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Zhao G, Chen X, Zhu M, Liu Y, Wang Y. Exploring the application and future outlook of Artificial intelligence in pancreatic cancer. Front Oncol 2024; 14:1345810. [PMID: 38450187 PMCID: PMC10915754 DOI: 10.3389/fonc.2024.1345810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024] Open
Abstract
Pancreatic cancer, an exceptionally malignant tumor of the digestive system, presents a challenge due to its lack of typical early symptoms and highly invasive nature. The majority of pancreatic cancer patients are diagnosed when curative surgical resection is no longer possible, resulting in a poor overall prognosis. In recent years, the rapid progress of Artificial intelligence (AI) in the medical field has led to the extensive utilization of machine learning and deep learning as the prevailing approaches. Various models based on AI technology have been employed in the early screening, diagnosis, treatment, and prognostic prediction of pancreatic cancer patients. Furthermore, the development and application of three-dimensional visualization and augmented reality navigation techniques have also found their way into pancreatic cancer surgery. This article provides a concise summary of the current state of AI technology in pancreatic cancer and offers a promising outlook for its future applications.
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Affiliation(s)
- Guohua Zhao
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning, China
| | - Xi Chen
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning, China
- Department of Clinical integration of traditional Chinese and Western medicine, Liaoning University of Traditional Chinese Medicine, Liaoning, China
| | - Mengying Zhu
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning, China
- Department of Clinical integration of traditional Chinese and Western medicine, Liaoning University of Traditional Chinese Medicine, Liaoning, China
| | - Yang Liu
- Department of Ophthalmology, First Hospital of China Medical University, Liaoning, China
| | - Yue Wang
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning, China
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Yu X, Zhang L, He Q, Huang Y, Wu P, Xin S, Zhang Q, Zhao S, Sun H, Lei G, Zhang T, Jiang J. Development and validation of an interpretable Markov-embedded multilabel model for predicting risks of multiple postoperative complications among surgical inpatients: a multicenter prospective cohort study. Int J Surg 2024; 110:130-143. [PMID: 37830953 PMCID: PMC10793770 DOI: 10.1097/js9.0000000000000817] [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: 06/21/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND When they encounter various highly related postoperative complications, existing risk evaluation tools that focus on single or any complications are inadequate in clinical practice. This seriously hinders complication management because of the lack of a quantitative basis. An interpretable multilabel model framework that predicts multiple complications simultaneously is urgently needed. MATERIALS AND METHODS The authors included 50 325 inpatients from a large multicenter cohort (2014-2017). The authors separated patients from one hospital for external validation and randomly split the remaining patients into training and internal validation sets. A MARKov-EmbeDded (MARKED) multilabel model was proposed, and three models were trained for comparison: binary relevance, a fully connected network (FULLNET), and a deep neural network. Performance was mainly evaluated using the area under the receiver operating characteristic curve (AUC). The authors interpreted the model using Shapley Additive Explanations. Complication-specific risk and risk source inference were provided at the individual level. RESULTS There were 26 292, 6574, and 17 459 inpatients in the training, internal validation, and external validation sets, respectively. For the external validation set, MARKED achieved the highest average AUC (0.818, 95% CI: 0.771-0.864) across eight outcomes [compared with binary relevance, 0.799 (0.748-0.849), FULLNET, 0.806 (0.756-0.856), and deep neural network, 0.815 (0.765-0.866)]. Specifically, the AUCs of MARKED were above 0.9 for cardiac complications [0.927 (0.894-0.960)], neurological complications [0.905 (0.870-0.941)], and mortality [0.902 (0.867-0.937)]. Serum albumin, surgical specialties, emergency case, American Society of Anesthesiologists score, age, and sex were the six most important preoperative variables. The interaction between complications contributed more than the preoperative variables, and formed a hierarchical chain of risk factors, mild complications, and severe complications. CONCLUSION The authors demonstrated the advantage of MARKED in terms of performance and interpretability. The authors expect that the identification of high-risk patients and the inference of the risk source for specific complications will be valuable for clinical decision-making.
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Affiliation(s)
| | - Luwen Zhang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College
| | - Qing He
- The National Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing
| | - Yuguang Huang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences
| | - Peng Wu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College
| | - Shijie Xin
- Department of Vascular and Thyroid Surgery, The First Hospital of China Medical University, Shenyang, Liaoning Province, People’s Republic of China
| | | | - Shengxiu Zhao
- Department of Nursing, Qinghai Provincial People’s Hospital, Xining, Qinghai Province
| | - Hong Sun
- Department of Otolaryngology Head and Neck Surgery
| | - Guanghua Lei
- Department of Orthopedics, Xiangya Hospital of Central South University, Changsha, Hunan Province
| | | | - Jingmei Jiang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College
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Wang Y, Deng Y, Tan Y, Zhou M, Jiang Y, Liu B. A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke. BMC Med Inform Decis Mak 2023; 23:215. [PMID: 37833724 PMCID: PMC10576378 DOI: 10.1186/s12911-023-02293-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 09/11/2023] [Indexed: 10/15/2023] Open
Abstract
OBJECTIVE To evaluate RSF and Cox models for mortality prediction of hemorrhagic stroke (HS) patients in intensive care unit (ICU). METHODS In the training set, the optimal models were selected using five-fold cross-validation and grid search method. In the test set, the bootstrap method was used to validate. The area under the curve(AUC) was used for discrimination, Brier Score (BS) was used for calibration, positive predictive value(PPV), negative predictive value(NPV), and F1 score were combined to compare. RESULTS A total of 2,990 HS patients were included. For predicting the 7-day mortality, the mean AUCs for RSF and Cox regression were 0.875 and 0.761, while the mean BS were 0.083 and 0.108. For predicting the 28-day mortality, the mean AUCs for RSF and Cox regression were 0.794 and 0.649, while the mean BS were 0.129 and 0.174. The mean AUCs of RSF and Cox versus conventional scores for predicting patients' 7-day mortality were 0.875 (RSF), 0.761 (COX), 0.736 (SAPS II), 0.723 (OASIS), 0.632 (SIRS), and 0.596 (SOFA), respectively. CONCLUSIONS RSF provided a better clinical reference than Cox. Creatine, temperature, anion gap and sodium were important variables in both models.
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Affiliation(s)
- Yuxin Wang
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China
| | - Yuhan Deng
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China
| | - Yinliang Tan
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China
| | - Meihong Zhou
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China
| | - Yong Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- China National Clinical Research Center for Neurological Diseases, Beijing, China.
| | - Baohua Liu
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China.
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Abu-Khudir R, Hafsa N, Badr BE. Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning. Diagnostics (Basel) 2023; 13:3091. [PMID: 37835833 PMCID: PMC10572229 DOI: 10.3390/diagnostics13193091] [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: 06/01/2023] [Revised: 09/08/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.
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Affiliation(s)
- Rasha Abu-Khudir
- Chemistry Department, College of Science, King Faisal University, P.O. Box 380, Hofuf 31982, Al-Ahsa, Saudi Arabia
- Chemistry Department, Biochemistry Branch, Faculty of Science, Tanta University, Tanta 31527, Egypt
| | - Noor Hafsa
- Computer Science Department, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Hofuf 31982, Al-Ahsa, Saudi Arabia;
| | - Badr E. Badr
- Egyptian Ministry of Labor, Training and Research Department, Tanta 31512, Egypt;
- Botany Department, Microbiology Unit, Faculty of Science, Tanta University, Tanta 31527, Egypt
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Sijithra PC, Santhi N, Ramasamy N. A review study on early detection of pancreatic ductal adenocarcinoma using artificial intelligence assisted diagnostic methods. Eur J Radiol 2023; 166:110972. [PMID: 37454557 DOI: 10.1016/j.ejrad.2023.110972] [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: 06/26/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive, chemo-refractory and recalcitrant cancer and increases the number of deaths. With just around 1 in 4 individuals having respectable tumours, PDAC is frequently discovered when it is in an advanced stage. Accordingly, ED of PDAC improves patient survival. Subsequently, this paper reviews the early detection of PDAC, initially, the work presented an overview of PDAC. Subsequently, it reviews the molecular biology of pancreatic cancer and the development of molecular biomarkers are represented. This article illustrates the importance of identifying PDCA, the Immune Microenvironment of Pancreatic Cancer. Consequently, in this review, traditional and non-traditional imaging techniques are elucidated, traditional and non-traditional methods like endoscopic ultrasound, Multidetector CT, CT texture analysis, PET-CT, magnetic resonance imaging, diffusion-weighted imaging, secondary signs of pancreatic cancer, and molecular imaging. The use of artificial intelligence in pancreatic cancer, novel MRI techniques, and the future directions of AI for PDAC detection and prognosis is then described. Additionally, the research problem definition and motivation, current trends and developments, state of art of survey, and objective of the research are demonstrated in the review. Consequently, this review concluded that Artificial Intelligence Assisted Diagnostic Methods with MRI images can be proposed in future to improve the specificity and the sensitivity of the work, and to classify malignant PDAC with greater accuracy.
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Affiliation(s)
- P C Sijithra
- Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kanyakumari District, Tamilnadu, India.
| | - N Santhi
- Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kanyakumari District, Tamilnadu, India
| | - N Ramasamy
- Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kanyakumari District, Tamilnadu, India
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Jan Z, El Assadi F, Abd-alrazaq A, Jithesh PV. Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review (Preprint).. [DOI: 10.2196/preprints.44248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
BACKGROUND
Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.
OBJECTIVE
This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.
METHODS
A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.
RESULTS
Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.
CONCLUSIONS
This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.
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Lee KS, Kim ES. Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease. Diagnostics (Basel) 2022; 12:2740. [PMID: 36359583 PMCID: PMC9689865 DOI: 10.3390/diagnostics12112740] [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: 10/03/2022] [Revised: 11/03/2022] [Accepted: 11/06/2022] [Indexed: 08/29/2023] Open
Abstract
This study reviews the recent progress of explainable artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The source of data was eight original studies in PubMed. The search terms were "gastrointestinal" (title) together with "random forest" or "explainable artificial intelligence" (abstract). The eligibility criteria were the dependent variable of GID or a strongly associated disease, the intervention(s) of artificial intelligence, the outcome(s) of accuracy and/or the area under the receiver operating characteristic curve (AUC), the outcome(s) of variable importance and/or the Shapley additive explanations (SHAP), a publication year of 2020 or later, and the publication language of English. The ranges of performance measures were reported to be 0.70-0.98 for accuracy, 0.04-0.25 for sensitivity, and 0.54-0.94 for the AUC. The following factors were discovered to be top-10 predictors of gastrointestinal bleeding in the intensive care unit: mean arterial pressure (max), bicarbonate (min), creatinine (max), PMN, heart rate (mean), Glasgow Coma Scale, age, respiratory rate (mean), prothrombin time (max) and aminotransferase aspartate (max). In a similar vein, the following variables were found to be top-10 predictors for the intake of almond, avocado, broccoli, walnut, whole-grain barley, and/or whole-grain oat: Roseburia undefined, Lachnospira spp., Oscillibacter undefined, Subdoligranulum spp., Streptococcus salivarius subsp. thermophiles, Parabacteroides distasonis, Roseburia spp., Anaerostipes spp., Lachnospiraceae ND3007 group undefined, and Ruminiclostridium spp. Explainable artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of GID.
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Affiliation(s)
- Kwang-Sig Lee
- AI Center, Korea University Anam Hospital, Seoul 02841, Korea
| | - Eun Sun Kim
- Department of Gastroenterology, Korea University Anam Hospital, Seoul 02841, Korea
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Lee KS, Ham BJ. Machine Learning on Early Diagnosis of Depression. Psychiatry Investig 2022; 19:597-605. [PMID: 36059048 PMCID: PMC9441463 DOI: 10.30773/pi.2022.0075] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/23/2022] [Indexed: 11/27/2022] Open
Abstract
To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were "depression" (title) and "random forest" (abstract). The eligibility criteria were the dependent variable of depression, the interventions of machine learning (the decision tree, the naïve Bayesian, the random forest, the support vector machine and/or the artificial neural network), the outcomes of accuracy and/or the area under the receiver operating characteristic curve (AUC) for the early diagnosis of depression, the publication year of 2000 or later, the publication language of English and the publication journal of SCIE/SSCI. Different machine learning methods would be appropriate for different types of data for the early diagnosis of depression, e.g., logistic regression, the random forest, the support vector machine and/or the artificial neural network in the case of numeric data, the random forest in the case of genomic data. Their performance measures reported varied within 60.1-100.0 for accuracy and 64.0-96.0 for the AUC. Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression.
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Affiliation(s)
- Kwang-Sig Lee
- AI Center, Korea University Anam Hospital, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Mental Health, Korea University Anam Hospital, Seoul, Republic of Korea
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Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med 2022; 11:jcm11082265. [PMID: 35456357 PMCID: PMC9031863 DOI: 10.3390/jcm11082265] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.
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Affiliation(s)
- Ștefan Busnatu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
| | - Alexandra Bolocan
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - George E. D. Petrescu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Dan Nicolae Păduraru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Iulian Năstasă
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Mircea Lupușoru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Marius Geantă
- Centre for Innovation in Medicine, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Octavian Andronic
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 50044 Bucharest, Romania
- Correspondence:
| | - Henrique Martins
- Faculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilha, Portugal;
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Schlanger D, Graur F, Popa C, Moiș E, Al Hajjar N. The role of artificial intelligence in pancreatic surgery: a systematic review. Updates Surg 2022; 74:417-429. [PMID: 35237939 DOI: 10.1007/s13304-022-01255-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/10/2022] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI), including machine learning (ML), is being slowly incorporated in medical practice, to provide a more precise and personalized approach. Pancreatic surgery is an evolving field, which offers the only curative option for patients with pancreatic cancer. Increasing amounts of data are available in medicine: AI and ML can help incorporate large amounts of information in clinical practice. We conducted a systematic review, based on PRISMA criteria, of studies that explored the use of AI or ML algorithms in pancreatic surgery. To our knowledge, this is the first systematic review on this topic. Twenty-five eligible studies were included in this review; 12 studies with implications in the preoperative diagnosis, while 13 studies had implications in patient evolution. Preoperative diagnosis, such as predicting the malignancy of IPMNs, differential diagnosis between pancreatic cystic lesions, classification of different pancreatic tumours, and establishment of the correct management for each of these lesions, can be facilitated through different AI or ML algorithms. Postoperative evolution can also be predicted, and some studies reported prediction models for complications, including postoperative pancreatic fistula, while other studies have analysed the implications for prognosis evaluation (from predicting a textbook outcome, the risk of metastasis or relapse, or the mortality rate and survival). One study discussed the possibility of predicting an intraoperative complication-massive intraoperative bleeding. Artificial intelligence and machine learning models have promising applications in pancreatic surgery, in the preoperative period (high-accuracy diagnosis) and postoperative setting (prognosis evaluation and complication prediction), and the intraoperative applications have been less explored.
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Affiliation(s)
- D Schlanger
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania
| | - F Graur
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania. .,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania.
| | - C Popa
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania
| | - E Moiș
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania
| | - N Al Hajjar
- "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania street Emil Isac no 13, 400023, Cluj-Napoca, Romania.,Surgery Department, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. O. Fodor", Cluj-Napoca, Romania. Street Croitorilor no 19-21, 400162, Cluj-Napoca, Romania
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Lee KS, Kim G, Ham BJ. ORIGINAL ARTICLE: Associations of antidepressant medication with its various predictors including particulate matter: Machine learning analysis using national health insurance data. J Psychiatr Res 2022; 147:67-78. [PMID: 35026595 DOI: 10.1016/j.jpsychires.2022.01.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/23/2021] [Accepted: 01/03/2022] [Indexed: 10/19/2022]
Abstract
This study uses machine learning and population-based data to analyze major determinants of antidepressant medication including the concentration of particulate matter under 2.5 μm (PM2.5). Retrospective cohort data came from Korea National Health Insurance Service claims data for 43,251 participants, who were aged 15-79 years, lived in the same districts of Seoul and had no history of antidepressant medication during 2002-2012. The dependent variable was antidepressant-free months during 2013-2015 and the 30 independent variables for 2012 were included (demographic/socioeconomic information, health information, district-level information including PM2.5). Random forest variable importance, the contribution of a variable for the performance of the model, was used for identifying major predictors of antidepressant-free months. Based on random forest variable importance, the top 15 determinants of antidepressant medication during 2013-2015 included cardiovascular disease (0.0054), age (0.0047), household income (0.0037), gender (0.0027), the district-level proportion of recipients of national basic living security program benefits (0.0019), district-level social satisfaction (0.0013), diabetes mellitus (0.0012), January 2012 PM2.5 (0.0011), district-level street ratio (0.0010), drinker (0.0009), chronic obstructive pulmonary disease (0.0008), district-level economic satisfaction (0.0006), exercise (0.0005), March 2012 PM2.5 (0.0005) and November 2012 PM2.5 (0.0004). Besides these predictors, smoker and district-level deprivation index are found to be influential most widely, given that they ranked within the top 10 most often in sub-group analysis. In conclusion, antidepressant medication has strong associations with neighborhood conditions including socioeconomic satisfaction and the seasonality of particulate matter. Strong interventions for these factors are really needed for the effective management of major depressive disorder.
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Affiliation(s)
- Kwang-Sig Lee
- AI Center, Korea University College of Medicine, Seoul, South Korea
| | - Geunyeong Kim
- Korea University Graduate School of Policy Studies, Seoul, South Korea
| | - Byung-Joo Ham
- Department of Mental Health, Korea University College of Medicine, Seoul, South Korea.
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Chen X, Fu R, Shao Q, Chen Y, Ye Q, Li S, He X, Zhu J. Application of artificial intelligence to pancreatic adenocarcinoma. Front Oncol 2022; 12:960056. [PMID: 35936738 PMCID: PMC9353734 DOI: 10.3389/fonc.2022.960056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 06/24/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Pancreatic cancer (PC) is one of the deadliest cancers worldwide although substantial advancement has been made in its comprehensive treatment. The development of artificial intelligence (AI) technology has allowed its clinical applications to expand remarkably in recent years. Diverse methods and algorithms are employed by AI to extrapolate new data from clinical records to aid in the treatment of PC. In this review, we will summarize AI's use in several aspects of PC diagnosis and therapy, as well as its limits and potential future research avenues. METHODS We examine the most recent research on the use of AI in PC. The articles are categorized and examined according to the medical task of their algorithm. Two search engines, PubMed and Google Scholar, were used to screen the articles. RESULTS Overall, 66 papers published in 2001 and after were selected. Of the four medical tasks (risk assessment, diagnosis, treatment, and prognosis prediction), diagnosis was the most frequently researched, and retrospective single-center studies were the most prevalent. We found that the different medical tasks and algorithms included in the reviewed studies caused the performance of their models to vary greatly. Deep learning algorithms, on the other hand, produced excellent results in all of the subdivisions studied. CONCLUSIONS AI is a promising tool for helping PC patients and may contribute to improved patient outcomes. The integration of humans and AI in clinical medicine is still in its infancy and requires the in-depth cooperation of multidisciplinary personnel.
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Affiliation(s)
- Xi Chen
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Ruibiao Fu
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Qian Shao
- Department of Surgical Ward 1, Ningbo Women and Children’s Hospital, Ningbo, China
| | - Yan Chen
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Qinghuang Ye
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Sheng Li
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiongxiong He
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Jinhui Zhu
- Department of General Surgery, Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Jinhui Zhu,
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Comment on "Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study". Int J Surg 2021; 94:106117. [PMID: 34537395 DOI: 10.1016/j.ijsu.2021.106117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 09/11/2021] [Indexed: 11/23/2022]
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