes were individual components of the primary outcome, other in-hospital complications, and 1-year mortality and any reintervention. We split our data into training (70%) and test (30%) sets. Using 10-fold cross-validation, six ML models were trained using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score. The top 10 predictive features in our final model were determined based on variable importance scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median area deprivation index, proximal clamp site, prior aortic surgery, and concomitant procedures. RESULTS Overall, 12,027 patients were included. The primary outcome of in-hospital MACE occurred in 630 patients (5.2%). Compared with patients without a primary outcome, those who developed in-hospital MACE were older with more comorbidities, demonstrated poorer functional status, had more complex aneurysms, and were more likely to require concomitant procedures. Our best performing prediction model for in-hospital MACE was XGBoost, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). Comparatively, logistic regression had an AUROC of 0.71 (95% confidence interval, 0.70-0.73). For secondary outcomes, XGBoost achieved AUROCs between 0.84 and 0.94. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.05. These findings highlight the excellent predictive performance of the XGBoost model. The top three predictive features in our algorithm for in-hospital MACE following open AAA repair were: (1) coronary artery disease; (2) American Society of Anesthesiologists classification; and (3) proximal clamp site. Model performance remained robust on all subgroup analyses. CONCLUSIONS Open AAA repair outcomes can be accurately predicted using preoperative data with our ML models, which perform better than logistic regression. Our automated algorithms can help guide risk-mitigation strategies for patients being considered for open AAA repair to improve outcomes.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada
| | - Badr Aljabri
- Department of Surgery, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Raj Verma
- School of Medicine, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin, Ireland
| | - Derek Beaton
- Data Science and Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Naomi Eisenberg
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Douglas S Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Department of Anesthesia, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Thomas L Forbes
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Ori D Rotstein
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Division of General Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Muhammad Mamdani
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada; Data Science and Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Graham Roche-Nagle
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia.
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6
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Acosta-Mérida MA. DATA GOVERNANCE in digital surgery. Cir Esp 2023:S2173-5077(23)00237-5. [PMID: 38042295 DOI: 10.1016/j.cireng.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 10/12/2023] [Indexed: 12/04/2023]
Abstract
Technological and computer advances have led to a "new era" of Surgery called Digital Surgery. In it, the management of information is the key. The development of Artificial Intelligence requires "Big Data" to create its algorithms. The use of digital technology for the systematic capture of data from the surgical process raises ethical issues of privacy, property, and consent. The use of these out-of-control data creates uncertainty and can be a source of mistrust and refusal by surgeons to allow its use, requiring a framework for the correct management of them. This paper exposes the current situation of Data Governance in Digital Surgery, the challenges posed and the lines of action necessary to resolve the areas of uncertainty that have arisen in the process, in which the surgeon must play a relevant role.
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Affiliation(s)
- María Asunción Acosta-Mérida
- Servicio de Cirugía General y Aparato Digestivo, Hospital Universitario de Gran Canaria Dr. Negrín, Las Palmas de Gran Canaria, Spain.
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Wang Y, Zhou M, Ding Y, Li X, Zhou Z, Xie T, Shi Z, Fu W. A radiomics model for predicting the outcome of endovascular abdominal aortic aneurysm repair based on machine learning. Vascular 2023; 31:654-663. [PMID: 35440250 DOI: 10.1177/17085381221091061] [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] [Indexed: 11/15/2022]
Abstract
OBJECTIVE This study aimed to develop a radiomics model to predict the outcome of endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA), based on machine learning (ML) algorithms. METHODS We retrospectively reviewed 711 patients with infra-renal AAA who underwent elective EVAR procedures between January 2016 and December 2019 at our single center. The radiomics features of AAA were extracted using Pyradiomics. Pearson correlation analysis, analysis of variance (ANOVA), least absolute shrinkage, and selection operator (LASSO) regression were applied to determine the predictors for EVAR-related severe adverse events (SAEs). Eighty percent of patients were classified as the training set and the remaining 20 percent of patients were classified as the test set. The selected features were used to build a radiomics model in training set using different ML algorithms. The performance of each model was assessed using the area under the curve (AUC) from the receiver operating characteristic (ROC) curve in the test set. RESULTS A total of 493 patients were enrolled in this study, the mean follow-up time was 32 months. During the follow-up, 156 (31.6%) patients experienced EVAR-related SAEs. A total of 1223 radiomics features were extracted from each patient, of which 30 radiomics features were finally identified. The quantitative performance assessment and the ROC curves indicated that the logistics regression (LR) model had better predictive value than others, with accuracy, 0.86; AUC, 0.93; and F1 score, 0.91. The Rad-score waterfall plot showed that the overall amount of error was small both in the training set and in the test set. Calibration curve showed that the calibration degree of the training set and the test set were good (p > 0.05). Decision curve analysis (threshold 0.32) demonstrated that the model had good clinical applicability. CONCLUSION Our radiomics model could be used as an efficient and adjunctive tool to predict the outcome after EVAR.
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Affiliation(s)
- Yonggang Wang
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Min Zhou
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yong Ding
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xu Li
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhenyu Zhou
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Tianchen Xie
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhenyu Shi
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weiguo Fu
- Department of Vascular Surgery, National Clinical Research Center for Interventional Medicine, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
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Geronzi L, Haigron P, Martinez A, Yan K, Rochette M, Bel-Brunon A, Porterie J, Lin S, Marin-Castrillon DM, Lalande A, Bouchot O, Daniel M, Escrig P, Tomasi J, Valentini PP, Biancolini ME. Assessment of shape-based features ability to predict the ascending aortic aneurysm growth. Front Physiol 2023; 14:1125931. [PMID: 36950300 PMCID: PMC10025384 DOI: 10.3389/fphys.2023.1125931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/24/2023] [Indexed: 03/08/2023] Open
Abstract
The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR-). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR- (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR- (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease.
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Affiliation(s)
- Leonardo Geronzi
- Department of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, Italy
- Ansys France, Villeurbanne, France
- *Correspondence: Leonardo Geronzi,
| | - Pascal Haigron
- LTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, France
| | - Antonio Martinez
- Department of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, Italy
- Ansys France, Villeurbanne, France
| | - Kexin Yan
- Ansys France, Villeurbanne, France
- LaMCoS, Laboratoire de Mécanique des Contacts et des Structures, CNRS UMR5259, INSA Lyon, University of Lyon, Villeurbanne, France
| | | | - Aline Bel-Brunon
- LaMCoS, Laboratoire de Mécanique des Contacts et des Structures, CNRS UMR5259, INSA Lyon, University of Lyon, Villeurbanne, France
| | - Jean Porterie
- Cardiac Surgery Department, Rangueil University Hospital, Toulouse, France
| | - Siyu Lin
- IMVIA Laboratory, University of Burgundy, Dijon, France
- Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | - Diana Marcela Marin-Castrillon
- IMVIA Laboratory, University of Burgundy, Dijon, France
- Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | - Alain Lalande
- IMVIA Laboratory, University of Burgundy, Dijon, France
- Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | - Olivier Bouchot
- Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, Dijon, France
| | - Morgan Daniel
- LTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, France
| | - Pierre Escrig
- LTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, France
| | - Jacques Tomasi
- LTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, France
| | - Pier Paolo Valentini
- Department of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, Italy
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9
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Fan ZX, Wang CB, Fang LB, Ma L, Niu TT, Wang ZY, Lu JF, Yuan BY, Liu GZ. Risk factors and a Bayesian network model to predict ischemic stroke in patients with dilated cardiomyopathy. Front Neurosci 2022; 16:1043922. [PMID: 36440270 PMCID: PMC9683474 DOI: 10.3389/fnins.2022.1043922] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 10/25/2022] [Indexed: 04/03/2024] Open
Abstract
OBJECTIVE This study aimed to identify risk factors and create a predictive model for ischemic stroke (IS) in patients with dilated cardiomyopathy (DCM) using the Bayesian network (BN) approach. MATERIALS AND METHODS We collected clinical data of 634 patients with DCM treated at three referral management centers in Beijing between 2016 and 2021, including 127 with and 507 without IS. The patients were randomly divided into training (441 cases) and test (193 cases) sets at a ratio of 7:3. A BN model was established using the Tabu search algorithm with the training set data and verified with the test set data. The BN and logistic regression models were compared using the area under the receiver operating characteristic curve (AUC). RESULTS Multivariate logistic regression analysis showed that hypertension, hyperlipidemia, atrial fibrillation/flutter, estimated glomerular filtration rate (eGFR), and intracardiac thrombosis were associated with IS. The BN model found that hyperlipidemia, atrial fibrillation (AF) or atrial flutter, eGFR, and intracardiac thrombosis were closely associated with IS. Compared to the logistic regression model, the BN model for IS performed better or equally well in the training and test sets, with respective accuracies of 83.7 and 85.5%, AUC of 0.763 [95% confidence interval (CI), 0.708-0.818] and 0.822 (95% CI, 0.748-0.896), sensitivities of 20.2 and 44.2%, and specificities of 98.3 and 97.3%. CONCLUSION Hypertension, hyperlipidemia, AF or atrial flutter, low eGFR, and intracardiac thrombosis were good predictors of IS in patients with DCM. The BN model was superior to the traditional logistic regression model in predicting IS in patients with DCM and is, therefore, more suitable for early IS detection and diagnosis, and could help prevent the occurrence and recurrence of IS in this patient cohort.
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Affiliation(s)
- Ze-Xin Fan
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Chao-Bin Wang
- Department of Neurology, Beijing Fangshan District Liangxiang Hospital, Beijing, China
| | - Li-Bo Fang
- Department of Neurology, Beijing Fuxing Hospital, Capital Medical University, Beijing, China
| | - Lin Ma
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Tian-Tong Niu
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Ze-Yi Wang
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Jian-Feng Lu
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Bo-Yi Yuan
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Guang-Zhi Liu
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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10
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Fransvea P, Fransvea G, Liuzzi P, Sganga G, Mannini A, Costa G. Study and validation of an explainable machine learning-based mortality prediction following emergency surgery in the elderly: A prospective observational study. Int J Surg 2022; 107:106954. [PMID: 36229017 DOI: 10.1016/j.ijsu.2022.106954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/07/2022] [Accepted: 10/03/2022] [Indexed: 10/31/2022]
Abstract
INTRODUCTION The heterogeneity of procedures and the variety of comorbidities of the patients undergoing surgery in an emergency setting makes perioperative risk stratification, planning, and risk mitigation crucial. In this optic, Machine Learning has the capability of deriving data-driven predictions based on multivariate interactions of thousands of instances. Our aim was to cross-validate and test interpretable models for the prediction of post-operative mortality after any surgery in an emergency setting on elderly patients. METHODS This study is a secondary analysis derived from the FRAILESEL study, a multi-center (N = 29 emergency care units), nationwide, observational prospective study with data collected between 06-2017 and 06-2018 investigating perioperative outcomes of elderly patients (age≥65 years) undergoing emergency surgery. Demographic and clinical data, medical and surgical history, preoperative risk factors, frailty, biochemical blood examination, vital parameters, and operative details were collected and the primary outcome was set to the 30-day mortality. RESULTS Of the 2570 included patients (50.66% males, median age 77 [IQR = 13] years) 238 (9.26%) were in the non-survivors group. The best performing solution (MultiLayer Perceptron) resulted in a test accuracy of 94.9% (sensitivity = 92.0%, specificity = 95.2%). Model explanations showed how non-chronic cardiac-related comorbidities reduced activities of daily living, low consciousness levels, high creatinine and low saturation increase the risk of death following surgery. CONCLUSIONS In this prospective observational study, a robustly cross-validated model resulted in better predictive performance than existing tools and scores in literature. By using only preoperative features and by deriving patient-specific explanations, the model provides crucial information during shared decision-making processes required for risk mitigation procedures.
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Affiliation(s)
- Pietro Fransvea
- Emergency Surgery and Trauma, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Largo A. Gemelli 8, Rome, Italy The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera, PI, Italy IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy Surgery Center, Colorectal Surgery Unit - Fondazione Policlinico Campus Bio-Medico, University Hospital of University Campus Bio-Medico of Rome, Rome, Italy
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11
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Lam K, Abràmoff MD, Balibrea JM, Bishop SM, Brady RR, Callcut RA, Chand M, Collins JW, Diener MK, Eisenmann M, Fermont K, Neto MG, Hager GD, Hinchliffe RJ, Horgan A, Jannin P, Langerman A, Logishetty K, Mahadik A, Maier-Hein L, Antona EM, Mascagni P, Mathew RK, Müller-Stich BP, Neumuth T, Nickel F, Park A, Pellino G, Rudzicz F, Shah S, Slack M, Smith MJ, Soomro N, Speidel S, Stoyanov D, Tilney HS, Wagner M, Darzi A, Kinross JM, Purkayastha S. A Delphi consensus statement for digital surgery. NPJ Digit Med 2022; 5:100. [PMID: 35854145 PMCID: PMC9296639 DOI: 10.1038/s41746-022-00641-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 06/24/2022] [Indexed: 12/13/2022] Open
Abstract
The use of digital technology is increasing rapidly across surgical specialities, yet there is no consensus for the term ‘digital surgery’. This is critical as digital health technologies present technical, governance, and legal challenges which are unique to the surgeon and surgical patient. We aim to define the term digital surgery and the ethical issues surrounding its clinical application, and to identify barriers and research goals for future practice. 38 international experts, across the fields of surgery, AI, industry, law, ethics and policy, participated in a four-round Delphi exercise. Issues were generated by an expert panel and public panel through a scoping questionnaire around key themes identified from the literature and voted upon in two subsequent questionnaire rounds. Consensus was defined if >70% of the panel deemed the statement important and <30% unimportant. A final online meeting was held to discuss consensus statements. The definition of digital surgery as the use of technology for the enhancement of preoperative planning, surgical performance, therapeutic support, or training, to improve outcomes and reduce harm achieved 100% consensus agreement. We highlight key ethical issues concerning data, privacy, confidentiality and public trust, consent, law, litigation and liability, and commercial partnerships within digital surgery and identify barriers and research goals for future practice. Developers and users of digital surgery must not only have an awareness of the ethical issues surrounding digital applications in healthcare, but also the ethical considerations unique to digital surgery. Future research into these issues must involve all digital surgery stakeholders including patients.
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Affiliation(s)
- Kyle Lam
- Department of Surgery and Cancer, Imperial College, London, UK.,Institute of Global Health Innovation, Imperial College London, London, UK
| | - Michael D Abràmoff
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA.,Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
| | - José M Balibrea
- Department of Gastrointestinal Surgery, Hospital Clínic de Barcelona, Barcelona, Spain.,Universitat de Barcelona, Barcelona, Spain
| | | | - Richard R Brady
- Newcastle Centre for Bowel Disease Research Hub, Newcastle University, Newcastle, UK.,Department of Colorectal Surgery, Newcastle Hospitals, Newcastle, UK
| | | | - Manish Chand
- Department of Surgery and Interventional Sciences, University College London, London, UK
| | - Justin W Collins
- CMR Surgical Limited, Cambridge, UK.,Department of Surgery and Interventional Sciences, University College London, London, UK
| | - Markus K Diener
- Department of General and Visceral Surgery, University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Matthias Eisenmann
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kelly Fermont
- Solicitor of the Senior Courts of England and Wales, Independent Researcher, Bristol, UK
| | - Manoel Galvao Neto
- Endovitta Institute, Sao Paulo, Brazil.,FMABC Medical School, Santo Andre, Brazil
| | - Gregory D Hager
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, MD, USA.,Department of Computer Science, The Johns Hopkins University, Baltimore, MD, USA
| | | | - Alan Horgan
- Department of Colorectal Surgery, Newcastle Hospitals, Newcastle, UK
| | - Pierre Jannin
- LTSI, Inserm UMR 1099, University of Rennes 1, Rennes, France
| | - Alexander Langerman
- Otolaryngology, Head & Neck Surgery and Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.,International Centre for Surgical Safety, Li Ka Shing Knowledge Institute, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | | | | | - Lena Maier-Hein
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany.,Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.,Medical Faculty, Heidelberg University, Heidelberg, Germany.,LKSK Institute of St. Michael's Hospital, Toronto, ON, Canada
| | | | - Pietro Mascagni
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,IHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France.,ICube, University of Strasbourg, Strasbourg, France
| | - Ryan K Mathew
- School of Medicine, University of Leeds, Leeds, UK.,Department of Neurosurgery, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Beat P Müller-Stich
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.,National Center for Tumor Diseases, Heidelberg, Germany
| | - Thomas Neumuth
- Innovation Center Computer Assisted Surgery (ICCAS), Universität Leipzig, Leipzig, Germany
| | - Felix Nickel
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Adrian Park
- Department of Surgery, Anne Arundel Medical Center, School of Medicine, Johns Hopkins University, Annapolis, MD, USA
| | - Gianluca Pellino
- Department of Advanced Medical and Surgical Sciences, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy.,Colorectal Surgery, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Frank Rudzicz
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.,Vector Institute for Artificial Intelligence, Toronto, ON, Canada.,Unity Health Toronto, Toronto, ON, Canada.,Surgical Safety Technologies Inc, Toronto, ON, Canada
| | - Sam Shah
- Faculty of Future Health, College of Medicine and Dentistry, Ulster University, Birmingham, UK
| | - Mark Slack
- CMR Surgical Limited, Cambridge, UK.,Department of Urogynaecology, Addenbrooke's Hospital, Cambridge, UK.,University of Cambridge, Cambridge, UK
| | - Myles J Smith
- The Royal Marsden Hospital, London, UK.,Institute of Cancer Research, London, UK
| | - Naeem Soomro
- Department of Urology, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Stefanie Speidel
- Division of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany.,Centre for Tactile Internet with Human-in-the-Loop (CeTI), TU Dresden, Dresden, Germany
| | - Danail Stoyanov
- Wellcome/ESPRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Henry S Tilney
- Department of Surgery and Cancer, Imperial College, London, UK.,Department of Colorectal Surgery, Frimley Health NHS Foundation Trust, Frimley, UK
| | - Martin Wagner
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.,National Center for Tumor Diseases, Heidelberg, Germany
| | - Ara Darzi
- Department of Surgery and Cancer, Imperial College, London, UK.,Institute of Global Health Innovation, Imperial College London, London, UK
| | - James M Kinross
- Department of Surgery and Cancer, Imperial College, London, UK.
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12
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van de Sande D, van Genderen ME, Verhoef C, Huiskens J, Gommers D, van Unen E, Schasfoort RA, Schepers J, van Bommel J, Grünhagen DJ. Optimizing discharge after major surgery using an artificial intelligence-based decision support tool (DESIRE): An external validation study. Surgery 2022; 172:663-669. [PMID: 35525621 DOI: 10.1016/j.surg.2022.03.031] [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: 11/11/2021] [Revised: 03/15/2022] [Accepted: 03/21/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND In the DESIRE study (Discharge aftEr Surgery usIng aRtificial intElligence), we have previously developed and validated a machine learning concept in 1,677 gastrointestinal and oncology surgery patients that can predict safe hospital discharge after the second postoperative day. Despite strong model performance (area under the receiver operating characteristics curve of 0.88) in an academic surgical population, it remains unknown whether these findings can be translated to other hospitals and surgical populations. We therefore aimed to determine the generalizability of the previously developed machine learning concept. METHODS We externally validated the machine learning concept in gastrointestinal and oncology surgery patients admitted to 3 nonacademic hospitals in The Netherlands between January 2017 and June 2021, who remained admitted 2 days after surgery. Primary outcome was the ability to predict hospital interventions after the second postoperative day, which were defined as unplanned reoperations, radiological interventions, and/or intravenous antibiotics administration. Four forest models were locally trained and evaluated with respect to area under the receiver operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS All models were trained on 1,693 epsiodes, of which 731 (29.9%) required a hospital intervention and demonstrated strong performance (area under the receiver operating characteristics curve only varied 4%). The best model achieved an area under the receiver operating characteristics curve of 0.83 (95% confidence interval [0.81-0.85]), sensitivity of 77.9% (0.67-0.87), specificity of 79.2% (0.72-0.85), positive predictive value of 61.6% (0.54-0.69), and negative predictive value of 89.3% (0.85-0.93). CONCLUSION This study showed that a previously developed machine learning concept can predict safe discharge in different surgical populations and hospital settings (academic versus nonacademic) by training a model on local patient data. Given its high accuracy, integration of the machine learning concept into the clinical workflow could expedite surgical discharge and aid hospitals in addressing capacity challenges by reducing avoidable bed-days.
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Affiliation(s)
- Davy van de Sande
- Department of Adult Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands. https://twitter.com/davy_sande
| | - Michel E van Genderen
- Department of Adult Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands.
| | - Cornelis Verhoef
- Department of Surgical Oncology, Erasmus MC Cancer Institute University Medical Center, Rotterdam, The Netherlands
| | | | - Diederik Gommers
- Department of Adult Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | | | - Judith Schepers
- Department of Business Intelligence, Treant Care Group, Emmen, The Netherlands
| | - Jasper van Bommel
- Department of Adult Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Dirk J Grünhagen
- Department of Surgical Oncology, Erasmus MC Cancer Institute University Medical Center, Rotterdam, The Netherlands
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13
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Nabaei M. Cerebral aneurysm evolution modeling from microstructural computational models to machine learning: A review. Comput Biol Chem 2022; 98:107676. [DOI: 10.1016/j.compbiolchem.2022.107676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 02/13/2022] [Accepted: 03/30/2022] [Indexed: 11/03/2022]
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14
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Luo C, Zhu Y, Zhu Z, Li R, Chen G, Wang Z. A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure. J Transl Med 2022; 20:136. [PMID: 35303896 PMCID: PMC8932070 DOI: 10.1186/s12967-022-03340-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/06/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Predicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units. METHODS Using a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients' clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. The logistic regression model and a common risk score for mortality were used for comparison. The eICU Collaborative Research Database dataset was used for external validation. RESULTS The performance of the machine learning model was superior to that of conventional risk predictive methods, with the area under curve 0.831 (95% CI 0.820-0.843) and acceptable calibration. In external validation, the model had an area under the curve of 0.809 (95% CI 0.805-0.814). Risk stratification through the model was specific when the hospital mortality was very low, low, moderate, high, and very high (2.0%, 10.2%, 11.5%, 21.2% and 56.2%, respectively). The decision curve analysis verified that the machine learning model is the best clinically valuable in predicting mortality risk. CONCLUSION Using readily available clinical data in the intensive care unit, we built a machine learning-based mortality risk tool with prediction accuracy superior to that of linear regression model and common risk scores. The risk tool may support clinicians in assessing individual patients and making individualized treatment.
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Affiliation(s)
- Cida Luo
- South China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, 511400, Guangdong, China.,School of Life Sciences, South China Normal University, Guangzhou, 510631, Guangdong, China
| | - Yi Zhu
- Department of Cardiology, Guangzhou Panyu Central Hospital, Guangzhou, 511400, Guangdong, China
| | - Zhou Zhu
- South China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, 511400, Guangdong, China.,School of Life Sciences, South China Normal University, Guangzhou, 510631, Guangdong, China
| | - Ranxi Li
- South China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, 511400, Guangdong, China.,School of Life Sciences, South China Normal University, Guangzhou, 510631, Guangdong, China
| | - Guoqin Chen
- Department of Cardiology, Guangzhou Panyu Central Hospital, Guangzhou, 511400, Guangdong, China.
| | - Zhang Wang
- South China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, 511400, Guangdong, China. .,School of Life Sciences, South China Normal University, Guangzhou, 510631, Guangdong, China.
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15
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Pan T, Jiang X, Liu H, Liu Y, Fu W, Dong Z. Prediction of 2-Year Major Adverse Limb Event-Free Survival After Percutaneous Transluminal Angioplasty and Stenting for Lower Limb Atherosclerosis Obliterans: A Machine Learning-Based Study. Front Cardiovasc Med 2022; 9:783336. [PMID: 35224037 PMCID: PMC8863671 DOI: 10.3389/fcvm.2022.783336] [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: 09/26/2021] [Accepted: 01/06/2022] [Indexed: 11/23/2022] Open
Abstract
Background The current scoring systems could not predict prognosis after endovascular therapy for peripheral artery disease. Machine learning could make predictions for future events by learning a specific pattern from existing data. This study aimed to demonstrate machine learning could make an accurate prediction for 2-year major adverse limb event-free survival (MFS) after percutaneous transluminal angioplasty (PTA) and stenting for lower limb atherosclerosis obliterans (ASO). Methods A lower limb ASO cohort of 392 patients who received PTA and stenting was split to the training set and test set by 4:1 in chronological order. Demographic, medical, and imaging data were used to build machine learning models to predict 2-year MFS. The discrimination and calibration of artificial neural network (ANN) and random forest models were compared with the logistic regression model, using the area under the receiver operating curve (ROCAUC) with DeLong test, and the calibration curve with Hosmer–Lemeshow goodness-of-fit test, respectively. Results The ANN model (ROCAUC = 0.80, 95% CI: 0.68–0.89) but not the random forest model (ROCAUC = 0.78, 95% CI: 0.66–0.87) significantly outperformed the logistic regression model (ROCAUC = 0.73, 95% CI: 0.60–0.83, P = 0.01 and P = 0.24). The ANN model the logistic regression model demonstrated good calibration performance (P = 0.73 and P = 0.28), while the random forest model showed poor calibration (P < 0.01). The calibration curve of the ANN model was visually the closest to the perfectly calibrated line. Conclusion Machine learning models could accurately predict 2-year MFS after PTA and stenting for lower limb ASO, in which the ANN model had better discrimination and calibration. Machine learning-derived prediction tools might be clinically useful to automatically identify candidates for PTA and stenting.
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Affiliation(s)
- Tianyue Pan
- Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Xiaolang Jiang
- Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Hao Liu
- Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Yifan Liu
- Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Weiguo Fu
- Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
- *Correspondence: Weiguo Fu
| | - Zhihui Dong
- Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
- Zhihui Dong
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16
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Mijwil MM, Aggarwal K. A diagnostic testing for people with appendicitis using machine learning techniques. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:7011-7023. [PMID: 35095329 PMCID: PMC8785023 DOI: 10.1007/s11042-022-11939-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/09/2021] [Accepted: 01/03/2022] [Indexed: 05/07/2023]
Abstract
Appendicitis is a common disease that occurs particularly often in childhood and adolescence. The accurate diagnosis of acute appendicitis is the most significant precaution to avoid severe unnecessary surgery. In this paper, the author presents a machine learning (ML) technique to predict appendix illness whether it is acute or subacute, especially between 10 and 30 years and whether it requires an operation or just taking medication for treatment. The dataset has been collected from public hospital-based citizens between 2016 and 2019. The predictive results of the models achieved by different ML techniques (Logistic Regression, Naïve Bayes, Generalized Linear, Decision Tree, Support Vector Machine, Gradient Boosted Tree, Random Forest) are compared. The covered dataset are 625 specimens and the total of the medical records that are applied in this paper include 371 males (60.22%) and 254 females (40.12%). According to the dataset, the records consist of 318 (50.88%) operated and 307 (49.12%) unoperated patients. It is observed that the random forest algorithm obtains the optimal result with an accurately predicted result of 83.75%, precision of 84.11%, sensitivity of 81.08%, and the specificity of 81.01%. Moreover, an estimation method based on ML techniques is improved and enhanced to detect individuals with acute appendicitis.
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Affiliation(s)
- Maad M. Mijwil
- Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq
| | - Karan Aggarwal
- Electronics and Communication Engineering Department, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India
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17
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Abstract
In this paper, we focus on providing a narrative review of healthcare services in which artificial intelligence (AI) based services are used as part of the operations and analyze key elements to create successful AI-based services for healthcare. The benefits of AI in healthcare are measured by how AI is improving the healthcare outcomes, help caregivers in work, and reducing healthcare costs. AI market in healthcare sector have also a high market potential with 28% global compound annual growth rate. This paper will collect outcomes from multiple perspectives of healthcare sector including financial, health improvement, and care outcome as well as provide proposals and key factors for successful implementation of AI methods in healthcare. It is shown in this paper that AI implementation in healthcare can provide cost reduction and same time provide better health outcome for all.
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18
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Vellameeran FA, Brindha T. An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps. BIO-ALGORITHMS AND MED-SYSTEMS 2021. [DOI: 10.1515/bams-2020-0069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Abstract
Objectives
To make a clear literature review on state-of-the-art heart disease prediction models.
Methods
It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed.
Results
The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions.
Conclusions
The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.
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Affiliation(s)
| | - Thomas Brindha
- Department of Information Technology , Noorul Islam Centre for Higher Education , Kanyakumari , India
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19
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Understanding current states of machine learning approaches in medical informatics: a systematic literature review. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00538-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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20
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Amigo N, Valencia A, Wu W, Patnaik S, Finol E. Cerebral aneurysm rupture status classification using statistical and machine learning methods. Proc Inst Mech Eng H 2021; 235:655-662. [PMID: 33685288 DOI: 10.1177/09544119211000477] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Morphological characterization and fluid dynamics simulations were carried out to classify the rupture status of 71 (36 unruptured, 35 ruptured) patient specific cerebral aneurysms using a machine learning approach together with statistical techniques. Eleven morphological and six hemodynamic parameters were evaluated individually and collectively for significance as rupture status predictors. The performance of each parameter was inspected using hypothesis testing, accuracy, confusion matrix, and the area under the receiver operating characteristic curve. Overall, the size ratio exhibited the best performance, followed by the diastolic wall shear stress, and systolic wall shear stress. The prediction capability of all 17 parameters together was evaluated using eight different machine learning algorithms. The logistic regression achieved the highest accuracy (0.75), whereas the random forest had the highest area under curve value among all the classifiers (0.82), surpassing the performance exhibited by the size ratio. Hence, we propose the random forest model as a tool that can help improve the rupture status prediction of cerebral aneurysms.
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Affiliation(s)
- Nicolás Amigo
- Escuela de Data Science, Facultad de Estudios Interdisciplinarios, Universidad Mayor, Santiago, Chile
| | - Alvaro Valencia
- Departamento de Ingeniera Mecánica, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago, Chile
| | - Wei Wu
- Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX, USA.,Cardiovascular Division, College of Medicine, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Sourav Patnaik
- Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX, USA.,Department of Bioengineering, University of Texas at Dallas, Dallas, TX, USA
| | - Ender Finol
- Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX, USA
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21
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Melstrom LG, Rodin AS, Rossi LA, Fu P, Fong Y, Sun V. Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning. J Surg Oncol 2020; 123:52-60. [PMID: 32974930 DOI: 10.1002/jso.26232] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 09/11/2020] [Indexed: 12/16/2022]
Abstract
In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We will also describe the critical role of artificial intelligence and machine-learning methodology in the efficient translation of PGHD, PROs, and traditional outcome measures into meaningful patient care models.
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Affiliation(s)
- Laleh G Melstrom
- Department of Surgery, City of Hope National Medical Center, Duarte, California, USA
| | - Andrei S Rodin
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, USA
| | - Lorenzo A Rossi
- Applied AI and Data Science Department, City of Hope National Medical Center, Duarte, California, USA
| | - Paul Fu
- Department of Pediatrics, City of Hope National Medical Center, Duarte, California, USA
| | - Yuman Fong
- Department of Surgery, City of Hope National Medical Center, Duarte, California, USA
| | - Virginia Sun
- Department of Surgery, City of Hope National Medical Center, Duarte, California, USA.,Department of Population Sciences, City of Hope National Medical Center, Duarte, California, USA
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22
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Affiliation(s)
- Elif Bilgic
- Department of Surgery, Division of Surgical Education, McGill University, McGill University Health Centre, 1650 Cedar Avenue, #D6.136, Montreal, Quebec H3G 1A4, Canada
| | - Sofia Valanci-Aroesty
- Department of Surgery, Division of Experimental Surgery, McGill University, McGill University Health Centre, 1650 Cedar Avenue, #D6.136, Montreal, Quebec H3G 1A4, Canada
| | - Gerald M Fried
- Department of Surgery, McGill University, McGill University Health Centre, 1650 Cedar Avenue, #D6.136, Montreal, Quebec H3G 1A4, Canada.
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Mangano A, Valle V, Dreifuss NH, Aguiluz G, Masrur MA. Role of Artificial Intelligence (AI) in Surgery: Introduction, General Principles, and Potential Applications. Surg Technol Int 2020; 38:17-21. [PMID: 33370842 DOI: 10.52198/21.sti.38.so1369] [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: 06/12/2023]
Abstract
AI (Artificial intelligence) is an interdisciplinary field aimed at the development of algorithms to endow machines with the capability of executing cognitive tasks. The number of publications regarding AI and surgery has increased dramatically over the last two decades. This phenomenon can partly be explained by the exponential growth in computing power available to the largest AI training runs. AI can be classified into different sub-domains with extensive potential clinical applications in the surgical setting. AI will increasingly become a major component of clinical practice in surgery. The aim of the present Narrative Review is to give a general introduction and summarized overview of AI, as well as to present additional remarks on potential surgical applications and future perspectives in surgery.
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Affiliation(s)
- Alberto Mangano
- Division of General, Minimally Invasive and Robotic Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Valentina Valle
- Division of General, Minimally Invasive and Robotic Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Nicolas H Dreifuss
- Division of General, Minimally Invasive and Robotic Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Gabriela Aguiluz
- Division of General, Minimally Invasive and Robotic Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Mario A Masrur
- Division of General, Minimally Invasive and Robotic Surgery, University of Illinois at Chicago, Chicago, IL, USA
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Raffort J, Adam C, Carrier M, Ballaith A, Coscas R, Jean-Baptiste E, Hassen-Khodja R, Chakfé N, Lareyre F. Artificial intelligence in abdominal aortic aneurysm. J Vasc Surg 2020; 72:321-333.e1. [PMID: 32093909 DOI: 10.1016/j.jvs.2019.12.026] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 12/07/2019] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the only curative treatment relies on open or endovascular repair. The decision to treat relies on the evaluation of the risk of AAA growth and rupture, which can be difficult to assess in practice. Artificial intelligence (AI) has revealed new insights into the management of cardiovascular diseases, but its application in AAA has so far been poorly described. The aim of this review was to summarize the current knowledge on the potential applications of AI in patients with AAA. METHODS A comprehensive literature review was performed. The MEDLINE database was searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search strategy used a combination of keywords and included studies using AI in patients with AAA published between May 2019 and January 2000. Two authors independently screened titles and abstracts and performed data extraction. The search of published literature identified 34 studies with distinct methodologies, aims, and study designs. RESULTS AI was used in patients with AAA to improve image segmentation and for quantitative analysis and characterization of AAA morphology, geometry, and fluid dynamics. AI allowed computation of large data sets to identify patterns that may be predictive of AAA growth and rupture. Several predictive and prognostic programs were also developed to assess patients' postoperative outcomes, including mortality and complications after endovascular aneurysm repair. CONCLUSIONS AI represents a useful tool in the interpretation and analysis of AAA imaging by enabling automatic quantitative measurements and morphologic characterization. It could be used to help surgeons in preoperative planning. AI-driven data management may lead to the development of computational programs for the prediction of AAA evolution and risk of rupture as well as postoperative outcomes. AI could also be used to better evaluate the indications and types of surgical treatment and to plan the postoperative follow-up. AI represents an attractive tool for decision-making and may facilitate development of personalized therapeutic approaches for patients with AAA.
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Affiliation(s)
- Juliette Raffort
- Clinical Chemistry Laboratory, University Hospital of Nice, Nice, France; Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France
| | - Cédric Adam
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, Paris, France
| | - Marion Carrier
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, Paris, France
| | - Ali Ballaith
- Department of Vascular Surgery, University Hospital of Nice, Nice, France
| | - Raphael Coscas
- Department of Vascular Surgery, Ambroise Paré University Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Boulogne, France; Inserm U1018 Team 5, Versailles-Saint-Quentin et Paris-Saclay Universities, Versailles, France
| | - Elixène Jean-Baptiste
- Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France; Department of Vascular Surgery, University Hospital of Nice, Nice, France
| | - Réda Hassen-Khodja
- Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France; Department of Vascular Surgery, University Hospital of Nice, Nice, France
| | - Nabil Chakfé
- Department of Vascular Surgery and Kidney Transplantation, University Hospital of Strasbourg, and GEPROVAS, Strasbourg, France
| | - Fabien Lareyre
- Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France; Department of Vascular Surgery, University Hospital of Nice, Nice, France.
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25
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Artificial intelligence, machine learning, vascular surgery, automatic image processing. Implications for clinical practice. ANGIOLOGIA 2020. [DOI: 10.20960/angiologia.00177] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Raffort J, Adam C, Carrier M, Lareyre F. Fundamentals in Artificial Intelligence for Vascular Surgeons. Ann Vasc Surg 2019; 65:254-260. [PMID: 31857229 DOI: 10.1016/j.avsg.2019.11.037] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 11/17/2019] [Accepted: 11/21/2019] [Indexed: 12/31/2022]
Abstract
Artificial intelligence (AI) corresponds to a broad discipline that aims to design systems, which display properties of human intelligence. While it has led to many advances and applications in daily life, its introduction in medicine is still in its infancy. AI has created interesting perspectives for medical research and clinical practice but has been sometimes associated with hype leading to a misunderstanding of its real capabilities. Here, we aim to introduce the fundamental notions of AI and to bring an overview of its potential applications for medical and surgical practice. In the limelight of current knowledge, limits and challenges to face as well as future directions are discussed.
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Affiliation(s)
- Juliette Raffort
- Clinical Chemistry Laboratory, University Hospital of Nice, Nice, France; Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France.
| | - Cédric Adam
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, Paris, France
| | - Marion Carrier
- Laboratory of Applied Mathematics and Computer Science (MICS), CentraleSupélec, Université Paris-Saclay, Paris, France
| | - Fabien Lareyre
- Université Côte d'Azur, CHU, Inserm U1065, C3M, Nice, France; Department of Vascular Surgery, University Hospital of Nice, Nice, France
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Rodriguez F, Scheinker D, Harrington RA. Promise and Perils of Big Data and Artificial Intelligence in Clinical Medicine and Biomedical Research. Circ Res 2019; 123:1282-1284. [PMID: 30566055 DOI: 10.1161/circresaha.118.314119] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Fatima Rodriguez
- From the Division of Cardiovascular Medicine, Cardiovascular Institute (F.R., R.A.H.), Department of Medicine (F.R., R.A.H.)
| | - David Scheinker
- Department of Management Science and Engineering (D.S.), Stanford University, CA
| | - Robert A Harrington
- From the Division of Cardiovascular Medicine, Cardiovascular Institute (F.R., R.A.H.), Department of Medicine (F.R., R.A.H.)
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Abstract
OBJECTIVES This survey aims at reviewing the literature related to Clinical Information Systems (CIS), Hospital Information Systems (HIS), Electronic Health Record (EHR) systems, and how collected data can be analyzed by Artificial Intelligence (AI) techniques. METHODS We selected the major journals (11 journals) collecting papers (more than 7,000) over the last five years from the top members of the research community, and read and analyzed the papers (more than 200) covering the topics. Then, we completed the analysis using search engines to also include papers from major conferences over the same five years. RESULTS We defined a taxonomy of major features and research areas of CIS, HIS, EHR systems. We also defined a taxonomy for the use of Artificial Intelligence (AI) techniques on healthcare data. In the light of these taxonomies, we report on the most relevant papers from the literature. CONCLUSIONS We highlighted some major research directions and issues which seem to be promising and to need further investigations over a medium- or long-term period.
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Affiliation(s)
- Carlo Combi
- Dipartimento di Informatica, Università degli Studi di Verona, Verona, Italy
| | - Giuseppe Pozzi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
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Park E, Chang HJ, Nam HS. A Bayesian Network Model for Predicting Post-stroke Outcomes With Available Risk Factors. Front Neurol 2018; 9:699. [PMID: 30245663 PMCID: PMC6137617 DOI: 10.3389/fneur.2018.00699] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 08/02/2018] [Indexed: 11/13/2022] Open
Abstract
Bayesian network is an increasingly popular method in modeling uncertain and complex problems, because its interpretability is often more useful than plain prediction. To satisfy the core requirement in medical research to obtain interpretable prediction with high accuracy, we constructed an inference engine for post-stroke outcomes based on Bayesian network classifiers. The prediction system that was trained on data of 3,605 patients with acute stroke forecasts the functional independence at 3 months and the mortality 1 year after stroke. Feature selection methods were applied to eliminate less relevant and redundant features from 76 risk variables. The Bayesian network classifiers were trained with a hill-climbing searching for the qualified network structure and parameters measured by maximum description length. We evaluated and optimized the proposed system to increase the area under the receiver operating characteristic curve (AUC) while ensuring acceptable sensitivity for the class-imbalanced data. The performance evaluation demonstrated that the Bayesian network with selected features by wrapper-type feature selection can predict 3-month functional independence with an AUC of 0.889 using only 19 risk variables and 1-year mortality with an AUC of 0.893 using 24 variables. The Bayesian network with 50 features filtered by information gain can predict 3-month functional independence with an AUC of 0.875 and 1-year mortality with an AUC of 0.895. We also built an online prediction service, Yonsei Stroke Outcome Inference System, to substantialize the proposed solution for patients with stroke.
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Affiliation(s)
- Eunjeong Park
- Cardiovascular Research Institute, College of Medicine, Yonsei University, Seoul, South Korea
| | - Hyuk-Jae Chang
- Department of Cardiology, College of Medicine, Yonsei University, Seoul, South Korea
| | - Hyo Suk Nam
- Department of Neurology, College of Medicine, Yonsei University, Seoul, South Korea
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Abstract
OBJECTIVE The aim of this review was to summarize major topics in artificial intelligence (AI), including their applications and limitations in surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments. SUMMARY BACKGROUND DATA AI is composed of various subfields that each provide potential solutions to clinical problems. Each of the core subfields of AI reviewed in this piece has also been used in other industries such as the autonomous car, social networks, and deep learning computers. METHODS A review of AI papers across computer science, statistics, and medical sources was conducted to identify key concepts and techniques within AI that are driving innovation across industries, including surgery. Limitations and challenges of working with AI were also reviewed. RESULTS Four main subfields of AI were defined: (1) machine learning, (2) artificial neural networks, (3) natural language processing, and (4) computer vision. Their current and future applications to surgical practice were introduced, including big data analytics and clinical decision support systems. The implications of AI for surgeons and the role of surgeons in advancing the technology to optimize clinical effectiveness were discussed. CONCLUSIONS Surgeons are well positioned to help integrate AI into modern practice. Surgeons should partner with data scientists to capture data across phases of care and to provide clinical context, for AI has the potential to revolutionize the way surgery is taught and practiced with the promise of a future optimized for the highest quality patient care.
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Affiliation(s)
| | - Guy Rosman
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, MA
| | - Daniela Rus
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, MA
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Samad MD, Ulloa A, Wehner GJ, Jing L, Hartzel D, Good CW, Williams BA, Haggerty CM, Fornwalt BK. Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning. JACC Cardiovasc Imaging 2018; 12:681-689. [PMID: 29909114 DOI: 10.1016/j.jcmg.2018.04.026] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 04/17/2018] [Accepted: 04/26/2018] [Indexed: 10/14/2022]
Abstract
OBJECTIVES The goal of this study was to use machine learning to more accurately predict survival after echocardiography. BACKGROUND Predicting patient outcomes (e.g., survival) following echocardiography is primarily based on ejection fraction (EF) and comorbidities. However, there may be significant predictive information within additional echocardiography-derived measurements combined with clinical electronic health record data. METHODS Mortality was studied in 171,510 unselected patients who underwent 331,317 echocardiograms in a large regional health system. The authors investigated the predictive performance of nonlinear machine learning models compared with that of linear logistic regression models using 3 different inputs: 1) clinical variables, including 90 cardiovascular-relevant International Classification of Diseases, Tenth Revision, codes, and age, sex, height, weight, heart rate, blood pressures, low-density lipoprotein, high-density lipoprotein, and smoking; 2) clinical variables plus physician-reported EF; and 3) clinical variables and EF, plus 57 additional echocardiographic measurements. Missing data were imputed with a multivariate imputation by using a chained equations algorithm (MICE). The authors compared models versus each other and baseline clinical scoring systems by using a mean area under the curve (AUC) over 10 cross-validation folds and across 10 survival durations (6 to 60 months). RESULTS Machine learning models achieved significantly higher prediction accuracy (all AUC >0.82) over common clinical risk scores (AUC = 0.61 to 0.79), with the nonlinear random forest models outperforming logistic regression (p < 0.01). The random forest model including all echocardiographic measurements yielded the highest prediction accuracy (p < 0.01 across all models and survival durations). Only 10 variables were needed to achieve 96% of the maximum prediction accuracy, with 6 of these variables being derived from echocardiography. Tricuspid regurgitation velocity was more predictive of survival than LVEF. In a subset of studies with complete data for the top 10 variables, multivariate imputation by chained equations yielded slightly reduced predictive accuracies (difference in AUC of 0.003) compared with the original data. CONCLUSIONS Machine learning can fully utilize large combinations of disparate input variables to predict survival after echocardiography with superior accuracy.
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Affiliation(s)
- Manar D Samad
- Department of Imaging Science and Innovation, Geisinger, Danville, Pennsylvania
| | - Alvaro Ulloa
- Department of Imaging Science and Innovation, Geisinger, Danville, Pennsylvania
| | - Gregory J Wehner
- Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky
| | - Linyuan Jing
- Department of Imaging Science and Innovation, Geisinger, Danville, Pennsylvania
| | - Dustin Hartzel
- Department of Imaging Science and Innovation, Geisinger, Danville, Pennsylvania
| | | | - Brent A Williams
- Department of Epidemiology and Health Services Research, Geisinger, Danville, Pennsylvania
| | | | - Brandon K Fornwalt
- Department of Imaging Science and Innovation, Geisinger, Danville, Pennsylvania; Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky; Department of Radiology, Geisinger, Danville, Pennsylvania.
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On the Use of Geometric Modeling to Predict Aortic Aneurysm Rupture. Ann Vasc Surg 2017; 44:190-196. [DOI: 10.1016/j.avsg.2017.05.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 03/30/2017] [Accepted: 05/10/2017] [Indexed: 11/21/2022]
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Ambient intelligence for health environments. J Biomed Inform 2016; 64:207-210. [DOI: 10.1016/j.jbi.2016.10.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 10/13/2016] [Accepted: 10/15/2016] [Indexed: 11/23/2022]
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