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Kong X, Wang Z, Sun J, Qi X, Qiu Q, Ding X. Facial recognition for disease diagnosis using a deep learning convolutional neural network: a systematic review and meta-analysis. Postgrad Med J 2024:qgae061. [PMID: 39102373 DOI: 10.1093/postmj/qgae061] [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: 12/11/2023] [Revised: 03/20/2024] [Accepted: 04/24/2024] [Indexed: 08/07/2024]
Abstract
BACKGROUND With the rapid advancement of deep learning network technology, the application of facial recognition technology in the medical field has received increasing attention. OBJECTIVE This study aims to systematically review the literature of the past decade on facial recognition technology based on deep learning networks in the diagnosis of rare dysmorphic diseases and facial paralysis, among other conditions, to determine the effectiveness and applicability of this technology in disease identification. METHODS This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for literature search and retrieved relevant literature from multiple databases, including PubMed, on 31 December 2023. The search keywords included deep learning convolutional neural networks, facial recognition, and disease recognition. A total of 208 articles on facial recognition technology based on deep learning networks in disease diagnosis over the past 10 years were screened, and 22 articles were selected for analysis. The meta-analysis was conducted using Stata 14.0 software. RESULTS The study collected 22 articles with a total sample size of 57 539 cases, of which 43 301 were samples with various diseases. The meta-analysis results indicated that the accuracy of deep learning in facial recognition for disease diagnosis was 91.0% [95% CI (87.0%, 95.0%)]. CONCLUSION The study results suggested that facial recognition technology based on deep learning networks has high accuracy in disease diagnosis, providing a reference for further development and application of this technology.
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Affiliation(s)
- Xinru Kong
- Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan City, Shandong Province 250355, China
- Department of Vertigo Center, Air Force Specialized Medical Center, Beijing 100142, China
| | - Ziyue Wang
- Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan City, Shandong Province 250355, China
| | - Jie Sun
- Rizhao Central Hospital, Rizhao, Shandong 276800, China
| | - Xianghua Qi
- Department of Neurology II, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan City, Shandong Province 25000, China
| | - Qianhui Qiu
- Department of Otolaryngology and Head and Neck Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Cardiovsacular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Science, Guangzhou 510000, China
| | - Xiao Ding
- Department of Neurology II, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan City, Shandong Province 25000, China
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Amanzholova A, Coşkun A. Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study. Front Big Data 2024; 7:1359703. [PMID: 38586474 PMCID: PMC10995364 DOI: 10.3389/fdata.2024.1359703] [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: 12/22/2023] [Accepted: 02/20/2024] [Indexed: 04/09/2024] Open
Abstract
Efficiently detecting and treating cancer at an early stage is crucial to improve the overall treatment process and mitigate the risk of disease progression. In the realm of research, the utilization of artificial intelligence technologies holds significant promise for enhancing advanced cancer diagnosis. Nonetheless, a notable hurdle arises when striving for precise cancer-stage diagnoses through the analysis of gene sets. Issues such as limited sample volumes, data dispersion, overfitting, and the use of linear classifiers with simple parameters hinder prediction performance. This study introduces an innovative approach for predicting early and late-stage cancers by integrating hybrid deep neural networks. A deep neural network classifier, developed using the open-source TensorFlow library and Keras network, incorporates a novel method that combines genetic algorithms, Extreme Learning Machines (ELM), and Deep Belief Networks (DBN). Specifically, two evolutionary techniques, DBN-ELM-BP and DBN-ELM-ELM, are proposed and evaluated using data from The Cancer Genome Atlas (TCGA), encompassing mRNA expression, miRNA levels, DNA methylation, and clinical information. The models demonstrate outstanding prediction accuracy (89.35%-98.75%) in distinguishing between early- and late-stage cancers. Comparative analysis against existing methods in the literature using the same cancer dataset reveals the superiority of the proposed hybrid method, highlighting its enhanced accuracy in cancer stage prediction.
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Affiliation(s)
- Alina Amanzholova
- Graduate School of Natural and Applied Sciences, Department of Computer Engineering, Gazi University, Ankara, Türkiye
- Khoja Akhmet Yassawi International Kazakh-Turkish University, Faculty of Engineering, Department of Computer Engineering, Turkistan, Kazakhstan
| | - Aysun Coşkun
- Department of Computer Engineering, Faculty of Technology, Gazi University, Ankara, Türkiye
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Zhang N, Jiang Z, Li M, Zhang D. A novel multi-feature learning model for disease diagnosis using face skin images. Comput Biol Med 2024; 168:107837. [PMID: 38086142 DOI: 10.1016/j.compbiomed.2023.107837] [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: 04/06/2023] [Revised: 11/15/2023] [Accepted: 12/07/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Facial skin characteristics can provide valuable information about a patient's underlying health conditions. OBJECTIVE In practice, there are often samples with divergent characteristics (commonly known as divergent samples) that can be attributed to environmental factors, living conditions, or genetic elements. These divergent samples significantly degrade the accuracy of diagnoses. METHODOLOGY To tackle this problem, we propose a novel multi-feature learning method called Multi-Feature Learning with Centroid Matrix (MFLCM), which aims to mitigate the influence of divergent samples on the accurate classification of samples located on the boundary. In this approach, we introduce a novel discriminator that incorporates a centroid matrix strategy and simultaneously adapt it to a classifier in a unified model. We effectively apply the centroid matrix to the embedding feature spaces, which are transformed from the multi-feature observation space, by calculating a relaxed Hamming distance. The purpose of the centroid vectors for each category is to act as anchors, ensuring that samples from the same class are positioned close to their corresponding centroid vector while being pushed further away from the remaining centroids. RESULTS Validation of the proposed method with clinical facial skin dataset showed that the proposed method achieved F1 scores of 92.59%, 83.35%, 82.84% and 85.46%, respectively for the detection the Healthy, Diabetes Mellitus (DM), Fatty Liver (FL) and Chronic Renal Failure (CRF). CONCLUSION Experimental results demonstrate the superiority of the proposed method compared with typical classifiers single-view-based and state-of-the-art multi-feature approaches. To the best of our knowledge, this study represents the first to demonstrate concept of multi-feature learning using only facial skin images as an effective non-invasive approach for simultaneously identifying DM, FL and CRF in Han Chinese, the largest ethnic group in the world.
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Affiliation(s)
- Nannan Zhang
- The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen, China.
| | - Zhixing Jiang
- The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen, China.
| | - Mu Li
- Harbin Institute of Technology at Shenzhen, Shenzhen, China.
| | - David Zhang
- The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen, China.
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Zhang N, Jiang Z, Li J, Zhang D. Multiple color representation and fusion for diabetes mellitus diagnosis based on back tongue images. Comput Biol Med 2023; 155:106652. [PMID: 36805220 DOI: 10.1016/j.compbiomed.2023.106652] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/02/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023]
Abstract
Tongue images have been proved to be effective in diabetes mellitus (DM) diagnosis. Without requirement of collecting blood sample, tongue image based diagnosis approach is non-invasive and convenient for the patients. Meanwhile, the colors of tongues play an important in aiding accurate diagnosis. However, the tongues' colors fall on a small color gamut that makes it difficult for the existing color descripts to identify and distinguish the tiny difference of the tongues. To tackle this problem, we introduce a novel color descriptor by representing the colors with the clustering centers, namely color centroid points, of the color points sampled from tongue images. In order to boost the capacity of the descriptor, we extend it into three color spaces, i.e., RGB, HSV and LAB to mine a rich set of color information and exploit the complementary information among the three spaces. Since there exist correlation and complementarity among the features extracted from the three color spaces, we propose a novel multiple color features fusion method for DM diagnosis. Particularly, two projections are learned to project the multiple features to their corresponding shared and specific subspaces, in which their similarity and diversity are firstly measured by the Euclidean Distance and Hilbert Schmidt Independence Criterion (HSIC), respectively. To fully exploit the similar and complementary information, the two components are jointly transformed to their label vector, efficiently embedding the discriminant prior into the model, leading to significant improvement in the diagnosis outcomes. Experimental results on clinical tongue dataset substantiated the effectiveness of our proposed clustering-based color descriptor and the proposed multiple colors fusion approach. Overall, the proposed pipeline for the diagnosis of DM using back tongue images, achieved an average accuracy of up to 93.38%, indicating its potential toward realization of a clinical diagnostic tool for DM. Without loss generality, we also assessed the performance of the novel multiple features fusion method on two public datasets. The experiments prove the superiority of our multiple features learning model on general real-life application.
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Affiliation(s)
- Nannan Zhang
- The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen, China.
| | - Zhixing Jiang
- The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen, China.
| | - JinXing Li
- Harbin Institute of Technology at Shenzhen, Shenzhen, China.
| | - David Zhang
- The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen, China.
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Khong FY, Connie T, Goh MKO, Wong LP, Teh PS, Choo AL. Non-invasive health prediction from visually observable features. F1000Res 2022; 10:918. [PMID: 35528954 PMCID: PMC9039370 DOI: 10.12688/f1000research.72894.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/17/2022] [Indexed: 11/20/2022] Open
Abstract
Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person’s health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches.
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Affiliation(s)
- Fan Yi Khong
- Faculty of Information Science and Technology, Multimedia University, Melaka, Melaka, 75450, Malaysia
| | - Tee Connie
- Faculty of Information Science and Technology, Multimedia University, Melaka, Melaka, 75450, Malaysia
| | - Michael Kah Ong Goh
- Faculty of Information Science and Technology, Multimedia University, Melaka, Melaka, 75450, Malaysia
| | - Li Pei Wong
- School of Computer Sciences, Universiti Sains Malaysia, Penang, Penang, 11800, Malaysia
| | - Pin Shen Teh
- Department of Operations, Technology, Events and Hospitality Management, Faculty of Business and Law, Manchester Metropolitan University, Manchester, Manchester, M15 6BH, UK
| | - Ai Ling Choo
- iRadar Sdn. Bhd., Melaka, Melaka, 75450, Malaysia
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He S, Su H, Li Y, Xu W, Wang X, Han D. Detecting obstructive sleep apnea by craniofacial image–based deep learning. Sleep Breath 2022; 26:1885-1895. [DOI: 10.1007/s11325-022-02571-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/18/2021] [Accepted: 01/19/2022] [Indexed: 11/24/2022]
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Su Z, Liang B, Shi F, Gelfond J, Šegalo S, Wang J, Jia P, Hao X. Deep learning-based facial image analysis in medical research: a systematic review protocol. BMJ Open 2021; 11:e047549. [PMID: 34764164 PMCID: PMC8587597 DOI: 10.1136/bmjopen-2020-047549] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION Deep learning techniques are gaining momentum in medical research. Evidence shows that deep learning has advantages over humans in image identification and classification, such as facial image analysis in detecting people's medical conditions. While positive findings are available, little is known about the state-of-the-art of deep learning-based facial image analysis in the medical context. For the consideration of patients' welfare and the development of the practice, a timely understanding of the challenges and opportunities faced by research on deep-learning-based facial image analysis is needed. To address this gap, we aim to conduct a systematic review to identify the characteristics and effects of deep learning-based facial image analysis in medical research. Insights gained from this systematic review will provide a much-needed understanding of the characteristics, challenges, as well as opportunities in deep learning-based facial image analysis applied in the contexts of disease detection, diagnosis and prognosis. METHODS Databases including PubMed, PsycINFO, CINAHL, IEEEXplore and Scopus will be searched for relevant studies published in English in September, 2021. Titles, abstracts and full-text articles will be screened to identify eligible articles. A manual search of the reference lists of the included articles will also be conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was adopted to guide the systematic review process. Two reviewers will independently examine the citations and select studies for inclusion. Discrepancies will be resolved by group discussions till a consensus is reached. Data will be extracted based on the research objective and selection criteria adopted in this study. ETHICS AND DISSEMINATION As the study is a protocol for a systematic review, ethical approval is not required. The study findings will be disseminated via peer-reviewed publications and conference presentations. PROSPERO REGISTRATION NUMBER CRD42020196473.
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Affiliation(s)
- Zhaohui Su
- Center on Smart and Connected Health Technologies, Mays Cancer Center, School of Nursing, UT Health San Antonio, San Antonio, Texas, USA
| | - Bin Liang
- Department of Radiation Oncology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - J Gelfond
- Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, Texas, UK
| | - Sabina Šegalo
- Department of Microbiology, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Jing Wang
- College of Nursing, Florida State University, Tallahassee, Florida, USA
| | - Peng Jia
- Department of Land Surveying and Geo-Informatics, University of Twente, Enschede, Netherlands
- International Initiative on Spatial Lifecourse Epidemiology (ISLE), Enschede, UK
| | - Xiaoning Hao
- Division of Health Security Research, National Health Commission of the People's Republic of China, Beijing, Beijing, China
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Katsanis SH, Claes P, Doerr M, Cook-Deegan R, Tenenbaum JD, Evans BJ, Lee MK, Anderton J, Weinberg SM, Wagner JK. A survey of U.S. public perspectives on facial recognition technology and facial imaging data practices in health and research contexts. PLoS One 2021; 16:e0257923. [PMID: 34648520 PMCID: PMC8516205 DOI: 10.1371/journal.pone.0257923] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/13/2021] [Indexed: 12/01/2022] Open
Abstract
Facial imaging and facial recognition technologies, now common in our daily lives, also are increasingly incorporated into health care processes, enabling touch-free appointment check-in, matching patients accurately, and assisting with the diagnosis of certain medical conditions. The use, sharing, and storage of facial data is expected to expand in coming years, yet little is documented about the perspectives of patients and participants regarding these uses. We developed a pair of surveys to gather public perspectives on uses of facial images and facial recognition technologies in healthcare and in health-related research in the United States. We used Qualtrics Panels to collect responses from general public respondents using two complementary and overlapping survey instruments; one focused on six types of biometrics (including facial images and DNA) and their uses in a wide range of societal contexts (including healthcare and research) and the other focused on facial imaging, facial recognition technology, and related data practices in health and research contexts specifically. We collected responses from a diverse group of 4,048 adults in the United States (2,038 and 2,010, from each survey respectively). A majority of respondents (55.5%) indicated they were equally worried about the privacy of medical records, DNA, and facial images collected for precision health research. A vignette was used to gauge willingness to participate in a hypothetical precision health study, with respondents split as willing to (39.6%), unwilling to (30.1%), and unsure about (30.3%) participating. Nearly one-quarter of respondents (24.8%) reported they would prefer to opt out of the DNA component of a study, and 22.0% reported they would prefer to opt out of both the DNA and facial imaging component of the study. Few indicated willingness to pay a fee to opt-out of the collection of their research data. Finally, respondents were offered options for ideal governance design of their data, as "open science"; "gated science"; and "closed science." No option elicited a majority response. Our findings indicate that while a majority of research participants might be comfortable with facial images and facial recognition technologies in healthcare and health-related research, a significant fraction expressed concern for the privacy of their own face-based data, similar to the privacy concerns of DNA data and medical records. A nuanced approach to uses of face-based data in healthcare and health-related research is needed, taking into consideration storage protection plans and the contexts of use.
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Affiliation(s)
- Sara H. Katsanis
- Mary Ann & J. Milburn Smith Child Health Outcomes, Research and Evaluation Center, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, United States of America
- Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Peter Claes
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, MIRC, KU Leuven, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Megan Doerr
- Sage Bionetworks, Seattle, Washington, United States of America
| | - Robert Cook-Deegan
- School for the Future of Innovation in Society, Arizona State University, Washington, District of Columbia, United States of America
| | - Jessica D. Tenenbaum
- Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, North Carolina, United States of America
| | - Barbara J. Evans
- Levin College of Law, University of Florida, Gainesville, Florida, United States of America
- Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Myoung Keun Lee
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Joel Anderton
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Seth M. Weinberg
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Jennifer K. Wagner
- School of Engineering Design, Technology, and Professional Programs, Pennsylvania State University, University Park, Pennsylvania, United States of America
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Su Z, McDonnell D, Bentley BL, He J, Shi F, Cheshmehzangi A, Ahmad J, Jia P. Addressing Biodisaster X Threats With Artificial Intelligence and 6G Technologies: Literature Review and Critical Insights. J Med Internet Res 2021; 23:e26109. [PMID: 33961583 PMCID: PMC8153034 DOI: 10.2196/26109] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 01/21/2021] [Accepted: 04/07/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND With advances in science and technology, biotechnology is becoming more accessible to people of all demographics. These advances inevitably hold the promise to improve personal and population well-being and welfare substantially. It is paradoxical that while greater access to biotechnology on a population level has many advantages, it may also increase the likelihood and frequency of biodisasters due to accidental or malicious use. Similar to "Disease X" (describing unknown naturally emerging pathogenic diseases with a pandemic potential), we term this unknown risk from biotechnologies "Biodisaster X." To date, no studies have examined the potential role of information technologies in preventing and mitigating Biodisaster X. OBJECTIVE This study aimed to explore (1) what Biodisaster X might entail and (2) solutions that use artificial intelligence (AI) and emerging 6G technologies to help monitor and manage Biodisaster X threats. METHODS A review of the literature on applying AI and 6G technologies for monitoring and managing biodisasters was conducted on PubMed, using articles published from database inception through to November 16, 2020. RESULTS Our findings show that Biodisaster X has the potential to upend lives and livelihoods and destroy economies, essentially posing a looming risk for civilizations worldwide. To shed light on Biodisaster X threats, we detailed effective AI and 6G-enabled strategies, ranging from natural language processing to deep learning-based image analysis to address issues ranging from early Biodisaster X detection (eg, identification of suspicious behaviors), remote design and development of pharmaceuticals (eg, treatment development), and public health interventions (eg, reactive shelter-at-home mandate enforcement), as well as disaster recovery (eg, sentiment analysis of social media posts to shed light on the public's feelings and readiness for recovery building). CONCLUSIONS Biodisaster X is a looming but avoidable catastrophe. Considering the potential human and economic consequences Biodisaster X could cause, actions that can effectively monitor and manage Biodisaster X threats must be taken promptly and proactively. Rather than solely depending on overstretched professional attention of health experts and government officials, it is perhaps more cost-effective and practical to deploy technology-based solutions to prevent and control Biodisaster X threats. This study discusses what Biodisaster X could entail and emphasizes the importance of monitoring and managing Biodisaster X threats by AI techniques and 6G technologies. Future studies could explore how the convergence of AI and 6G systems may further advance the preparedness for high-impact, less likely events beyond Biodisaster X.
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Affiliation(s)
- Zhaohui Su
- Center on Smart and Connected Health Technologies, Mays Cancer Center, School of Nursing, UT Health San Antonio, San Antonio, TX, United States
| | - Dean McDonnell
- Department of Humanities, Institute of Technology Carlow, Carlow, Ireland
| | - Barry L Bentley
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, United Kingdom
| | - Jiguang He
- Centre for Wireless Communications, University of Oulu, Oulu, Finland
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence, Shanghai, China
| | - Ali Cheshmehzangi
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
- Network for Education and Research on Peace and Sustainability, Hiroshima University, Hiroshima, Japan
| | - Junaid Ahmad
- Prime Institute of Public Health, Peshawar Medical College, Peshawar, Pakistan
| | - Peng Jia
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- International Institute of Spatial Lifecourse Epidemiology, Hong Kong, China
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Liao J, Liu L, Duan H, Huang Y, Zhou L, Chen L, Wang C. Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D-DSA Images (Preprint). JMIR Med Inform 2021; 10:e28880. [PMID: 35294371 PMCID: PMC8968557 DOI: 10.2196/28880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/27/2021] [Accepted: 01/16/2022] [Indexed: 11/26/2022] Open
Abstract
Background It is hard to distinguish cerebral aneurysms from overlapping vessels in 2D digital subtraction angiography (DSA) images due to these images’ lack of spatial information. Objective The aims of this study were to (1) construct a deep learning diagnostic system to improve the ability to detect posterior communicating artery aneurysms on 2D DSA images and (2) validate the efficiency of the deep learning diagnostic system in 2D DSA aneurysm detection. Methods We proposed a 2-stage detection system. First, we established the region localization stage to automatically locate specific detection regions of raw 2D DSA sequences. Second, in the intracranial aneurysm detection stage, we constructed a bi-input+RetinaNet+convolutional long short-term memory (C-LSTM) framework to compare its performance for aneurysm detection with that of 3 existing frameworks. Each of the frameworks had a 5-fold cross-validation scheme. The receiver operating characteristic curve, the area under the curve (AUC) value, mean average precision, sensitivity, specificity, and accuracy were used to assess the abilities of different frameworks. Results A total of 255 patients with posterior communicating artery aneurysms and 20 patients without aneurysms were included in this study. The best AUC values of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks were 0.95, 0.96, 0.92, and 0.97, respectively. The mean sensitivities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 89% (range 67.02%-98.43%), 88% (range 65.76%-98.06%), 87% (range 64.53%-97.66%), 89% (range 67.02%-98.43%), and 90% (range 68.30%-98.77%), respectively. The mean specificities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 80% (range 56.34%-94.27%), 89% (range 67.02%-98.43%), 86% (range 63.31%-97.24%), 93% (range 72.30%-99.56%), and 90% (range 68.30%-98.77%), respectively. The mean accuracies of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 84.50% (range 69.57%-93.97%), 88.50% (range 74.44%-96.39%), 86.50% (range 71.97%-95.22%), 91% (range 77.63%-97.72%), and 90% (range 76.34%-97.21%), respectively. Conclusions According to our results, more spatial and temporal information can help improve the performance of the frameworks. Therefore, the bi-input+RetinaNet+C-LSTM framework had the best performance when compared to that of the other frameworks. Our study demonstrates that our system can assist physicians in detecting intracranial aneurysms on 2D DSA images.
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Affiliation(s)
- JunHua Liao
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
- College of Computer Science, Sichuan University, Chengdu, China
| | - LunXin Liu
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - HaiHan Duan
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
| | - YunZhi Huang
- School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - LiangXue Zhou
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - LiangYin Chen
- College of Computer Science, Sichuan University, Chengdu, China
| | - ChaoHua Wang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
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Cancino RS, Su Z, Mesa R, Tomlinson GE, Wang J. The Impact of COVID-19 on Cancer Screening: Challenges and Opportunities. JMIR Cancer 2020; 6:e21697. [PMID: 33027039 PMCID: PMC7599065 DOI: 10.2196/21697] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 09/24/2020] [Accepted: 09/30/2020] [Indexed: 12/15/2022] Open
Abstract
Cancer is a leading cause of death in the United States and across the globe. Cancer screening is an effective preventive measure that can reduce cancer incidence and mortality. While cancer screening is integral to cancer control and prevention, due to the COVID-19 outbreak many screenings have either been canceled or postponed, leaving a vast number of patients without access to recommended health care services. This disruption to cancer screening services may have a significant impact on patients, health care practitioners, and health systems. In this paper, we aim to offer a comprehensive view of the impact of COVID-19 on cancer screening. We present the challenges COVID-19 has exerted on patients, health care practitioners, and health systems as well as potential opportunities that could help address these challenges.
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Affiliation(s)
- Ramon S Cancino
- Department of Family & Community Medicine, Joe R & Teresa Lozano Long School of Medicine, UT Health San Antonio, San Antonio, TX, United States
- Center on Smart and Connected Health Technologies, Mays Cancer Center, School of Nursing, UT Health San Antonio, San Antonio, TX, United States
| | - Zhaohui Su
- Center on Smart and Connected Health Technologies, Mays Cancer Center, School of Nursing, UT Health San Antonio, San Antonio, TX, United States
| | - Ruben Mesa
- Center on Smart and Connected Health Technologies, Mays Cancer Center, School of Nursing, UT Health San Antonio, San Antonio, TX, United States
- Department of Medicine, Joe R & Teresa Lozano Long School of Medicine, UT Health San Antonio, San Antonio, TX, United States
| | - Gail E Tomlinson
- Center on Smart and Connected Health Technologies, Mays Cancer Center, School of Nursing, UT Health San Antonio, San Antonio, TX, United States
- Department of Pediatrics, Joe R & Teresa Lozano Long School of Medicine, UT Health San Antonio, San Antonio, TX, United States
| | - Jing Wang
- Center on Smart and Connected Health Technologies, Mays Cancer Center, School of Nursing, UT Health San Antonio, San Antonio, TX, United States
- School of Nursing, UT Health San Antonio, San Antonio, TX, United States
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