1
|
Trincado MT, Morales-Conde S, Bellido-Luque J, Gallego MA. Digital surgery. Cir Esp 2024:S2173-5077(24)00144-3. [PMID: 38852618 DOI: 10.1016/j.cireng.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Affiliation(s)
- Miguel Toledano Trincado
- Coordinador de la Unidad Cirugía Esofagogástrica y pared abdominal, Hospital Universitario Rio Hortega, Valladolid, Spain
| | - Salvador Morales-Conde
- Jefe de Servicio de Cirugía General y del Aparato Digestivo, Hospital Universitario Virgen Macarena, Sevilla, Spain; Jefe de Servicio de Cirugía General y del Aparato Digestivo, Hospital Quironsalud Sagrado Corazón, Sevilla, Spain
| | - Juan Bellido-Luque
- Unidad de cirugía Hepatobiliar, Hospital Universitario Virgen Macarena, Facultad de Medicina, Universidad de Sevilla, Spain; Hospital QuirónSalud Sagrado Corazón, Sevilla, Spain; Coordinador de la sección de Cirugía Mínimamente invasiva e Innovación Tecnológica de la AEC, Spain.
| | - Mario Alvarez Gallego
- Servicio de Cirugía General y del Aparato Digestivo, Hospital Universitario La Paz, Madrid, Spain
| |
Collapse
|
2
|
Toledano Trincado M, Bellido-Luque J, Álvarez Gallego M. Robotic surgery as a driver of surgical digitalization. Cir Esp 2024:S2173-5077(24)00135-2. [PMID: 38801975 DOI: 10.1016/j.cireng.2024.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 05/04/2024] [Indexed: 05/29/2024]
Abstract
Classical surgery, also called analog surgery, is transmitted to us by our mentors, whose knowledge has been delegated from generation to generation throughout the history of surgery. Its main limitations are limited surgical precision and dependence on the surgeon's skill to achieve surgical goals. So-called digital surgery incorporates the most advanced technology, with the aim of improving the results of all phases of the surgical process. Robotic platforms are currently considered to be one of the main drivers of the digital transformation of surgery. They bring considerable advances to the digitalization of surgery, including: higher quality visualization, more controlled and stable movements with elimination of tremor, minimized risk of errors, data integration throughout the patient's surgical process, use of various systems for better surgical planning, application of virtual and augmented reality, telementoring, and artificial intelligence.
Collapse
|
3
|
Chen KA, Joisa CU, Stem JM, Guillem JG, Gomez SM, Kapadia MR. Prediction of Ureteral Injury During Colorectal Surgery Using Machine Learning. Am Surg 2023; 89:5702-5710. [PMID: 37133432 PMCID: PMC10622328 DOI: 10.1177/00031348231173981] [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] [Indexed: 05/04/2023]
Abstract
BACKGROUND Ureteral injury (UI) is a rare but devastating complication during colorectal surgery. Ureteral stents may reduce UI but carry risks themselves. Risk predictors for UI could help target the use of stents, but previous efforts have relied on logistic regression (LR), shown moderate accuracy, and used intraoperative variables. We sought to use an emerging approach in predictive analytics, machine learning, to create a model for UI. METHODS Patients who underwent colorectal surgery were identified in the National Surgical Quality Improvement Program (NSQIP) database. Patients were split into training, validation, and test sets. The primary outcome was UI. Three machine learning approaches were tested including random forest (RF), gradient boosting (XGB), and neural networks (NN), and compared with traditional LR. Model performance was assessed using area under the curve (AUROC). RESULTS The data set included 262,923 patients, of whom 1519 (.578%) experienced UI. Of the modeling techniques, XGB performed the best, with an AUROC score of .774 (95% CI .742-.807) compared with .698 (95% CI .664-.733) for LR. Random forest and NN performed similarly with scores of .738 and .763, respectively. Type of procedure, work RVUs, indication for surgery, and mechanical bowel prep showed the strongest influence on model predictions. CONCLUSIONS Machine learning-based models significantly outperformed LR and previous models and showed high accuracy in predicting UI during colorectal surgery. With proper validation, they could be used to support decision making regarding the placement of ureteral stents preoperatively.
Collapse
Affiliation(s)
- Kevin A. Chen
- Department of Surgery, University of North Carolina at Chapel Hill, NC, USA
| | - Chinmaya U. Joisa
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, NC, USA
| | - Jonathan M. Stem
- Department of Surgery, University of North Carolina at Chapel Hill, NC, USA
| | - Jose G. Guillem
- Department of Surgery, University of North Carolina at Chapel Hill, NC, USA
| | - Shawn M. Gomez
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, NC, USA
| | - Muneera R. Kapadia
- Department of Surgery, University of North Carolina at Chapel Hill, NC, USA
| |
Collapse
|
4
|
Chen KA, Goffredo P, Hu D, Joisa CU, Guillem JG, Gomez SM, Kapadia MR. Estimating Risk of Locoregional Failure and Overall Survival in Anal Cancer Following Chemoradiation: A Machine Learning Approach. J Gastrointest Surg 2023; 27:1925-1935. [PMID: 37407899 PMCID: PMC10528925 DOI: 10.1007/s11605-023-05755-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 06/03/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND Optimal treatment of anal squamous cell carcinoma (ASCC) is definitive chemoradiation. Patients with persistent or recurrent disease require abdominoperineal resection (APR). Current models for predicting need for APR and overall survival are limited by low accuracy or small datasets. This study sought to use machine learning (ML) to develop more accurate models for locoregional failure and overall survival for ASCC. METHODS This study used the National Cancer Database from 2004-2018, divided into training, validation, and test sets. We included patients with stage I-III ASCC who underwent chemoradiation. Our primary outcomes were need for APR and 3-year overall survival. Random forest (RF), gradient boosting (XGB), and neural network (NN) ML-based models were developed and compared with logistic regression (LR). Accuracy was assessed using area under the receiver operating characteristic curve (AUROC). RESULTS APR was required in 5.3% (1,015/18,978) of patients. XGB performed best with AUROC of 0.813, compared with 0.691 for LR. Tumor size, lymphovascular invasion, and tumor grade showed the strongest influence on model predictions. Mortality was 23.6% (7,988/33,834). AUROC for XGB and LR were similar at 0.766 and 0.748, respectively. For this model, age, radiation dose, sex, and insurance status were the most influential variables. CONCLUSIONS We developed and internally validated machine learning-based models for predicting outcomes in ASCC and showed higher accuracy versus LR for locoregional failure, but not overall survival. After external validation, these models may assist clinicians with identifying patients with ASCC at high risk of treatment failure.
Collapse
Affiliation(s)
- Kevin A Chen
- Divison of Gastrointestinal Surgery, Department of Surgery, University of North Carolina at Chapel Hill, 100 Manning Drive, 4038 Burnett Womack Building, CB #7050, Chapel Hill, NC, 27599, USA
| | - Paolo Goffredo
- Division of Colon & Rectal Surgery, Department of Surgery, University of Minnesota, 420 Delaware St SE, MN, 55455, Minneapolis, USA
| | - David Hu
- Department of Biostatistics, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 3101 McGavran-Greenberg Hall, CB #7420, Chapel Hill, NC, 27599-7420, USA
| | - Chinmaya U Joisa
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, 10202C Mary Ellen Jones Building, Chapel Hill, NC, 27599, USA
| | - Jose G Guillem
- Divison of Gastrointestinal Surgery, Department of Surgery, University of North Carolina at Chapel Hill, 100 Manning Drive, 4038 Burnett Womack Building, CB #7050, Chapel Hill, NC, 27599, USA
| | - Shawn M Gomez
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, 10202C Mary Ellen Jones Building, Chapel Hill, NC, 27599, USA
| | - Muneera R Kapadia
- Divison of Gastrointestinal Surgery, Department of Surgery, University of North Carolina at Chapel Hill, 100 Manning Drive, 4038 Burnett Womack Building, CB #7050, Chapel Hill, NC, 27599, USA.
| |
Collapse
|
5
|
Henn J, Hatterscheidt S, Sahu A, Buness A, Dohmen J, Arensmeyer J, Feodorovici P, Sommer N, Schmidt J, Kalff JC, Matthaei H. Machine Learning for Decision-Support in Acute Abdominal Pain - Proof of Concept and Central Considerations. Zentralbl Chir 2023; 148:376-383. [PMID: 37562397 DOI: 10.1055/a-2125-1559] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used here as a decision-support and relieve the time and personnel resource shortage.Patients with acute abdominal pain presenting to the Department of Surgery at Bonn University Hospital in 2020 and 2021 were retrospectively analyzed. Clinical parameters as well as laboratory values were used as predictors. After randomly splitting into a training and test data set (ratio 80 to 20), three ML algorithms were comparatively trained and validated. The entire procedure was repeated 20 times.A total of 1357 patients were identified and included in the analysis, with one in five (n = 276, 20.3%) requiring emergency abdominal surgery within 24 hours. Patients operated on were more likely to be male (p = 0.026), older (p = 0.006), had more gastrointestinal symptoms (nausea: p < 0.001, vomiting p < 0.001) as well as a more recent onset of pain (p < 0.001). Tenderness (p < 0.001) and guarding (p < 0.001) were more common in surgically treated patients and blood analyses showed increased inflammation levels (white blood cell count: p < 0.001, CRP: p < 0.001) and onset of organ dysfunction (creatinine: p < 0.014, quick p < 0.001). Of the three trained algorithms, the tree-based methods (h2o random forest and cforest) showed the best performance. The algorithms classified patients, i.e., predicted surgery, with a median AUC ROC of 0.81 and 0.79 and AUC PRC of 0.56 in test sets.A proof-of-concept was achieved with the development of an ML model for predicting timely surgical therapy for acute abdomen. The ML algorithm can be a valuable tool in decision-making. Especially in the context of heavily used medical resources, the algorithm can help to use these scarce resources more effectively. Technological progress, especially regarding artificial intelligence, increasingly enables evidence-based approaches in surgery but requires a strictly interdisciplinary approach. In the future, the use and handling of ML should be integrated into surgical training.
Collapse
Affiliation(s)
- Jonas Henn
- Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Simon Hatterscheidt
- Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Anshupa Sahu
- Institute for Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
| | - Andreas Buness
- Institute for Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
| | - Jonas Dohmen
- Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Jan Arensmeyer
- Division of Thoracic Surgery, Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Philipp Feodorovici
- Division of Thoracic Surgery, Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Nils Sommer
- Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Joachim Schmidt
- Division of Thoracic Surgery, Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
- Department of Thoracic Surgery, Helios Hospital Bonn Rhein-Sieg, Bonn, Germany
| | - Jörg C Kalff
- Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Hanno Matthaei
- Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| |
Collapse
|
6
|
Welvaars K, van den Bekerom MPJ, Doornberg JN, van Haarst EP. Evaluating machine learning algorithms to Predict 30-day Unplanned REadmission (PURE) in Urology patients. BMC Med Inform Decis Mak 2023; 23:108. [PMID: 37312177 DOI: 10.1186/s12911-023-02200-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 05/18/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND Unplanned hospital readmissions are serious medical adverse events, stressful to patients, and expensive for hospitals. This study aims to develop a probability calculator to predict unplanned readmissions (PURE) within 30-days after discharge from the department of Urology, and evaluate the respective diagnostic performance characteristics of the PURE probability calculator developed with machine learning (ML) algorithms comparing regression versus classification algorithms. METHODS Eight ML models (i.e. logistic regression, LASSO regression, RIDGE regression, decision tree, bagged trees, boosted trees, XGBoost trees, RandomForest) were trained on 5.323 unique patients with 52 different features, and evaluated on diagnostic performance of PURE within 30 days of discharge from the department of Urology. RESULTS Our main findings were that performances from classification to regression algorithms had good AUC scores (0.62-0.82), and classification algorithms showed a stronger overall performance as compared to models trained with regression algorithms. Tuning the best model, XGBoost, resulted in an accuracy of 0.83, sensitivity of 0.86, specificity of 0.57, AUC of 0.81, PPV of 0.95, and a NPV of 0.31. CONCLUSIONS Classification models showed stronger performance than regression models with reliable prediction for patients with high probability of readmission, and should be considered as first choice. The tuned XGBoost model shows performance that indicates safe clinical appliance for discharge management in order to prevent an unplanned readmission at the department of Urology.
Collapse
Affiliation(s)
- Koen Welvaars
- Data Science Team, OLVG, Jan Tooropstraat 164, 1061 AE, Amsterdam, the Netherlands.
- Department of Orthopaedic Surgery, UMCG, Groningen, Netherlands.
| | - Michel P J van den Bekerom
- Department of Orthopaedic Surgery, OLVG, Amsterdam, Netherlands
- Faculty of Behavioural and Movement Sciences, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, the Netherlands
| | - Job N Doornberg
- Department of Orthopaedic Surgery, UMCG, Groningen, Netherlands
| | | |
Collapse
|
7
|
Giuffrè M, Moretti R, Tiribelli C. Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease. Int J Mol Sci 2023; 24:ijms24065229. [PMID: 36982303 PMCID: PMC10049444 DOI: 10.3390/ijms24065229] [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: 02/01/2023] [Revised: 02/27/2023] [Accepted: 03/08/2023] [Indexed: 03/11/2023] Open
Abstract
The human gut microbiome plays a crucial role in human health and has been a focus of increasing research in recent years. Omics-based methods, such as metagenomics, metatranscriptomics, and metabolomics, are commonly used to study the gut microbiome because they provide high-throughput and high-resolution data. The vast amount of data generated by these methods has led to the development of computational methods for data processing and analysis, with machine learning becoming a powerful and widely used tool in this field. Despite the promising results of machine learning-based approaches for analyzing the association between microbiota and disease, there are several unmet challenges. Small sample sizes, disproportionate label distribution, inconsistent experimental protocols, or a lack of access to relevant metadata can all contribute to a lack of reproducibility and translational application into everyday clinical practice. These pitfalls can lead to false models, resulting in misinterpretation biases for microbe–disease correlations. Recent efforts to address these challenges include the construction of human gut microbiota data repositories, improved data transparency guidelines, and more accessible machine learning frameworks; implementation of these efforts has facilitated a shift in the field from observational association studies to experimental causal inference and clinical intervention.
Collapse
Affiliation(s)
- Mauro Giuffrè
- Department of Medical, Surgical and Health Sciences, University of Trieste, 34149 Trieste, Italy
- Department of Internal Medicine, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Rita Moretti
- Department of Medical, Surgical and Health Sciences, University of Trieste, 34149 Trieste, Italy
- Fondazione Italiana Fegato-Onlus, The Liver-Brain Unit “Rita Moretti”, 34149 Trieste, Italy
| | - Claudio Tiribelli
- Fondazione Italiana Fegato-Onlus, The Liver-Brain Unit “Rita Moretti”, 34149 Trieste, Italy
- Correspondence:
| |
Collapse
|
8
|
Sabharwal R, Miah SJ, Fosso Wamba S. Extending artificial intelligence research in the clinical domain: a theoretical perspective. ANNALS OF OPERATIONS RESEARCH 2022:1-32. [PMID: 36407943 PMCID: PMC9641309 DOI: 10.1007/s10479-022-05035-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.
Collapse
Affiliation(s)
- Renu Sabharwal
- Newcastle Business School, The University of Newcastle, Callaghan, NSW Australia
| | - Shah J. Miah
- Newcastle Business School, The University of Newcastle, Callaghan, NSW Australia
| | | |
Collapse
|
9
|
Pettit RW, Fullem R, Cheng C, Amos CI. Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. Emerg Top Life Sci 2021; 5:ETLS20210246. [PMID: 34927670 PMCID: PMC8786279 DOI: 10.1042/etls20210246] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022]
Abstract
AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.
Collapse
Affiliation(s)
- Rowland W. Pettit
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
| | - Robert Fullem
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, U.S.A
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, U.S.A
| | - Christopher I. Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, U.S.A
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, U.S.A
| |
Collapse
|