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Yue F, Li S, Wu L, Chen X, Zhu J. Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm. Photodiagnosis Photodyn Ther 2024; 50:104426. [PMID: 39615559 DOI: 10.1016/j.pdpdt.2024.104426] [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: 09/16/2024] [Revised: 11/19/2024] [Accepted: 11/27/2024] [Indexed: 12/06/2024]
Abstract
The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive, radioactive, insufficiently sensitive and accurate. This study demonstrates the possibility of using blood plasma autofluorescence spectroscopy and Artificial Neural Network (ANN) algorithm for the rapid and accurate diagnosis of latent and active pulmonary TB from healthy subjects. The fluorescence spectra of blood plasma from 18 healthy volunteers, 12 individuals with latent TB infections and 80 active TB patients are measured and analyzed. By optimizing the ANN structure and activation functions, the ANN three-classification model achieves average classification accuracy of 96.3 %, and the accuracy of healthy persons, latent TB infections and active TB patients are 100 %, 83.3 % and 97.5 %, respectively, which is much better than the results of traditional Principal component analysis (PCA) and Linear discriminant analysis (LDA) method. To the best of our knowledge, this is the first research work of differentiating latent, active pulmonary TB cases from healthy samples with autofluorescence spectroscopy. As a rapid, accurate, safe, label-free, non-invasive and cost-effective technique, it can be developed as a promising diagnostic tool for the screening of pulmonary TB disease in the early stage.
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Affiliation(s)
- Fengjiao Yue
- College of Physics, Sichuan University, Chengdu, China
| | - Si Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Lijuan Wu
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Xuerong Chen
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China; Department of Respiratory Medicine, The Third Hospital of Shenzhen City, Southern University of Science and Technology, Shenzhen, China; Shenzhen Clinical Research Center for Tuberculosis, Shenzhen, China.
| | - Jianhua Zhu
- College of Physics, Sichuan University, Chengdu, China.
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Kaczmarczyk K, Zakynthinaki M, Barton G, Baran M, Wit A. Biomechanical comparison of two surgical methods for Hallux Valgus deformity: Exploring the use of artificial neural networks as a decision-making tool for orthopedists. PLoS One 2024; 19:e0297504. [PMID: 38349907 PMCID: PMC10863859 DOI: 10.1371/journal.pone.0297504] [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: 07/06/2023] [Accepted: 01/06/2024] [Indexed: 02/15/2024] Open
Abstract
Hallux Valgus foot deformity affects gait performance. Common treatment options include distal oblique metatarsal osteotomy and chevron osteotomy. Nonetheless, the current process of selecting the appropriate osteotomy method poses potential biases and risks, due to its reliance on subjective human judgment and interpretation. The inherent variability among clinicians, the potential influence of individual clinical experiences, or inherent measurement limitations may contribute to inconsistent evaluations. To address this, incorporating objective tools like neural networks, renowned for effective classification and decision-making support, holds promise in identifying optimal surgical approaches. The objective of this cross-sectional study was twofold. Firstly, it aimed to investigate the feasibility of classifying patients based on the type of surgery. Secondly, it sought to explore the development of a decision-making tool to assist orthopedists in selecting the optimal surgical approach. To achieve this, gait parameters of twenty-three women with moderate to severe Hallux Valgus were analyzed. These patients underwent either distal oblique metatarsal osteotomy or chevron osteotomy. The parameters exhibiting differences in preoperative and postoperative values were identified through various statistical tests such as normalization, Shapiro-Wilk, non-parametric Wilcoxon, Student t, and paired difference tests. Two artificial neural networks were constructed for patient classification based on the type of surgery and to simulate an optimal surgery type considering postoperative walking speed. The results of the analysis demonstrated a strong correlation between surgery type and postoperative gait parameters, with the first neural network achieving a remarkable 100% accuracy in classification. Additionally, cases were identified where there was a mismatch with the surgeon's decision. Our findings highlight the potential of artificial neural networks as a complementary tool for surgeons in making informed decisions. Addressing the study's limitations, future research may investigate a wider range of orthopedic procedures, examine additional gait parameters and use more diverse and extensive datasets to enhance statistical robustness.
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Affiliation(s)
- Katarzyna Kaczmarczyk
- Faculty of Rehabilitation, Józef Piłsudski Academy of Physical Education, Warsaw, Poland
| | - Maria Zakynthinaki
- School of Chemical and Environmental Engineering, Technical University of Crete, Chania, Greece
| | - Gabor Barton
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom
| | - Mateusz Baran
- Faculty of Rehabilitation, Józef Piłsudski Academy of Physical Education, Warsaw, Poland
| | - Andrzej Wit
- Faculty of Rehabilitation, Józef Piłsudski Academy of Physical Education, Warsaw, Poland
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Shahidi F, Rennert-May E, D'Souza AG, Crocker A, Faris P, Leal J. Machine learning risk estimation and prediction of death in continuing care facilities using administrative data. Sci Rep 2023; 13:17708. [PMID: 37853045 PMCID: PMC10584843 DOI: 10.1038/s41598-023-43943-9] [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: 05/04/2023] [Accepted: 09/30/2023] [Indexed: 10/20/2023] Open
Abstract
In this study, we aimed to identify the factors that were associated with mortality among continuing care residents in Alberta, during the coronavirus disease 2019 (COVID-19) pandemic. We achieved this by leveraging and linking various administrative datasets together. Then, we examined pre-processing methods in terms of prediction performance. Finally, we developed several machine learning models and compared the results of these models in terms of performance. We conducted a retrospective cohort study of all continuing care residents in Alberta, Canada, from March 1, 2020, to March 31, 2021. We used a univariable and a multivariable logistic regression (LR) model to identify predictive factors of 60-day all-cause mortality by estimating odds ratios (ORs) with a 95% confidence interval. To determine the best sensitivity-specificity cut-off point, the Youden index was employed. We developed several machine learning models to determine the best model regarding performance. In this cohort study, increased age, male sex, symptoms, previous admissions, and some specific comorbidities were associated with increased mortality. Machine learning and pre-processing approaches offer a potentially valuable method for improving risk prediction for mortality, but more work is needed to show improvement beyond standard risk factors.
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Affiliation(s)
- Faezehsadat Shahidi
- Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada
| | - Elissa Rennert-May
- Community Health Sciences, University of Calgary, Calgary, AB, Canada
- Department of Medicine, University of Calgary, Calgary, AB, Canada
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
- Department of Microbiology, Immunology, and Infectious Diseases, University of Calgary, Calgary, AB, Canada
- Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
- AMR - One Health Consortium, University of Calgary, Calgary, AB, Canada
| | - Adam G D'Souza
- Centre for Health Informatics, University of Calgary, Calgary, AB, Canada
- Analytics, Alberta Health Services, Calgary, AB, Canada
| | - Alysha Crocker
- Clinical Information Systems, Alberta Health Services, Calgary, AB, Canada
| | - Peter Faris
- Community Health Sciences, University of Calgary, Calgary, AB, Canada
- Analytics, Alberta Health Services, Calgary, AB, Canada
| | - Jenine Leal
- Community Health Sciences, University of Calgary, Calgary, AB, Canada.
- O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.
- Department of Microbiology, Immunology, and Infectious Diseases, University of Calgary, Calgary, AB, Canada.
- AMR - One Health Consortium, University of Calgary, Calgary, AB, Canada.
- Infection Prevention and Control, Alberta Health Services, Calgary, AB, Canada.
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Huang C, Luo B, Wang G, Chen P, Ren J. Development and validation of a prediction model for intrapartum cesarean delivery based on the artificial neural networks approach: a protocol for a prospective nested case-control study. BMJ Open 2023; 13:e066753. [PMID: 36828664 PMCID: PMC9972428 DOI: 10.1136/bmjopen-2022-066753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 02/12/2023] [Indexed: 02/26/2023] Open
Abstract
INTRODUCTION Although intrapartum caesarean delivery can resolve dystocia, it would still lead to several adverse outcomes for mothers and children. The obstetric care professionals need effective tools that can help them to identify the possibility and risk factors of intrapartum caesarean delivery, and further implement interventions to avoid unnecessary caesarean birth. This study aims to develop a prediction model for intrapartum caesarean delivery with real-life data based on the artificial neural networks approach. METHODS AND ANALYSIS This study is a prospective nested case-control design. Pregnant women who plan to deliver vaginally will be recruited in a tertiary hospital in Southwest China from March 2022 to March 2024. The clinical data of prelabour, intrapartum period and psychosocial information will be collected. The case group will be the women who finally have a baby with intrapartum caesarean deliveries, and the control group will be those who deliver a baby vaginally. An artificial neural networks approach with the backpropagation algorithm multilayer perceptron topology will be performed to construct the prediction model. ETHICS AND DISSEMINATION Ethical approval for data collection was granted by the Ethics Committee of West China Second University Hospital, Sichuan University, and the ethical number is 2021 (204). Written informed consent will be obtained from all participants and they can withdraw from the study at any time. The results of this study will be published in peer-review journal.
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Affiliation(s)
- Chuanya Huang
- Department of Nursing, West China Second University, Sichuan University, Chengdu, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, People's Republic of China
- West China School of Nursing, Sichuan University, Chengdu, People's Republic of China
| | - Biru Luo
- Department of Nursing, West China Second University, Sichuan University, Chengdu, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, People's Republic of China
- West China School of Nursing, Sichuan University, Chengdu, People's Republic of China
| | - Guoyu Wang
- Department of Nursing, West China Second University, Sichuan University, Chengdu, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, People's Republic of China
- West China School of Nursing, Sichuan University, Chengdu, People's Republic of China
| | - Peng Chen
- School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, China
| | - Jianhua Ren
- Department of Nursing, West China Second University, Sichuan University, Chengdu, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, People's Republic of China
- West China School of Nursing, Sichuan University, Chengdu, People's Republic of China
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Sundaram A, Li Y, Abdel-Khalik H. Denoising Algorithm for Subtle Anomaly Detection. NUCL TECHNOL 2022. [DOI: 10.1080/00295450.2022.2027147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Arvind Sundaram
- Purdue University, 205 Gates Road, West Lafayette, Indiana 47906
| | - Yeni Li
- Purdue University, 205 Gates Road, West Lafayette, Indiana 47906
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Zaborowicz M, Zaborowicz K, Biedziak B, Garbowski T. Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters. SENSORS 2022; 22:s22020637. [PMID: 35062599 PMCID: PMC8777593 DOI: 10.3390/s22020637] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/07/2022] [Accepted: 01/12/2022] [Indexed: 02/06/2023]
Abstract
Dental age is one of the most reliable methods for determining a patient’s age. The timing of teething, the period of tooth replacement, or the degree of tooth attrition is an important diagnostic factor in the assessment of an individual’s developmental age. It is used in orthodontics, pediatric dentistry, endocrinology, forensic medicine, and pathomorphology, but also in scenarios regarding international adoptions and illegal immigrants. The methods used to date are time-consuming and not very precise. For this reason, artificial intelligence methods are increasingly used to estimate the age of a patient. The present work is a continuation of the work of Zaborowicz et al. In the presented research, a set of 21 original indicators was used to create deep neural network models. The aim of this study was to verify the ability to generate a more accurate deep neural network model compared to models produced previously. The quality parameters of the produced models were as follows. The MAE error of the produced models, depending on the learning set used, was between 2.34 and 4.61 months, while the RMSE error was between 5.58 and 7.49 months. The correlation coefficient R2 ranged from 0.92 to 0.96.
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Affiliation(s)
- Maciej Zaborowicz
- Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-627 Poznan, Poland;
- Correspondence: (M.Z.); (K.Z.)
| | - Katarzyna Zaborowicz
- Department of Orthodontics and Craniofacial Anomalies, Poznan University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznan, Poland;
- Correspondence: (M.Z.); (K.Z.)
| | - Barbara Biedziak
- Department of Orthodontics and Craniofacial Anomalies, Poznan University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznan, Poland;
| | - Tomasz Garbowski
- Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-627 Poznan, Poland;
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Zaborowicz K, Biedziak B, Olszewska A, Zaborowicz M. Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods. SENSORS (BASEL, SWITZERLAND) 2021; 21:6008. [PMID: 34577221 PMCID: PMC8473021 DOI: 10.3390/s21186008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 12/13/2022]
Abstract
The analog methods used in the clinical assessment of the patient's chronological age are subjective and characterized by low accuracy. When using those methods, there is a noticeable discrepancy between the chronological age and the age estimated based on relevant scientific studies. Innovations in the field of information technology are increasingly used in medicine, with particular emphasis on artificial intelligence methods. The paper presents research aimed at developing a new, effective methodology for the assessment of the chronological age using modern IT methods. In this paper, a study was conducted to determine the features of pantomographic images that support the determination of metric age, and neural models were produced to support the process of identifying the age of children and adolescents. The whole conducted work was a new methodology of metric age assessment. The result of the conducted study is a set of 21 original indicators necessary for the assessment of the chronological age with the use of computer image analysis and neural modelling, as well as three non-linear models of radial basis function networks (RBF), whose accuracy ranges from 96 to 99%. The result of the research are three neural models that determine the chronological age.
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Affiliation(s)
- Katarzyna Zaborowicz
- Department of Craniofacial Anomalies, Poznań University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznań, Poland
| | - Barbara Biedziak
- Department of Craniofacial Anomalies, Poznań University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznań, Poland
| | - Aneta Olszewska
- Department of Craniofacial Anomalies, Poznań University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznań, Poland
| | - Maciej Zaborowicz
- Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-637 Poznań, Poland
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Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study. Int J Surg 2021; 93:106050. [PMID: 34388677 DOI: 10.1016/j.ijsu.2021.106050] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/26/2021] [Accepted: 08/05/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND or Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of mortality in the world with the overall 5-year survival rate of 6%. The survival of patients with PDAC is closely related to recurrence and therefore it is necessary to identify the risk factors for recurrence. This study uses artificial intelligence approaches and multi-center registry data to analyze the recurrence of pancreatic cancer after surgery and its major determinants. METHODS Data came from 4846 patients enrolled in a multi-center registry system, the Korea Tumor Registry System (KOTUS). The random forest and the Cox proportional-hazards model (the Cox model) were applied and compared for the prediction of disease-free survival. Variable importance, the contribution of a variable for the performance of the model, was used for identifying major predictors of disease-free survival after surgery. The C-Index was introduced as a criterion for validating the models trained. RESULTS Based on variable importance from the random forest, major predictors of disease-free survival after surgery were tumor size (0.00310), tumor grade (0.00211), TNM stage (0.00211), T stage (0.00146) and lymphovascular invasion (0.00125). The coefficients of these variables were statistically significant in the Cox model (p < 0.05). The C-Index averages of the random forest and the Cox model were 0.6805 and 0.7738, respectively. CONCLUSIONS This is the first artificial-intelligence study with multi-center registry data to predict disease-free survival after the surgery of pancreatic cancer. The findings of this methodological study demonstrate that artificial intelligence can provide a valuable decision-support system for treating patients undergoing surgery for pancreatic cancer. However, at present, further studies are needed to demonstrate the actual benefit of applying machine learning algorithms in clinical practice.
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Aronsson L, Andersson R, Ansari D. Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas. PLoS One 2021; 16:e0249206. [PMID: 33765078 PMCID: PMC7993879 DOI: 10.1371/journal.pone.0249206] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 03/14/2021] [Indexed: 12/12/2022] Open
Abstract
Prediction of long-term survival in patients with invasive intraductal papillary mucinous neoplasm (IPMN) of the pancreas may aid in patient assessment, risk stratification and personalization of treatment. This study aimed to investigate the predictive ability of artificial neural networks (ANN) and LASSO regression in terms of 5-year disease-specific survival. ANN work in a non-linear fashion, having a potential advantage in analysis of variables with complex correlations compared to regression models. LASSO is a type of regression analysis facilitating variable selection and regularization. A total of 440 patients undergoing surgical treatment for invasive IPMN of the pancreas registered in the Surveillance, Epidemiology and End Results (SEER) database between 2004 and 2016 were analyzed. The dataset was prior to analysis randomly split into a modelling and test set (7:3). The accuracy, precision and F1 score for predicting mortality were 0.82, 0.83 and 0.89, respectively for ANN with variable selection compared to 0.79, 0.85 and 0.87, respectively for the LASSO-model. ANN using all variables showed similar accuracy, precision and F1 score of 0.81, 0.85 and 0.88, respectively compared to a logistic regression analysis. McNemar´s test showed no statistical difference between the models. The models showed high and similar performance with regard to accuracy and precision for predicting 5-year survival status.
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Affiliation(s)
- Linus Aronsson
- Department of Surgery, Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Roland Andersson
- Department of Surgery, Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Daniel Ansari
- Department of Surgery, Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
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Varrecchia T, Castiglia SF, Ranavolo A, Conte C, Tatarelli A, Coppola G, Di Lorenzo C, Draicchio F, Pierelli F, Serrao M. An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters. PLoS One 2021; 16:e0244396. [PMID: 33606730 PMCID: PMC7894951 DOI: 10.1371/journal.pone.0244396] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 12/08/2020] [Indexed: 01/16/2023] Open
Abstract
Introduction Gait deficits are debilitating in people with Parkinson’s disease (PwPD), which inevitably deteriorate over time. Gait analysis is a valuable method to assess disease-specific gait patterns and their relationship with the clinical features and progression of the disease. Objectives Our study aimed to i) develop an automated diagnostic algorithm based on machine-learning techniques (artificial neural networks [ANNs]) to classify the gait deficits of PwPD according to disease progression in the Hoehn and Yahr (H-Y) staging system, and ii) identify a minimum set of gait classifiers. Methods We evaluated 76 PwPD (H-Y stage 1–4) and 67 healthy controls (HCs) by computerized gait analysis. We computed the time-distance parameters and the ranges of angular motion (RoMs) of the hip, knee, ankle, trunk, and pelvis. Principal component analysis was used to define a subset of features including all gait variables. An ANN approach was used to identify gait deficits according to the H-Y stage. Results We identified a combination of a small number of features that distinguished PwPDs from HCs (one combination of two features: knee and trunk rotation RoMs) and identified the gait patterns between different H-Y stages (two combinations of four features: walking speed and hip, knee, and ankle RoMs; walking speed and hip, knee, and trunk rotation RoMs). Conclusion The ANN approach enabled automated diagnosis of gait deficits in several symptomatic stages of Parkinson’s disease. These results will inspire future studies to test the utility of gait classifiers for the evaluation of treatments that could modify disease progression.
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Affiliation(s)
- Tiwana Varrecchia
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone Rome, Rome, Italy
- * E-mail:
| | - Stefano Filippo Castiglia
- Department of Medico-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Latina, Italy
| | - Alberto Ranavolo
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone Rome, Rome, Italy
| | | | - Antonella Tatarelli
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone Rome, Rome, Italy
- Department of Human Neurosciences, University of Rome Sapienza, Rome, Italy
| | - Gianluca Coppola
- Department of Medico-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Latina, Italy
| | - Cherubino Di Lorenzo
- Department of Medico-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Latina, Italy
| | - Francesco Draicchio
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone Rome, Rome, Italy
| | - Francesco Pierelli
- Department of Medico-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Latina, Italy
| | - Mariano Serrao
- Department of Medico-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Latina, Italy
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Jin X, Ding Z, Li T, Xiong J, Tian G, Liu J. Comparison of MPL-ANN and PLS-DA models for predicting the severity of patients with acute pancreatitis: An exploratory study. Am J Emerg Med 2021; 44:85-91. [PMID: 33582613 DOI: 10.1016/j.ajem.2021.01.044] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/27/2020] [Accepted: 01/16/2021] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Acute pancreatitis (AP) is a common inflammatory disorder that may develop into severe AP (SAP), resulting in life-threatening complications and even death. The purpose of this study was to explore two different machine learning models of multilayer perception-artificial neural network (MPL-ANN) and partial least squares-discrimination (PLS-DA) to diagnose and predict AP patients' severity. METHODS The MPL-ANN and PLS-DA models were established using candidate markers from 15 blood routine parameters and five serum biochemical indexes of 133 mild acute pancreatitis (MAP) patients, 167 SAP (including 88 moderately SAP) patients, and 69 healthy controls (HCs). The independent parameters and combined model's diagnostic efficiency in AP severity differentiation were analyzed using the area under the receiver operating characteristic curve (AUC). RESULTS The neutrophil to lymphocyte ratio (NLR) is the most useful marker in 20 parameters for screening AP patients [AUC = 0.990, 95% confidence interval (CI): 0.984-0.997, sensitivity 94.3%, specificity 98.6%]. The MPL-ANN model based on six optimal parameters exhibited better diagnostic and predict performance (AUC = 0.984, 95% CI: 0.960-1.00, sensitivity 92.7%, specificity 93.3%, accuracy 93.0%) than the PLS-DA model based on five optimal parameters (AUC = 0.912, 95% CI: 0.853-0.971, sensitivity 87.8%, specificity 84.4%, accuracy 84.8%) in discriminating MAP patients from SAP patients. CONCLUSION The results demonstrated that the MPL-ANN model based on routine blood and serum biochemical indexes provides a reliable and straightforward daily clinical practice tool to predict AP patients' severity.
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Affiliation(s)
- Xinrui Jin
- Department of Laboratory Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China
| | - Zixuan Ding
- Department of Laboratory Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China
| | - Tao Li
- Network manage center, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China
| | - Jie Xiong
- Department of Laboratory Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China; Department of Laboratory Medicine, General Hospital of Chengdu Military Region, Chengdu, Sichuan 610083, China
| | - Gang Tian
- Department of Laboratory Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China
| | - Jinbo Liu
- Department of Laboratory Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China.
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12
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Lai Q, Spoletini G, Mennini G, Laureiro ZL, Tsilimigras DI, Pawlik TM, Rossi M. Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review. World J Gastroenterol 2020; 26:6679-6688. [PMID: 33268955 PMCID: PMC7673961 DOI: 10.3748/wjg.v26.i42.6679] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 09/14/2020] [Accepted: 10/01/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Prediction of survival after the treatment of hepatocellular carcinoma (HCC) has been widely investigated, yet remains inadequate. The application of artificial intelligence (AI) is emerging as a valid adjunct to traditional statistics due to the ability to process vast amounts of data and find hidden interconnections between variables. AI and deep learning are increasingly employed in several topics of liver cancer research, including diagnosis, pathology, and prognosis. AIM To assess the role of AI in the prediction of survival following HCC treatment. METHODS A web-based literature search was performed according to the Preferred Reporting Items for Systemic Reviews and Meta-Analysis guidelines using the keywords "artificial intelligence", "deep learning" and "hepatocellular carcinoma" (and synonyms). The specific research question was formulated following the patient (patients with HCC), intervention (evaluation of HCC treatment using AI), comparison (evaluation without using AI), and outcome (patient death and/or tumor recurrence) structure. English language articles were retrieved, screened, and reviewed by the authors. The quality of the papers was assessed using the Risk of Bias In Non-randomized Studies of Interventions tool. Data were extracted and collected in a database. RESULTS Among the 598 articles screened, nine papers met the inclusion criteria, six of which had low-risk rates of bias. Eight articles were published in the last decade; all came from eastern countries. Patient sample size was extremely heterogenous (n = 11-22926). AI methodologies employed included artificial neural networks (ANN) in six studies, as well as support vector machine, artificial plant optimization, and peritumoral radiomics in the remaining three studies. All the studies testing the role of ANN compared the performance of ANN with traditional statistics. Training cohorts were used to train the neural networks that were then applied to validation cohorts. In all cases, the AI models demonstrated superior predictive performance compared with traditional statistics with significantly improved areas under the curve. CONCLUSION AI applied to survival prediction after HCC treatment provided enhanced accuracy compared with conventional linear systems of analysis. Improved transferability and reproducibility will facilitate the widespread use of AI methodologies.
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Affiliation(s)
- Quirino Lai
- Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
| | - Gabriele Spoletini
- General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario A Gemelli IRCCS, Rome 00100, Italy
| | - Gianluca Mennini
- Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
| | - Zoe Larghi Laureiro
- Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
| | | | | | - Massimo Rossi
- Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
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Balaban DV, Jinga M. Digital histology in celiac disease: A practice changer. Artif Intell Gastroenterol 2020; 1:1-4. [DOI: 10.35712/aig.v1.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/18/2020] [Accepted: 07/20/2020] [Indexed: 02/06/2023] Open
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Digital histology in celiac disease: A practice changer. Artif Intell Gastroenterol 2020. [DOI: 10.35712/wjg.v1.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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Wang X, Yang B, Moody JB, Tang J. Improved myocardial perfusion PET imaging using artificial neural networks. Phys Med Biol 2020; 65:145010. [PMID: 32244234 DOI: 10.1088/1361-6560/ab8687] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Myocardial perfusion (MP) PET imaging plays a key role in risk assessment and stratification of patients with coronary artery disease. In this work, we proposed a patch-based artificial neural network (ANN) fusion approach that integrates information from the ML and the post-smoothed ML reconstruction to improve MP PET imaging. The proposed method was applied to images reconstructed from different noise levels to enhance quantification and task-based MP defect detection. Using the XCAT phantom, we simulated three MP PET imaging cases, one with normal perfusion and the other two with non-transmural and transmural regionally reduced perfusion of the left ventricular (LV) myocardium. The proposed ANN fusion technique was quantitatively evaluated in terms of the noise versus bias and noise versus contrast tradeoff, and compared with the post-smoothed ML reconstruction. Using the channelized Hotelling observer, we evaluated the detectability of the non-transmural and transmural defects through the receiver operating characteristic analysis. The quantitative results demonstrated that the ANN enhancement method reduced bias and improved contrast while reaching comparable noise to what the post-smoothed ML reconstruction achieved. Moreover, the ANN fusion technique significantly improved the defect detectability of both the non-transmural and transmural defects. In addition to the simulation study, we further evaluated the proposed method using patient data. Compared with the post-smoothed ML reconstruction, the ANN fusion improved the tradeoff between noise and the mean value on the LV myocardium, indicating its potential clinical application in MP PET imaging.
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Affiliation(s)
- Xinhui Wang
- Department of Electrical and Computer Engineering, Oakland University, Rochester, MI, United States of America
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Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:7239780. [PMID: 31428186 PMCID: PMC6679853 DOI: 10.1155/2019/7239780] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 07/02/2019] [Indexed: 12/19/2022]
Abstract
The diagnostic performance of an artificial neural network model for chronic HBV-induced liver fibrosis reverse is not well established. Our research aims to construct an ANN model for estimating noninvasive predictors of fibrosis reverse in chronic HBV patients after regular antiviral therapy. In our study, 141 consecutive patients requiring liver biopsy at baseline and 1.5 years were enrolled. Several serum biomarkers and liver stiffness were measured during antiviral therapy in both reverse and nonreverse groups. Statistically significant variables between two groups were selected to form an input layer of the ANN model. The ROC (receiver-operating characteristic) curve and AUC (area under the curve) were calculated for comparison of effectiveness of the ANN model and logistic regression model in predicting HBV-induced liver fibrosis reverse. The prevalence of fibrosis reverse of HBV patients was about 39% (55/141) after 78-week antiviral therapy. The Ishak scoring system was used to assess fibrosis reverse. Our study manifested that AST (aspartate aminotransferase; importance coefficient = 0.296), PLT (platelet count; IC = 0.159), WBC (white blood cell; IC = 0.142), CHE (cholinesterase; IC = 0.128), LSM (liver stiffness measurement; IC = 0.125), ALT (alanine aminotransferase; IC = 0.110), and gender (IC = 0.041) were the most crucial predictors of reverse. The AUC of the ANN model and logistic model was 0.809 ± 0.062 and 0.756 ± 0.059, respectively. In our study, we concluded that the ANN model with variables consisting of AST, PLT, WBC, CHE, LSM, ALT, and gender may be useful in diagnosing liver fibrosis reverse for chronic HBV-induced liver fibrosis patients.
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Personalized Pancreatic Cancer Management: A Systematic Review of How Machine Learning Is Supporting Decision-making. Pancreas 2019; 48:598-604. [PMID: 31090660 DOI: 10.1097/mpa.0000000000001312] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
This review critically analyzes how machine learning is being used to support clinical decision-making in the management of potentially resectable pancreatic cancer. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, electronic searches of MEDLINE, Embase, PubMed, and Cochrane Database were undertaken. Studies were assessed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) checklist. In total 89,959 citations were retrieved. Six studies met the inclusion criteria. Three studies were Markov decision-analysis models comparing neoadjuvant therapy versus upfront surgery. Three studies predicted survival time using Bayesian modeling (n = 1) and artificial neural network (n = 1), and one study explored machine learning algorithms including Bayesian network, decision trees, k-nearest neighbor, and artificial neural networks. The main methodological issues identified were limited data sources, which limits generalizability and potentiates bias; lack of external validation; and the need for transparency in methods of internal validation, consecutive sampling, and selection of candidate predictors. The future direction of research relies on expanding our view of the multidisciplinary team to include professionals from computing and data science with algorithms developed in conjunction with clinicians and viewed as aids, not replacement, to traditional clinical decision-making.
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Ertiaei A, Ataeinezhad Z, Bitaraf M, Sheikhrezaei A, Saberi H. Application of an artificial neural network model for early outcome prediction of gamma knife radiosurgery in patients with trigeminal neuralgia and determining the relative importance of risk factors. Clin Neurol Neurosurg 2019; 179:47-52. [PMID: 30825722 DOI: 10.1016/j.clineuro.2018.11.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 10/27/2018] [Accepted: 11/07/2018] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Stereotactic radiosurgery (SRS) is a minimally invasive modality for the treatment of trigeminal neuralgia (TN). Outcome prediction of this modality is very important for proper case selection. The aim of this study was to create artificial neural networks (ANN) to predict the clinical outcomes after gamma knife radiosurgery (GKRS) in patients with TN, based on preoperative clinical factors. PATIENTS AND METHODS We used the clinical findings of 155 patients who were underwent GKRS (from March 2000 to march 2015) at Iran Gamma Knife center, Teheran, Iran. Univariate analysis was performed for a long list of risk factors, and those with P-Value < 0.2 were used to create back-propagation ANN models to predict pain reduction and hypoesthesia after GKRS. Pain reduction was defined as BNI score 3a or lower and hypoesthesia was defined as BNI score 3 or 4. RESULTS Typical trigeminal neuralgia (TTN) (P-Value = 0.018) and age>65 (P-Value = 0.040) were significantly associated with successful pain reduction and three other variables including radiation dosage >85 (P-Value = 0.098), negative history of diabetes mellitus (P-Value = 0.133) and depression (P-Value = 0.190). On the other hand, radio dosage>85 (P-Value = 0.008) was significantly associated with hypoesthesia, other related risk factors (with p-Value<0.2), were history of multiple sclerosis (P-Value = 0.106), pain duration more than 10 years before GKRS (P-Value = 0.115), history of depression (P-Value = 0.139), history of percutaneous ablative procedures (P-Value = 0.148) and history of diabetes mellitus (P-Value = 0.169).ANN models could predict pain reduction and hypoesthesia with the accuracy of 84.5% and 91.5% respectively. By mutual elimination of each factor in this model we could also evaluate the contribution of each factor in the predictive performance of ANN. CONCLUSIONS The findings show that artificial neural networks can predict post operative outcomes in patients who underwent GKRS with a high level of accuracy. Also the contribution of each factor in the prediction of outcomes can be determined using the trained network.
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Affiliation(s)
- Abolhassan Ertiaei
- Department of Neurosurgery, Imam Khomeini Hospital, Tehran University of Medical Science, Tehran, Iran.
| | - Zohreh Ataeinezhad
- Department of Neurosurgery, Imam Khomeini Hospital, Tehran University of Medical Science, Tehran, Iran
| | - MohammadAli Bitaraf
- Department of Neurosurgery, Imam Khomeini Hospital, Tehran University of Medical Science, Tehran, Iran
| | - Abdolreza Sheikhrezaei
- Department of Neurosurgery, Imam Khomeini Hospital, Tehran University of Medical Science, Tehran, Iran
| | - Hooshang Saberi
- Department of Neurosurgery, Imam Khomeini Hospital, Tehran University of Medical Science, Tehran, Iran
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Strijker M, Chen JW, Mungroop TH, Jamieson NB, van Eijck CH, Steyerberg EW, Wilmink JW, Groot Koerkamp B, van Laarhoven HW, Besselink MG. Systematic review of clinical prediction models for survival after surgery for resectable pancreatic cancer. Br J Surg 2019; 106:342-354. [PMID: 30758855 DOI: 10.1002/bjs.11111] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 11/02/2018] [Accepted: 12/11/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND As more therapeutic options for pancreatic cancer are becoming available, there is a need to improve outcome prediction to support shared decision-making. A systematic evaluation of prediction models in resectable pancreatic cancer is lacking. METHODS This systematic review followed the CHARMS and PRISMA guidelines. PubMed, Embase and Cochrane Library databases were searched up to 11 October 2017. Studies reporting development or validation of models predicting survival in resectable pancreatic cancer were included. Models without performance measures, reviews, abstracts or more than 10 per cent of patients not undergoing resection in postoperative models were excluded. Studies were appraised critically. RESULTS After screening 4403 studies, 22 (44 319 patients) were included. There were 19 model development/update studies and three validation studies, altogether concerning 21 individual models. Two studies were deemed at low risk of bias. Eight models were developed for the preoperative setting and 13 for the postoperative setting. Most frequently included parameters were differentiation grade (11 of 21 models), nodal status (8 of 21) and serum albumin (7 of 21). Treatment-related variables were included in three models. The C-statistic/area under the curve values ranged from 0·57 to 0·90. Based on study design, validation methods and the availability of web-based calculators, two models were identified as the most promising. CONCLUSION Although a large number of prediction models for resectable pancreatic cancer have been reported, most are at high risk of bias and have not been validated externally. This overview of prognostic factors provided practical recommendations that could help in designing easily applicable prediction models to support shared decision-making.
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Affiliation(s)
- M Strijker
- Department of Surgery, Cancer Centre Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - J W Chen
- Department of Surgery, Cancer Centre Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - T H Mungroop
- Department of Surgery, Cancer Centre Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - N B Jamieson
- West of Scotland Pancreatic Unit, Glasgow Royal Infirmary, University of Glasgow, Glasgow, UK
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - C H van Eijck
- Department of Surgery, Erasmus Medical Centre, Rotterdam, the Netherlands
| | - E W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, the Netherlands
| | - J W Wilmink
- Department of Medical Oncology, Cancer Centre Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - B Groot Koerkamp
- Department of Surgery, Erasmus Medical Centre, Rotterdam, the Netherlands
| | - H W van Laarhoven
- Department of Medical Oncology, Cancer Centre Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - M G Besselink
- Department of Surgery, Cancer Centre Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
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Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS One 2019; 14:e0212356. [PMID: 30779785 PMCID: PMC6380578 DOI: 10.1371/journal.pone.0212356] [Citation(s) in RCA: 140] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 01/31/2019] [Indexed: 12/12/2022] Open
Abstract
Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. We provide a seminal review of the applications of ANN to health care organizational decision-making. We screened 3,397 articles from six databases with coverage of Health Administration, Computer Science and Business Administration. We extracted study characteristics, aim, methodology and context (including level of analysis) from 80 articles meeting inclusion criteria. Articles were published from 1997-2018 and originated from 24 countries, with a plurality of papers (26 articles) published by authors from the United States. Types of ANN used included ANN (36 articles), feed-forward networks (25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%. The majority of ANN informed decision-making at the micro level (61 articles), between patients and health care providers. Fewer ANN were deployed for intra-organizational (meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 articles) decision-making. Our review identifies key characteristics and drivers for market uptake of ANN for health care organizational decision-making to guide further adoption of this technique.
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Affiliation(s)
- Nida Shahid
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- Toronto Health Economics and Technology Assessment (THETA) Collaborative, University Health Network, Toronto, Canada
| | - Tim Rappon
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
| | - Whitney Berta
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
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Harbaugh RE. Editorial. Artificial neural networks for neurosurgical diagnosis, prognosis, and management. Neurosurg Focus 2018; 45:E3. [PMID: 30453456 DOI: 10.3171/2018.8.focus18438] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Walczak S, Velanovich V. An Evaluation of Artificial Neural Networks in Predicting Pancreatic Cancer Survival. J Gastrointest Surg 2017; 21:1606-1612. [PMID: 28776157 DOI: 10.1007/s11605-017-3518-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 07/20/2017] [Indexed: 01/31/2023]
Abstract
OBJECTIVE This study aims to evaluate the development of an artificial neural network (ANN) method for predicting the survival likelihood of pancreatic adenocarcinoma patients. The ANN predictive model should produce results with a 90% sensitivity. METHODS A prospective examination of the records for 283 consecutive pancreatic adenocarcinoma patients is used to identify 219 records with complete data. These records are then used to create two unique samples which are then used to train and validate an ANN predictive model. Numerous network architectures are evaluated, following recommended ANN development protocols. RESULTS Several backpropagation-trained ANNs were produced that satisfied the 90% sensitivity requirement. An ANN model with over a 91% sensitivity is selected because even though it did not have the highest sensitivity, it was able to achieve over 38% specificity. CONCLUSIONS ANN models can accurately predict the 7-month survival of pancreatic adenocarcinoma patients, both with and without resection, at a 91% sensitivity and 38% specificity. This implies that ANN models may be useful objective decision tools in complex treatment decisions. This information may be used by patients and surgeons in determining optimal treatment plans that minimize regret and improve the quality of life for these patients.
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Affiliation(s)
- Steven Walczak
- School of Information and Florida Center for Cybersecurity, University of South Florida, 4202 E. Fowler Ave., CIS 1040, Tampa, FL, 33620, USA.
| | - Vic Velanovich
- Division of General Surgery, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
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Abstract
Logistic Regression Model (LRM) and artificial neural networks (ANNs) as two nonlinear models have been used to establish a novel two-stage hybrid modeling procedure for prediction of metastasis in advanced colorectal carcinomas. Two different datasets were used in training and testing procedures. For the first stage of hybrid modeling procedure, LRM was used to evaluate the contribution of DNA sequence copy number aberrations detected by Comparative Genomic Hybridization in advanced colorectal carcinoma and its metastasis. Then, the most effective parameters were selected by the LRM. Selected effective parameters among 565 detected chromosomal gains and losses were as follows: gain of 20q11.2, loss of 1q42, loss of 13q34, gain of 5q12, gain of 17p13, loss of 2q22, loss of 11q24 and gain of 2p11.2. Consequently, neural network models were constructed and fed by the parameters selected by LRM to build hybrid predictors on the two databases during self-consistency and jackknife tests, and performance of the hybrid model was verified. The results showed that our two-stage hybrid model approach is very promising for prediction of metastasis in advanced colorectal carcinomas.
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Lopez-de-Andres A, Hernandez-Barrera V, Lopez R, Martin-Junco P, Jimenez-Trujillo I, Alvaro-Meca A, Salinero-Fort MA, Jimenez-Garcia R. Predictors of in-hospital mortality following major lower extremity amputations in type 2 diabetic patients using artificial neural networks. BMC Med Res Methodol 2016; 16:160. [PMID: 27876006 PMCID: PMC5120563 DOI: 10.1186/s12874-016-0265-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Accepted: 11/16/2016] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Outcome prediction is important in the clinical decision-making process. Artificial neural networks (ANN) have been used to predict the risk of post-operative events, including survival, and are increasingly being used in complex medical decision making. We aimed to use ANN analysis to estimate predictive factors of in-hospital mortality (IHM) in patients with type 2 diabetes (T2DM) after major lower extremity amputation (LEA) in Spain. METHODS We design a retrospective, observational study using ANN models. We used the Spanish National Hospital Discharge Database to select all hospital admissions of major LEA procedure in T2DM patients. MAIN OUTCOME MEASURES Predictors of IHM using 4 ANN models: i) with all discharge diagnosis included in the database; ii) with all discharge diagnosis included in the database, excluding infectious diseases; iii) comorbidities included in the Charlson Comorbidities Index; iv) comorbidities included in the Elixhauser Comorbidity Index. RESULTS From 2003 to 2013, 40,857 major LEAs in patients with T2DM were identified with a 10.0% IHM. We found that Elixhauser Comorbidity Index model performed better in terms of sensitivity, specificity and precision than Charlson Comorbidity Index model (0.7634 vs 0.7444; 0.9602 vs 0.9121; 0.9511 vs 0.888, respectively). The area under the ROC curve for Elixhauser comorbidity model was 91.7% (95% CI 90.3-93.0) and for Charlson comorbidity model was 88.9% (95% CI; 87.590.2) p = 0.043. Models including all discharge diagnosis with and without infectious diseases showed worse results. In the Elixhauser Comorbidity Index model the most sensitive parameter was age (variable sensitive ratio [VSR] 1.451) followed by female sex (VSR 1.433), congestive heart failure (VSR 1.341), renal failure (VSR 1.274) and chronic pulmonary disease (VSR 1.266). CONCLUSIONS Elixhauser Comorbidity Index is a superior comorbidity risk-adjustment model for major LEA survival prediction in patients with T2DM than Charlson Comorbidity Index model using ANN models. Female sex, congestive heart failure, and renal failure are strong predictors of mortality in these patients.
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Affiliation(s)
- Ana Lopez-de-Andres
- Preventive Medicine and Public Health Teaching and Research Unit, Health Sciences Faculty, Rey Juan Carlos University, Avda. de Atenas s/n, 28922, Alcorcón, Comunidad de Madrid, Spain.
| | - Valentin Hernandez-Barrera
- Preventive Medicine and Public Health Teaching and Research Unit, Health Sciences Faculty, Rey Juan Carlos University, Avda. de Atenas s/n, 28922, Alcorcón, Comunidad de Madrid, Spain
| | | | | | - Isabel Jimenez-Trujillo
- Preventive Medicine and Public Health Teaching and Research Unit, Health Sciences Faculty, Rey Juan Carlos University, Avda. de Atenas s/n, 28922, Alcorcón, Comunidad de Madrid, Spain
| | - Alejandro Alvaro-Meca
- Preventive Medicine and Public Health Teaching and Research Unit, Health Sciences Faculty, Rey Juan Carlos University, Avda. de Atenas s/n, 28922, Alcorcón, Comunidad de Madrid, Spain
| | - Miguel Angel Salinero-Fort
- Dirección Técnica de Docencia e Investigación, Gerencia Atención Primaria, Madrid, Comunidad de Madrid, Spain
| | - Rodrigo Jimenez-Garcia
- Preventive Medicine and Public Health Teaching and Research Unit, Health Sciences Faculty, Rey Juan Carlos University, Avda. de Atenas s/n, 28922, Alcorcón, Comunidad de Madrid, Spain
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Chen J, Chen J, Ding HY, Pan QS, Hong WD, Xu G, Yu FY, Wang YM. Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients. Asian Pac J Cancer Prev 2016; 16:5095-9. [PMID: 26163648 DOI: 10.7314/apjcp.2015.16.12.5095] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. MATERIALS AND METHODS A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. RESULTS The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). CONCLUSIONS The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.
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Affiliation(s)
- Jian Chen
- Department of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China E-mail : ,
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Karthikesalingam A, Attallah O, Ma X, Bahia SS, Thompson L, Vidal-Diez A, Choke EC, Bown MJ, Sayers RD, Thompson MM, Holt PJ. An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study. PLoS One 2015; 10:e0129024. [PMID: 26176943 PMCID: PMC4503678 DOI: 10.1371/journal.pone.0129024] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Accepted: 05/03/2015] [Indexed: 12/16/2022] Open
Abstract
Background Lifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques. Methods Patients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk. Results 761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p<0.001) Conclusion This study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data.
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Affiliation(s)
- Alan Karthikesalingam
- Department of Outcomes Research, St George’s Vascular Institute, London, SW17 0QT, United Kingdom
| | - Omneya Attallah
- College of Engineering and Applied Science, Aston University, Birmingham, B4 7ET, United Kingdom
- Department of Electronics and Communications Engineering, Arab Academy for Science and Technology and Maritime Transport, Alexandria, Egypt
| | - Xianghong Ma
- College of Engineering and Applied Science, Aston University, Birmingham, B4 7ET, United Kingdom
| | - Sandeep Singh Bahia
- Department of Outcomes Research, St George’s Vascular Institute, London, SW17 0QT, United Kingdom
- * E-mail:
| | - Luke Thompson
- Department of Outcomes Research, St George’s Vascular Institute, London, SW17 0QT, United Kingdom
| | - Alberto Vidal-Diez
- Department of Outcomes Research, St George’s Vascular Institute, London, SW17 0QT, United Kingdom
- Department of Community Health Sciences, St George’s University of London, London, SW17 0QT, United Kingdom
| | - Edward C. Choke
- Vascular Surgery Group, University of Leicester, Robert Kilpatrick Clinical Sciences Building, Leicester Royal Infirmary, Leicester, LE2 7LX, United Kingdom
| | - Matt J. Bown
- Vascular Surgery Group, University of Leicester, Robert Kilpatrick Clinical Sciences Building, Leicester Royal Infirmary, Leicester, LE2 7LX, United Kingdom
| | - Robert D. Sayers
- Vascular Surgery Group, University of Leicester, Robert Kilpatrick Clinical Sciences Building, Leicester Royal Infirmary, Leicester, LE2 7LX, United Kingdom
| | - Matt M. Thompson
- Department of Outcomes Research, St George’s Vascular Institute, London, SW17 0QT, United Kingdom
| | - Peter J. Holt
- Department of Outcomes Research, St George’s Vascular Institute, London, SW17 0QT, United Kingdom
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Chen J, Pan QS, Hong WD, Pan J, Zhang WH, Xu G, Wang YM. Use of an artificial neural network to predict risk factors of nosocomial infection in lung cancer patients. Asian Pac J Cancer Prev 2015; 15:5349-53. [PMID: 25041000 DOI: 10.7314/apjcp.2014.15.13.5349] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Statistical methods to analyze and predict the related risk factors of nosocomial infection in lung cancer patients are various, but the results are inconsistent. A total of 609 patients with lung cancer were enrolled to allow factor comparison using Student's t-test or the Mann-Whitney test or the Chi-square test. Variables that were significantly related to the presence of nosocomial infection were selected as candidates for input into the final ANN model. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of nosocomial infection from lung cancer in this entire study population was 20.1% (165/609), nosocomial infections occurring in sputum specimens (85.5%), followed by blood (6.73%), urine (6.0%) and pleural effusions (1.82%). It was shown that long term hospitalization (≥ 22 days, P= 0.000), poor clinical stage (IIIb and IV stage, P=0.002), older age (≥ 61 year old, P=0.023), and use the hormones were linked to nosocomial infection and the ANN model consisted of these four factors .The artificial neural network model with variables consisting of age, clinical stage, time of hospitalization, and use of hormones should be useful for predicting nosocomial infection in lung cancer cases.
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Affiliation(s)
- Jie Chen
- Department of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China E-mail :
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Al Haidan A, Abu-Hammad O, Dar-Odeh N. Predicting tooth surface loss using genetic algorithms-optimized artificial neural networks. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2014; 2014:106236. [PMID: 25114713 PMCID: PMC4120478 DOI: 10.1155/2014/106236] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Revised: 06/16/2014] [Accepted: 06/16/2014] [Indexed: 11/29/2022]
Abstract
Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.
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Affiliation(s)
- Ali Al Haidan
- College of Dentistry, Taibah University, Al Madina Al Munawara, Saudi Arabia
| | - Osama Abu-Hammad
- College of Dentistry, Taibah University, Al Madina Al Munawara, Saudi Arabia
| | - Najla Dar-Odeh
- College of Dentistry, Taibah University, Al Madina Al Munawara, Saudi Arabia
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Yang Y, Chen H, Wang D, Luo W, Zhu B, Zhang Z. Diagnosis of pancreatic carcinoma based on combined measurement of multiple serum tumor markers using artificial neural network analysis. Chin Med J (Engl) 2014; 127:1891-1896. [PMID: 24824251 DOI: 10.3760/cma.j.issn.0366-6999.20133101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2025] Open
Abstract
BACKGROUND Artificial neural network (ANN) has demonstrated the ability to assimilate information from multiple sources to enable the detection of subtle and complex patterns. In this research, we evaluated an ANN model in the diagnosis of pancreatic cancer using multiple serum markers. METHODS In this retrospective analysis, 913 serum specimens collected at the Department of General Surgery of Beijing Friendship Hospital were analyzed for carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 125 (CA125), and carcinoembryonic antigen (CEA). The three tumor marker values were used as inputs into an ANN and randomized into a training set of 658 (70.31% were malignant) and a test set of the remaining 255 samples (70.69% were malignant). The samples were also evaluated using a Logistic regression (LR) model. RESULTS The ANN-derived composite index was superior to each of the serum tumor markers alone and the Logistic regression model. The areas under receiver operating characteristic curves (AUROC) was 0.905 (95% confidence Interval (CI) 0.868-0.942) for ANN, 0.812 (95% CI 0.762-0.863) for the Logistic regression model, 0.845 (95% CI 0.798-0.893) for CA19-9, 0.795 (95% CI 0.738-0.851) for CA125, and 0.800 (95% CI 0.746-0.854) for CEA. ANN analysis of multiple markers yielded a high level of diagnostic accuracy (83.53%) compared to LR (74.90%). CONCLUSION The performance of ANN model in the diagnosis of pancreatic cancer is better than the single tumor marker and LR model.
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Affiliation(s)
- Yingchi Yang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Hui Chen
- Institute of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Dong Wang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Wei Luo
- Institute of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Biyun Zhu
- Institute of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Zhongtao Zhang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
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van den Heever M, Mittal A, Haydock M, Windsor J. The use of intelligent database systems in acute pancreatitis--a systematic review. Pancreatology 2013; 14:9-16. [PMID: 24555973 DOI: 10.1016/j.pan.2013.11.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2013] [Revised: 10/15/2013] [Accepted: 11/18/2013] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Acute pancreatitis (AP) is a complex disease with multiple aetiological factors, wide ranging severity, and multiple challenges to effective triage and management. Databases, data mining and machine learning algorithms (MLAs), including artificial neural networks (ANNs), may assist by storing and interpreting data from multiple sources, potentially improving clinical decision-making. AIMS 1) Identify database technologies used to store AP data, 2) collate and categorise variables stored in AP databases, 3) identify the MLA technologies, including ANNs, used to analyse AP data, and 4) identify clinical and non-clinical benefits and obstacles in establishing a national or international AP database. METHODS Comprehensive systematic search of online reference databases. The predetermined inclusion criteria were all papers discussing 1) databases, 2) data mining or 3) MLAs, pertaining to AP, independently assessed by two reviewers with conflicts resolved by a third author. RESULTS Forty-three papers were included. Three data mining technologies and five ANN methodologies were reported in the literature. There were 187 collected variables identified. ANNs increase accuracy of severity prediction, one study showed ANNs had a sensitivity of 0.89 and specificity of 0.96 six hours after admission--compare APACHE II (cutoff score ≥8) with 0.80 and 0.85 respectively. Problems with databases were incomplete data, lack of clinical data, diagnostic reliability and missing clinical data. CONCLUSION This is the first systematic review examining the use of databases, MLAs and ANNs in the management of AP. The clinical benefits these technologies have over current systems and other advantages to adopting them are identified.
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Affiliation(s)
| | - Anubhav Mittal
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - Matthew Haydock
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - John Windsor
- Department of Surgery, University of Auckland, Auckland, New Zealand.
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Talukdar R, Nageshwar Reddy D. Predictors of adverse outcomes in acute pancreatitis: new horizons. Indian J Gastroenterol 2013; 32:143-51. [PMID: 23475525 DOI: 10.1007/s12664-013-0306-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2012] [Accepted: 02/03/2013] [Indexed: 02/04/2023]
Abstract
Acute pancreatitis (AP) continues to be a clinical challenge. The mortality of patients with AP with adverse outcomes like organ failure and infected necrosis can be as high as 43 %. Highly accurate predictors of adverse outcomes are necessary to identify the high-risk patients so that they can be meticulously monitored and managed. However, there are no ideal predictors till date. Over the past several years, a number of single- and multi-parameter predictors have been identified and tested for prediction of adverse outcomes in AP. Out of the different tools tested, blood urea nitrogen and the harmless acute pancreatitis score appears to be useful and feasible in the management of AP under Indian conditions. Other single-parameter predictors like serum creatinine, hematocrit, erythrocyte sedimentation rate, C-reactive protein, and D-dimer need to be put to further tests in high-quality prospective studies with large sample size at the community level. Multi-parameter prediction tools like the bedside index of severity of acute pancreatitis may not be appealing in day-to-day clinical practice.
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Affiliation(s)
- Rupjyoti Talukdar
- Asian Institute of Gastroenterology, 6-3-661, Somajiguda, Hyderabad 500 082, India.
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Hong WD, Chen XR, Jin SQ, Huang QK, Zhu QH, Pan JY. Use of an artificial neural network to predict persistent organ failure in patients with acute pancreatitis. Clinics (Sao Paulo) 2013; 68:27-31. [PMID: 23420153 PMCID: PMC3548405 DOI: 10.6061/clinics/2013(01)rc01] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Wan-dong Hong
- The First Affiliated Hospital of Wenzhou Medical College, Department of Gastroenterology and Hepatology, Zhejiang Province, People's Republic of China
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Performance Evaluation of Levenberg-Marquardt Technique in Error Reduction for Diabetes Condition Classification. ACTA ACUST UNITED AC 2013. [DOI: 10.1016/j.procs.2013.05.455] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Ansari D, Nilsson J, Andersson R, Regnér S, Tingstedt B, Andersson B. Artificial neural networks predict survival from pancreatic cancer after radical surgery. Am J Surg 2013; 205:1-7. [PMID: 23245432 DOI: 10.1016/j.amjsurg.2012.05.032] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2011] [Revised: 03/19/2012] [Accepted: 05/10/2012] [Indexed: 12/11/2022]
Abstract
BACKGROUND Artificial neural networks (ANNs) are nonlinear pattern recognition techniques that can be used as a tool in medical decision making. The objective of this study was to develop an ANN model for predicting survival in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS A flexible nonlinear survival model based on ANNs was designed by using clinical and histopathological data from 84 patients who underwent resection for PDAC. RESULTS Seven of 33 potential risk variables were selected to construct the ANN, including lymph node metastasis, differentiation, body mass index, age, resection margin status, peritumoral inflammation, and American Society of Anesthesiologists grade. Three variables (ie, lymph node metastasis, leukocyte count, and tumor location) were significant according to Cox regression analysis. Harrell's concordance index for the ANN model was .79, and for Cox regression it was .67. CONCLUSIONS For the first time, ANNs have been used to successfully predict individual long-term survival for patients after radical surgery for PDAC.
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Affiliation(s)
- Daniel Ansari
- Department of Surgery, Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
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Ansari D, Chen BC, Dong L, Zhou MT, Andersson R. Pancreatic cancer: translational research aspects and clinical implications. World J Gastroenterol 2012; 18:1417-1424. [PMID: 22509073 PMCID: PMC3319937 DOI: 10.3748/wjg.v18.i13.1417] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2011] [Revised: 12/29/2011] [Accepted: 01/18/2012] [Indexed: 02/06/2023] Open
Abstract
Despite improvements in surgical techniques and adjuvant chemotherapy, the overall mortality rates in pancreatic cancer have generally remained relatively unchanged and the 5-year survival rate is actually below 2%. This paper will address the importance of achieving an early diagnosis and identifying markers for prognosis and response to therapy such as genes, proteins, microRNAs or epigenetic modifications. However, there are still major hurdles when translating investigational biomarkers into routine clinical practice. Furthermore, novel ways of secondary screening in high-risk individuals, such as artificial neural networks and modern imaging, will be discussed. Drug resistance is ubiquitous in pancreatic cancer. Several mechanisms of drug resistance have already been revealed, including human equilibrative nucleoside transporter-1 status, multidrug resistance proteins, aberrant signaling pathways, microRNAs, stromal influence, epithelial-mesenchymal transition-type cells and recently the presence of cancer stem cells/cancer-initiating cells. These factors must be considered when developing more customized types of intervention ("personalized medicine"). In the future, multifunctional nanoparticles that combine a specific targeting agent, an imaging probe, a cell-penetrating agent, a biocompatible polymer and an anti-cancer drug may become valuable for the management of patients with pancreatic cancer.
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Viceconti M. A tentative taxonomy for predictive models in relation to their falsifiability. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2011; 369:4149-4161. [PMID: 21969670 DOI: 10.1098/rsta.2011.0227] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The growing importance of predictive models in biomedical research raises some concerns on the correct methodological approach to the falsification of such models, as they are developed in interdisciplinary research contexts between physics, biology and medicine. In each of these research sectors, there are established methods to develop cause-effect explanations for observed phenomena, which can be used to predict: epidemiological models, biochemical models, biophysical models, Bayesian models, neural networks, etc. Each research sector has accepted processes to verify how correct these models are (falsification). But interdisciplinary research imposes a broader perspective, which encompasses all possible models in a general methodological framework of falsification. The present paper proposes a general definition of 'scientific model' that makes it possible to categorize predictive models into broad categories. For each of these categories, generic falsification strategies are proposed, except for the so-called 'abductive' models. For this category, which includes artificial neural networks, Bayesian models and integrative models, the definition of a generic falsification strategy requires further investigation by researchers and philosophers of science.
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Affiliation(s)
- Marco Viceconti
- Laboratorio di Tecnologia Medica, Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136 Bologna, Italy.
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Andersson B, Andersson R, Ohlsson M, Nilsson J. Prediction of severe acute pancreatitis at admission to hospital using artificial neural networks. Pancreatology 2011; 11:328-335. [PMID: 21757970 DOI: 10.1159/000327903] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2010] [Accepted: 03/25/2011] [Indexed: 12/11/2022]
Abstract
BACKGROUND/AIMS Artificial neural networks (ANNs) are non-linear pattern recognition techniques, which can be used as a tool in medical decision-making. The aim of this study was to construct and validate an ANN model for early prediction of the severity of acute pancreatitis (AP). METHODS Patients treated for AP from 2002 to 2005 (n = 139) and from 2007 to 2009 (n = 69) were analyzed to develop and validate the ANN model. Severe AP was defined according to the Atlanta criteria. RESULTS ANNs selected 6 of 23 potential risk variables as relevant for severity prediction, including duration of pain until arrival at the emergency department, creatinine, hemoglobin, alanine aminotransferase, heart rate, and white blood cell count. The discriminatory power for prediction of progression to a severe course, determined from the area under the receiver-operating characteristic curve, was 0.92 for the ANN model, 0.84 for the logistic regression model (p = 0.030), and 0.63 for the APACHE II score (p < 0.001). The numbers of correctly classified patients for a sensitivity of 50 and 75% were significantly higher for the ANN model than for logistic regression (p = 0.002) and APACHE II (p < 0.001). CONCLUSION The ANN model identified 6 risk variables available at the time of admission, including duration of pain, a finding not being presented as a risk factor before. The severity classification developed proved to be superior to APACHE II. and IAP.
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Lievaart J, Reneau J, Kremer W, Barkema H. Short communication: Influence of sampling interval on the accuracy of predicting bulk milk somatic cell count. J Dairy Sci 2011; 94:804-7. [DOI: 10.3168/jds.2010-3567] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2010] [Accepted: 11/02/2010] [Indexed: 11/19/2022]
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Dar-Odeh NS, Alsmadi OM, Bakri F, Abu-Hammour Z, Shehabi AA, Al-Omiri MK, Abu-Hammad SMK, Al-Mashni H, Saeed MB, Muqbil W, Abu-Hammad OA. Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks. Adv Appl Bioinform Chem 2010; 3:7-13. [PMID: 21918622 PMCID: PMC3170012 DOI: 10.2147/aabc.s10177] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Objective To construct and optimize a neural network that is capable of predicting the occurrence of recurrent aphthous ulceration (RAU) based on a set of appropriate input data. Participants and methods Artificial neural networks (ANN) software employing genetic algorithms to optimize the architecture neural networks was used. Input and output data of 86 participants (predisposing factors and status of the participants with regards to recurrent aphthous ulceration) were used to construct and train the neural networks. The optimized neural networks were then tested using untrained data of a further 10 participants. Results The optimized neural network, which produced the most accurate predictions for the presence or absence of recurrent aphthous ulceration was found to employ: gender, hematological (with or without ferritin) and mycological data of the participants, frequency of tooth brushing, and consumption of vegetables and fruits. Conclusions Factors appearing to be related to recurrent aphthous ulceration and appropriate for use as input data to construct ANNs that predict recurrent aphthous ulceration were found to include the following: gender, hemoglobin, serum vitamin B12, serum ferritin, red cell folate, salivary candidal colony count, frequency of tooth brushing, and the number of fruits or vegetables consumed daily.
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Talukdar R, Vege SS. Recent developments in acute pancreatitis. Clin Gastroenterol Hepatol 2009; 7:S3-9. [PMID: 19896095 DOI: 10.1016/j.cgh.2009.07.037] [Citation(s) in RCA: 56] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2009] [Revised: 07/25/2009] [Accepted: 07/28/2009] [Indexed: 02/07/2023]
Abstract
The incidence of acute pancreatitis (AP) has been increasing worldwide, but the major etiologies remain gallstones and alcohol. Several studies have reported that smoking is an independent risk factor for developing AP. Classification of AP has traditionally used the categories of mild and severe disease. However, a new intermediate category of moderately severe AP has been described with intermediate characteristics including a high incidence of local complications but a low mortality. Assessment criteria that can serve as early predictors of AP severity are often complex and not sufficiently accurate. However, several recently described criteria that rely on criteria such as the body mass index, physical findings, and simple laboratory measurements could prove useful if validated in large prospective studies. Many issues related to the therapy of AP are still unresolved. Although preliminary studies support the importance of early volume expansion for the treatment of acute pancreatitis, optimization of the amount and type of fluids will require further studies. Similarly, preliminary studies suggest that enteral nutrition might benefit patients with AP and could even be useful early in the course of disease. However, the timing and type of fluids as well as the intestinal infusion site require further study. Finally, issues related to the prophylactic use of antibiotics in patients with severe AP have not been resolved. While the process of clinical investigation moves slowly, progress has been made in clinical studies of AP.
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Affiliation(s)
- Rupjyoti Talukdar
- Miles and Shirley Fiterman Center for Digestive Diseases, Mayo Clinic, Rochester, Minnesota 55905, USA
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The changing character of acute pancreatitis: epidemiology, etiology, and prognosis. Curr Gastroenterol Rep 2009; 11:97-103. [PMID: 19281696 DOI: 10.1007/s11894-009-0016-4] [Citation(s) in RCA: 137] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Acute pancreatitis continues to be a diagnostic and therapeutic challenge for physicians and surgeons. It ranks third in the list of hospital discharges for gastro-intestinal diseases. In most patients the cause is either gallstones or alcoholism. The overall mortality is less than 5%, but severe acute pancreatitis leads to prolonged hospitalization and much higher mortality. There are important differences in disease susceptibility and case fatality rates: the incidence is higher in blacks than in whites, and mortality is higher in older patients than in younger patients. Reports from various countries reveal that the frequency of acute pancreatitis is increasing, perhaps in relation to rising obesity rates, which would increase the likelihood of gallstone pancreatitis. Conversely, mortality rates for acute pancreatitis are declining in many, but not all, reports.
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Lowenfels AB, Maisonneuve P. Pancreas: A new model to predict mortality in acute pancreatitis. Nat Rev Gastroenterol Hepatol 2009; 6:203-4. [PMID: 19347010 DOI: 10.1038/nrgastro.2009.36] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/08/2022]
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