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Gurumurthy P, Alagarsamy M, Kuppusamy S, Ponnusamy NC. M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images. NETWORK (BRISTOL, ENGLAND) 2024; 35:319-346. [PMID: 38279811 DOI: 10.1080/0954898x.2024.2306988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 12/11/2023] [Indexed: 01/29/2024]
Abstract
Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification methods, such as retinal imaging and dual-energy X-ray absorptiometry (DXA), is limited. This study presents a groundbreaking system known as Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD), designed to provide highly accurate predictions of CVD. The M2AI-CVD framework employs a four-fold methodology: First, it rigorously evaluates image quality and processes lower-quality images for further analysis. Subsequently, it uses the Entropy-based Fuzzy C Means (EnFCM) algorithm for precise image segmentation. The Multi-Modal Boltzmann Machine (MMBM) is then employed to extract relevant features from various data modalities, while the Genetic Algorithm (GA) selects the most informative features. Finally, a ZFNet Convolutional Neural Network (ZFNetCNN) classifies images, effectively distinguishing between CVD and Non-CVD cases. The research's culmination, tested across five distinct datasets, yields outstanding results, with an accuracy of 95.89%, sensitivity of 96.89%, and specificity of 98.7%. This multi-modal AI approach offers a promising solution for the accurate and early detection of cardiovascular diseases, significantly improving the prospects of timely intervention and improved patient outcomes in the realm of cardiovascular health.
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Affiliation(s)
- Premalatha Gurumurthy
- Department of Electronics and Communication Engineering, Prathyusha Engineering College, Tiruvallur, Tamil Nadu, India
| | - Manjunathan Alagarsamy
- Department of Electronics and Communication Engineering, K.Ramakrishnan College of Technology, Trichy, Tamil Nadu, India
| | - Sangeetha Kuppusamy
- Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India
| | - Niranjana Chitra Ponnusamy
- Department of Electronics and Communication Engineering, Kongunadu College of Engineering and Technology, Trichy, Tamil Nadu, India
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Na S, Jeong H, Kim I, Hong SM, Shim J, Yoon IH, Cho KH. Distribution coefficient prediction using multimodal machine learning based on soil adsorption factors, XRF, and XRD spectrum data. JOURNAL OF HAZARDOUS MATERIALS 2024; 478:135285. [PMID: 39121738 DOI: 10.1016/j.jhazmat.2024.135285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 07/08/2024] [Accepted: 07/20/2024] [Indexed: 08/12/2024]
Abstract
The distribution coefficient (Kd) plays a crucial role in predicting the migration behavior of radionuclides in the soil environment. However, Kd depends on the complexities of geological and environmental factors, and existing models often do not reflect the unique soil properties. We propose a multimodal technique to predict Kd values for radionuclide adsorption in soils surrounding nuclear facilities in Republic of Korea. We integrated and trained three sub-networks reflecting different data domains: soil adsorption factors for physicochemical conditions, X-ray fluorescence (XRF) data, and X-ray diffraction (XRD) spectra for inherent soil properties. Our multimodal model achieved high performance, with a coefficient of determination (R2) of 0.84 and root mean squared error (RMSE) of 0.89 for natural log-transformed Kd. This is the first study to develop a multimodal model that simultaneously incorporates inherent soil properties and adsorption factors to predict Kd. We investigated influential peaks in XRD spectra and also revealed that pH and calcium oxide (CaO) were significant variables in soil adsorption factors and XRF data, respectively. These results promote the use of a multimodal model to predict Kd values by integrating data from different domains, providing a cost-effective and novel approach to elucidate the mechanisms of radionuclide adsorption in soil.
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Affiliation(s)
- Seongyeon Na
- Department of Civil, Urban, Earth and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Heewon Jeong
- Future and Fusion Lab of Architectural, Civil and Environmental Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Ilgook Kim
- Decommissioning Technology Research Division, Korea Atomic Energy Research Institute, Daejeon 34057, Republic of Korea
| | - Seok Min Hong
- Department of Civil, Urban, Earth and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Jaegyu Shim
- Department of Civil, Urban, Earth and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - In-Ho Yoon
- Decommissioning Technology Research Division, Korea Atomic Energy Research Institute, Daejeon 34057, Republic of Korea.
| | - Kyung Hwa Cho
- School of Civil, Environmental, and Architectural Engineering, Korea University, Seoul 02841, Republic of Korea.
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Oikonomou EK, Khera R. Artificial intelligence-enhanced patient evaluation: bridging art and science. Eur Heart J 2024:ehae415. [PMID: 38976371 DOI: 10.1093/eurheartj/ehae415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/23/2024] [Accepted: 06/18/2024] [Indexed: 07/10/2024] Open
Abstract
The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, PO Box 208017, New Haven, 06520-8017 CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, 195 Church St, 6th Floor, New Haven, CT 06510, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, 100 College Street, New Haven, 06511 CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06510 CT, USA
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Sawaf B, Swed S, Alibrahim H, Bohsas H, Dave T, Nasif MN, Hafez W, Tashrifwala FAA, Jabban YKE, Al-Rassas S, Saleh HH, Zaidi ARZ, Alghalyini B, Mohamed SA, Mohamed WF, Farwati A, Seijari MN, Battikh N, Elnagar B, Iqbal S, Robles-Velasco K, Cherrez-Ojeda I. Triglyceride-Glucose Index as Predictor for Hypertension, CHD and STROKE Risk among Non-Diabetic Patients: A NHANES Cross-Sectional Study 2001-2020. J Epidemiol Glob Health 2024:10.1007/s44197-024-00269-7. [PMID: 38954387 DOI: 10.1007/s44197-024-00269-7] [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: 02/20/2024] [Accepted: 06/16/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Cardiovascular disease (CVD) is a leading cause of global mortality. Early intervention and prevention of CVD depend on accurately predicting the risk of CVD. This study aimed to investigate the association between the TyG index and the risk of coronary heart disease (CHD), congestive heart failure (CHF), heart attack (HA), stroke, and hypertension (HTN) among patients without diabetes in the United States. METHODS In this retrospective, cross-sectional study, we used data from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2020. We conducted several regression analysis models and calculated the sensitivity and specificity of (TyG) index for predicting the onset of CHD, CHF, HA, stroke, and HTN. RESULTS A total of 10,937 individuals without diabetes participated in our study. Individuals with a TyG index greater than 8.96 displayed significant increasing in various parameters, including BMI, systolic/diastolic blood pressure, total cholesterol, LDL, and Apo-B levels (p < 0.001). Almost all regression models ensured that a higher TyGI value was associated with higher odds of having CHD, CHF, HA, stroke, and HTN, which patients with a TyGI value higher than 8.96 have odds ratios of 2.24-5.58 for CHD, 1.68-4.42 for stroke, 2.45-3.77 for HA and 1.75-3.93 for HTN comparing than patients with a TyGI value lower than 8.11 (p-value < 0.05).We evaluated the predictive value of the TyG index for each endpoint, obtaining the following area under the curve (AUC) values: 54.75% for CHF (95% CI: 0.542-0.614), 52.32% for stroke (95% CI: 0.529-0.584), 55.67% for HA (95% CI: 0.595-0.646), 55.59% for HTN (95% CI: 0.574-0.597), and 50.31% for CHD (95% CI: 0.592-0.646). CONCLUSION The TyG index showed a strong correlation with cardiovascular risk factors in individuals without diabetes, however it was a poor predictor of almost studied cardiovascular diseases.
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Affiliation(s)
- Bisher Sawaf
- Department of Internal Medicine, Hamad Medical Corporation, Doha, Qatar
- Independent Researcher, THE GLOBEST TEAM
| | - Sarya Swed
- Faculty of Medicine, Aleppo University, Aleppo, Syria.
- Independent Researcher, THE GLOBEST TEAM , .
| | - Hidar Alibrahim
- Faculty of Medicine, Aleppo University, Aleppo, Syria
- Independent Researcher, THE GLOBEST TEAM
| | - Haidara Bohsas
- Faculty of Medicine, Aleppo University, Aleppo, Syria
- Independent Researcher, THE GLOBEST TEAM
| | - Tirth Dave
- Bukovinian State Medical University, Chernivtsi, Ukraine
- Independent Researcher, THE GLOBEST TEAM
| | - Mohamad Nour Nasif
- Faculty of Medicine, Aleppo University, Aleppo, Syria
- Independent Researcher, THE GLOBEST TEAM
| | - Wael Hafez
- NMC Royal Hospital, 16Th Street, Khalifa City, Abu Dhabi, UAE
- Department of Internal Medicine, Medical Research and Clinical Studies Institute, The National Research Centre, Cairo, Egypt
- Independent Researcher, THE GLOBEST TEAM
| | | | | | - Safwan Al-Rassas
- Faculty of Medicine, Thamar University, Dhamar, Yemen
- Independent Researcher, THE GLOBEST TEAM
| | - Heba Haj Saleh
- Faculty of Medicine, Aleppo University, Aleppo, Syria
- Independent Researcher, THE GLOBEST TEAM
| | - Abdul Rehman Zia Zaidi
- Department of Family and Community Medicine, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Baraa Alghalyini
- Department of Family and Community Medicine, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Shaymaa Abdelmaboud Mohamed
- Department of Cardiology, National Heart Institute, Ibn Al Nafees Square, AL KIT KAT, Agouza, Giza Governorate, Egypt
- Department of Cardiology, Al Salam Specialist Hospital, Building 1, Road 39, Block 941, Riffa, 80278, Bahrain
| | | | - Amr Farwati
- Department of Internal Medicine, Hamad Medical Corporation, Doha, Qatar
- Independent Researcher, THE GLOBEST TEAM
| | - Mohammed Najdat Seijari
- Department of Internal Medicine, Hamad Medical Corporation, Doha, Qatar
- Independent Researcher, THE GLOBEST TEAM
| | - Naim Battikh
- John H. Stroger, Jr. Hospital of Cook County, Chicago, USA
| | - Basma Elnagar
- Lecturer of Cardiovascular Medicine, Cardiovascular Department, Faculty of Medicine, Tanta University, Tanta, Egypt
| | - Seema Iqbal
- Khyber Medical College, University of Peshawar, Peshawar, 25120, Khyber Pakhtunkhwa, Pakistan
| | - Karla Robles-Velasco
- Universidad Espíritu Santo, Samborondón, Ecuador
- Respiralab Research Group, Guayaquil, Ecuador
- Independent Researcher, THE GLOBEST TEAM
| | - Ivan Cherrez-Ojeda
- Universidad Espíritu Santo, Samborondón, Ecuador
- Respiralab Research Group, Guayaquil, Ecuador
- Independent Researcher, THE GLOBEST TEAM
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Shi XH, Ju L, Dong L, Zhang RH, Shao L, Yan YN, Wang YX, Fu XF, Chen YZ, Ge ZY, Wei WB. Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images. Ophthalmol Retina 2024; 8:666-677. [PMID: 38280426 DOI: 10.1016/j.oret.2024.01.019] [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: 07/31/2023] [Revised: 01/03/2024] [Accepted: 01/19/2024] [Indexed: 01/29/2024]
Abstract
OBJECTIVE We aimed to develop a deep learning system capable of identifying subjects with cognitive impairment quickly and easily based on multimodal ocular images. DESIGN Cross sectional study. SUBJECTS Participants of Beijing Eye Study 2011 and patients attending Beijing Tongren Eye Center and Beijing Tongren Hospital Physical Examination Center. METHODS We trained and validated a deep learning algorithm to assess cognitive impairment using retrospectively collected data from the Beijing Eye Study 2011. Cognitive impairment was defined as a Mini-Mental State Examination score < 24. Based on fundus photographs and OCT images, we developed 5 models based on the following sets of images: macula-centered fundus photographs, optic disc-centered fundus photographs, fundus photographs of both fields, OCT images, and fundus photographs of both fields with OCT (multimodal). The performance of the models was evaluated and compared in an external validation data set, which was collected from patients attending Beijing Tongren Eye Center and Beijing Tongren Hospital Physical Examination Center. MAIN OUTCOME MEASURES Area under the curve (AUC). RESULTS A total of 9424 retinal photographs and 4712 OCT images were used to develop the model. The external validation sets from each center included 1180 fundus photographs and 590 OCT images. Model comparison revealed that the multimodal performed best, achieving an AUC of 0.820 in the internal validation set, 0.786 in external validation set 1, and 0.784 in external validation set 2. We evaluated the performance of the multi-model in different sexes and different age groups; there were no significant differences. The heatmap analysis showed that signals around the optic disc in fundus photographs and the retina and choroid around the macular and optic disc regions in OCT images were used by the multimodal to identify participants with cognitive impairment. CONCLUSIONS Fundus photographs and OCT can provide valuable information on cognitive function. Multimodal models provide richer information compared with single-mode models. Deep learning algorithms based on multimodal retinal images may be capable of screening cognitive impairment. This technique has potential value for broader implementation in community-based screening or clinic settings. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Xu Han Shi
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lie Ju
- Beijing Airdoc Technology Co., Ltd., Beijing, China; Augmented Intelligence and Multimodal Analytics (AIM) for Health Lab, Faculty of Information Technology, Monash University, Clayton, Australia; Faculty of Engineering, Monash University, Clayton, Australia
| | - Li Dong
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Rui Heng Zhang
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lei Shao
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yan Ni Yan
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ya Xing Wang
- Beijing Ophthalmology and Visual Science Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Capital Medical University, Beijing, China
| | - Xue Fei Fu
- Beijing Airdoc Technology Co., Ltd., Beijing, China
| | | | - Zong Yuan Ge
- Beijing Airdoc Technology Co., Ltd., Beijing, China; Augmented Intelligence and Multimodal Analytics (AIM) for Health Lab, Faculty of Information Technology, Monash University, Clayton, Australia; Faculty of Engineering, Monash University, Clayton, Australia
| | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
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Chawla R. Retina: Current research. Indian J Ophthalmol 2024; 72:S1. [PMID: 38131532 PMCID: PMC10833165 DOI: 10.4103/ijo.ijo_3029_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023] Open
Affiliation(s)
- Rohan Chawla
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India E-mail:
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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 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
| | - 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
| | - 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
| | - 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
| | - 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
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