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Yuan P, Ma ZH, Yan Y, Li SJ, Wang J, Wu Q. Artificial Intelligence-Based Classification of Anatomical Sites in Esophagogastroduodenoscopy Images. Int J Gen Med 2024; 17:6127-6138. [PMID: 39691834 PMCID: PMC11649499 DOI: 10.2147/ijgm.s481127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Accepted: 06/03/2024] [Indexed: 12/19/2024] Open
Abstract
Background A full examination of gastrointestinal tract is an essential prerequisite for effectively detecting gastrointestinal lesions. However, there is a lack of efficient tools to analyze and recognize gastric anatomy locations, preventing the complete portrayal of entire stomach. This study aimed to evaluate the effectiveness of artificial intelligence in identifying gastric anatomy sites by analyzing esophagogastroduodenoscopy images. Methods Using endoscopic images, we proposed a system called the Artificial Intelligence of Medicine (AIMED) through convolutional neural networks and MobileNetV3-large. The performance of artificial intelligence in the recognition of anatomic sites in esophagogastroduodenoscopy images was evaluated by considering many cases. Primary outcomes included diagnostic accuracy, sensitivity, and specificity. Results A total of 160,308 images from 27 categories of the upper endoscopy anatomy classification were included in this retrospective research. As a test group, 16031 esophagogastroduodenoscopy images with 27 categories were used to evaluate AIMED's performance in identifying gastric anatomy sites. The convolutional neural network's accuracy, sensitivity, and specificity were determined to be 99.40%, 91.85%, and 99.69%, respectively. Conclusion The AIMED system achieved high accuracy with regard to recognizing gastric anatomy sites, and it could assist the operator in enhancing the quality control of the used endoscope. Moreover, it could contribute to a more standardized endoscopic performance. Overall, our findings prove that artificial-intelligence-based systems can be indispensable to the endoscopic revolution (Clinical trial registration number: NCT04384575 (12/05/2020)).
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Affiliation(s)
- Peng Yuan
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China
| | - Zhong-Hua Ma
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China
| | - Yan Yan
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China
| | - Shi-Jie Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China
| | - Jing Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China
| | - Qi Wu
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China
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Jin Y, Ma M, Yan Y, Guo Y, Feng Y, Chen C, Zhong Y, Huang K, Xia H, Libo Y, Si Y, Zou J. A convenient machine learning model to predict full stomach and evaluate the safety and comfort improvements of preoperative oral carbohydrate in patients undergoing elective painless gastrointestinal endoscopy. Ann Med 2023; 55:2292778. [PMID: 38109932 PMCID: PMC10732178 DOI: 10.1080/07853890.2023.2292778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 12/04/2023] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND AND AIMS Assessment of the patient's gastric contents is the key to avoiding aspiration incidents, however, there is no effective method to determine whether elective painless gastrointestinal endoscopy (GIE) patients have a full stomach or an empty stomach. And previous studies have shown that preoperative oral carbohydrates (POCs) can improve the discomfort induced by fasting, but there are different perspectives on their safety. This study aimed to develop a convenient, accurate machine learning (ML) model to predict full stomach. And based on the model outcomes, evaluate the safety and comfort improvements of POCs in empty- and full stomach groups. METHODS We enrolled 1386 painless GIE patients between October 2022 and January 2023 in Nanjing First Hospital, and 1090 patients without POCs were used to construct five different ML models to identify full stomach. The metrics of discrimination and calibration validated the robustness of the models. For the best-performance model, we further interpreted it through SHapley Additive exPlanations (SHAP) and constructed a web calculator to facilitate clinical use. We evaluated the safety and comfort improvements of POCs by propensity score matching (PSM) in the two groups, respectively. RESULTS Random Forest (RF) model showed the greatest discrimination with the area under the receiver operating characteristic curve (AUROC) 0.837 [95% confidence interval (CI): 79.1-88.2], F1 71.5%, and best calibration with a Brier score of 15.2%. The web calculator can be visited at https://medication.shinyapps.io/RF_model/. PSM results demonstrated that POCs significantly reduced the full stomach incident in empty stomach group (p < 0.05), but no differences in full stomach group (p > 0.05). Comfort improved in both groups and was more significant in empty stomach group. CONCLUSIONS The developed convenient RF model predicted full stomach with high accuracy and interpretability. POCs were safe and comfortably improved in both groups, with more benefit in empty stomach group. These findings may guide the patients' gastrointestinal preparation.
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Affiliation(s)
- Yuzhan Jin
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Mingtao Ma
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Anesthesiology, Leping People’s Hospital, Jiangxi, China
| | - Yuqing Yan
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yaoyi Guo
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yue Feng
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Chen Chen
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Yi Zhong
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Kaizong Huang
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
| | - Huaming Xia
- Nanjing Xiaheng Network System Co., Ltd., Nanjing, China
| | - Yan Libo
- Jiangsu Kaiyuan Pharmaceutical Co., Ltd., Nanjing, China
| | - Yanna Si
- Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jianjun Zou
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
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Wu S, Zhang R, Yan J, Li C, Liu Q, Wang L, Wang H. High-Speed and Accurate Diagnosis of Gastrointestinal Disease: Learning on Endoscopy Images Using Lightweight Transformer with Local Feature Attention. Bioengineering (Basel) 2023; 10:1416. [PMID: 38136007 PMCID: PMC10741161 DOI: 10.3390/bioengineering10121416] [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: 11/18/2023] [Revised: 12/04/2023] [Accepted: 12/10/2023] [Indexed: 12/24/2023] Open
Abstract
In response to the pressing need for robust disease diagnosis from gastrointestinal tract (GIT) endoscopic images, we proposed FLATer, a fast, lightweight, and highly accurate transformer-based model. FLATer consists of a residual block, a vision transformer module, and a spatial attention block, which concurrently focuses on local features and global attention. It can leverage the capabilities of both convolutional neural networks (CNNs) and vision transformers (ViT). We decomposed the classification of endoscopic images into two subtasks: a binary classification to discern between normal and pathological images and a further multi-class classification to categorize images into specific diseases, namely ulcerative colitis, polyps, and esophagitis. FLATer has exhibited exceptional prowess in these tasks, achieving 96.4% accuracy in binary classification and 99.7% accuracy in ternary classification, surpassing most existing models. Notably, FLATer could maintain impressive performance when trained from scratch, underscoring its robustness. In addition to the high precision, FLATer boasted remarkable efficiency, reaching a notable throughput of 16.4k images per second, which positions FLATer as a compelling candidate for rapid disease identification in clinical practice.
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Affiliation(s)
- Shibin Wu
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (S.W.); (R.Z.); (J.Y.)
| | - Ruxin Zhang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (S.W.); (R.Z.); (J.Y.)
| | - Jiayi Yan
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (S.W.); (R.Z.); (J.Y.)
| | - Chengquan Li
- School of Clinical Medicine, Tsinghua University, Beijing 100084, China;
| | - Qicai Liu
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China;
| | - Liyang Wang
- School of Clinical Medicine, Tsinghua University, Beijing 100084, China;
| | - Haoqian Wang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (S.W.); (R.Z.); (J.Y.)
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Kim HJ, Gong EJ, Bang CS. Application of Machine Learning Based on Structured Medical Data in Gastroenterology. Biomimetics (Basel) 2023; 8:512. [PMID: 37999153 PMCID: PMC10669027 DOI: 10.3390/biomimetics8070512] [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: 08/09/2023] [Revised: 10/12/2023] [Accepted: 10/26/2023] [Indexed: 11/25/2023] Open
Abstract
The era of big data has led to the necessity of artificial intelligence models to effectively handle the vast amount of clinical data available. These data have become indispensable resources for machine learning. Among the artificial intelligence models, deep learning has gained prominence and is widely used for analyzing unstructured data. Despite the recent advancement in deep learning, traditional machine learning models still hold significant potential for enhancing healthcare efficiency, especially for structured data. In the field of medicine, machine learning models have been applied to predict diagnoses and prognoses for various diseases. However, the adoption of machine learning models in gastroenterology has been relatively limited compared to traditional statistical models or deep learning approaches. This narrative review provides an overview of the current status of machine learning adoption in gastroenterology and discusses future directions. Additionally, it briefly summarizes recent advances in large language models.
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Affiliation(s)
- Hye-Jin Kim
- Department of Internal Medicine, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea; (H.-J.K.); (E.-J.G.)
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24253, Republic of Korea
- Institute of New Frontier Research, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea
| | - Eun-Jeong Gong
- Department of Internal Medicine, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea; (H.-J.K.); (E.-J.G.)
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24253, Republic of Korea
- Institute of New Frontier Research, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea
| | - Chang-Seok Bang
- Department of Internal Medicine, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea; (H.-J.K.); (E.-J.G.)
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24253, Republic of Korea
- Institute of New Frontier Research, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea
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Ho KMA, Rosenfeld A, Hogan Á, McBain H, Duku M, Wolfson PB, Wilson A, Cheung SM, Hennelly L, Macabodbod L, Graham DG, Sehgal V, Banerjee A, Lovat LB. Development and validation of a multivariable risk factor questionnaire to detect oesophageal cancer in 2-week wait patients. Clin Res Hepatol Gastroenterol 2023; 47:102087. [PMID: 36669752 PMCID: PMC10602932 DOI: 10.1016/j.clinre.2023.102087] [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: 08/31/2022] [Revised: 01/09/2023] [Accepted: 01/15/2023] [Indexed: 01/19/2023]
Abstract
INTRODUCTION Oesophageal cancer is associated with poor health outcomes. Upper GI (UGI) endoscopy is the gold standard for diagnosis but is associated with patient discomfort and low yield for cancer. We used a machine learning approach to create a model which predicted oesophageal cancer based on questionnaire responses. METHODS We used data from 2 separate prospective cross-sectional studies: the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study and predicting RIsk of diSease using detailed Questionnaires (RISQ) study. We recruited patients from National Health Service (NHS) suspected cancer pathways as well as patients with known cancer. We identified patient characteristics and questionnaire responses which were most associated with the development of oesophageal cancer. Using the SPIT dataset, we trained seven different machine learning models, selecting the best area under the receiver operator curve (AUC) to create our final model. We further applied a cost function to maximise cancer detection. We then independently validated the model using the RISQ dataset. RESULTS 807 patients were included in model training and testing, split in a 70:30 ratio. 294 patients were included in model validation. The best model during training was regularised logistic regression using 17 features (median AUC: 0.81, interquartile range (IQR): 0.69-0.85). For testing and validation datasets, the model achieved an AUC of 0.71 (95% CI: 0.61-0.81) and 0.92 (95% CI: 0.88-0.96) respectively. At a set cut off, our model achieved a sensitivity of 97.6% and specificity of 59.1%. We additionally piloted the model in 12 patients with gastric cancer; 9/12 (75%) of patients were correctly classified. CONCLUSIONS We have developed and validated a risk stratification tool using a questionnaire approach. This could aid prioritising patients at high risk of having oesophageal cancer for endoscopy. Our tool could help address endoscopic backlogs caused by the COVID-19 pandemic.
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Affiliation(s)
- Kai Man Alexander Ho
- Division of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK.
| | - Avi Rosenfeld
- Department of Computer Science, Jerusalem College of Technology, Havaad Haleumi 21, Givat Mordechai 91160 Jerusalem, Israel
| | - Áine Hogan
- Division of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK
| | - Hazel McBain
- Division of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK
| | - Margaret Duku
- Division of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK
| | - Paul Bd Wolfson
- Division of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK
| | - Ashley Wilson
- Division of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK
| | - Sharon My Cheung
- Division of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK
| | - Laura Hennelly
- Division of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK
| | - Lester Macabodbod
- Division of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK
| | - David G Graham
- Department of Gastrointestinal Services, University College London Hospital, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London NW1 2BU, UK
| | - Vinay Sehgal
- Department of Gastrointestinal Services, University College London Hospital, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London NW1 2BU, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, 222 Euston Road, London NW1 2DA, UK; Department of Cardiology, St Bartholomew's Hospital, Barts Health NHS Trust, London EC1A 7BE, UK
| | - Laurence B Lovat
- Division of Surgery and Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Charles Bell House, 43-45 Foley Street, London W1W 7TY, UK; Department of Gastrointestinal Services, University College London Hospital, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London NW1 2BU, UK
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Yuan P, Bai R, Yan Y, Li S, Wang J, Cao C, Wu Q. Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence. Front Neurosci 2023; 16:1118087. [PMID: 36865000 PMCID: PMC9971730 DOI: 10.3389/fnins.2022.1118087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 12/30/2022] [Indexed: 02/16/2023] Open
Abstract
Gastrointestinal endoscopy has been identified as an important tool for cancer diagnosis and therapy, particularly for treating patients with early gastric cancer (EGC). It is well known that the quality of gastroscope images is a prerequisite for achieving a high detection rate of gastrointestinal lesions. Owing to manual operation of gastroscope detection, in practice, it possibly introduces motion blur and produces low-quality gastroscope images during the imaging process. Hence, the quality assessment of gastroscope images is the key process in the detection of gastrointestinal endoscopy. In this study, we first present a novel gastroscope image motion blur (GIMB) database that includes 1,050 images generated by imposing 15 distortion levels of motion blur on 70 lossless images and the associated subjective scores produced with the manual operation of 15 viewers. Then, we design a new artificial intelligence (AI)-based gastroscope image quality evaluator (GIQE) that leverages the newly proposed semi-full combination subspace to learn multiple kinds of human visual system (HVS) inspired features for providing objective quality scores. The results of experiments conducted on the GIMB database confirm that the proposed GIQE showed more effective performance compared with its state-of-the-art peers.
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Affiliation(s)
- Peng Yuan
- The Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital and Institute, Beijing, China
| | - Ruxue Bai
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Yan Yan
- The Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital and Institute, Beijing, China
| | - Shijie Li
- The Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital and Institute, Beijing, China
| | - Jing Wang
- The Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital and Institute, Beijing, China
| | - Changqi Cao
- The Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital and Institute, Beijing, China
| | - Qi Wu
- The Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital and Institute, Beijing, China
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Afrash MR, Shafiee M, Kazemi-Arpanahi H. Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors. BMC Gastroenterol 2023; 23:6. [PMID: 36627564 PMCID: PMC9832798 DOI: 10.1186/s12876-022-02626-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 12/19/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Gastric cancer is one of the leading causes of death worldwide. Screening for gastric cancer greatly relies on endoscopy and pathology biopsy, which are invasive and pose financial burdens. Thus, the prevention of the disease by modifying lifestyle-related behaviors and dietary habits or even the prevention of risk factor formation is of great importance. This study aimed to construct an inexpensive, non-invasive, fast, and high-precision diagnostic model using six machine learning (ML) algorithms to classify patients at high or low risk of developing gastric cancer by analyzing individual lifestyle factors. METHODS This retrospective study used the data of 2029 individuals from the gastric cancer database of Ayatollah Taleghani Hospital in Abadan City, Iran. The data were randomly separated into training and test sets (ratio 0.7:0.3). Six ML methods, including multilayer perceptron (MLP), support vector machine (SVM) (linear kernel), SVM (RBF kernel), k-nearest neighbors (KNN) (K = 1, 3, 7, 9), random forest (RF), and eXtreme Gradient Boosting (XGBoost), were trained to construct prognostic models before and after performing the relief feature selection method. Finally, to evaluate the models' performance, the metrics derived from the confusion matrix were calculated via a test split and cross-validation. RESULTS This study found 11 important influence factors for the risk of gastric cancer, such as Helicobacter pylori infection, high salt intake, and chronic atrophic gastritis, among other factors. Comparisons indicated that the XGBoost had the best performance for the risk prediction of gastric cancer. CONCLUSIONS The results suggest that based on simple baseline patient data, the ML techniques have the potential to start the prescreening of gastric cancer and identify high-risk individuals who should proceed with invasive examinations. Our model could also considerably lessen the number of cases that need endoscopic surveillance. Future studies are required to validate the efficacy of the models in a larger and multicenter population.
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Affiliation(s)
- Mohammad Reza Afrash
- grid.411705.60000 0001 0166 0922Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran
| | - Mohsen Shafiee
- Department of Nursing, Abadan University of Medical Sciences, Abadan, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
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