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Grzybowski A, Jin K, Zhou J, Pan X, Wang M, Ye J, Wong TY. Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review. Ophthalmol Ther 2024; 13:2125-2149. [PMID: 38913289 PMCID: PMC11246322 DOI: 10.1007/s40123-024-00981-4] [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: 02/19/2024] [Accepted: 04/15/2024] [Indexed: 06/25/2024] Open
Abstract
We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.
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Affiliation(s)
- Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznań , Poland.
| | - Kai Jin
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jingxin Zhou
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiangji Pan
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Meizhu Wang
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Juan Ye
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Tien Y Wong
- School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
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Wei R. Enhancing predictive models for sarcopenia: Suggestions for improved interpretability, feature inclusion, and stratified analyses. Geriatr Gerontol Int 2024; 24:818. [PMID: 38877791 DOI: 10.1111/ggi.14927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Accepted: 06/05/2024] [Indexed: 06/16/2024]
Affiliation(s)
- Ruigang Wei
- School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, China
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Patterson EJ, Bounds AD, Wagner SK, Kadri-Langford R, Taylor R, Daly D. Oculomics: A Crusade Against the Four Horsemen of Chronic Disease. Ophthalmol Ther 2024; 13:1427-1451. [PMID: 38630354 PMCID: PMC11109082 DOI: 10.1007/s40123-024-00942-x] [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: 02/02/2024] [Accepted: 03/25/2024] [Indexed: 05/22/2024] Open
Abstract
Chronic, non-communicable diseases present a major barrier to living a long and healthy life. In many cases, early diagnosis can facilitate prevention, monitoring, and treatment efforts, improving patient outcomes. There is therefore a critical need to make screening techniques as accessible, unintimidating, and cost-effective as possible. The association between ocular biomarkers and systemic health and disease (oculomics) presents an attractive opportunity for detection of systemic diseases, as ophthalmic techniques are often relatively low-cost, fast, and non-invasive. In this review, we highlight the key associations between structural biomarkers in the eye and the four globally leading causes of morbidity and mortality: cardiovascular disease, cancer, neurodegenerative disease, and metabolic disease. We observe that neurodegenerative disease is a particularly promising target for oculomics, with biomarkers detected in multiple ocular structures. Cardiovascular disease biomarkers are present in the choroid, retinal vasculature, and retinal nerve fiber layer, and metabolic disease biomarkers are present in the eyelid, tear fluid, lens, and retinal vasculature. In contrast, only the tear fluid emerged as a promising ocular target for the detection of cancer. The retina is a rich source of oculomics data, the analysis of which has been enhanced by artificial intelligence-based tools. Although not all biomarkers are disease-specific, limiting their current diagnostic utility, future oculomics research will likely benefit from combining data from various structures to improve specificity, as well as active design, development, and optimization of instruments that target specific disease signatures, thus facilitating differential diagnoses.
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Affiliation(s)
| | | | - Siegfried K Wagner
- Moorfields Eye Hospital NHS Trust, 162 City Road, London, EC1V 2PD, UK
- UCL Institute of Ophthalmology, University College London, 11-43 Bath Street, London, EC1V 9EL, UK
| | | | - Robin Taylor
- Occuity, The Blade, Abbey Square, Reading, Berkshire, RG1 3BE, UK
| | - Dan Daly
- Occuity, The Blade, Abbey Square, Reading, Berkshire, RG1 3BE, UK
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Zhang Y, Li S, Wu W, Zhao Y, Han J, Tong C, Luo N, Zhang K. Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES. BioData Min 2024; 17:12. [PMID: 38644481 PMCID: PMC11034020 DOI: 10.1186/s13040-024-00363-3] [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: 01/26/2024] [Accepted: 04/09/2024] [Indexed: 04/23/2024] Open
Abstract
BACKGROUND Recent researches have found a strong correlation between the triglyceride-glucose (TyG) index or the atherogenic index of plasma (AIP) and cardiovascular disease (CVD) risk. However, there is a lack of research on non-invasive and rapid prediction of cardiovascular risk. We aimed to develop and validate a machine-learning model for predicting cardiovascular risk based on variables encompassing clinical questionnaires and oculomics. METHODS We collected data from the Korean National Health and Nutrition Examination Survey (KNHANES). The training dataset (80% from the year 2008 to 2011 KNHANES) was used for machine learning model development, with internal validation using the remaining 20%. An external validation dataset from the year 2012 assessed the model's predictive capacity for TyG-index or AIP in new cases. We included 32122 participants in the final dataset. Machine learning models used 25 algorithms were trained on oculomics measurements and clinical questionnaires to predict the range of TyG-index and AIP. The area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score were used to evaluate the performance of our machine learning models. RESULTS Based on large-scale cohort studies, we determined TyG-index cut-off points at 8.0, 8.75 (upper one-third values), 8.93 (upper one-fourth values), and AIP cut-offs at 0.318, 0.34. Values surpassing these thresholds indicated elevated cardiovascular risk. The best-performing algorithm revealed TyG-index cut-offs at 8.0, 8.75, and 8.93 with internal validation AUCs of 0.812, 0.873, and 0.911, respectively. External validation AUCs were 0.809, 0.863, and 0.901. For AIP at 0.34, internal and external validation achieved similar AUCs of 0.849 and 0.842. Slightly lower performance was seen for the 0.318 cut-off, with AUCs of 0.844 and 0.836. Significant gender-based variations were noted for TyG-index at 8 (male AUC=0.832, female AUC=0.790) and 8.75 (male AUC=0.874, female AUC=0.862) and AIP at 0.318 (male AUC=0.853, female AUC=0.825) and 0.34 (male AUC=0.858, female AUC=0.831). Gender similarity in AUC (male AUC=0.907 versus female AUC=0.906) was observed only when the TyG-index cut-off point equals 8.93. CONCLUSION We have established a simple and effective non-invasive machine learning model that has good clinical value for predicting cardiovascular risk in the general population.
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Affiliation(s)
- Yuqi Zhang
- School of Computer Science & Engineering, Beihang University, Beijing, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Sijin Li
- Department of Cardiology, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weijie Wu
- Department of Cardiology, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Yanqing Zhao
- Department of Interventional Radiology & Vascular Surgery, Peking University Third Hospital, Beijing, China
| | - Jintao Han
- Department of Interventional Radiology & Vascular Surgery, Peking University Third Hospital, Beijing, China
| | - Chao Tong
- School of Computer Science & Engineering, Beihang University, Beijing, China.
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.
| | - Niansang Luo
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Kun Zhang
- Department of Cardiology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
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Choi JY, Kim H, Kim JK, Lee IS, Ryu IH, Kim JS, Yoo TK. Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era. Med Biol Eng Comput 2024; 62:449-463. [PMID: 37889431 DOI: 10.1007/s11517-023-02952-6] [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: 04/21/2023] [Accepted: 10/14/2023] [Indexed: 10/28/2023]
Abstract
Recently, fundus photography (FP) is being increasingly used. Corneal curvature is an essential factor in refractive errors and is associated with several pathological corneal conditions. As FP-based examination systems have already been widely distributed, it would be helpful for telemedicine to extract information such as corneal curvature using FP. This study aims to develop a deep learning model based on FP for corneal curvature prediction by categorizing corneas into steep, regular, and flat groups. The EfficientNetB0 architecture with transfer learning was used to learn FP patterns to predict flat, regular, and steep corneas. In validation, the model achieved a multiclass accuracy of 0.727, a Matthews correlation coefficient of 0.519, and an unweighted Cohen's κ of 0.590. The areas under the receiver operating characteristic curves for binary prediction of flat and steep corneas were 0.863 and 0.848, respectively. The optic nerve and its peripheral areas were the main focus of the model. The developed algorithm shows that FP can potentially be used as an imaging modality to estimate corneal curvature in the post-COVID-19 era, whereby patients may benefit from the detection of abnormal corneal curvatures using FP in the telemedicine setting.
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Affiliation(s)
- Joon Yul Choi
- Department of Biomedical Engineering, Yonsei University, Wonju, South Korea
| | | | - Jin Kuk Kim
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
| | - In Sik Lee
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
| | - Ik Hee Ryu
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
- Research and Development Department, VISUWORKS, Seoul, South Korea
| | - Jung Soo Kim
- Research and Development Department, VISUWORKS, Seoul, South Korea
| | - Tae Keun Yoo
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea.
- Research and Development Department, VISUWORKS, Seoul, South Korea.
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Guo J, He Q, She C, Liu H, Li Y. A machine learning-based online web calculator to aid in the diagnosis of sarcopenia in the US community. Digit Health 2024; 10:20552076241283247. [PMID: 39360239 PMCID: PMC11445774 DOI: 10.1177/20552076241283247] [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: 03/15/2024] [Accepted: 08/28/2024] [Indexed: 10/04/2024] Open
Abstract
Background Sarcopenia places a heavy healthcare burden on individuals and society. Recognizing sarcopenia and intervening at an early stage is critical. However, there is no simple and easy-to-use prediction tool for diagnosing sarcopenia. The aim of this study was to construct a well-performing online web calculator based on a machine learning approach to predict the risk of low lean body mass (LBM) to assist in the diagnosis of sarcopenia. Methods Data from the National Health and Nutritional Examination Surveys 1999-2004 were selected for model construction, and the included data were randomly divided into training and validation sets in the ratio of 75:25. Six machine learning methods- Classification and Regression Trees, Logistic Regression, Neural Network, Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost)-were used to develop the model. They are screened for features and evaluated for performance. The best-performing models were further developed as an online web calculator for clinical applications. Results There were 3046 participants enrolled in the study and 815 (26.8%) participants with LBM. Through feature screening, height, waist circumference, race, and age were used as machine learning features to construct the model. After performance evaluation and sensitivity analysis, the XGBoost-based model was determined to be the best model with better discriminative performance, clinical utility, and robustness. Conclusion The XGBoost-based model in this study has excellent performance, and the online web calculator based on it can easily and quickly predict the risk of LBM to aid in the diagnosis of sarcopenia in adults over the age of 60.
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Affiliation(s)
- Jiale Guo
- Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Qionghan He
- Department of Infectious Disease, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Chunjie She
- Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Hefeng Liu
- Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Yehai Li
- Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China
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Seok M, Kim W, Kim J. Machine Learning for Sarcopenia Prediction in the Elderly Using Socioeconomic, Infrastructure, and Quality-of-Life Data. Healthcare (Basel) 2023; 11:2881. [PMID: 37958025 PMCID: PMC10649858 DOI: 10.3390/healthcare11212881] [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: 10/02/2023] [Revised: 10/30/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023] Open
Abstract
Since the WHO's 2021 aging redefinition emphasizes "healthy aging" by focusing on the elderly's ability to perform daily activities, sarcopenia, which is defined as the loss of skeletal muscle mass, is now becoming a critical health concern, especially in South Korea with a rapidly aging population. Therefore, we develop a prediction model for sarcopenia by using machine learning (ML) techniques based on the Korea National Health and Nutrition Examination Survey (KNHANES) data 2008-2011, in which we focus on the role of socioeconomic status (SES), social infrastructure, and quality of life (QoL) in the prevalence of sarcopenia. We successfully identify sarcopenia with approximately 80% accuracy by using random forest (RF) and LightGBM (LGB), CatBoost (CAT), and a deep neural network (DNN). For prediction reliability, we achieve area under curve (AUC) values of 0.831, 0.868, and 0.773 for both genders, males, and females, respectively. Especially when using only male data, all the models consistently exhibit better performance overall. Furthermore, using the SHapley Additive exPlanations (SHAP) analysis, we find several common key features, which mainly contribute to model building. These include SES features, such as monthly household income, housing type, marriage status, and social infrastructure accessibility. Furthermore, the causal relationships of household income, per capita neighborhood sports facility area, and life satisfaction are analyzed to establish an effective prediction model for sarcopenia management in an aging population.
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Affiliation(s)
- Minje Seok
- Computer Engineering Department, Gachon University, Seongnam 13120, Republic of Korea;
| | - Wooseong Kim
- Computer Engineering Department, Gachon University, Seongnam 13120, Republic of Korea;
| | - Jiyoun Kim
- Convergence Health Science, Gachon University, Incheon 21936, Republic of Korea;
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Li H, Cao J, Grzybowski A, Jin K, Lou L, Ye J. Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images. Healthcare (Basel) 2023; 11:1739. [PMID: 37372857 PMCID: PMC10298137 DOI: 10.3390/healthcare11121739] [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: 04/15/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications.
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Affiliation(s)
- Huimin Li
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China; (H.L.); (J.C.); (K.J.)
| | - Jing Cao
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China; (H.L.); (J.C.); (K.J.)
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, 60-836 Poznan, Poland;
| | - Kai Jin
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China; (H.L.); (J.C.); (K.J.)
| | - Lixia Lou
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China; (H.L.); (J.C.); (K.J.)
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital School of Medicine Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou 310009, China; (H.L.); (J.C.); (K.J.)
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Veronese N, Koyanagi A, Barbagallo M, Dominguez L, Maggi S, Soysal P, Bolzetta F, Ruotolo G, Castagna A, Smith L. Pain Increases the Risk for Sarcopenia in Community-Dwelling Adults: Results From the English Longitudinal Study of Ageing. J Gerontol A Biol Sci Med Sci 2023; 78:1013-1019. [PMID: 36808253 PMCID: PMC10235197 DOI: 10.1093/gerona/glad062] [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: 09/15/2022] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Pain and sarcopenia are common in older people. Cross-sectional studies have reported a significant association between these two conditions, but cohort studies exploring pain as a potential risk factor for sarcopenia are scarce. Given this background, the aim of the present work was to investigate the association between pain (and its severity) at baseline, and the incidence of sarcopenia over 10 years of follow-up in a large representative sample of the English older adult population. METHODS Pain was diagnosed using self-reported information and categorized as mild to severe pain at four sites (low back, hip, knee, and feet). Incident sarcopenia was defined as having low handgrip strength and low skeletal muscle mass during the follow-up period. The association between pain at baseline and incident sarcopenia was assessed using an adjusted logistic regression analysis, and reported as odds ratios (ORs) with their 95% confidence intervals (CIs). RESULTS The 4 102 participants without sarcopenia at baseline had a mean ± standard deviation age of 69.7 ± 7.2 years, and they were mainly male (55.6%). Pain was present in 35.3% of the sample. Over 10 years of follow-up, 13.9% of the participants developed sarcopenia. After adjusting for 12 potential confounders, people with pain reported a significantly higher risk of sarcopenia (OR = 1.46: 95% CI: 1.18-1.82). However, only severe pain was significantly associated with incident sarcopenia, without significant differences across the four sites assessed. CONCLUSIONS The presence of pain, particularly severe pain, was associated with a significantly higher risk of incident sarcopenia.
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Affiliation(s)
- Nicola Veronese
- Geriatric Unit, Department of Internal Medicine and Geriatrics, University of Palermo, Palermo, Italy
| | - Ai Koyanagi
- Research and Development Unit, Parc Sanitari Sant Joan de Déu, CIBERSAM, ISCIII, Barcelona, Spain
- ICREA, Barcelona, Spain
| | - Mario Barbagallo
- Geriatric Unit, Department of Internal Medicine and Geriatrics, University of Palermo, Palermo, Italy
| | - Ligia J Dominguez
- Geriatric Unit, Department of Internal Medicine and Geriatrics, University of Palermo, Palermo, Italy
- Faculty of Medicine and Surgery, Kore University of Enna, Enna, Italy
| | - Stefania Maggi
- National Research Council, Neuroscience Institute, Padua, Italy
| | - Pinar Soysal
- Department of Geriatric Medicine, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Francesco Bolzetta
- Medical Department, Geriatric Unit, Azienda ULSS (Unità Locale Socio Sanitaria), Dolo-Mirano, Italy
| | | | | | - Lee Smith
- Centre for Health, Performance, and Wellbeing, Anglia Ruskin University, Cambridge, UK
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Seok M, Kim W. Sarcopenia Prediction for Elderly People Using Machine Learning: A Case Study on Physical Activity. Healthcare (Basel) 2023; 11:healthcare11091334. [PMID: 37174876 PMCID: PMC10178078 DOI: 10.3390/healthcare11091334] [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: 03/12/2023] [Revised: 04/21/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
Sarcopenia is a well-known age-related disease that can lead to musculoskeletal disorders and chronic metabolic syndromes, such as sarcopenic obesity. Numerous studies have researched the relationship between sarcopenia and various risk factors, leading to the development of predictive models based on these factors. In this study, we explored the impact of physical activity (PA) in daily life and obesity on sarcopenia prediction. PA is easier to measure using personal devices, such as smartphones and watches, or lifelogs, than using other factors that require medical equipment and examination. To demonstrate the feasibility of sarcopenia prediction using PA, we trained various machine learning models, including gradient boosting machine (GBM), xgboost (XGB), lightgbm (LGB), catboost (CAT), logistic regression, support vector classifier, k-nearest neighbors, random forest (RF), multi-layer perceptron, and deep neural network (DNN), using data samples from the Korea National Health and Nutrition Examination Survey. Among the models, the DNN achieved the most precise accuracy on average, 81%, with PA features across all data combinations, and the accuracy increased up to 90% with the addition of obesity information, such as total fat mass and fat percentage. Considering the difficulty of measuring the obesity feature, when adding waist circumference to the PA features, the DNN recorded the highest accuracy of 84%. This model accuracy could be improved by using separate training sets according to gender. As a result of measurement with various metrics for accurate evaluation of models, GBM, XGB, LGB, CAT, RF, and DNN demonstrated significant predictive performance using only PA features including waist circumference, with AUC values at least around 0.85 and often approaching or exceeding 0.9. We also found the key features for a highly performing model such as the quantified PA value and metabolic equivalent score in addition to a simple obesity measure such as body mass index (BMI) and waist circumference using SHAP analysis.
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Affiliation(s)
- Minje Seok
- Computer Engineering Department, Gachon University, Seongnam 13120, Gyeonggi, Republic of Korea
| | - Wooseong Kim
- Computer Engineering Department, Gachon University, Seongnam 13120, Gyeonggi, Republic of Korea
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Integrating oculomics with genomics reveals imaging biomarkers for preventive and personalized prediction of arterial aneurysms. EPMA J 2023; 14:73-86. [PMID: 36866161 PMCID: PMC9971392 DOI: 10.1007/s13167-023-00315-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/15/2023] [Indexed: 02/15/2023]
Abstract
Objective Arterial aneurysms are life-threatening but usually asymptomatic before requiring hospitalization. Oculomics of retinal vascular features (RVFs) extracted from retinal fundus images can reflect systemic vascular properties and therefore were hypothesized to provide valuable information on detecting the risk of aneurysms. By integrating oculomics with genomics, this study aimed to (i) identify predictive RVFs as imaging biomarkers for aneurysms and (ii) evaluate the value of these RVFs in supporting early detection of aneurysms in the context of predictive, preventive and personalized medicine (PPPM). Methods This study involved 51,597 UK Biobank participants who had retinal images available to extract oculomics of RVFs. Phenome-wide association analyses (PheWASs) were conducted to identify RVFs associated with the genetic risks of the main types of aneurysms, including abdominal aortic aneurysm (AAA), thoracic aneurysm (TAA), intracranial aneurysm (ICA) and Marfan syndrome (MFS). An aneurysm-RVF model was then developed to predict future aneurysms. The performance of the model was assessed in both derivation and validation cohorts and was compared with other models employing clinical risk factors. An RVF risk score was derived from our aneurysm-RVF model to identify patients with an increased risk of aneurysms. Results PheWAS identified a total of 32 RVFs that were significantly associated with the genetic risks of aneurysms. Of these, the number of vessels in the optic disc ('ntreeA') was associated with both AAA (β = -0.36, P = 6.75e-10) and ICA (β = -0.11, P = 5.51e-06). In addition, the mean angles between each artery branch ('curveangle_mean_a') were commonly associated with 4 MFS genes (FBN1: β = -0.10, P = 1.63e-12; COL16A1: β = -0.07, P = 3.14e-09; LOC105373592: β = -0.06, P = 1.89e-05; C8orf81/LOC441376: β = 0.07, P = 1.02e-05). The developed aneurysm-RVF model showed good discrimination ability in predicting the risks of aneurysms. In the derivation cohort, the C-index of the aneurysm-RVF model was 0.809 [95% CI: 0.780-0.838], which was similar to the clinical risk model (0.806 [0.778-0.834]) but higher than the baseline model (0.739 [0.733-0.746]). Similar performance was observed in the validation cohort, with a C-index of 0.798 (0.727-0.869) for the aneurysm-RVF model, 0.795 (0.718-0.871) for the clinical risk model and 0.719 (0.620-0.816) for the baseline model. An aneurysm risk score was derived from the aneurysm-RVF model for each study participant. The individuals in the upper tertile of the aneurysm risk score had a significantly higher risk of aneurysm compared to those in the lower tertile (hazard ratio = 17.8 [6.5-48.8], P = 1.02e-05). Conclusion We identified a significant association between certain RVFs and the risk of aneurysms and revealed the impressive capability of using RVFs to predict the future risk of aneurysms by a PPPM approach. Our finds have great potential to support not only the predictive diagnosis of aneurysms but also a preventive and more personalized screening plan which may benefit both patients and the healthcare system. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s13167-023-00315-7.
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Barriada RG, Masip D. An Overview of Deep-Learning-Based Methods for Cardiovascular Risk Assessment with Retinal Images. Diagnostics (Basel) 2022; 13:diagnostics13010068. [PMID: 36611360 PMCID: PMC9818382 DOI: 10.3390/diagnostics13010068] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Cardiovascular diseases (CVDs) are one of the most prevalent causes of premature death. Early detection is crucial to prevent and address CVDs in a timely manner. Recent advances in oculomics show that retina fundus imaging (RFI) can carry relevant information for the early diagnosis of several systemic diseases. There is a large corpus of RFI systematically acquired for diagnosing eye-related diseases that could be used for CVDs prevention. Nevertheless, public health systems cannot afford to dedicate expert physicians to only deal with this data, posing the need for automated diagnosis tools that can raise alarms for patients at risk. Artificial Intelligence (AI) and, particularly, deep learning models, became a strong alternative to provide computerized pre-diagnosis for patient risk retrieval. This paper provides a novel review of the major achievements of the recent state-of-the-art DL approaches to automated CVDs diagnosis. This overview gathers commonly used datasets, pre-processing techniques, evaluation metrics and deep learning approaches used in 30 different studies. Based on the reviewed articles, this work proposes a classification taxonomy depending on the prediction target and summarizes future research challenges that have to be tackled to progress in this line.
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Gu YJ, Chen LM, Gu ME, Xu HX, Li J, Wu LY. Body mass index-based predictions and personalized clinical strategies for colorectal cancer in the context of PPPM. EPMA J 2022; 13:615-632. [PMID: 36505896 PMCID: PMC9727065 DOI: 10.1007/s13167-022-00306-0] [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: 08/13/2022] [Accepted: 11/07/2022] [Indexed: 12/03/2022]
Abstract
Currently colorectal cancer (CRC) is the third most prevalent cancer worldwide. Body mass index (BMI) is frequently used in CRC screening and risk assessment to quantitatively evaluate weight. However, the impact of BMI on clinical strategies for CRC has received little attention. Within the framework of the predictive, preventive, and personalized medicine (3PM/PPPM), we hypothesized that BMI stratification would affect the primary, secondary, and tertiary care options for CRC and we conducted a critical evidence-based review. BMI dynamically influences CRC outcomes, which helps avoiding adverse treatment effects. The outcome of surgical and radiation treatment is adversely affected by overweight (BMI ≥ 30) or underweight (BMI < 20). A number of interventions, such as enhanced recovery after surgery and robotic surgery, can be applied to CRC at all levels of BMI. BMI-controlling modalities such as exercise, diet control, nutritional therapy, and medications may be potentially beneficial for patients with CRC. Patients with overweight are advised to lose weight through diet, medication, and physical activity while patients suffering of underweight require more focus on nutrition. BMI assists patients with CRC in better managing their weight, which decreases the incidence of adverse prognostic events during treatment. BMI is accessible, noninvasive, and highly predictive of clinical outcomes in CRC. The cost-benefit of the PPPM paradigm in developing countries can be advanced, and the clinical benefit for patients can be improved with the promotion of BMI-based clinical strategy models for CRC.
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Affiliation(s)
- Yun-Jia Gu
- grid.412540.60000 0001 2372 7462Yueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, No.110 Ganhe Road, Shanghai, 200437 China ,grid.412540.60000 0001 2372 7462Shanghai Qigong Research Institute, Shanghai University of Traditional Chinese Medicine, No. 650 South Wanping Road, Shanghai, 200030 China
| | - Li-Ming Chen
- grid.412540.60000 0001 2372 7462Yueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, No.110 Ganhe Road, Shanghai, 200437 China
| | - Mu-En Gu
- grid.412540.60000 0001 2372 7462Yueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, No.110 Ganhe Road, Shanghai, 200437 China
| | - Hong-Xiao Xu
- grid.412540.60000 0001 2372 7462Yueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, No.110 Ganhe Road, Shanghai, 200437 China
| | - Jing Li
- grid.412540.60000 0001 2372 7462Yueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, No.110 Ganhe Road, Shanghai, 200437 China
| | - Lu-Yi Wu
- grid.412540.60000 0001 2372 7462Yueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, No.110 Ganhe Road, Shanghai, 200437 China
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Liu Z, Wang Y, Shen F, Zhang Z, Gong J, Fu C, Shen C, Li R, Jing G, Cai S, Zhang Z, Sun Y, Tong T. Radiomics based on readout-segmented echo-planar imaging (RS-EPI) diffusion-weighted imaging (DWI) for prognostic risk stratification of patients with rectal cancer: a two-centre, machine learning study using the framework of predictive, preventive, and personalized medicine. EPMA J 2022; 13:633-647. [PMID: 36505889 PMCID: PMC9727035 DOI: 10.1007/s13167-022-00303-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/01/2022] [Indexed: 11/14/2022]
Abstract
Background Currently, the rate of recurrence or metastasis (ROM) remains high in rectal cancer (RC) patients treated with the standard regimen. The potential of diffusion-weighted imaging (DWI) in predicting ROM risk has been reported, but the efficacy is insufficient. Aims This study investigated the potential of a new sequence called readout-segmented echo-planar imaging (RS-EPI) DWI in predicting the ROM risk of patients with RC using machine learning methods to achieve the principle of predictive, preventive, and personalized medicine (PPPM) application in RC treatment. Methods A total of 195 RC patients from two centres who directly received total mesorectal excision were retrospectively enrolled in our study. Machine learning methods, including recursive feature elimination (RFE), the synthetic minority oversampling technique (SMOTE), and the support vector machine (SVM) classifier, were used to construct models based on clinical-pathological factors (clinical model), radiomic features from RS-EPI DWI (radiomics model), and their combination (merged model). The Harrell concordance index (C-index) and the area under the time-dependent receiver operating characteristic curve (AUC) were calculated to evaluate the predictive performance at 1 year, 3 years, and 5 years. Kaplan‒Meier analysis was performed to evaluate the ability to stratify patients according to the risk of ROM. Findings The merged model performed well in predicting tumour ROM in patients with RC at 1 year, 3 years, and 5 years in both cohorts (AUC = 0.887/0.813/0.794; 0.819/0.795/0.783) and was significantly superior to the clinical model (AUC = 0.87 [95% CI: 0.80-0.93] vs. 0.71 [95% CI: 0.59-0.81], p = 0.009; C-index = 0.83 [95% CI: 0.76-0.90] vs. 0.68 [95% CI: 0.56-0.79], p = 0.002). It also had a significant ability to differentiate patients with a high and low risk of ROM (HR = 12.189 [95% CI: 4.976-29.853], p < 0.001; HR = 6.427 [95% CI: 2.265-13.036], p = 0.002). Conclusion Our developed merged model based on RS-EPI DWI accurately predicted and effectively stratified patients with RC according to the ROM risk at an early stage with an individualized profile, which may be able to assist physicians in individualizing the treatment protocols and promote a meaningful paradigm shift in RC treatment from traditional reactive medicine to PPPM. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-022-00303-3.
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Affiliation(s)
- Zonglin Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yueming Wang
- Department of Anatomy and Physiology, Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Fu Shen
- Department of Radiology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Zhiyuan Zhang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Caixia Fu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | - Changqing Shen
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Rong Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Guodong Jing
- Department of Radiology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Sanjun Cai
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Zhen Zhang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yiqun Sun
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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