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Mukherjee J, Sharma R, Dutta P, Bhunia B. Artificial intelligence in healthcare: a mastery. Biotechnol Genet Eng Rev 2024; 40:1659-1708. [PMID: 37013913 DOI: 10.1080/02648725.2023.2196476] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 03/22/2023] [Indexed: 04/05/2023]
Abstract
There is a vast development of artificial intelligence (AI) in recent years. Computational technology, digitized data collection and enormous advancement in this field have allowed AI applications to penetrate the core human area of specialization. In this review article, we describe current progress achieved in the AI field highlighting constraints on smooth development in the field of medical AI sector, with discussion of its implementation in healthcare from a commercial, regulatory and sociological standpoint. Utilizing sizable multidimensional biological datasets that contain individual heterogeneity in genomes, functionality and milieu, precision medicine strives to create and optimize approaches for diagnosis, treatment methods and assessment. With the arise of complexity and expansion of data in the health-care industry, AI can be applied more frequently. The main application categories include indications for diagnosis and therapy, patient involvement and commitment and administrative tasks. There has recently been a sharp rise in interest in medical AI applications due to developments in AI software and technology, particularly in deep learning algorithms and in artificial neural network (ANN). In this overview, we enlisted the major categories of issues that AI systems are ideally equipped to resolve followed by clinical diagnostic tasks. It also includes a discussion of the future potential of AI, particularly for risk prediction in complex diseases, and the difficulties, constraints and biases that must be meticulously addressed for the effective delivery of AI in the health-care sector.
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Affiliation(s)
- Jayanti Mukherjee
- Department of Pharmaceutical Chemistry, CMR College of Pharmacy Affiliated to Jawaharlal Nehru Technological University, Hyderabad, Telangana, India
| | - Ramesh Sharma
- Department of Bioengineering, National Institute of Technology, Agartala, India
| | - Prasenjit Dutta
- Department of Production Engineering, National Institute of Technology, Agartala, India
| | - Biswanath Bhunia
- Department of Bioengineering, National Institute of Technology, Agartala, India
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2
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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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Miranda O, Fan P, Qi X, Wang H, Brannock MD, Kosten TR, Ryan ND, Kirisci L, Wang L. DeepBiomarker2: Prediction of Alcohol and Substance Use Disorder Risk in Post-Traumatic Stress Disorder Patients Using Electronic Medical Records and Multiple Social Determinants of Health. J Pers Med 2024; 14:94. [PMID: 38248795 PMCID: PMC10817272 DOI: 10.3390/jpm14010094] [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: 11/14/2023] [Revised: 01/03/2024] [Accepted: 01/10/2024] [Indexed: 01/23/2024] Open
Abstract
Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. We developed DeepBiomarker2 by leveraging deep learning and natural language processing to analyze lab tests, medication use, diagnosis, social determinants of health (SDoH) parameters, and psychotherapy for outcome prediction. To increase the model's interpretability, we further refined our contribution analysis to identify key features by scaling with a factor from a reference feature. We applied DeepBiomarker2 to analyze the EMR data of 38,807 patients from the University of Pittsburgh Medical Center diagnosed with post-traumatic stress disorder (PTSD) to determine their risk of developing alcohol and substance use disorder (ASUD). DeepBiomarker2 predicted whether a PTSD patient would have a diagnosis of ASUD within the following 3 months with an average c-statistic (receiver operating characteristic AUC) of 0.93 and average F1 score, precision, and recall of 0.880, 0.895, and 0.866 in the test sets, respectively. Our study found that the medications clindamycin, enalapril, penicillin, valacyclovir, Xarelto/rivaroxaban, moxifloxacin, and atropine and the SDoH parameters access to psychotherapy, living in zip codes with a high normalized vegetative index, Gini index, and low-income segregation may have potential to reduce the risk of ASUDs in PTSD. In conclusion, the integration of SDoH information, coupled with the refined feature contribution analysis, empowers DeepBiomarker2 to accurately predict ASUD risk. Moreover, the model can further identify potential indicators of increased risk along with medications with beneficial effects.
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Affiliation(s)
- Oshin Miranda
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA; (O.M.); (P.F.); (X.Q.)
| | - Peihao Fan
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA; (O.M.); (P.F.); (X.Q.)
| | - Xiguang Qi
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA; (O.M.); (P.F.); (X.Q.)
| | - Haohan Wang
- School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA;
| | | | - Thomas R. Kosten
- Menninger Department of Psychiatry, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Neal David Ryan
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA;
| | - Levent Kirisci
- Center for Education and Drug Abuse Research, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA;
| | - Lirong Wang
- Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA; (O.M.); (P.F.); (X.Q.)
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Woodman RJ, Mangoni AA. A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future. Aging Clin Exp Res 2023; 35:2363-2397. [PMID: 37682491 PMCID: PMC10627901 DOI: 10.1007/s40520-023-02552-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023]
Abstract
The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.
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Affiliation(s)
- Richard J Woodman
- Centre of Epidemiology and Biostatistics, College of Medicine and Public Health, Flinders University, GPO Box 2100, Adelaide, SA, 5001, Australia.
| | - Arduino A Mangoni
- Discipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, SA, Australia
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Ahmed MAO, Abbas IA, AbdelSatar Y. HDSNE a new unsupervised multiple image database fusion learning algorithm with flexible and crispy production of one database: a proof case study of lung infection diagnose In chest X-ray images. BMC Med Imaging 2023; 23:134. [PMID: 37718458 PMCID: PMC10506286 DOI: 10.1186/s12880-023-01078-3] [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: 10/23/2022] [Accepted: 08/16/2023] [Indexed: 09/19/2023] Open
Abstract
Continuous release of image databases with fully or partially identical inner categories dramatically deteriorates the production of autonomous Computer-Aided Diagnostics (CAD) systems for true comprehensive medical diagnostics. The first challenge is the frequent massive bulk release of medical image databases, which often suffer from two common drawbacks: image duplication and corruption. The many subsequent releases of the same data with the same classes or categories come with no clear evidence of success in the concatenation of those identical classes among image databases. This issue stands as a stumbling block in the path of hypothesis-based experiments for the production of a single learning model that can successfully classify all of them correctly. Removing redundant data, enhancing performance, and optimizing energy resources are among the most challenging aspects. In this article, we propose a global data aggregation scale model that incorporates six image databases selected from specific global resources. The proposed valid learner is based on training all the unique patterns within any given data release, thereby creating a unique dataset hypothetically. The Hash MD5 algorithm (MD5) generates a unique hash value for each image, making it suitable for duplication removal. The T-Distributed Stochastic Neighbor Embedding (t-SNE), with a tunable perplexity parameter, can represent data dimensions. Both the Hash MD5 and t-SNE algorithms are applied recursively, producing a balanced and uniform database containing equal samples per category: normal, pneumonia, and Coronavirus Disease of 2019 (COVID-19). We evaluated the performance of all proposed data and the new automated version using the Inception V3 pre-trained model with various evaluation metrics. The performance outcome of the proposed scale model showed more respectable results than traditional data aggregation, achieving a high accuracy of 98.48%, along with high precision, recall, and F1-score. The results have been proved through a statistical t-test, yielding t-values and p-values. It's important to emphasize that all t-values are undeniably significant, and the p-values provide irrefutable evidence against the null hypothesis. Furthermore, it's noteworthy that the Final dataset outperformed all other datasets across all metric values when diagnosing various lung infections with the same factors.
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Affiliation(s)
- Muhammad Atta Othman Ahmed
- Department of Computer Science, Faculty of Computers and Information, Luxor University, Luxor, 85951, Egypt.
| | - Ibrahim A Abbas
- Mathematics Department, Faculty of Science, Sohag University, Sohag, 82511, Egypt
| | - Yasser AbdelSatar
- Mathematics Department, Faculty of Science, Sohag University, Sohag, 82511, Egypt
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Abdulazeem H, Whitelaw S, Schauberger G, Klug SJ. A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLoS One 2023; 18:e0274276. [PMID: 37682909 PMCID: PMC10491005 DOI: 10.1371/journal.pone.0274276] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.
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Affiliation(s)
- Hebatullah Abdulazeem
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Sera Whitelaw
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Gunther Schauberger
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Stefanie J. Klug
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
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Pungitore S, Subbian V. Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:313-331. [PMID: 37637723 PMCID: PMC10449760 DOI: 10.1007/s41666-023-00143-4] [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: 07/08/2022] [Revised: 04/12/2023] [Accepted: 07/28/2023] [Indexed: 08/29/2023]
Abstract
Temporal electronic health record (EHR) data are often preferred for clinical prediction tasks because they offer more complete representations of a patient's pathophysiology than static data. A challenge when working with temporal EHR data is problem formulation, which includes defining the time windows of interest and the prediction task. Our objective was to conduct a systematic review that assessed the definition and reporting of concepts relevant to temporal clinical prediction tasks. We searched PubMed® and IEEE Xplore® databases for studies from January 1, 2010 applying machine learning models to EHR data for patient outcome prediction. Publications applying time-series methods were selected for further review. We identified 92 studies and summarized them by clinical context and definition and reporting of the prediction problem. For the time windows of interest, 12 studies did not discuss window lengths, 57 used a single set of window lengths, and 23 evaluated the relationship between window length and model performance. We also found that 72 studies had appropriate reporting of the prediction task. However, evaluation of prediction problem formulation for temporal EHR data was complicated by heterogeneity in assessing and reporting of these concepts. Even among studies modeling similar clinical outcomes, there were variations in terminology used to describe the prediction problem, rationale for window lengths, and determination of the outcome of interest. As temporal modeling using EHR data expands, minimal reporting standards should include time-series specific concerns to promote rigor and reproducibility in future studies and facilitate model implementation in clinical settings. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-023-00143-4.
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Affiliation(s)
- Sarah Pungitore
- Program in Applied Mathematics, Department of Mathematics, 617 N Santa Rita Ave, Tucson, AZ 85721 USA
| | - Vignesh Subbian
- Department of Biomedical Engineering, The University of Arizona, Tucson, AZ 85721-0020 USA
- Department of Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721-0020 USA
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8
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Amirahmadi A, Ohlsson M, Etminani K. Deep learning prediction models based on EHR trajectories: A systematic review. J Biomed Inform 2023; 144:104430. [PMID: 37380061 DOI: 10.1016/j.jbi.2023.104430] [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: 10/07/2022] [Revised: 06/08/2023] [Accepted: 06/17/2023] [Indexed: 06/30/2023]
Abstract
BACKGROUND Electronic health records (EHRs) are generated at an ever-increasing rate. EHR trajectories, the temporal aspect of health records, facilitate predicting patients' future health-related risks. It enables healthcare systems to increase the quality of care through early identification and primary prevention. Deep learning techniques have shown great capacity for analyzing complex data and have been successful for prediction tasks using complex EHR trajectories. This systematic review aims to analyze recent studies to identify challenges, knowledge gaps, and ongoing research directions. METHODS For this systematic review, we searched Scopus, PubMed, IEEE Xplore, and ACM databases from Jan 2016 to April 2022 using search terms centered around EHR, deep learning, and trajectories. Then the selected papers were analyzed according to publication characteristics, objectives, and their solutions regarding existing challenges, such as the model's capacity to deal with intricate data dependencies, data insufficiency, and explainability. RESULTS After removing duplicates and out-of-scope papers, 63 papers were selected, which showed rapid growth in the number of research in recent years. Predicting all diseases in the next visit and the onset of cardiovascular diseases were the most common targets. Different contextual and non-contextual representation learning methods are employed to retrieve important information from the sequence of EHR trajectories. Recurrent neural networks and the time-aware attention mechanism for modeling long-term dependencies, self-attentions, convolutional neural networks, graphs for representing inner visit relations, and attention scores for explainability were frequently used among the reviewed publications. CONCLUSIONS This systematic review demonstrated how recent breakthroughs in deep learning methods have facilitated the modeling of EHR trajectories. Research on improving the ability of graph neural networks, attention mechanisms, and cross-modal learning to analyze intricate dependencies among EHRs has shown good progress. There is a need to increase the number of publicly available EHR trajectory datasets to allow for easier comparison among different models. Also, very few developed models can handle all aspects of EHR trajectory data.
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Affiliation(s)
- Ali Amirahmadi
- Center for Applied Intelligent Systems Research, Halmstad University, Sweden.
| | - Mattias Ohlsson
- Center for Applied Intelligent Systems Research, Halmstad University, Sweden; Computational Biology & Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Sweden
| | - Kobra Etminani
- Center for Applied Intelligent Systems Research, Halmstad University, Sweden
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Kurt B, Gürlek B, Keskin S, Özdemir S, Karadeniz Ö, Kırkbir İB, Kurt T, Ünsal S, Kart C, Baki N, Turhan K. Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques. Med Biol Eng Comput 2023; 61:1649-1660. [PMID: 36848010 PMCID: PMC9969040 DOI: 10.1007/s11517-023-02800-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 01/31/2023] [Indexed: 03/01/2023]
Abstract
The study aimed to develop a clinical diagnosis system to identify patients in the GD risk group and reduce unnecessary oral glucose tolerance test (OGTT) applications for pregnant women who are not in the GD risk group using deep learning algorithms. With this aim, a prospective study was designed and the data was taken from 489 patients between the years 2019 and 2021, and informed consent was obtained. The clinical decision support system for the diagnosis of GD was developed using the generated dataset with deep learning algorithms and Bayesian optimization. As a result, a novel successful decision support model was developed using RNN-LSTM with Bayesian optimization that gave 95% sensitivity and 99% specificity on the dataset for the diagnosis of patients in the GD risk group by obtaining 98% AUC (95% CI (0.95-1.00) and p < 0.001). Thus, with the clinical diagnosis system developed to assist physicians, it is planned to save both cost and time, and reduce possible adverse effects by preventing unnecessary OGTT for patients who are not in the GD risk group.
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Affiliation(s)
- Burçin Kurt
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Beril Gürlek
- Faculty of Medicine, Department of Gynecology and Obstetrics, Recep Tayyip Erdoğan University, Rize, Turkey
| | - Seda Keskin
- Faculty of Medicine, Department of Gynecology and Obstetrics, Ordu University, Ordu, Turkey
| | - Sinem Özdemir
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Özlem Karadeniz
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - İlknur Buçan Kırkbir
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Tuğba Kurt
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Serbülent Ünsal
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Cavit Kart
- Faculty of Medicine, Department of Gynecology and Obstetrics, Karadeniz Technical University, Trabzon, Turkey
| | - Neslihan Baki
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Kemal Turhan
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
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El-Sappagh S, Alonso-Moral JM, Abuhmed T, Ali F, Bugarín-Diz A. Trustworthy artificial intelligence in Alzheimer’s disease: state of the art, opportunities, and challenges. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10415-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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11
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Mahendran N, Vincent P M DR. Deep belief network-based approach for detecting Alzheimer's disease using the multi-omics data. Comput Struct Biotechnol J 2023; 21:1651-1660. [PMID: 36874164 PMCID: PMC9978469 DOI: 10.1016/j.csbj.2023.02.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/10/2023] [Accepted: 02/11/2023] [Indexed: 02/15/2023] Open
Abstract
Alzheimer's disease (AD) is the most uncertain form of Dementia in terms of finding out the mechanism. AD does not have a vital genetic factor to relate to. There were no reliable techniques and methods to identify the genetic risk factors associated with AD in the past. Most of the data available were from the brain images. However, recently, there have been drastic advancements in the high-throughput techniques in bioinformatics. It has led to focused researches in discovering the AD causing genetic risk factors. Recent analysis has resulted in considerable prefrontal cortex data with which classification and prediction models can be developed for AD. We have developed a Deep Belief Network-based prediction model using the DNA Methylation and Gene Expression Microarray Data, with High Dimension Low Sample Size (HDLSS) issues. To overcome the HDLSS challenge, we performed a two-layer feature selection considering the biological aspects of the features as well. In the two-layered feature selection approach, first the differentially expressed genes and differentially methylated positions are identified, then both the datasets are combined using Jaccard similarity measure. As the second step, an ensemble-based feature selection approach is implemented to further narrow down the gene selection. The results show that the proposed feature selection technique outperforms the existing commonly used feature selection techniques, such as Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Correlation-based Feature Selection (CBS). Furthermore, the Deep Belief Network-based prediction model performs better than the widely used Machine Learning models. Also, the multi-omics dataset shows promising results compared to the single omics.
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Affiliation(s)
- Nivedhitha Mahendran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Durai Raj Vincent P M
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
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12
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Hong CG, Chen ML, Duan R, Wang X, Pang ZL, Ge LT, Lu M, Xie H, Liu ZZ. Transplantation of Nasal Olfactory Mucosa Mesenchymal Stem Cells Benefits Alzheimer's Disease. Mol Neurobiol 2022; 59:7323-7336. [PMID: 36173534 DOI: 10.1007/s12035-022-03044-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 09/13/2022] [Indexed: 11/25/2022]
Abstract
Alzheimer's disease (AD) is a common neurodegenerative disease that contributes to 60-70% of dementia in elderly people and is currently incurable. Current treatments only relieve the symptoms of AD and slow its progression. Achieving effective neural regeneration to ameliorate cognitive impairment is a major challenge in the treatment of AD. For the first time, we alleviated symptoms of AD in APPswe/PS1dE9 mice (hereafter referred to as AD mice) by transplantation of olfactory mucosa mesenchymal stem cells (OM-MSCs). Our study demonstrated that OM-MSC transplantation promotes amyloid-β (Aβ) clearance, downregulates the inflammatory response, and increases the M2/M1 ratio; OM-MSCs promote the conversion of BV2 (microglia) from M1 to M2 and also Aβ clearance in SH-SY5YAPPswe (AD cell model). OM-MSC-transplanted AD mice show improved cognitive learning and locomotive behavior. Our study suggests that OM-MSC transplantation could alleviate the symptoms of AD and promote Aβ clearance through immunomodulation, thus demonstrating the great potential and social value of OM-MSC treatment for AD patients.
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Affiliation(s)
- Chun-Gu Hong
- Department of Orthopedics, Movement System Injury and Repair Research Center, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Meng-Lu Chen
- Department of Sports Medicine, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Ran Duan
- Department of Sports Medicine, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Xin Wang
- Department of Sports Medicine, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Zhi-Lin Pang
- Department of Sports Medicine, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Li-Te Ge
- Department of Neurosurgery, Second Affiliated Hospital of Hunan Normal University (921 Hospital of PLA), Changsha, 410081, Hunan, China.,Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China
| | - Ming Lu
- Department of Neurosurgery, Second Affiliated Hospital of Hunan Normal University (921 Hospital of PLA), Changsha, 410081, Hunan, China.
| | - Hui Xie
- Department of Orthopedics, Movement System Injury and Repair Research Center, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. .,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. .,Department of Sports Medicine, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
| | - Zheng-Zhao Liu
- Department of Orthopedics, Movement System Injury and Repair Research Center, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. .,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. .,Department of Sports Medicine, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
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Andjelkovic J, Ljubic B, Hai AA, Stanojevic M, Pavlovski M, Diaz W, Obradovic Z. Sequential machine learning in prediction of common cancers. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:8459-8486. [PMID: 35039756 PMCID: PMC8754556 DOI: 10.1007/s12652-021-03612-z] [Citation(s) in RCA: 147] [Impact Index Per Article: 73.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 11/18/2021] [Indexed: 05/03/2023]
Abstract
Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Engineering, Indus Institute of Technology and Engineering, Indus University, Ahmedabad, 382115 India
| | | | - Ruchi Singla
- Department of Research, Innovations, Sponsored Projects and Entrepreneurship, CGC Landran, Mohali, India
| | - Muhammad Fazal Ijaz
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006 South Korea
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Sun X, Guo W, Shen J. Toward attention-based learning to predict the risk of brain degeneration with multimodal medical data. Front Neurosci 2022; 16:1043626. [PMID: 36741058 PMCID: PMC9889549 DOI: 10.3389/fnins.2022.1043626] [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: 09/13/2022] [Accepted: 12/12/2022] [Indexed: 01/20/2023] Open
Abstract
Introduction Brain degeneration is commonly caused by some chronic diseases, such as Alzheimer's disease (AD) and diabetes mellitus (DM). The risk prediction of brain degeneration aims to forecast the situation of disease progression of patients in the near future based on their historical health records. It is beneficial for patients to make an accurate clinical diagnosis and early prevention of disease. Current risk predictions of brain degeneration mainly rely on single-modality medical data, such as Electronic Health Records (EHR) or magnetic resonance imaging (MRI). However, only leveraging EHR or MRI data for the pertinent and accurate prediction is insufficient because of single-modality information (e.g., pixel or volume information of image data or clinical context information of non-image data). Methods Several deep learning-based methods have used multimodal data to predict the risks of specified diseases. However, most of them simply integrate different modalities in an early, intermediate, or late fusion structure and do not care about the intra-modal and intermodal dependencies. A lack of these dependencies would lead to sub-optimal prediction performance. Thus, we propose an encoder-decoder framework for better risk prediction of brain degeneration by using MRI and EHR. An encoder module is one of the key components and mainly focuses on feature extraction of input data. Specifically, we introduce an encoder module, which integrates intra-modal and inter-modal dependencies with the spatial-temporal attention and cross-attention mechanism. The corresponding decoder module is another key component and mainly parses the features from the encoder. In the decoder module, a disease-oriented module is used to extract the most relevant disease representation features. We take advantage of a multi-head attention module followed by a fully connected layer to produce the predicted results. Results As different types of AD and DM influence the nature and severity of brain degeneration, we evaluate the proposed method for three-class prediction of AD and three-class prediction of DM. Our results show that the proposed method with integrated MRI and EHR data achieves an accuracy of 0.859 and 0.899 for the risk prediction of AD and DM, respectively. Discussion The prediction performance is significantly better than the benchmarks, including MRI-only, EHR-only, and state-of-the-art multimodal fusion methods.
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Affiliation(s)
- Xiaofei Sun
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Weiwei Guo
- EchoX Technology Limited, Hong Kong, Hong Kong SAR, China
| | - Jing Shen
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, China
- *Correspondence: Jing Shen,
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Bai J, Sun B, Chu X, Wang T, Li H, Huang Q. Neighborhood rough set-based multi-attribute prediction approach and its application of gout patients. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108127] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Li Q, Zhang Y, Liang H, Gong H, Jiang L, Liu Q, Shen L. Deep learning based neuronal soma detection and counting for Alzheimer's disease analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106023. [PMID: 33744751 DOI: 10.1016/j.cmpb.2021.106023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 02/21/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's Disease (AD) is associated with neuronal damage and decrease. Micro-Optical Sectioning Tomography (MOST) provides an approach to acquire high-resolution images for neuron analysis in the whole-brain. Application of this technique to AD mouse brain enables us to investigate neuron changes during the progression of AD pathology. However, how to deal with the huge amount of data becomes the bottleneck. METHODS Using MOST technology, we acquired 3D whole-brain images of six AD mice, and sampled the imaging data of four regions in each mouse brain for AD progression analysis. To count the number of neurons, we proposed a deep learning based method by detecting neuronal soma in the neuronal images. In our method, the neuronal images were first cut into small cubes, then a Convolutional Neural Network (CNN) classifier was designed to detect the neuronal soma by classifying the cubes into three categories, "soma", "fiber", and "background". RESULTS Compared with the manual method and currently available NeuroGPS software, our method demonstrates faster speed and higher accuracy in identifying neurons from the MOST images. By applying our method to various brain regions of 6-month-old and 12-month-old AD mice, we found that the amount of neurons in three brain regions (lateral entorhinal cortex, medial entorhinal cortex, and presubiculum) decreased slightly with the increase of age, which is consistent with the experimental results previously reported. CONCLUSION This paper provides a new method to automatically handle the huge amounts of data and accurately identify neuronal soma from the MOST images. It also provides the potential possibility to construct a whole-brain neuron projection to reveal the impact of AD pathology on mouse brain.
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Affiliation(s)
- Qiufu Li
- Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China; AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen 518060, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China
| | - Yu Zhang
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Hanbang Liang
- Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China; AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen 518060, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China
| | - Hui Gong
- National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
| | - Liang Jiang
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, 518055, China.
| | - Qiong Liu
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, 518055, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China.
| | - Linlin Shen
- Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China; AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen 518060, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China.
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