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Li X, Li L, Wei J, Zhang P, Turchenko V, Vempala N, Kabakov E, Habib F, Gupta A, Huang H, Lee K. Using Advanced Convolutional Neural Network Approaches to Reveal Patient Age, Gender, and Weight Based on Tongue Images. BIOMED RESEARCH INTERNATIONAL 2024; 2024:5551209. [PMID: 39118805 PMCID: PMC11309814 DOI: 10.1155/2024/5551209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 06/15/2024] [Accepted: 07/04/2024] [Indexed: 08/10/2024]
Abstract
The human tongue has been long believed to be a window to provide important insights into a patient's health in medicine. The present study introduced a novel approach to predict patient age, gender, and weight inferences based on tongue images using pretrained deep convolutional neural networks (CNNs). Our results demonstrated that the deep CNN models (e.g., ResNeXt) trained on dorsal tongue images produced excellent results for age prediction with a Pearson correlation coefficient of 0.71 and a mean absolute error (MAE) of 8.5 years. We also obtained an excellent classification of gender, with a mean accuracy of 80% and an AUC (area under the receiver operating characteristic curve) of 88%. ResNeXt model also obtained a moderate level of accuracy for weight prediction, with a Pearson correlation coefficient of 0.39 and a MAE of 9.06 kg. These findings support our hypothesis that the human tongue contains crucial information about a patient. This study demonstrated the feasibility of using the pretrained deep CNNs along with a large tongue image dataset to develop computational models to predict patient medical conditions for noninvasive, convenient, and inexpensive patient health monitoring and diagnosis.
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Affiliation(s)
- Xiaoyan Li
- Hangzhou Normal University Affiliated Hospital, Hangzhou, Zhejiang, China
- Computer ScienceUniversity of Toronto, Toronto, Ontario, Canada
| | - Li Li
- Hangzhou Normal University Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Jing Wei
- Hangzhou Normal University Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Pengwei Zhang
- Hangzhou Normal University Affiliated Hospital, Hangzhou, Zhejiang, China
| | | | | | | | - Faisal Habib
- Mathematics, Analytics, and Data Science LabFields Institute for Research in Mathematical Sciences, Toronto, Ontario, Canada
| | - Arvind Gupta
- Computer ScienceUniversity of Toronto, Toronto, Ontario, Canada
| | - Huaxiong Huang
- Computer ScienceUniversity of Toronto, Toronto, Ontario, Canada
- Mathematics and StatisticsYork University, Toronto, Ontario, Canada
| | - Kang Lee
- Computer ScienceUniversity of Toronto, Toronto, Ontario, Canada
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Dou S, Ma G, Liang Y, Fu G, Shen J, Fu L, Wang Q, Li T, Cong B, Li S. Preliminary exploratory research on the application value of oral and intestinal meta-genomics in predicting subjects' occupations-A case study of the distinction between students and migrant workers. Front Microbiol 2024; 14:1330603. [PMID: 38390220 PMCID: PMC10883652 DOI: 10.3389/fmicb.2023.1330603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/26/2023] [Indexed: 02/24/2024] Open
Abstract
Background In the field of forensic science, accurately determining occupation of an individual can greatly assist in resolving cases such as criminal investigations or disaster victim identifications. However, estimating occupation can be challenging due to the intricate relationship between occupation and various factors, including gender, age, living environment, health status, medication use, and lifestyle habits such as alcohol consumption and smoking. All of these factors can impact the composition of oral or gut microbial community of an individual. Methods and results In this study, we collected saliva and feces samples from individuals representing different occupational sectors, specifically students and manual laborers. We then performed metagenomic sequencing on the DNA extracted from these samples to obtain data that could be analyzed for taxonomic and functional annotations in five different databases. The correlation between occupation with microbial information was assisted from the perspective of α and β diversity, showing that individuals belonging to the two occupations hold significantly different oral and gut microbial communities, and that this correlation is basically not affected by gender, drinking, and smoking in our datasets. Finally, random forest (RF) models were built with recursive feature elimination (RFE) processes. Models with 100% accuracy in both training and testing sets were constructed based on three species in saliva samples or on a single pathway annotated by the KEGG database in fecal samples, namely, "ko04145" or Phagosome. Conclusion Although this study may have limited representativeness due to its small sample size, it provides preliminary evidence of the potential of using microbiome information for occupational inference.
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Affiliation(s)
- Shujie Dou
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Guanju Ma
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Yu Liang
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Guangping Fu
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Jie Shen
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Lihong Fu
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Qian Wang
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Tao Li
- Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen, China
| | - Bin Cong
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
- Hainan Tropical Forensic Medicine Academician Workstation, Haikou, China
| | - Shujin Li
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
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Maskey N, Hameedi S, Dawoud A, Jacksi K, Al-Sadoon OHR, Salahuddin ABE. DCPV: A Taxonomy for Deep Learning Model in Computer Aided System for Human Age Detection. LECTURE NOTES IN NETWORKS AND SYSTEMS 2023:64-79. [DOI: 10.1007/978-3-031-35308-6_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants. Transl Psychiatry 2022; 12:397. [PMID: 36130921 PMCID: PMC9492670 DOI: 10.1038/s41398-022-02162-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/04/2022] [Accepted: 09/08/2022] [Indexed: 11/12/2022] Open
Abstract
Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies suggest that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to characterize MDD. However, current conclusions are usually drawn based on structural MRI information collected from Caucasian participants. The universality of this biomarker needs to be further validated by subjects with different ethnic/racial backgrounds and by different types of data. Here we make use of the REST-meta-MDD, a large scale resting-state fMRI dataset collected from multiple cohort participants in China. We develop a stacking machine learning model based on 1101 healthy controls, which estimates a subject's chronological age from fMRI with promising accuracy. The trained model is then applied to 1276 MDD patients from 24 sites. We observe that MDD patients exhibit a +4.43 years (p < 0.0001, Cohen's d = 0.31, 95% CI: 2.23-3.88) higher brain-predicted age difference (brain-PAD) compared to controls. In the MDD subgroup, we observe a statistically significant +2.09 years (p < 0.05, Cohen's d = 0.134525) brain-PAD in antidepressant users compared to medication-free patients. The statistical relationship observed is further checked by three different machine learning algorithms. The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients. The utilization of functional brain connectivity for age estimation verifies existing findings from a new dimension.
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Adaptive feature selection framework for DNA methylation-based age prediction. Soft comput 2022. [DOI: 10.1007/s00500-022-06844-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Zhang J, Fu H, Xu Y. Age Prediction of Human Based on DNA Methylation by Blood Tissues. Genes (Basel) 2021; 12:genes12060870. [PMID: 34204075 PMCID: PMC8228382 DOI: 10.3390/genes12060870] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/27/2021] [Accepted: 06/05/2021] [Indexed: 12/14/2022] Open
Abstract
In recent years, scientists have found a close correlation between DNA methylation and aging in epigenetics. With the in-depth research in the field of DNA methylation, researchers have established a quantitative statistical relationship to predict the individual ages. This work used human blood tissue samples to study the association between age and DNA methylation. We built two predictors based on healthy and disease data, respectively. For the health data, we retrieved a total of 1191 samples from four previous reports. By calculating the Pearson correlation coefficient between age and DNA methylation values, 111 age-related CpG sites were selected. Gradient boosting regression was utilized to build the predictive model and obtained the R2 value of 0.86 and MAD of 3.90 years on testing dataset, which were better than other four regression methods as well as Horvath’s results. For the disease data, 354 rheumatoid arthritis samples were retrieved from a previous study. Then, 45 CpG sites were selected to build the predictor and the corresponded MAD and R2 were 3.11 years and 0.89 on the testing dataset respectively, which showed the robustness of our predictor. Our results were better than the ones from other four regression methods. Finally, we also analyzed the twenty-four common CpG sites in both healthy and disease datasets which illustrated the functional relevance of the selected CpG sites.
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Soedarsono N, Hanafi MS, Auerkari E. Biological age estimation using DNA methylation analysis: A systematic review. SCIENTIFIC DENTAL JOURNAL 2021. [DOI: 10.4103/sdj.sdj_27_20] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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MapReduce-Based Parallel Genetic Algorithm for CpG-Site Selection in Age Prediction. Genes (Basel) 2019; 10:genes10120969. [PMID: 31775313 PMCID: PMC6947642 DOI: 10.3390/genes10120969] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Revised: 11/12/2019] [Accepted: 11/15/2019] [Indexed: 11/23/2022] Open
Abstract
Genomic biomarkers such as DNA methylation (DNAm) are employed for age prediction. In recent years, several studies have suggested the association between changes in DNAm and its effect on human age. The high dimensional nature of this type of data significantly increases the execution time of modeling algorithms. To mitigate this problem, we propose a two-stage parallel algorithm for selection of age related CpG-sites. The algorithm first attempts to cluster the data into similar age ranges. In the next stage, a parallel genetic algorithm (PGA), based on the MapReduce paradigm (MR-based PGA), is used for selecting age-related features of each individual age range. In the proposed method, the execution of the algorithm for each age range (data parallel), the evaluation of chromosomes (task parallel) and the calculation of the fitness function (data parallel) are performed using a novel parallel framework. In this paper, we consider 16 different healthy DNAm datasets that are related to the human blood tissue and that contain the relevant age information. These datasets are combined into a single unioned set, which is in turn randomly divided into two sets of train and test data with a ratio of 7:3, respectively. We build a Gradient Boosting Regressor (GBR) model on the selected CpG-sites from the train set. To evaluate the model accuracy, we compared our results with state-of-the-art approaches that used these datasets, and observed that our method performs better on the unseen test dataset with a Mean Absolute Deviation (MAD) of 3.62 years, and a correlation (R2) of 95.96% between age and DNAm. In the train data, the MAD and R2 are 1.27 years and 99.27%, respectively. Finally, we evaluate our method in terms of the effect of parallelization in computation time. The algorithm without parallelization requires 4123 min to complete, whereas the parallelized execution on 3 computing machines having 32 processing cores each, only takes a total of 58 min. This shows that our proposed algorithm is both efficient and scalable.
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Gillespie SL, Hardy LR, Anderson CM. Patterns of DNA methylation as an indicator of biological aging: State of the science and future directions in precision health promotion. Nurs Outlook 2019; 67:337-344. [PMID: 31248628 DOI: 10.1016/j.outlook.2019.05.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 05/13/2019] [Accepted: 05/15/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND A rapidly expanding literature suggests that individuals of the same chronological age show significant variation in biological age. PURPOSE The purpose of this article is to review the literature surrounding epigenetic age as estimated by DNA methylation, involving the addition or removal of methyl groups to DNA that can alter gene expression without changing the DNA sequence. METHODS This state of the science literature review summarizes current approaches in epigenetic age determination and applications of aging algorithms. FINDINGS A number of algorithms estimate epigenetic age using DNA methylation markers, primarily among adults. Algorithm application has focused on determining predictive value for risk of disease and death and identifying antecedents to age acceleration. Several studies have incorporated epigenetic age to evaluate intervention effectiveness. DISCUSSION As the research community continues to refine aging algorithms, there may be opportunity to promote health from a precision health perspective.
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Affiliation(s)
- Shannon L Gillespie
- Martha S. Pitzer Center for Women, Children, & Youth, College of Nursing, The Ohio State University, Columbus, OH.
| | - Lynda R Hardy
- Center for Research and Health Analytics, College of Nursing, The Ohio State University, Columbus, OH
| | - Cindy M Anderson
- Martha S. Pitzer Center for Women, Children, & Youth, College of Nursing, The Ohio State University, Columbus, OH
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