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Asteris PG, Gandomi AH, Armaghani DJ, Kokoris S, Papandreadi AT, Roumelioti A, Papanikolaou S, Tsoukalas MZ, Triantafyllidis L, Koutras EI, Bardhan A, Mohammed AS, Naderpour H, Paudel S, Samui P, Ntanasis-Stathopoulos I, Dimopoulos MA, Terpos E. Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm. Eur J Intern Med 2024; 125:67-73. [PMID: 38458880 DOI: 10.1016/j.ejim.2024.02.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/23/2024] [Accepted: 02/29/2024] [Indexed: 03/10/2024]
Abstract
It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice.
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Affiliation(s)
- Panagiotis G Asteris
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
| | - Amir H Gandomi
- Faculty of Engineering & IT, University of Technology Sydney, Sydney, NSW 2007, Australia; University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
| | - Danial J Armaghani
- School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - Styliani Kokoris
- Laboratory of Hematology and Hospital Blood Transfusion Department, University General Hospital "Attikon", National and Kapodistrian University of Athens, Medical School, Greece
| | - Anastasia T Papandreadi
- Software and Applications Department, University General Hospital "Attikon", National and Kapodistrian University of Athens, Medical School, Greece
| | - Anna Roumelioti
- Department of Hematology and Lymphoma BMTU, Evangelismos General Hospital, Athens, Greece
| | - Stefanos Papanikolaou
- NOMATEN Centre of Excellence, National Center for Nuclear Research, ulica A. Sołtana 7, 05-400 Swierk/Otwock, Poland
| | - Markos Z Tsoukalas
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
| | - Leonidas Triantafyllidis
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
| | - Evangelos I Koutras
- Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
| | - Abidhan Bardhan
- Civil Engineering Department, National Institute of Technology Patna, Bihar, India
| | - Ahmed Salih Mohammed
- Engineering Department, American University of Iraq, Sulaimani, Kurdistan-Region, Iraq
| | - Hosein Naderpour
- Institute of Industrial Science, University of Tokyo, Tokyo, Japan
| | - Satish Paudel
- Department of Civil and Environmental Engineering, University of Nevada, Reno, US
| | - Pijush Samui
- Civil Engineering Department, National Institute of Technology Patna, Bihar, India
| | - Ioannis Ntanasis-Stathopoulos
- Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece
| | - Meletios A Dimopoulos
- Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece
| | - Evangelos Terpos
- Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece.
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Jaksik R, Szumała K, Dinh KN, Śmieja J. Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival. Int J Mol Sci 2024; 25:3661. [PMID: 38612473 PMCID: PMC11011391 DOI: 10.3390/ijms25073661] [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: 02/15/2024] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease's complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction method for mutation and copy number variation data. Using the TCGA dataset, we identified 32 molecular features that allowed the construction of a 2-year survival prediction model with an AUC of 0.839. The selected features were additionally tested on an independent CPTAC-3 dataset, achieving an AUC of 0.815 in nested cross-validation, which confirmed the robustness of the identified features.
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Affiliation(s)
- Roman Jaksik
- Department of Systems Biology and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland;
| | - Kamila Szumała
- Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland;
| | - Khanh Ngoc Dinh
- Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, New York, NY 10027, USA;
| | - Jarosław Śmieja
- Department of Systems Biology and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland;
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Yang L, Chen JJ, Sheng-Xian Teo B, Zhang J, Jiang M. Research Progress on the Antitumor Molecular Mechanism of Ginsenoside Rh2. THE AMERICAN JOURNAL OF CHINESE MEDICINE 2024; 52:217-230. [PMID: 38291582 DOI: 10.1142/s0192415x24500095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Cancer has evolved into a substantial public health concern as the second-leading cause of mortality globally. Radiotherapy and chemotherapy have been the two most widely used cancer therapies in recent years; however, both have drawbacks. Therefore, the focus has shifted to the creation of herbal medicines, the extraction of active ingredients, replacement therapy, and the adverse effects of these medications. Ginsenoside Rh2, which is extracted from ginseng, has been identified in many cancer cells. The immune system of the body is strengthened by ginsenoside Rh2, which can also cause the proliferation, death, and differentiation of tumor cells through various pathways. For instance, it inhibits the expression of the NF-[Formula: see text]B signaling pathway and induces cell apoptosis, affects the expression levels of mitochondrial apoptosis proteins Bcl-2 and Bax, and cooperates with the PD-1 blockade to reactivate T cells to promote an antitumor immune response. Furthermore, ginsenosides Rh2 has the effect of reversing the toxic effect of chemotherapy drugs on normal cells, reducing myocardial damage, and relieving bone marrow function suppression. For clinical applications, it is mainly used as an adjuvant drug for preoperative neoadjuvant chemotherapy, postoperative adjuvant chemotherapy, and rescue treatment of advanced cancer. This paper summarizes the pharmacological action and mechanism of ginsenosides Rh2 in all kinds of cancer and looks forward to its future development and application.
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Affiliation(s)
- Lan Yang
- Institute of Analytical Psychology, City University of Macau, Macau, P. R. China
| | - Jenny Jie Chen
- MSU Centre for Complementary and Alternative Medicine (MyCAM), Management and Science University, Shah Alam 40100, Malaysia
| | - Brian Sheng-Xian Teo
- MSU Centre for Complementary and Alternative Medicine (MyCAM), Management and Science University, Shah Alam 40100, Malaysia
| | - Jiong Zhang
- Department of Traditional Chinese Medicine Internal Medicine, Henan University of Traditional Chinese Medicine, Zhengzhou 450000, P. R. China
| | - Mingqiang Jiang
- Department of Breast, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450003, P. R. China
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Sadeghi MH, Sina S, Omidi H, Farshchitabrizi AH, Alavi M. Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities. Pol J Radiol 2024; 89:e30-e48. [PMID: 38371888 PMCID: PMC10867948 DOI: 10.5114/pjr.2024.134817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 12/27/2023] [Indexed: 02/20/2024] Open
Abstract
Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis using medical imaging modalities, as well as tumour differentiation and radiomics. Data extraction, analysis, and synthesis were performed to summarize the characteristics and findings of the selected studies. The review emphasizes the potential of DL in enhancing the diagnosis of ovarian cancer by accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated their effectiveness in categorizing ovarian tissues and achieving comparable diagnostic performance to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. Nevertheless, additional research and validation are necessary to ensure the dependability and applicability of DL models in everyday clinical settings.
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Affiliation(s)
| | - Sedigheh Sina
- Shiraz University, Shiraz, Iran
- Radiation Research Center, Shiraz University, Shiraz, Iran
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Taddese AA, Tilahun BC, Awoke T, Atnafu A, Mamuye A, Mengiste SA. Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis. Front Oncol 2024; 13:1216326. [PMID: 38273847 PMCID: PMC10809847 DOI: 10.3389/fonc.2023.1216326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 11/13/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction Gynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications. Methods The study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model. Results The review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias. Conclusion This review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.
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Affiliation(s)
- Asefa Adimasu Taddese
- Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
| | - Binyam Chakilu Tilahun
- Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
| | - Tadesse Awoke
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Asmamaw Atnafu
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
- Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Adane Mamuye
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
- School of Information Technology and Engineering, Addis Ababa University, Addis Ababa, Ethiopia
| | - Shegaw Anagaw Mengiste
- Department of Business, History and Social Sciences, University of Southeastern Norway, Vestfold, Vestfold, Norway
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Rokhshad R, Salehi SN, Yavari A, Shobeiri P, Esmaeili M, Manila N, Motamedian SR, Mohammad-Rahimi H. Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis. Oral Radiol 2024; 40:1-20. [PMID: 37855976 DOI: 10.1007/s11282-023-00715-5] [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: 05/09/2023] [Accepted: 09/23/2023] [Indexed: 10/20/2023]
Abstract
PURPOSE This study aims to review deep learning applications for detecting head and neck cancer (HNC) using magnetic resonance imaging (MRI) and radiographic data. METHODS Through January 2023, a PubMed, Scopus, Embase, Google Scholar, IEEE, and arXiv search were carried out. The inclusion criteria were implementing head and neck medical images (computed tomography (CT), positron emission tomography (PET), MRI, Planar scans, and panoramic X-ray) of human subjects with segmentation, object detection, and classification deep learning models for head and neck cancers. The risk of bias was rated with the quality assessment of diagnostic accuracy studies (QUADAS-2) tool. For the meta-analysis diagnostic odds ratio (DOR) was calculated. Deeks' funnel plot was used to assess publication bias. MIDAS and Metandi packages were used to analyze diagnostic test accuracy in STATA. RESULTS From 1967 studies, 32 were found eligible after the search and screening procedures. According to the QUADAS-2 tool, 7 included studies had a low risk of bias for all domains. According to the results of all included studies, the accuracy varied from 82.6 to 100%. Additionally, specificity ranged from 66.6 to 90.1%, sensitivity from 74 to 99.68%. Fourteen studies that provided sufficient data were included for meta-analysis. The pooled sensitivity was 90% (95% CI 0.820.94), and the pooled specificity was 92% (CI 95% 0.87-0.96). The DORs were 103 (27-251). Publication bias was not detected based on the p-value of 0.75 in the meta-analysis. CONCLUSION With a head and neck screening deep learning model, detectable screening processes can be enhanced with high specificity and sensitivity.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
| | - Seyyede Niloufar Salehi
- Executive Secretary of Research Committee, Board Director of Scientific Society, Dental Faculty, Azad University, Tehran, Iran
| | - Amirmohammad Yavari
- Student Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Parnian Shobeiri
- School of Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Mahdieh Esmaeili
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Nisha Manila
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
- Department of Diagnostic Sciences, Louisiana State University Health Science Center School of Dentistry, Louisiana, USA
| | - Saeed Reza Motamedian
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany.
- Dentofacial Deformities Research Center, Research Institute of Dental, Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjou Blvd, Tehran, Iran.
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
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Smith LA, Oakden-Rayner L, Bird A, Zeng M, To MS, Mukherjee S, Palmer LJ. Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. Lancet Digit Health 2023; 5:e872-e881. [PMID: 38000872 DOI: 10.1016/s2589-7500(23)00177-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 06/26/2023] [Accepted: 08/29/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND Machine learning and deep learning models have been increasingly used to predict long-term disease progression in patients with chronic obstructive pulmonary disease (COPD). We aimed to summarise the performance of such prognostic models for COPD, compare their relative performances, and identify key research gaps. METHODS We conducted a systematic review and meta-analysis to compare the performance of machine learning and deep learning prognostic models and identify pathways for future research. We searched PubMed, Embase, the Cochrane Library, ProQuest, Scopus, and Web of Science from database inception to April 6, 2023, for studies in English using machine learning or deep learning to predict patient outcomes at least 6 months after initial clinical presentation in those with COPD. We included studies comprising human adults aged 18-90 years and allowed for any input modalities. We reported area under the receiver operator characteristic curve (AUC) with 95% CI for predictions of mortality, exacerbation, and decline in forced expiratory volume in 1 s (FEV1). We reported the degree of interstudy heterogeneity using Cochran's Q test (significant heterogeneity was defined as p≤0·10 or I2>50%). Reporting quality was assessed using the TRIPOD checklist and a risk-of-bias assessment was done using the PROBAST checklist. This study was registered with PROSPERO (CRD42022323052). FINDINGS We identified 3620 studies in the initial search. 18 studies were eligible, and, of these, 12 used conventional machine learning and six used deep learning models. Seven models analysed exacerbation risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·69-0·85]) and there was significant heterogeneity (I2 97%, p<0·0001). 11 models analysed mortality risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·74-0·80]) with significant degrees of heterogeneity (I2 60%, p=0·027). Two studies assessed decline in lung function and were unable to be pooled. Machine learning and deep learning models did not show significant improvement over pre-existing disease severity scores in predicting exacerbations (p=0·24). Three studies directly compared machine learning models against pre-existing severity scores for predicting mortality and pooled performance did not differ (p=0·57). Of the five studies that performed external validation, performance was worse than or equal to regression models. Incorrect handling of missing data, not reporting model uncertainty, and use of datasets that were too small relative to the number of predictive features included provided the largest risks of bias. INTERPRETATION There is limited evidence that conventional machine learning and deep learning prognostic models demonstrate superior performance to pre-existing disease severity scores. More rigorous adherence to reporting guidelines would reduce the risk of bias in future studies and aid study reproducibility. FUNDING None.
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Affiliation(s)
- Luke A Smith
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia.
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia
| | - Alix Bird
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia
| | - Minyan Zeng
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia
| | - Minh-Son To
- Health Data and Clinical Trials, Flinders University, Bedford Park, SA, Australia; South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sutapa Mukherjee
- Department of Respiratory and Sleep Medicine, Southern Adelaide Local Health Network (SALHN), Bedford Park, SA, Australia; Adelaide Institute for Sleep Health/Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Lyle J Palmer
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia
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Štancl P, Karlić R. Machine learning for pan-cancer classification based on RNA sequencing data. Front Mol Biosci 2023; 10:1285795. [PMID: 38028533 PMCID: PMC10667476 DOI: 10.3389/fmolb.2023.1285795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023] Open
Abstract
Despite recent improvements in cancer diagnostics, 2%-5% of all malignancies are still cancers of unknown primary (CUP), for which the tissue-of-origin (TOO) cannot be determined at the time of presentation. Since the primary site of cancer leads to the choice of optimal treatment, CUP patients pose a significant clinical challenge with limited treatment options. Data produced by large-scale cancer genomics initiatives, which aim to determine the genomic, epigenomic, and transcriptomic characteristics of a large number of individual patients of multiple cancer types, have led to the introduction of various methods that use machine learning to predict the TOO of cancer patients. In this review, we assess the reproducibility, interpretability, and robustness of results obtained by 20 recent studies that utilize different machine learning methods for TOO prediction based on RNA sequencing data, including their reported performance on independent data sets and identification of important features. Our review investigates the strengths and weaknesses of different methods, checks the correspondence of their results, and identifies potential issues with datasets used for model training and testing, assessing their potential usefulness in a clinical setting and suggesting future improvements.
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Affiliation(s)
| | - Rosa Karlić
- Bioinformatics Group, Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
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Wang Y, Liu L, Wang C. Trends in using deep learning algorithms in biomedical prediction systems. Front Neurosci 2023; 17:1256351. [PMID: 38027475 PMCID: PMC10665494 DOI: 10.3389/fnins.2023.1256351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/25/2023] [Indexed: 12/01/2023] Open
Abstract
In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.
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Affiliation(s)
- Yanbu Wang
- School of Strength and Conditioning, Beijing Sport University, Beijing, China
| | - Linqing Liu
- Department of Physical Education, Peking University, Beijing, China
| | - Chao Wang
- Institute of Competitive Sports, Beijing Sport University, Beijing, China
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10
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Al-Rajab M, Lu J, Xu Q, Kentour M, Sawsa A, Shuweikeh E, Joy M, Arasaradnam R. A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset. PLoS One 2023; 18:e0286791. [PMID: 37917732 PMCID: PMC10621932 DOI: 10.1371/journal.pone.0286791] [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: 01/27/2023] [Accepted: 05/20/2023] [Indexed: 11/04/2023] Open
Abstract
Colon cancer is a significant global health problem, and early detection is critical for improving survival rates. Traditional detection methods, such as colonoscopies, can be invasive and uncomfortable for patients. Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. One approach is to use ML to analyse genetic data, or patient demographics and medical history, to predict the likelihood of colon cancer. However, due to the challenges imposed by variable gene expression and the high dimensionality of cancer-related datasets, traditional transductive ML applications have limited accuracy and risk overfitting. In this paper, we propose a new hybrid feature selection model called HMLFSM-Hybrid Machine Learning Feature Selection Model to improve colon cancer gene classification. We developed a multifilter hybrid model including a two-phase feature selection approach, combining Information Gain (IG) and Genetic Algorithms (GA), and minimum Redundancy Maximum Relevance (mRMR) coupling with Particle Swarm Optimization (PSO). We critically tested our model on three colon cancer genetic datasets and found that the new framework outperformed other models with significant accuracy improvements (95%, ~97%, and ~94% accuracies for datasets 1, 2, and 3 respectively). The results show that our approach improves the classification accuracy of colon cancer detection by highlighting important and relevant genes, eliminating irrelevant ones, and revealing the genes that have a direct influence on the classification process. For colon cancer gene analysis, and along with our experiments and literature review, we found that selective input feature extraction prior to feature selection is essential for improving predictive performance.
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Affiliation(s)
- Murad Al-Rajab
- College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
- School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| | - Joan Lu
- School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| | - Qiang Xu
- School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| | - Mohamed Kentour
- School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| | - Ahlam Sawsa
- School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
- Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom
| | - Emad Shuweikeh
- School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| | - Mike Joy
- University of Warwick, Coventry, United Kingdom
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Rai HM, Yoo J. A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics. J Cancer Res Clin Oncol 2023; 149:14365-14408. [PMID: 37540254 DOI: 10.1007/s00432-023-05216-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 07/26/2023] [Indexed: 08/05/2023]
Abstract
PURPOSE There are millions of people who lose their life due to several types of fatal diseases. Cancer is one of the most fatal diseases which may be due to obesity, alcohol consumption, infections, ultraviolet radiation, smoking, and unhealthy lifestyles. Cancer is abnormal and uncontrolled tissue growth inside the body which may be spread to other body parts other than where it has originated. Hence it is very much required to diagnose the cancer at an early stage to provide correct and timely treatment. Also, manual diagnosis and diagnostic error may cause of the death of many patients hence much research are going on for the automatic and accurate detection of cancer at early stage. METHODS In this paper, we have done the comparative analysis of the diagnosis and recent advancement for the detection of various cancer types using traditional machine learning (ML) and deep learning (DL) models. In this study, we have included four types of cancers, brain, lung, skin, and breast and their detection using ML and DL techniques. In extensive review we have included a total of 130 pieces of literature among which 56 are of ML-based and 74 are from DL-based cancer detection techniques. Only the peer reviewed research papers published in the recent 5-year span (2018-2023) have been included for the analysis based on the parameters, year of publication, feature utilized, best model, dataset/images utilized, and best accuracy. We have reviewed ML and DL-based techniques for cancer detection separately and included accuracy as the performance evaluation metrics to maintain the homogeneity while verifying the classifier efficiency. RESULTS Among all the reviewed literatures, DL techniques achieved the highest accuracy of 100%, while ML techniques achieved 99.89%. The lowest accuracy achieved using DL and ML approaches were 70% and 75.48%, respectively. The difference in accuracy between the highest and lowest performing models is about 28.8% for skin cancer detection. In addition, the key findings, and challenges for each type of cancer detection using ML and DL techniques have been presented. The comparative analysis between the best performing and worst performing models, along with overall key findings and challenges, has been provided for future research purposes. Although the analysis is based on accuracy as the performance metric and various parameters, the results demonstrate a significant scope for improvement in classification efficiency. CONCLUSION The paper concludes that both ML and DL techniques hold promise in the early detection of various cancer types. However, the study identifies specific challenges that need to be addressed for the widespread implementation of these techniques in clinical settings. The presented results offer valuable guidance for future research in cancer detection, emphasizing the need for continued advancements in ML and DL-based approaches to improve diagnostic accuracy and ultimately save more lives.
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Affiliation(s)
- Hari Mohan Rai
- School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea.
| | - Joon Yoo
- School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea
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Syukriya AJ, Bankeeree W, Prasongsuk S, Yanatatsaneejit P. In vitro antioxidant and anticancer activities of Smilax corbularia extract combined with Phellinus linteus extract against breast cancer cell lines. Biomed Rep 2023; 19:63. [PMID: 37614981 PMCID: PMC10442767 DOI: 10.3892/br.2023.1645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/17/2023] [Indexed: 08/25/2023] Open
Abstract
Treatment with extracts from whole herbs has been reported to synergistically enhance the anticancer activities of therapeutic agents in recent studies. The present study evaluated the antioxidant and anticancer activities of Smilax corbularia Kunth (S. corbularia) and Phellinus linteus (P. linteus) crude extracts individually and in combination. S. corbularia was extracted using ethanol, whereas P. linteus was extracted using hot water. Both crude extracts underwent physiochemical characterization. Subsequently, the possible antioxidant activities of both crude extracts, individually and in combination, were evaluated using 2,2-Diphenyl-1-picrylhydrazyl and 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) assays. Their effects on breast cancer cell cytotoxicity, proliferation and apoptosis were then assessed. The crude S. corbularia extract obtained was found to have a high level of total phenolic content, whilst the crude P. linteus extract had high levels of total polysaccharide content. The total phenolic content and total polysaccharide content results of the combinations depended on the respective ratios of the individual extracts. S. corbularia alone and combination 3 (which contained 75% S. corbularia: 25% P. linteus) demonstrated the greatest radical scavenging activity, followed by combination 1 (50% S. corbularia: 50% P. linteus), combination 2 (25% S. corbularia: 75% P. linteus) and P. linteus. The toxicity results of the extract samples on the cancer cells corresponded with their antioxidant activity. In particular, certain combinations demonstrated clearer inhibitory effects on cell proliferation against three types of breast cancer cells compared with those exerted by the two individual extracts. However, induction of apoptosis was limited, with the degree of apoptosis observed to be #x003C;5%. These findings suggested that treatment with combinations of these two extracts could confer enhanced antioxidant and antiproliferative effects on breast cancer cells. Therefore, the potential of these two extracts in combination as anticancer agents warrants further investigation.
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Affiliation(s)
| | - Wichanee Bankeeree
- Plant and Biomass Utilization Research Unit, Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
| | - Sehanat Prasongsuk
- Plant and Biomass Utilization Research Unit, Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
| | - Pattamawadee Yanatatsaneejit
- Human Genetics Research Group and Plant Breeding Laboratory, Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
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Rai HM. Cancer detection and segmentation using machine learning and deep learning techniques: a review. MULTIMEDIA TOOLS AND APPLICATIONS 2023. [DOI: 10.1007/s11042-023-16520-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 05/12/2023] [Accepted: 08/13/2023] [Indexed: 09/16/2023]
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Afrin H, Larson NB, Fatemi M, Alizad A. Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis. Cancers (Basel) 2023; 15:3139. [PMID: 37370748 PMCID: PMC10296633 DOI: 10.3390/cancers15123139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/02/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.
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Affiliation(s)
- Humayra Afrin
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Nicholas B. Larson
- Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Azra Alizad
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
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Wysocka M, Wysocki O, Zufferey M, Landers D, Freitas A. A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data. BMC Bioinformatics 2023; 24:198. [PMID: 37189058 DOI: 10.1186/s12859-023-05262-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 03/30/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings. METHODS This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. It focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the biomedical domain. For this, we retrieved and analyzed 42 studies focusing on emerging architectural and methodological advances, the encoding of biological domain knowledge and the integration of explainability methods. RESULTS We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction networks) and interpretability. This represents a fundamental functional shift towards models which can integrate mechanistic and statistical inference aspects. We introduce a concept of bio-centric interpretability and according to its taxonomy, we discuss representational methodologies for the integration of domain prior knowledge in such models. CONCLUSIONS The paper provides a critical outlook into contemporary methods for explainability and interpretability used in DL for cancer. The analysis points in the direction of a convergence between encoding prior knowledge and improved interpretability. We introduce bio-centric interpretability which is an important step towards formalisation of biological interpretability of DL models and developing methods that are less problem- or application-specific.
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Affiliation(s)
- Magdalena Wysocka
- Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK.
- Department of Computer Science, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK.
| | - Oskar Wysocki
- Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK.
- Department of Computer Science, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK.
- Idiap Research Institute, National University of Sciences, Rue Marconi 19, CH - 1920, Martigny, Switzerland.
| | - Marie Zufferey
- Idiap Research Institute, National University of Sciences, Rue Marconi 19, CH - 1920, Martigny, Switzerland
| | - Dónal Landers
- DeLondra Oncology Ltd, 38 Carlton Avenue, Wilmslow, SK9 4EP, UK
| | - André Freitas
- Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK
- Department of Computer Science, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK
- Idiap Research Institute, National University of Sciences, Rue Marconi 19, CH - 1920, Martigny, Switzerland
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Kober KM, Roy R, Conley Y, Dhruva A, Hammer MJ, Levine J, Olshen A, Miaskowski C. Prediction of morning fatigue severity in outpatients receiving chemotherapy: less may still be more. Support Care Cancer 2023; 31:253. [PMID: 37039882 DOI: 10.1007/s00520-023-07723-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 04/01/2023] [Indexed: 04/12/2023]
Abstract
INTRODUCTION Fatigue is the most common and debilitating symptom experienced by cancer patients undergoing chemotherapy (CTX). Prediction of symptom severity can assist clinicians to identify high-risk patients and provide education to decrease symptom severity. The purpose of this study was to predict the severity of morning fatigue in the week following the administration of CTX. METHODS Outpatients (n = 1217) completed questionnaires 1 week prior to and 1 week following administration of CTX. Morning fatigue was measured using the Lee Fatigue Scale (LFS). Separate prediction models for morning fatigue severity were created using 157 demographic, clinical, symptom, and psychosocial adjustment characteristics and either morning fatigue scores or individual fatigue item scores. Prediction models were created using two regression and five machine learning approaches. RESULTS Elastic net models provided the best fit across all models. For the EN model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out," "exhausted") were the strongest predictors. CONCLUSIONS This study is the first to use machine learning techniques to accurately predict the severity of morning fatigue from prior to through the week following the administration of CTX using total and individual item scores from the Lee Fatigue Scale (LFS). Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict morning fatigue severity.
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Affiliation(s)
- Kord M Kober
- School of Nursing, University of California, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
| | - Ritu Roy
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Yvette Conley
- School of Nursing, University of Pittsburg, Pittsburg, PA, USA
| | - Anand Dhruva
- School of Medicine, University of California, San Francisco, CA, USA
| | | | - Jon Levine
- School of Medicine, University of California, San Francisco, CA, USA
| | - Adam Olshen
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- School of Medicine, University of California, San Francisco, CA, USA
| | - Christine Miaskowski
- School of Nursing, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
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17
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Einoch Amor R, Levy J, Broza YY, Vangravs R, Rapoport S, Zhang M, Wu W, Leja M, Behar JA, Haick H. Liquid Biopsy-Based Volatile Organic Compounds from Blood and Urine and Their Combined Data Sets for Highly Accurate Detection of Cancer. ACS Sens 2023; 8:1450-1461. [PMID: 36926819 DOI: 10.1021/acssensors.2c02422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Liquid biopsy is seen as a prospective tool for cancer screening and tracking. However, the difficulty lies in effectively sieving, isolating, and overseeing cancer biomarkers from the backdrop of multiple disrupting cells and substances. The current study reports on the ability to perform liquid biopsy without the need to physically filter and/or isolate the cancer cells per se. This has been achieved through the detection and classification of volatile organic compounds (VOCs) emitted from the cancer cells found in the headspace of blood or urine samples or a combined data set of both. Spectrometric analysis shows that blood and urine contain complementary or overlapping VOC information on kidney cancer, gastric cancer, lung cancer, and fibrogastroscopy subjects. Based on this information, a nanomaterial-based chemical sensor array in conjugation with machine learning as well as data fusion of the signals achieved was carried out on various body fluids to assess the VOC profiles of cancer. The detection of VOC patterns by either Gas Chromatography-Mass Spectrometry (GC-MS) analysis or our sensor array achieved >90% accuracy, >80% sensitivity, and >80% specificity in different binary classification tasks. The hybrid approach, namely, analyzing the VOC datasets of blood and urine together, contributes an additional discrimination ability to the improvement (>3%) of the model's accuracy. The contribution of the hybrid approach for an additional discrimination ability to the improvement of the model's accuracy is examined and reported.
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Affiliation(s)
- Reef Einoch Amor
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Jeremy Levy
- The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering and Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Yoav Y Broza
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Reinis Vangravs
- Institute of Clinical and Preventive Medicine & Faculty of Medicine, University of Latvia, Riga LV-1004, Latvia.,Department of Research, Riga East University Hospital, Digestive Diseases Centre GASTRO, Riga 1586, Latvia
| | - Shelley Rapoport
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Min Zhang
- School of Chemistry and Molecular Engineering, Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai 200241, China
| | - Weiwei Wu
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Shaanxi 710126, P.R. China
| | - Marcis Leja
- Institute of Clinical and Preventive Medicine & Faculty of Medicine, University of Latvia, Riga LV-1004, Latvia.,Department of Research, Riga East University Hospital, Digestive Diseases Centre GASTRO, Riga 1586, Latvia
| | - Joachim A Behar
- The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering and Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
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Haslund-Gourley BS, Wigdahl B, Comunale MA. IgG N-glycan Signatures as Potential Diagnostic and Prognostic Biomarkers. Diagnostics (Basel) 2023; 13:diagnostics13061016. [PMID: 36980324 PMCID: PMC10047871 DOI: 10.3390/diagnostics13061016] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/02/2023] [Accepted: 03/05/2023] [Indexed: 03/30/2023] Open
Abstract
IgG N-glycans are an emerging source of disease-specific biomarkers. Over the last decade, the continued development of glycomic databases and the evolution of glyco-analytic methods have resulted in increased throughput, resolution, and sensitivity. IgG N-glycans promote adaptive immune responses through antibody-dependent cellular cytotoxicity (ADCC) and complement activation to combat infection or cancer and promote autoimmunity. In addition to the functional assays, researchers are examining the ability of protein-specific glycosylation to serve as biomarkers of disease. This literature review demonstrates that IgG N-glycans can discriminate between healthy controls, autoimmune disease, infectious disease, and cancer with high sensitivity. The literature also indicates that the IgG glycosylation patterns vary across disease state, thereby supporting their role as specific biomarkers. In addition, IgG N-glycans can be collected longitudinally from patients to track treatment responses or predict disease reoccurrence. This review focuses on IgG N-glycan profiles applied as diagnostics, cohort discriminators, and prognostics. Recent successes, remaining challenges, and upcoming approaches are critically discussed.
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Affiliation(s)
- Benjamin S Haslund-Gourley
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA 19129, USA
- Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA 19129, USA
| | - Brian Wigdahl
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA 19129, USA
- Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA 19129, USA
| | - Mary Ann Comunale
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA 19129, USA
- Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA 19129, USA
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AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions. Cancers (Basel) 2023; 15:cancers15041183. [PMID: 36831525 PMCID: PMC9953963 DOI: 10.3390/cancers15041183] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 02/15/2023] Open
Abstract
Skin cancer continues to remain one of the major healthcare issues across the globe. If diagnosed early, skin cancer can be treated successfully. While early diagnosis is paramount for an effective cure for cancer, the current process requires the involvement of skin cancer specialists, which makes it an expensive procedure and not easily available and affordable in developing countries. This dearth of skin cancer specialists has given rise to the need to develop automated diagnosis systems. In this context, Artificial Intelligence (AI)-based methods have been proposed. These systems can assist in the early detection of skin cancer and can consequently lower its morbidity, and, in turn, alleviate the mortality rate associated with it. Machine learning and deep learning are branches of AI that deal with statistical modeling and inference, which progressively learn from data fed into them to predict desired objectives and characteristics. This survey focuses on Machine Learning and Deep Learning techniques deployed in the field of skin cancer diagnosis, while maintaining a balance between both techniques. A comparison is made to widely used datasets and prevalent review papers, discussing automated skin cancer diagnosis. The study also discusses the insights and lessons yielded by the prior works. The survey culminates with future direction and scope, which will subsequently help in addressing the challenges faced within automated skin cancer diagnosis.
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Mohammed MA, Abdulkareem KH, Dinar AM, Zapirain BG. Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13040664. [PMID: 36832152 PMCID: PMC9955380 DOI: 10.3390/diagnostics13040664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 02/05/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.
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Affiliation(s)
- Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq
- eVIDA Lab, University of Deusto, 48007 Bilbao, Spain
- Correspondence: (M.A.M.); (B.G.Z.)
| | - Karrar Hameed Abdulkareem
- College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
- College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq
| | - Ahmed M. Dinar
- Computer Engineering Department, University of Technology- Iraq, Baghdad 19006, Iraq
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Zhang H, Xi Q, Zhang F, Li Q, Jiao Z, Ni X. Application of Deep Learning in Cancer Prognosis Prediction Model. Technol Cancer Res Treat 2023; 22:15330338231199287. [PMID: 37709267 PMCID: PMC10503281 DOI: 10.1177/15330338231199287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023] Open
Abstract
As an important branch of artificial intelligence and machine learning, deep learning (DL) has been widely used in various aspects of cancer auxiliary diagnosis, among which cancer prognosis is the most important part. High-accuracy cancer prognosis is beneficial to the clinical management of patients with cancer. Compared with other methods, DL models can significantly improve the accuracy of prediction. Therefore, this article is a systematic review of the latest research on DL in cancer prognosis prediction. First, the data type, construction process, and performance evaluation index of the DL model are introduced in detail. Then, the current mainstream baseline DL cancer prognosis prediction models, namely, deep neural networks, convolutional neural networks, deep belief networks, deep residual networks, and vision transformers, including network architectures, the latest application in cancer prognosis, and their respective characteristics, are discussed. Next, some key factors that affect the predictive performance of the model and common performance enhancement techniques are listed. Finally, the limitations of the DL cancer prognosis prediction model in clinical practice are summarized, and the future research direction is prospected. This article could provide relevant researchers with a comprehensive understanding of DL cancer prognostic models and is expected to promote the research progress of cancer prognosis prediction.
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Affiliation(s)
- Heng Zhang
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
| | - Qianyi Xi
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Fan Zhang
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Qixuan Li
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Zhuqing Jiao
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Xinye Ni
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
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Application of Deep Learning in College Physical Education Design under Flipped Classroom. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7368771. [PMID: 36156941 PMCID: PMC9507692 DOI: 10.1155/2022/7368771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/27/2022] [Accepted: 08/10/2022] [Indexed: 11/24/2022]
Abstract
With the development of information technology, teaching reform has also undergone major changes. The traditional college physical education teaching method cannot meet the needs of the majority of students, and the physical education teaching mode continues to be reformed. Microcourse is the most intuitive form of deep integration of information technology and physical education. From the perspective of the flipped classroom (FC), the physical education model has gradually changed from teacher centered to student centered. Deep learning (DL) emphasizes that learners have the ability to actively construct knowledge, effectively transfer knowledge, and solve real problems. This design applies DL and convolutional neural network to the teaching design of physical gymnastics in colleges and universities. The application of the DL teaching model based on FC in the microcourse teaching of gymnastics in colleges and universities is studied and evaluated. The results show that the current utilization of microcourse teaching resources is too low. Interest-oriented teaching microcourses cannot improve students' interests. The proportion of students who are interested is relatively small, and more than 50% of students are not interested. Teachers generally believe that the current gymnastics microcourse needs further optimization and improvement. The poor quality of microvideos and the lack of supervision and reward mechanism in the course are the main reasons for the insufficient students' interest. The complexity of the videos and the liveliness of the discussions are the main problems of low resource utilization. The student's interest in learning is greatly improved after the application of the designed model, and the proportion increases to 82.4%. The effect on ordinary college students is the most obvious, and the effect of microvideo learning has been significantly promoted. Design mode has the most obvious improvement in improving learning efficiency and autonomous learning ability. The improvement of learning ability has increased from 18% to 72%, and the improvement of learning efficiency has increased from 39% to 82%. Meanwhile, students' interest in learning is stimulated, and the utilization of resources is improved.
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A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6561622. [PMID: 36156967 PMCID: PMC9492356 DOI: 10.1155/2022/6561622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/18/2022] [Accepted: 07/22/2022] [Indexed: 11/17/2022]
Abstract
Context and Background: Since December 2019, the coronavirus (COVID-19) epidemic has sparked considerable alarm among the general community and significantly affected societal attitudes and perceptions. Apart from the disease itself, many people suffer from anxiety and depression due to the disease and the present threat of an outbreak. Due to the fast propagation of the virus and misleading/fake information, the issues of public discourse alter, resulting in significant confusion in certain places. Rumours are unproven facts or stories that propagate and promote sentiments of prejudice, hatred, and fear. Objective. The study’s objective is to propose a novel solution to detect fake news using state-of-the-art machines and deep learning models. Furthermore, to analyse which models outperformed in detecting the fake news. Method. In the research study, we adapted a COVID-19 rumours dataset, which incorporates rumours from news websites and tweets, together with information about the rumours. It is important to analyse data utilizing Natural Language Processing (NLP) and Deep Learning (DL) approaches. Based on the accuracy, precision, recall, and the f1 score, we can assess the effectiveness of the ML and DL algorithms. Results. The data adopted from the source (mentioned in the paper) have collected 9200 comments from Google and 34,779 Twitter postings filtered for phrases connected with COVID-19-related fake news. Experiment 1. The dataset was assessed using the following three criteria: veracity, stance, and sentiment. In these terms, we have different labels, and we have applied the DL algorithms separately to each term. We have used different models in the experiment such as (i) LSTM and (ii) Temporal Convolution Networks (TCN). The TCN model has more performance on each measurement parameter in the evaluated results. So, we have used the TCN model for the practical implication for better findings. Experiment 2. In the second experiment, we have used different state-of-the-art deep learning models and algorithms such as (i) Simple RNN; (ii) LSTM + Word Embedding; (iii) Bidirectional + Word Embedding; (iv) LSTM + CNN-1D; and (v) BERT. Furthermore, we have evaluated the performance of these models on all three datasets, e.g., veracity, stance, and sentiment. Based on our second experimental evaluation, the BERT has a superior performance over the other models compared.
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A Deep Neural Network for Gastric Cancer Prognosis Prediction Based on Biological Information Pathways. JOURNAL OF ONCOLOGY 2022; 2022:2965166. [PMID: 36117847 PMCID: PMC9481367 DOI: 10.1155/2022/2965166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/09/2022] [Accepted: 07/22/2022] [Indexed: 11/18/2022]
Abstract
Background Gastric cancer (GC) is one of the deadliest cancers in the world, with a 5-year overall survival rate of lower than 20% for patients with advanced GC. Genomic information is now frequently employed for precision cancer treatment due to the rapid advancements of high-throughput sequencing technologies. As a result, integrating multiomics data to construct predictive models for the GC patient prognosis is critical for tailored medical care. Results In this study, we integrated multiomics data to design a biological pathway-based gastric cancer sparse deep neural network (GCS-Net) by modifying the P-NET model for long-term survival prediction of GC. The GCS-Net showed higher accuracy (accuracy = 0.844), area under the curve (AUC = 0.807), and F1 score (F1 = 0.913) than traditional machine learning models. Furthermore, the GCS-Net not only enables accurate patient survival prognosis but also provides model interpretability capabilities lacking in most traditional deep neural networks to describe the complex biological process of prognosis. The GCS-Net suggested the importance of genes (UBE2C, JAK2, RAD21, CEP250, NUP210, PTPN1, CDC27, NINL, NUP188, and PLK4) and biological pathways (Mitotic Anaphase, Resolution of Sister Chromatid Cohesion, and SUMO E3 ligases) to GC, which is consistent with the results revealed in biological- and medical-related studies of GC. Conclusion The GCS-Net is an interpretable deep neural network built using biological pathway information whose structure represents a nonlinear hierarchical representation of genes and biological pathways. It can not only accurately predict the prognosis of GC patients but also suggest the importance of genes and biological pathways. The GCS-Net opens up new avenues for biological research and could be adapted for other cancer prediction and discovery activities as well.
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Wahab F, Zhao Y, Javeed D, Al-Adhaileh MH, Almaaytah SA, Khan W, Saeed MS, Kumar Shah R. An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6096289. [PMID: 36045979 PMCID: PMC9420579 DOI: 10.1155/2022/6096289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 11/17/2022]
Abstract
E-health has grown into a billion-dollar industry in the last decade. Its device's high throughput makes it an obvious target for cyberattacks, and these environments desperately need protection. In this scientific study, we presented an artificial intelligence (AI)-driven software-defined networking (SDN)-enabled intrusion detection system (IDS) to address increasing cyber threats in the E-health and internet of medical things (IoMT) environments. AI's success in various fields, including big data and intrusion detection systems, has prompted us to develop a flexible and cost-effective approach to protect such critical environments from cyberattacks. We present a hybrid model consisting of long short-term memory (LSTM) and gated recurrent unit (GRU). The proposed model was thoroughly evaluated using the publicly available CICDDoS2019 dataset and conventional evaluation measures. Furthermore, for proper validation, the proposed framework is compared with relevant classifiers, such as cu-GRU+ DNN and cu-BLSTM. We have further compared the proposed model with existing literature to prove its efficacy. Lastly, 10-fold cross-validation is also used to verify that our results are unbiased. The proposed approach has bypassed the current literature with extraordinary performance ramifications such as 99.01% accuracy, 99.04% precision, 98.80 percent recall, and 99.12% F1-score.
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Affiliation(s)
- Fazal Wahab
- College of Computer Science and Technology, Northeastern University, Shenyang 110169, China
| | - Yuhai Zhao
- College of Computer Science and Technology, Northeastern University, Shenyang 110169, China
| | - Danish Javeed
- Software College, Northeastern University, Shenyang 110169, China
| | - Mosleh Hmoud Al-Adhaileh
- Deanship of E-Learning and Distance Education, King Faisal University, P.O. Box 400, Al-Ahsa, Saudi Arabia
| | | | - Wasiat Khan
- Department of Software Engineering, University of Science and Technology Bannu, Bannu, Pakistan
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Machine learning models to prognose 30-Day Mortality in Postoperative Disseminated Cancer Patients. Surg Oncol 2022; 44:101810. [DOI: 10.1016/j.suronc.2022.101810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/14/2022] [Accepted: 07/03/2022] [Indexed: 11/18/2022]
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Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis. Cancers (Basel) 2022; 14:cancers14133215. [PMID: 35804988 PMCID: PMC9265023 DOI: 10.3390/cancers14133215] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary The rise of Big Data, the widespread use of Machine Learning, and the cheapening of omics techniques have allowed for the creation of more sophisticated and accurate models in biomedical research. This article presents the state-of-the-art predictive models of cancer prognosis that use multimodal data, considering clinical, molecular (omics and non-omics), and image data. The subject of study, the data modalities used, the data processing and modelling methods applied, the validation strategies involved, the integration strategies encompassed, and the evolution of prognostic predictive models are discussed. Finally, we discuss challenges and opportunities in this field of cancer research, with great potential impact on the clinical management of patients and, by extension, on the implementation of personalised and precision medicine. Abstract Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 state-of-the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression.
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Tufail AB, Anwar N, Othman MTB, Ullah I, Khan RA, Ma YK, Adhikari D, Rehman AU, Shafiq M, Hamam H. Early-Stage Alzheimer's Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22124609. [PMID: 35746389 PMCID: PMC9230850 DOI: 10.3390/s22124609] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/06/2022] [Accepted: 06/15/2022] [Indexed: 05/27/2023]
Abstract
Alzheimer's Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD/NC, AD/MCI, MCI/NC and one multiclass classification task AD/NC/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD/NC, 71.70% on AD/MCI, 62.25% on NC/MCI and 59.73% on AD/NC/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD.
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Affiliation(s)
- Ahsan Bin Tufail
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China; (A.B.T.); (Y.-K.M.)
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan
| | - Nazish Anwar
- Registered Medical Practitioner, Pakistan Medical Commission, Islamabad 44000, Pakistan;
| | - Mohamed Tahar Ben Othman
- Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
| | - Inam Ullah
- College of Internet of Things (IoT) Engineering, Changzhou Campus, Hohai University (HHU), Changzhou 213022, China;
| | - Rehan Ali Khan
- Department of Electrical Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan;
| | - Yong-Kui Ma
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China; (A.B.T.); (Y.-K.M.)
| | - Deepak Adhikari
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Ateeq Ur Rehman
- Department of Electrical Engineering, Government College University Lahore, Lahore 54000, Pakistan;
| | - Muhammad Shafiq
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
| | - Habib Hamam
- Faculty of Engineering, Université de Moncton, Moncton, NB E1A3E9, Canada;
- International Institute of Technology and Management, Libreville BP1989, Gabon
- Spectrum of Knowledge Production & Skills Development, Sfax 3027, Tunisia
- Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa
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Abstract
Melanoma skin cancer is one of the most dangerous types of skin cancer, which, if not diagnosed early, may lead to death. Therefore, an accurate diagnosis is needed to detect melanoma. Traditionally, a dermatologist utilizes a microscope to inspect and then provide a report on a biopsy for diagnosis; however, this diagnosis process is not easy and requires experience. Hence, there is a need to facilitate the diagnosis process while still yielding an accurate diagnosis. For this purpose, artificial intelligence techniques can assist the dermatologist in carrying out diagnosis. In this study, we considered the detection of melanoma through deep learning based on cutaneous image processing. For this purpose, we tested several convolutional neural network (CNN) architectures, including DenseNet201, MobileNetV2, ResNet50V2, ResNet152V2, Xception, VGG16, VGG19, and GoogleNet, and evaluated the associated deep learning models on graphical processing units (GPUs). A dataset consisting of 7146 images was processed using these models, and we compared the obtained results. The experimental results showed that GoogleNet can obtain the highest performance accuracy on both the training and test sets (74.91% and 76.08%, respectively).
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A Liver Damage Prediction Using Partial Differential Segmentation with Improved Convolutional Neural Network. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4055491. [PMID: 35265300 PMCID: PMC8898868 DOI: 10.1155/2022/4055491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/20/2021] [Accepted: 01/17/2022] [Indexed: 11/19/2022]
Abstract
Background The liver is one of the most significant and most essential organs in the human body. It is divided into two granular lobes, one on the right and one on the left, connected by a bile duct. The liver is essential in the removal of waste products from human food consumption, the creation of bile, the regulation of metabolic activities, the cleaning of the blood by sensitizing digestive management, and the storage of vitamins and minerals. To perform the classification of liver illnesses using computed tomography (CT scans), two critical phases must first be completed: liver segmentation and categorization. The most difficult challenge in categorizing liver disease is distinguishing the liver from the other organs near it. Methodology. Liver biopsy is a kind of invasive diagnostic procedure, widely regarded as the gold standard for accurately estimating the severity of liver disease. Noninvasive approaches for examining liver illnesses, such as blood serum markers and medical imaging (ultrasound, magnetic resonance MR, and CT) have also been developed. This approach uses the Partial Differential Technique (PDT) to separate the liver from the other organs and Level Set Methodology (LSM) for separating the cancer location from the surrounding tissue based on the projected pictures used as input. With the help of an Improved Convolutional Classifier, the categorization of different phases may be accomplished. Results Several accuracies, sensitivity, and specificity measurements are produced to assess the categorization of LSM using an Improved Convolutional classifier. Approximately, 97.5% of the performance accuracy of the liver categorization is achieved with a 94.5% continuous interval (CI) of [0.6775 1.0000] and an error rate of 2.1%. The suggested method's performance is compared to that of two existing algorithms, and the sensitivity and specificity provide an overall average of 96% and 93%, respectively, with 95% Continuous Interval of [0.7513 1.0000] and [0.7126 1.0000] for sensitivity and specificity, respectively.
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Tufail AB, Ullah K, Khan RA, Shakir M, Khan MA, Ullah I, Ma YK, Ali MS. On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer's Disease Using Neuroimaging Modalities and Data Augmentation Methods. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1302170. [PMID: 35186220 PMCID: PMC8856791 DOI: 10.1155/2022/1302170] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/17/2022] [Accepted: 01/20/2022] [Indexed: 12/15/2022]
Abstract
Alzheimer's disease (AD) is an irreversible illness of the brain impacting the functional and daily activities of elderly population worldwide. Neuroimaging sensory systems such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) measure the pathological changes in the brain associated with this disorder especially in its early stages. Deep learning (DL) architectures such as Convolutional Neural Networks (CNNs) are successfully used in recognition, classification, segmentation, detection, and other domains for data interpretation. Data augmentation schemes work alongside DL techniques and may impact the final task performance positively or negatively. In this work, we have studied and compared the impact of three data augmentation techniques on the final performances of CNN architectures in the 3D domain for the early diagnosis of AD. We have studied both binary and multiclass classification problems using MRI and PET neuroimaging modalities. We have found the performance of random zoomed in/out augmentation to be the best among all the augmentation methods. It is also observed that combining different augmentation methods may result in deteriorating performances on the classification tasks. Furthermore, we have seen that architecture engineering has less impact on the final classification performance in comparison to the data manipulation schemes. We have also observed that deeper architectures may not provide performance advantages in comparison to their shallower counterparts. We have further observed that these augmentation schemes do not alleviate the class imbalance issue.
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Affiliation(s)
- Ahsan Bin Tufail
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
- Department of Electrical and Computer Engineering, COMSATS University Islamabad Sahiwal Campus, Sahiwal, Pakistan
| | - Kalim Ullah
- Department of Zoology, Kohat University of Science and Technology, Kohat 26000, Pakistan
| | - Rehan Ali Khan
- Department of Electrical Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan
| | - Mustafa Shakir
- Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan
| | - Muhammad Abbas Khan
- Department of Electrical Engineering, Balochistan University of Information Technology,Engineering and Management Sciences, Quetta,Balochistan 87300, Pakistan
| | - Inam Ullah
- College of Internet of Things (IoT) Engineering, Hohai University (HHU), Changzhou Campus 213022, China
| | - Yong-Kui Ma
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
| | - Md. Sadek Ali
- Communication Research Laboratory, Department of Information and Communication Technology, Islamic University, Kushtia-7003, Bangladesh
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Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection. Cancers (Basel) 2022; 14:cancers14030606. [PMID: 35158874 PMCID: PMC8833500 DOI: 10.3390/cancers14030606] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/20/2022] [Accepted: 01/24/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Multiple myeloma is a malignant neoplasm of plasma cells with complex pathogenesis. With major progresses in multiple myeloma research, it is essential that we reconsider our methods for diagnosing and monitoring multiple myeloma disease. This fact needs the integration of serology, histology, radiology, and genetic data; therefore, multiple myeloma study has generated massive quantities of granular high-dimensional data exceeding human understanding. With improved computational techniques, artificial intelligence tools for data processing and analysis are becoming more and more relevant. Artificial intelligence represents a wide set of algorithms for which machine learning and deep learning are presently among the most impactful. This review focuses on artificial intelligence applications in multiple myeloma research, first illustrating machine learning and deep learning procedures and workflow, followed by how these algorithms are used for multiple myeloma diagnosis, prognosis, bone lesions identification, and evaluation of response to the treatment. Abstract Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. Machine learning is a branch of artificial intelligence that involves algorithms that analyse information, learn from that information, and then employ their discoveries to make abreast choice, while deep learning is a field of machine learning basically represented by algorithms inspired by the organization and function of the brain, named artificial neural networks. In this review, we examine the possibility of the artificial intelligence applications in multiple myeloma evaluation, and we report the most significant experimentations with respect to the machine and deep learning procedures in the relevant field. Multiple myeloma is one of the most common haematological malignancies in the world, and among them, it is one of the most difficult ones to cure due to the high occurrence of relapse and chemoresistance. Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery and therapy selection and by a better evaluation of its relapse and survival.
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Optimization: Molecular Communication Networks for Viral Disease Analysis Using Deep Leaning Autoencoder. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:9949328. [PMID: 34938362 PMCID: PMC8687764 DOI: 10.1155/2021/9949328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 10/03/2021] [Accepted: 11/12/2021] [Indexed: 11/25/2022]
Abstract
Developing new treatments for emerging infectious diseases in infectious and noninfectious diseases has attracted a particular attention. The emergence of viral diseases is expected to accelerate; these data indicate the need for a proactive approach to develop widely active family specific and cross family therapies for future disease outbreaks. Viral disease such as pneumonia, severe acute respiratory syndrome type 2, HIV infection, and Hepatitis-C virus can cause directly and indirectly cardiovascular disease (CVD). Emphasis should be placed not only on the development of broad-spectrum molecules and antibodies but also on host factor therapy, including the reutilization of previously approved or developing drugs. Another new class of therapeutics with great antiviral therapeutic potential is molecular communication networks using deep learning autoencoder (DL-AEs). The use of DL-AEs for diagnosis and prognosis prediction of infectious and noninfectious diseases has attracted a particular attention. MCN is map to molecular signaling and communication that are found inside and outside the human body where the goal is to develop a new black box mechanism that can serve the future robust healthcare industry (HCI). MCN has the ability to characterize the signaling process between cells and infectious disease locations at various levels of the human body called point-to-point MCN through DL-AE and provide targeted drug delivery (TDD) environment. Through MCN, and DL-AE healthcare provider can remotely measure biological signals and control certain processes in the required organism for the maintenance of the patient's health state. We use biomicrodevices to promote the real-time monitoring of human health and storage of the gathered data in the cloud. In this paper, we use the DL-based AE approach to design and implement a new drug source and target for the MCN under white Gaussian noise. Simulation results show that transceiver executions for a given medium model that reduces the bit error rate which can be learned. Then, next development of molecular diagnosis such as heart sounds is classified. Furthermore, biohealth interface for the inside and outside human body mechanism is presented, comparative perspective with up-to-date current situation about MCN.
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Classification of Initial Stages of Alzheimer’s Disease through Pet Neuroimaging Modality and Deep Learning: Quantifying the Impact of Image Filtering Approaches. MATHEMATICS 2021. [DOI: 10.3390/math9233101] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Alzheimer’s disease (AD) is a leading health concern affecting the elderly population worldwide. It is defined by amyloid plaques, neurofibrillary tangles, and neuronal loss. Neuroimaging modalities such as positron emission tomography (PET) and magnetic resonance imaging are routinely used in clinical settings to monitor the alterations in the brain during the course of progression of AD. Deep learning techniques such as convolutional neural networks (CNNs) have found numerous applications in healthcare and other technologies. Together with neuroimaging modalities, they can be deployed in clinical settings to learn effective representations of data for different tasks such as classification, segmentation, detection, etc. Image filtering methods are instrumental in making images viable for image processing operations and have found numerous applications in image-processing-related tasks. In this work, we deployed 3D-CNNs to learn effective representations of PET modality data to quantify the impact of different image filtering approaches. We used box filtering, median filtering, Gaussian filtering, and modified Gaussian filtering approaches to preprocess the images and use them for classification using 3D-CNN architecture. Our findings suggest that these approaches are nearly equivalent and have no distinct advantage over one another. For the multiclass classification task between normal control (NC), mild cognitive impairment (MCI), and AD classes, the 3D-CNN architecture trained using Gaussian-filtered data performed the best. For binary classification between NC and MCI classes, the 3D-CNN architecture trained using median-filtered data performed the best, while, for binary classification between AD and MCI classes, the 3D-CNN architecture trained using modified Gaussian-filtered data performed the best. Finally, for binary classification between AD and NC classes, the 3D-CNN architecture trained using box-filtered data performed the best.
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