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Ho CT, Tan ECH, Lee PC, Chu CJ, Huang YH, Huo TI, Su YH, Hou MC, Wu JC, Su CW. Conventional and machine-learning based risk score for patients with early-stage hepatocellular carcinoma. Clin Mol Hepatol 2024:cmh.2024.0103. [PMID: 38600872 DOI: 10.3350/cmh.2024.0103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 04/10/2024] [Indexed: 04/12/2024] Open
Abstract
Background/Aims The performance of machine-learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. Methods The study retrospectively enrolled 1411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. Results In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-to-monocyte ratio, and albumin bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATS-INF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Conclusions Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.
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Affiliation(s)
- Chun-Ting Ho
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Elise Chia-Hui Tan
- Department of Health Service Administration, College of Public Health, China Medical University, Taichung, Taiwan
| | - Pei-Chang Lee
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chi-Jen Chu
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Hsiang Huang
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Teh-Ia Huo
- Division of Basic Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Hui Su
- Department of Accounting, Soochow University, Taipei, Taiwan
| | - Ming-Chih Hou
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jaw-Ching Wu
- Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chien-Wei Su
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of General Medicine, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
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Ghods K, Azizi A, Jafari A, Ghods K. Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature. J Dent (Shiraz) 2023; 24:356-371. [PMID: 38149231 PMCID: PMC10749440 DOI: 10.30476/dentjods.2023.96835.1969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 01/04/2023] [Accepted: 03/05/2023] [Indexed: 12/28/2023]
Abstract
Statement of the Problem In recent years, the use of artificial intelligence (AI) has become increasingly popular in dentistry because it facilitates the process of diagnosis and clinical decision-making. However, AI holds multiple prominent drawbacks, which restrict its wide application today. It is necessary for dentists to be aware of AI's pros and cons before its implementation. Purpose Therefore, the present study was conducted to comprehensively review various applications of AI in all dental branches along with its advantages and disadvantages. Materials and Method For this review article, a complete query was carried out on PubMed and Google Scholar databases and the studies published during 2010-2022 were collected using the keywords "Artificial Intelligence", "Dentistry," "Machine learning," "Deep learning," and "Diagnostic System." Ultimately, 116 relevant articles focused on artificial intelligence in dentistry were selected and evaluated. Results In new research AI applications in detecting dental abnormalities and oral malignancies based on radiographic view and histopathological features, designing dental implants and crowns, determining tooth preparation finishing line, analyzing growth patterns, estimating biological age, predicting the viability of dental pulp stem cells, analyzing the gene expression of periapical lesions, forensic dentistry, and predicting the success rate of treatments, have been mentioned. Despite AI's benefits in clinical dentistry, three controversial challenges including ease of use, financial return on investment, and evidence of performance exist and need to be managed. Conclusion As evidenced by the obtained results, the most crucial progression of AI is in oral malignancies' diagnostic systems. However, AI's newest advancements in various branches of dentistry require further scientific work before being applied to clinical practice. Moreover, the immense use of AI in clinical dentistry is only achievable when its challenges are appropriately managed.
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Affiliation(s)
- Kimia Ghods
- Student of Dentistry, Membership of Dental Material Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Arash Azizi
- Dept. Oral Medicine, Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Aryan Jafari
- Student of Dentistry, Membership of Dental Material Research Center, Tehran
| | - Kian Ghods
- Dept. of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Canada
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Mir MM, Mir GM, Raina NT, Mir SM, Mir SM, Miskeen E, Alharthi MH, Alamri MMS. Application of Artificial Intelligence in Medical Education: Current Scenario and Future Perspectives. J Adv Med Educ Prof 2023; 11:133-140. [PMID: 37469385 PMCID: PMC10352669 DOI: 10.30476/jamp.2023.98655.1803] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Introduction Medical education is a lifetime learning process stretching from undergraduate to postgraduate, specialty training, and beyond. It also applies to various healthcare professionals, including doctors, nurses, and other allied healthcare professionals. Therefore, it is essential to acknowledge the immense role of artificial intelligence in medical education in the current era of rapidly growing technology. Methods High-quality data that met the study objectives were included. In addition, comprehensive investigations on articles available in reputable databases such as PubMed, Research Gate, PubMed central, Web of Science, and Google Scholar were considered for literature review. Results Artificial intelligence has fixed various issues in education during the last decade, including language processing, reasoning, planning, and cognitive modelling. Conclusion It can be used in medical education in the following forms: Virtual Inquiry System, Medical Distance Learning and Management, and Recording teaching videos in medical schools. It can also enhance the value of the non-analytical humanistic aspects of medicine. The goal of this review article was to present the implications of AI in medical education, now and in the coming years.
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Affiliation(s)
- Mohammad Muzaffar Mir
- Department of Basic Medical Sciences, College of Medicine, University of Bisha, Bisha, 61922, Kingdom of Saudi Arabia
| | - Gulzar Muzaffar Mir
- Department of Psychiatry, SKIMS Medical College, Bemina, Srinagar, 190018, J and K, India
| | - Nadeem Tufail Raina
- Department of Orthopedics, SKIMS Medical College, Bemina, Srinagar, 190018, J and K, India
| | - Saba Muzaffar Mir
- Department of Microbiology, Government Medical College, Srinagar, 190010, J and K, India
| | - Sadaf Muzaffar Mir
- Al-Falah School of Medical Science and Research Centre, Dhauj, Faridabad, 121004, Haryana, India
| | - Elhadi Miskeen
- Department of Obstetrics and Gynecology, College of Medicine, University of Bisha, Bisha, 61922, Kingdom of Saudi Arabia
| | - Muffarah Hamid Alharthi
- Department of Family Medicine, College of Medicine, University of Bisha, Bisha, 61922, Kingdom of Saudi Arabia
| | - Mohannad Mohammad S Alamri
- Department of Family Medicine, College of Medicine, University of Bisha, Bisha, 61922, Kingdom of Saudi Arabia
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Farid AB, Fathy EM, Sharaf Eldin A, Abd-Elmegid LA. Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM). PeerJ Comput Sci 2021; 7:e739. [PMID: 34901421 PMCID: PMC8627227 DOI: 10.7717/peerj-cs.739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/14/2021] [Indexed: 06/14/2023]
Abstract
In recent years, the software industry has invested substantial effort to improve software quality in organizations. Applying proactive software defect prediction will help developers and white box testers to find the defects earlier, and this will reduce the time and effort. Traditional software defect prediction models concentrate on traditional features of source code including code complexity, lines of code, etc. However, these features fail to extract the semantics of source code. In this research, we propose a hybrid model that is called CBIL. CBIL can predict the defective areas of source code. It extracts Abstract Syntax Tree (AST) tokens as vectors from source code. Mapping and word embedding turn integer vectors into dense vectors. Then, Convolutional Neural Network (CNN) extracts the semantics of AST tokens. After that, Bidirectional Long Short-Term Memory (Bi-LSTM) keeps key features and ignores other features in order to enhance the accuracy of software defect prediction. The proposed model CBIL is evaluated on a sample of seven open-source Java projects of the PROMISE dataset. CBIL is evaluated by applying the following evaluation metrics: F-measure and area under the curve (AUC). The results display that CBIL model improves the average of F-measure by 25% compared to CNN, as CNN accomplishes the top performance among the selected baseline models. In average of AUC, CBIL model improves AUC by 18% compared to Recurrent Neural Network (RNN), as RNN accomplishes the top performance among the selected baseline models used in the experiments.
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Affiliation(s)
- Ahmed Bahaa Farid
- Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, Helwan, Egypt
- Department of Information Systems, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt
| | - Enas Mohamed Fathy
- Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, Helwan, Egypt
| | - Ahmed Sharaf Eldin
- Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, Helwan, Egypt
- Department of Information Systems, Faculty of Information Technology and Computer Science, Sinai University, Sinai, Egypt
| | - Laila A. Abd-Elmegid
- Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, Helwan, Egypt
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Prasetyo SYJ, Hartomo KD, Paseleng MC. Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia. PeerJ Comput Sci 2021; 7:e415. [PMID: 34084916 PMCID: PMC8157165 DOI: 10.7717/peerj-cs.415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 02/05/2021] [Indexed: 06/12/2023]
Abstract
This study aims to develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images. VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). Comparison of prediction using Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn) and Multivariate Adaptive Regression Spline (MARS). Prediction results are interpolated using Inverse Distance Weight (IDW). This study was conducted in stages: (1) Image preprocessing; (2) calculating numerical data extracted from the LANDSAT band imagery using vegetation indices; (3) analyzing correlation coefficients between VI; (4) prediction using RF and CART; (5) comparing performances between RF and CART using ANN, SVM, k-nn, and MARS; (6) testing the accuracy of prediction using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE); (7) interpolating with IDW. Correlation coefficient of VI data shows a positive correlation, the lowest r (0.07) and the highest r (0.98). The experiments show that the RF and CART algorithms have efficiency and effectivity in determining the aridity areas better than the ANN, SVM, k-nn, and MARS algorithm. RF has a difference between the predicted results and 1.04% survey data MAPE and the smallest value close to zero is 0.05 MSE. CART has a difference between the predicted results and 1.05% survey data MAPE and the smallest value approaching to zero which is 0.05 MSE. The prediction results of VI show that in 2020 most of the study areas were low vegetation areas with the Normalized Difference Vegetation Index (NDVI) < 0.21, had an indication of drought with the Vegetation Health Index (VHI) < 31.10, had a Vegetation Condition Index (VCI) in some areas between 35%-50% (moderate drought) and < 35% (high drought). The Burn Area Index (dBAI) values are between -3, 971 and -2,376 that show the areas have a low fire risk, and index values are between -0, 208 and -0,412 that show the areas are starting vegetation growth. The result of this study shows that the machine learning algorithms is an accurate and stable algorithm in predicting the risks of drought and land fire based on the VI data extracted from the LANDSAT 8 OLL imagery. The VI data contain the record of vegetation condition and its environment, including humidity, temperatures, and the environmental vegetation health.
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Affiliation(s)
| | - Kristoko Dwi Hartomo
- Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Central Java, Indonesia
| | - Mila Chrismawati Paseleng
- Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Central Java, Indonesia
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