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Akpinar MH, Sengur A, Faust O, Tong L, Molinari F, Acharya UR. Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013-2023). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108253. [PMID: 38861878 DOI: 10.1016/j.cmpb.2024.108253] [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: 12/22/2023] [Revised: 04/22/2024] [Accepted: 05/25/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND AND OBJECTIVES Optical coherence tomography (OCT) has ushered in a transformative era in the domain of ophthalmology, offering non-invasive imaging with high resolution for ocular disease detection. OCT, which is frequently used in diagnosing fundamental ocular pathologies, such as glaucoma and age-related macular degeneration (AMD), plays an important role in the widespread adoption of this technology. Apart from glaucoma and AMD, we will also investigate pertinent pathologies, such as epiretinal membrane (ERM), macular hole (MH), macular dystrophy (MD), vitreomacular traction (VMT), diabetic maculopathy (DMP), cystoid macular edema (CME), central serous chorioretinopathy (CSC), diabetic macular edema (DME), diabetic retinopathy (DR), drusen, glaucomatous optic neuropathy (GON), neovascular AMD (nAMD), myopia macular degeneration (MMD) and choroidal neovascularization (CNV) diseases. This comprehensive review examines the role that OCT-derived images play in detecting, characterizing, and monitoring eye diseases. METHOD The 2020 PRISMA guideline was used to structure a systematic review of research on various eye conditions using machine learning (ML) or deep learning (DL) techniques. A thorough search across IEEE, PubMed, Web of Science, and Scopus databases yielded 1787 publications, of which 1136 remained after removing duplicates. Subsequent exclusion of conference papers, review papers, and non-open-access articles reduced the selection to 511 articles. Further scrutiny led to the exclusion of 435 more articles due to lower-quality indexing or irrelevance, resulting in 76 journal articles for the review. RESULTS During our investigation, we found that a major challenge for ML-based decision support is the abundance of features and the determination of their significance. In contrast, DL-based decision support is characterized by a plug-and-play nature rather than relying on a trial-and-error approach. Furthermore, we observed that pre-trained networks are practical and especially useful when working on complex images such as OCT. Consequently, pre-trained deep networks were frequently utilized for classification tasks. Currently, medical decision support aims to reduce the workload of ophthalmologists and retina specialists during routine tasks. In the future, it might be possible to create continuous learning systems that can predict ocular pathologies by identifying subtle changes in OCT images.
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Affiliation(s)
- Muhammed Halil Akpinar
- Department of Electronics and Automation, Vocational School of Technical Sciences, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Abdulkadir Sengur
- Electrical-Electronics Engineering Department, Technology Faculty, Firat University, Elazig, Turkey.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, United Kingdom
| | - Louis Tong
- Singapore Eye Research Institute, Singapore, Singapore
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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Miranda M, Santos-Oliveira J, Mendonça AM, Sousa V, Melo T, Carneiro Â. Human versus Artificial Intelligence: Validation of a Deep Learning Model for Retinal Layer and Fluid Segmentation in Optical Coherence Tomography Images from Patients with Age-Related Macular Degeneration. Diagnostics (Basel) 2024; 14:975. [PMID: 38786273 PMCID: PMC11119996 DOI: 10.3390/diagnostics14100975] [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: 03/29/2024] [Revised: 04/25/2024] [Accepted: 04/28/2024] [Indexed: 05/25/2024] Open
Abstract
Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches.
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Affiliation(s)
- Mariana Miranda
- Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, 4200 Porto, Portugal
| | - Joana Santos-Oliveira
- Department of Ophthalmology, Centro Hospitalar Universitário of São João, 4200 Porto, Portugal
| | - Ana Maria Mendonça
- Electrical and Computer Engineering Department, Faculty of Engineering of the University of Porto, 4200 Porto, Portugal
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200 Porto, Portugal
| | - Vânia Sousa
- Department of Ophthalmology, Centro Hospitalar Universitário of São João, 4200 Porto, Portugal
| | - Tânia Melo
- Electrical and Computer Engineering Department, Faculty of Engineering of the University of Porto, 4200 Porto, Portugal
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200 Porto, Portugal
| | - Ângela Carneiro
- Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, 4200 Porto, Portugal
- Department of Ophthalmology, Centro Hospitalar Universitário of São João, 4200 Porto, Portugal
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Park S, Moon J, Eun H, Hong JH, Lee K. Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants. J Clin Med 2024; 13:2089. [PMID: 38610854 PMCID: PMC11012712 DOI: 10.3390/jcm13072089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/02/2024] [Accepted: 04/02/2024] [Indexed: 04/14/2024] Open
Abstract
Background: Patent ductus arteriosus (PDA) is a prevalent congenital heart defect in premature infants, associated with significant morbidity and mortality. Accurate and timely diagnosis of PDA is crucial, given the vulnerability of this population. Methods: We introduce an artificial intelligence (AI)-based PDA diagnostic support system designed to assist medical professionals in diagnosing PDA in premature infants. This study utilized electronic health record (EHR) data from 409 premature infants spanning a decade at Severance Children's Hospital. Our system integrates a data viewer, data analyzer, and AI-based diagnosis supporter, facilitating comprehensive data presentation, analysis, and early symptom detection. Results: The system's performance was evaluated through diagnostic tests involving medical professionals. This early detection model achieved an accuracy rate of up to 84%, enabling detection up to 3.3 days in advance. In diagnostic tests, medical professionals using the system with the AI-based diagnosis supporter outperformed those using the system without the supporter. Conclusions: Our AI-based PDA diagnostic support system offers a comprehensive solution for medical professionals to accurately diagnose PDA in a timely manner in premature infants. The collaborative integration of medical expertise and technological innovation demonstrated in this study underscores the potential of AI-driven tools in advancing neonatal diagnosis and care.
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Affiliation(s)
- Seoyeon Park
- Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seoul 03722, Republic of Korea; (S.P.); (K.L.)
| | - Junhyung Moon
- Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seoul 03722, Republic of Korea; (S.P.); (K.L.)
| | - Hoseon Eun
- Department of Pediatrics, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seoul 03722, Republic of Korea;
| | - Jin-Hyuk Hong
- School of Integrated Technology, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Gwangju 61005, Republic of Korea;
| | - Kyoungwoo Lee
- Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seoul 03722, Republic of Korea; (S.P.); (K.L.)
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Choi JY, Ryu IH, Kim JK, Lee IS, Yoo TK. Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography. BMC Med Inform Decis Mak 2024; 24:25. [PMID: 38273286 PMCID: PMC10811871 DOI: 10.1186/s12911-024-02431-4] [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: 07/29/2023] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND The epiretinal membrane (ERM) is a common retinal disorder characterized by abnormal fibrocellular tissue at the vitreomacular interface. Most patients with ERM are asymptomatic at early stages. Therefore, screening for ERM will become increasingly important. Despite the high prevalence of ERM, few deep learning studies have investigated ERM detection in the color fundus photography (CFP) domain. In this study, we built a generative model to enhance ERM detection performance in the CFP. METHODS This deep learning study retrospectively collected 302 ERM and 1,250 healthy CFP data points from a healthcare center. The generative model using StyleGAN2 was trained using single-center data. EfficientNetB0 with StyleGAN2-based augmentation was validated using independent internal single-center data and external datasets. We randomly assigned healthcare center data to the development (80%) and internal validation (20%) datasets. Data from two publicly accessible sources were used as external validation datasets. RESULTS StyleGAN2 facilitated realistic CFP synthesis with the characteristic cellophane reflex features of the ERM. The proposed method with StyleGAN2-based augmentation outperformed the typical transfer learning without a generative adversarial network. The proposed model achieved an area under the receiver operating characteristic (AUC) curve of 0.926 for internal validation. AUCs of 0.951 and 0.914 were obtained for the two external validation datasets. Compared with the deep learning model without augmentation, StyleGAN2-based augmentation improved the detection performance and contributed to the focus on the location of the ERM. CONCLUSIONS We proposed an ERM detection model by synthesizing realistic CFP images with the pathological features of ERM through generative deep learning. We believe that our deep learning framework will help achieve a more accurate detection of ERM in a limited data setting.
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Affiliation(s)
- Joon Yul Choi
- Department of Biomedical Engineering, Yonsei University, Wonju, South Korea
| | - Ik Hee Ryu
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
- Research and development department, VISUWORKS, Seoul, South Korea
| | - Jin Kuk Kim
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
- Research and development department, VISUWORKS, Seoul, South Korea
| | - In Sik Lee
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
| | - Tae Keun Yoo
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea.
- Research and development department, VISUWORKS, Seoul, South Korea.
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Kim S, Yoon H, Lee J, Yoo S. Facial wrinkle segmentation using weighted deep supervision and semi-automatic labeling. Artif Intell Med 2023; 145:102679. [PMID: 37925209 DOI: 10.1016/j.artmed.2023.102679] [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: 01/15/2023] [Revised: 07/28/2023] [Accepted: 10/03/2023] [Indexed: 11/06/2023]
Abstract
Facial wrinkles are important indicators of human aging. Recently, a method using deep learning and a semi-automatic labeling was proposed to segment facial wrinkles, which showed much better performance than conventional image-processing-based methods. However, the difficulty of wrinkle segmentation remains challenging due to the thinness of wrinkles and their small proportion in the entire image. Therefore, performance improvement in wrinkle segmentation is still necessary. To address this issue, we propose a novel loss function that takes into account the thickness of wrinkles based on the semi-automatic labeling approach. First, considering the different spatial dimensions of the decoder in the U-Net architecture, we generated weighted wrinkle maps from ground truth. These weighted wrinkle maps were used to calculate the training losses more accurately than the existing deep supervision approach. This new loss computation approach is defined as weighted deep supervision in our study. The proposed method was evaluated using an image dataset obtained from a professional skin analysis device and labeled using semi-automatic labeling. In our experiment, the proposed weighted deep supervision showed higher Jaccard Similarity Index (JSI) performance for wrinkle segmentation compared to conventional deep supervision and traditional image processing methods. Additionally, we conducted experiments on the labeling using a semi-automatic labeling approach, which had not been explored in previous research, and compared it with human labeling. The semi-automatic labeling technology showed more consistent wrinkle labels than human-made labels. Furthermore, to assess the scalability of the proposed method to other domains, we applied it to retinal vessel segmentation. The results demonstrated superior performance of the proposed method compared to existing retinal vessel segmentation approaches. In conclusion, the proposed method offers high performance and can be easily applied to various biomedical domains and U-Net-based architectures. Therefore, the proposed approach will be beneficial for various biomedical imaging approaches. To facilitate this, we have made the source code of the proposed method publicly available at: https://github.com/resemin/WeightedDeepSupervision.
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Affiliation(s)
- Semin Kim
- AI R&D Center, Lululab Inc., 318, Dosan-daero, Gangnam-gu, Seoul, Republic of Korea.
| | - Huisu Yoon
- AI R&D Center, Lululab Inc., 318, Dosan-daero, Gangnam-gu, Seoul, Republic of Korea.
| | - Jongha Lee
- AI R&D Center, Lululab Inc., 318, Dosan-daero, Gangnam-gu, Seoul, Republic of Korea.
| | - Sangwook Yoo
- AI R&D Center, Lululab Inc., 318, Dosan-daero, Gangnam-gu, Seoul, Republic of Korea.
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Kaur G, Garg M, Gupta S, Juneja S, Rashid J, Gupta D, Shah A, Shaikh A. Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model. Diagnostics (Basel) 2023; 13:3152. [PMID: 37835895 PMCID: PMC10572820 DOI: 10.3390/diagnostics13193152] [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: 08/31/2023] [Revised: 09/23/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model's capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model's superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches.
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Affiliation(s)
- Gurjinder Kaur
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Meenu Garg
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Sheifali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Sapna Juneja
- Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia;
| | - Junaid Rashid
- Department of Data Science, Sejong University, Seoul 05006, Republic of Korea;
| | - Deepali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Asadullah Shah
- Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia;
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia;
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Hao S, Huang C, Heidari AA, Xu Z, Chen H, Alabdulkreem E, Elmannai H, Wang X. Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images. Biomed Signal Process Control 2023; 86:105147. [PMID: 37361197 PMCID: PMC10266503 DOI: 10.1016/j.bspc.2023.105147] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 04/22/2023] [Accepted: 06/08/2023] [Indexed: 06/28/2023]
Abstract
Since the outbreak of COVID-19, it has seriously endangered the health of human beings. Computer automatic segmentation of COVID-19 X-ray images is an important means to assist doctors in rapid and accurate diagnosis. Therefore, this paper proposes a modified FOA (EEFOA) with two optimization strategies added to the original FOA, including elite natural evolution (ENE) and elite random mutation (ERM). To be specific, ENE and ERM can effectively speed up the convergence and deal with the problem of local optima, respectively. The outstanding performance of EEFOA was confirmed by experimental results comparing EEFOA with the original FOA, other FOA variants, and advanced algorithms at CEC2014. After that, EEFOA is implemented for multi-threshold image segmentation (MIS) of COVID-19 X-ray images, where a 2D histogram consisting of the original greyscale image and the non-local means image is used to represent the image information, and Rényi's entropy is used as the objective function to find the maximum value. The evaluation results of the MIS segmentation experiments show that, whether high or low threshold, EEFOA can achieve higher quality segmentation results and greater robustness than other advanced segmentation methods.
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Affiliation(s)
- Shuhui Hao
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Changcheng Huang
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Ali Asghar Heidari
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Zhangze Xu
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Huiling Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Eatedal Alabdulkreem
- Department of Computer Science, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Xianchuan Wang
- Information Technology Center, Wenzhou Medical University, Wenzhou 325035, China
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Wei J, Chen T, Liu Y, Sun S, Yuan Z, Zhang Y, Xiong A, Li L, Wang Z, Yang L. Targeted bile acids metabolomics in cholesterol gallbladder polyps and gallstones: From analytical method development towards application to clinical samples. J Pharm Anal 2023; 13:1080-1087. [PMID: 37842658 PMCID: PMC10568091 DOI: 10.1016/j.jpha.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 10/17/2023] Open
Abstract
Bile acids (BAs) are synthesized by the liver from cholesterol through several complementary pathways and aberrant cholesterol metabolism plays pivotal roles in the pathogeneses of cholesterol gallbladder polyps (CGP) and cholesterol gallstones (CGS). To date, there is neither systematic study on BAs profile of CGP or CGS, nor the relationship between them. To explore the metabolomics profile of plasma BAs in healthy volunteers, CGP and CGS patients, an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated for simultaneous determination of 42 free and conjugated BAs in human plasma. The developed method was sensitive and reproducible to be applied for the quantification of BAs in the investigation of plasma samples. The results show that, compared to healthy volunteers, CGP and CGS were both characterized by the significant decrease in plasma BAs pool size, furthermore CGP and CGS shared aberrant BAs metabolic characteristics. Chenodeoxycholic acid, glycochenodeoxycholic acid, λ-muricholic acid, deoxycholic acid, and 7-ketolithocholic acid were shared potential markers of these two cholesterol gallbladder diseases. Subsequent analysis showed that clinical characteristics including cysteine, ornithine and body mass index might be closely related to metabolisms of certain BA modules. This work provides metabolomic information for the study of gallbladder diseases and analytical methodologies for clinical target analysis and efficacy evaluation related to BAs in medical institutions.
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Affiliation(s)
- Jiaojiao Wei
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Tao Chen
- Department of Biliary and Pancreatic Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yamin Liu
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Shuai Sun
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Zhiqing Yuan
- Department of Biliary and Pancreatic Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yixin Zhang
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Aizhen Xiong
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Linnan Li
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Zhengtao Wang
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Li Yang
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
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Li Y, Fu Y, Liu Y, Zhao D, Liu L, Bourouis S, Algarni AD, Zhong C, Wu P. An optimized machine learning method for predicting wogonin therapy for the treatment of pulmonary hypertension. Comput Biol Med 2023; 164:107293. [PMID: 37591162 DOI: 10.1016/j.compbiomed.2023.107293] [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: 04/16/2023] [Revised: 06/25/2023] [Accepted: 07/28/2023] [Indexed: 08/19/2023]
Abstract
Human health is at risk from pulmonary hypertension (PH), characterized by decreased pulmonary vascular resistance and constriction of the pulmonary vessels, resulting in right heart failure and dysfunction. Thus, preventing PH and monitoring its progression before treating it is vital. Wogonin, derived from the leaves of Scutellaria baicalensis Georgi, exhibits remarkable pharmacological activity. In this study, we examined the effectiveness of wogonin in mitigating the progression of PH in mice using right heart catheterization and hematoxylin-eosin (HE) staining. As an alternative to minimize the possibility of harming small animals, we present a scientifically effective feature selection method (BSCDWOA-KELM) that will allow us to develop a novel simpler noninvasive prediction method for wogonin in treating PH. In this method, we use the proposed enhanced whale optimizer (SCDWOA) in conjunction with the kernel extreme learning machine (KELM). Initially, we let SCDWOA perform global optimization experiments on the IEEE CEC2014 benchmark function set to verify its core advantages. Lastly, 12 public and PH datasets are examined for feature selection experiments using BSCDWOA-KELM. As shown in the experimental results for global optimization, the proposed SCDWOA has better convergence performance. Meanwhile, the proposed binary SCDWOA (BSCDWOA) significantly improves the ability of KELM to classify data. By utilizing the BSCDWOA-KELM, key indicators such as the Red blood cell (RBC), the Haemoglobin (HGB), the Lymphocyte percentage (LYM%), the Hematocrit (HCT), and the Red blood cell distribution width-size distribution (RDW-SD) can be efficiently screened in the Pulmonary hypertension dataset, and one of its most essential points is its accuracy of greater than 0.98. Consequently, the BSCDWOA-KELM introduced in this study can be used to predict wogonin therapy for treating pulmonary hypertension in a simple and noninvasive manner.
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Affiliation(s)
- Yupeng Li
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China.
| | - Yujie Fu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
| | - Yining Liu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan 610065, China.
| | - Sami Bourouis
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia.
| | - Abeer D Algarni
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Chuyue Zhong
- The First Clinical College, Wenzhou Medical University, Wenzhou 325000, China.
| | - Peiliang Wu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
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Zhao T, Fu C, Tie M, Sham CW, Ma H. RGSB-UNet: Hybrid Deep Learning Framework for Tumour Segmentation in Digital Pathology Images. Bioengineering (Basel) 2023; 10:957. [PMID: 37627842 PMCID: PMC10452008 DOI: 10.3390/bioengineering10080957] [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: 05/31/2023] [Revised: 08/06/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Colorectal cancer (CRC) is a prevalent gastrointestinal tumour with high incidence and mortality rates. Early screening for CRC can improve cure rates and reduce mortality. Recently, deep convolution neural network (CNN)-based pathological image diagnosis has been intensively studied to meet the challenge of time-consuming and labour-intense manual analysis of high-resolution whole slide images (WSIs). Despite the achievements made, deep CNN-based methods still suffer from some limitations, and the fundamental problem is that they cannot capture global features. To address this issue, we propose a hybrid deep learning framework (RGSB-UNet) for automatic tumour segmentation in WSIs. The framework adopts a UNet architecture that consists of the newly-designed residual ghost block with switchable normalization (RGS) and the bottleneck transformer (BoT) for downsampling to extract refined features, and the transposed convolution and 1 × 1 convolution with ReLU for upsampling to restore the feature map resolution to that of the original image. The proposed framework combines the advantages of the spatial-local correlation of CNNs and the long-distance feature dependencies of BoT, ensuring its capacity of extracting more refined features and robustness to varying batch sizes. Additionally, we consider a class-wise dice loss (CDL) function to train the segmentation network. The proposed network achieves state-of-the-art segmentation performance under small batch sizes. Experimental results on DigestPath2019 and GlaS datasets demonstrate that our proposed model produces superior evaluation scores and state-of-the-art segmentation results.
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Affiliation(s)
- Tengfei Zhao
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Chong Fu
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
- Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang 110819, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110819, China
| | - Ming Tie
- Science and Technology on Space Physics Laboratory, Beijing 100076, China
| | - Chiu-Wing Sham
- School of Computer Science, The University of Auckland, Auckland 1142, New Zealand
| | - Hongfeng Ma
- Dopamine Group Ltd., Auckland 1542, New Zealand
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11
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Chen SW, Chen JK, Hsieh YH, Chen WH, Liao YH, Lin YC, Chen MC, Tsai CT, Chai JW, Yuan SM. Improving Patient Safety in the X-ray Inspection Process with EfficientNet-Based Medical Assistance System. Healthcare (Basel) 2023; 11:2068. [PMID: 37510509 PMCID: PMC10379294 DOI: 10.3390/healthcare11142068] [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: 06/05/2023] [Revised: 07/14/2023] [Accepted: 07/16/2023] [Indexed: 07/30/2023] Open
Abstract
Patient safety is a paramount concern in the medical field, and advancements in deep learning and Artificial Intelligence (AI) have opened up new possibilities for improving healthcare practices. While AI has shown promise in assisting doctors with early symptom detection from medical images, there is a critical need to prioritize patient safety by enhancing existing processes. To enhance patient safety, this study focuses on improving the medical operation process during X-ray examinations. In this study, we utilize EfficientNet for classifying the 49 categories of pre-X-ray images. To enhance the accuracy even further, we introduce two novel Neural Network architectures. The classification results are then compared with the doctor's order to ensure consistency and minimize discrepancies. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising 49 different categories and over 12,000 training and testing sheets was collected from Taichung Veterans General Hospital. The research demonstrates a significant improvement in accuracy, surpassing a 4% enhancement compared to previous studies.
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Affiliation(s)
- Shyh-Wei Chen
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan
| | - Jyun-Kai Chen
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
| | - Yu-Heng Hsieh
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
| | - Wen-Hsien Chen
- Department of Radiology, Taichung Veterans General Hospital, Taichung 407219, Taiwan
- Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 407224, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 402202, Taiwan
| | - Ying-Hsiang Liao
- Department of Radiology, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - You-Cheng Lin
- Department of Radiology, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Ming-Chih Chen
- Department of Radiology, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Ching-Tsorng Tsai
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan
| | - Jyh-Wen Chai
- Department of Radiology, Taichung Veterans General Hospital, Taichung 407219, Taiwan
- Post-Baccalaureate Medicine, National Chung Hsing University, Taichung 402202, Taiwan
- College of Medicine, China Medical University, Taichung 406040, Taiwan
| | - Shyan-Ming Yuan
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
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12
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Dinesh MG, Bacanin N, Askar SS, Abouhawwash M. Diagnostic ability of deep learning in detection of pancreatic tumour. Sci Rep 2023; 13:9725. [PMID: 37322046 PMCID: PMC10272117 DOI: 10.1038/s41598-023-36886-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are crucial to improve diagnosis and treatment outcomes. In the medical field, several algorithms have been put into use. Valid and interpretable data are essential for effective diagnosis and therapy. There is much room for cutting-edge computer systems to develop. The main objective of this research is to predict pancreatic cancer early using deep learning and metaheuristic techniques. This research aims to create a deep learning and metaheuristic techniques-based system to predict pancreatic cancer early by analyzing medical imaging data, mainly CT scans, and identifying vital features and cancerous growths in the pancreas using Convolutional Neural Network (CNN) and YOLO model-based CNN (YCNN) models. Once diagnosed, the disease cannot be effectively treated, and its progression is unpredictable. That's why there's been a push in recent years to implement fully automated systems that can sense cancer at a prior stage and improve diagnosis and treatment. The paper aims to evaluate the effectiveness of the novel YCNN approach compared to other modern methods in predicting pancreatic cancer. To predict the vital features from the CT scan and the proportion of cancer feasts in the pancreas using the threshold parameters booked as markers. This paper employs a deep learning approach called a Convolutional Neural network (CNN) model to predict pancreatic cancer images. In addition, we use the YOLO model-based CNN (YCNN) to aid in the categorization process. Both biomarkers and CT image dataset is used for testing. The YCNN method was shown to perform well by a cent percent of accuracy compared to other modern techniques in a thorough review of comparative findings.
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Affiliation(s)
- M G Dinesh
- Department of Computer Science and Engineering, EASA College of Engineering and Technology, Coimbatore, India
| | | | - S S Askar
- Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia
| | - Mohamed Abouhawwash
- Department of Computational Mathematics, Science and Engineering (CMSE), College of Engineering, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt.
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13
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Hao Y, Liu S, Liu T, Huang X, Xie M, Wang D. Pulmonary Function Test and Obstructive Sleep Apnea Hypopnea Syndrome in Obese Adults: A Retrospective Study. Int J Chron Obstruct Pulmon Dis 2023; 18:1019-1030. [PMID: 37304766 PMCID: PMC10253010 DOI: 10.2147/copd.s409383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/28/2023] [Indexed: 06/13/2023] Open
Abstract
Objective We explore risk factors related to severe obstructive sleep apnea (OSA) in obese patients, including pulmonary ventilation function, diffusion function, and impulse oscillometry (IOS) data. Methods The medical records of 207 obese patients who were prepared to undergo bariatric surgery in a hospital from May 2020 to September 2021 were retrospectively reviewed. Polysomnography (PSG), pulmonary ventilation function, diffusion function, and IOS parameters were collected according to the ethical standards of the institutional research committee (registration number: KYLL-202008-144). Logistic regression analysis was used to analyze the related independent risk factors. Results There were significantly statistical difference in a number of pulmonary ventilation and diffusion function parameters among the non-OSAHS group, the mild-to-moderate OSA group, and the severe OSA group. However, only airway resistance parameters R5%, R10%, R15%, R20%, R25%, and R35% increased with increasing OSA severity and were positively correlated with apnea hypopnea index (AHI). Age (P = 0.012, 1.104 (1.022, 1.192)), body mass index (P< 0.0001, 1.12 (1.057, 1.187)), gender (P = 0.003, 4.129 (1.625, 10.49)), and R25% (P = 0.007, 1.018 (1.005, 1.031)) were independent risk factors for severe OSA. In patients aged 35 to 60, RV/TLC (P = 0.029, 1.272 (1.025, 1.577)) is an independent risk factor for severe OSA. Conclusion R25% was an independent risk factor for severe OSA in obese individuals, while RV/TLC was also an independent risk factor in those aged 35 to 60. Pulmonary function tests (PFTs), particularly IOS levels, are recommended to assess severe OSA in obese patients.
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Affiliation(s)
- Yijia Hao
- Cheeloo College of Medicine, Shandong University, Jinan, 250033, People’s Republic of China
| | - Shaozhuang Liu
- Division of Bariatric and Metabolic Surgery, Department of General Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
| | - Teng Liu
- Division of Bariatric and Metabolic Surgery, Department of General Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
| | - Xin Huang
- Division of Bariatric and Metabolic Surgery, Department of General Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
| | - Mengshuang Xie
- Department of Geriatrics & Key Laboratory of Cardiovascular Proteomics of Shandong Province, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
| | - Dexiang Wang
- Department of Pulmonary and Critical Care Medicine, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China
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14
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Venkatachala Appa Swamy M, Periyasamy J, Thangavel M, Khan SB, Almusharraf A, Santhanam P, Ramaraj V, Elsisi M. Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction. Diagnostics (Basel) 2023; 13:diagnostics13111942. [PMID: 37296794 DOI: 10.3390/diagnostics13111942] [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: 03/07/2023] [Revised: 05/04/2023] [Accepted: 05/11/2023] [Indexed: 06/12/2023] Open
Abstract
With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction.
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Affiliation(s)
| | - Jayalakshmi Periyasamy
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Muthamilselvan Thangavel
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Surbhi B Khan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
- Department of Data Science, School of Science, Engineering and Environment, University of Sanford, Manchester M5 4WT, UK
| | - Ahlam Almusharraf
- Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Prasanna Santhanam
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Vijayan Ramaraj
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Mahmoud Elsisi
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan
- Department of Electrical Engineering, Faculty of Engineering (Shoubra), Benha University, 108 Shoubra St., Cairo P.O. Box 11241, Egypt
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15
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Abbasi Habashi S, Koyuncu M, Alizadehsani R. A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques. Diagnostics (Basel) 2023; 13:1749. [PMID: 37238232 PMCID: PMC10217633 DOI: 10.3390/diagnostics13101749] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/19/2023] [Accepted: 04/29/2023] [Indexed: 05/28/2023] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.
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Affiliation(s)
| | - Murat Koyuncu
- Department of Information Systems Engineering, Atilim University, 06830 Ankara, Turkey;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
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16
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Brinza M, Schröder S, Ababii N, Gronenberg M, Strunskus T, Pauporte T, Adelung R, Faupel F, Lupan O. Two-in-One Sensor Based on PV4D4-Coated TiO 2 Films for Food Spoilage Detection and as a Breath Marker for Several Diseases. BIOSENSORS 2023; 13:bios13050538. [PMID: 37232899 DOI: 10.3390/bios13050538] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/03/2023] [Accepted: 05/08/2023] [Indexed: 05/27/2023]
Abstract
Certain molecules act as biomarkers in exhaled breath or outgassing vapors of biological systems. Specifically, ammonia (NH3) can serve as a tracer for food spoilage as well as a breath marker for several diseases. H2 gas in the exhaled breath can be associated with gastric disorders. This initiates an increasing demand for small and reliable devices with high sensitivity capable of detecting such molecules. Metal-oxide gas sensors present an excellent tradeoff, e.g., compared to expensive and large gas chromatographs for this purpose. However, selective identification of NH3 at the parts-per-million (ppm) level as well as detection of multiple gases in gas mixtures with one sensor remain a challenge. In this work, a new two-in-one sensor for NH3 and H2 detection is presented, which provides stable, precise, and very selective properties for the tracking of these vapors at low concentrations. The fabricated 15 nm TiO2 gas sensors, which were annealed at 610 °C, formed two crystal phases, namely anatase and rutile, and afterwards were covered with a thin 25 nm PV4D4 polymer nanolayer via initiated chemical vapor deposition (iCVD) and showed precise NH3 response at room temperature and exclusive H2 detection at elevated operating temperatures. This enables new possibilities in application fields such as biomedical diagnosis, biosensors, and the development of non-invasive technology.
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Affiliation(s)
- Mihai Brinza
- Center for Nanotechnology and Nanosensors, Department of Microelectronics and Biomedical Engineering, Technical University of Moldova, 168 Stefan cel Mare Av., MD-2004 Chisinau, Moldova
| | - Stefan Schröder
- Department of Materials Science, Chair for Multicomponent Materials, Faculty of Engineering, Kiel University, Kaiserstraße 2, D-24143 Kiel, Germany
| | - Nicolai Ababii
- Center for Nanotechnology and Nanosensors, Department of Microelectronics and Biomedical Engineering, Technical University of Moldova, 168 Stefan cel Mare Av., MD-2004 Chisinau, Moldova
| | - Monja Gronenberg
- Department of Materials Science, Chair for Functional Nanomaterials, Faculty of Engineering, Kiel University, Kaiserstraße 2, D-24143 Kiel, Germany
| | - Thomas Strunskus
- Department of Materials Science, Chair for Multicomponent Materials, Faculty of Engineering, Kiel University, Kaiserstraße 2, D-24143 Kiel, Germany
| | - Thierry Pauporte
- Institut de Recherche de Chimie Paris-IRCP, Chimie ParisTech, PSL Université, 11 rue Pierre et Marie Curie, 75231 Paris, Cedex 05, France
| | - Rainer Adelung
- Department of Materials Science, Chair for Functional Nanomaterials, Faculty of Engineering, Kiel University, Kaiserstraße 2, D-24143 Kiel, Germany
| | - Franz Faupel
- Department of Materials Science, Chair for Multicomponent Materials, Faculty of Engineering, Kiel University, Kaiserstraße 2, D-24143 Kiel, Germany
| | - Oleg Lupan
- Center for Nanotechnology and Nanosensors, Department of Microelectronics and Biomedical Engineering, Technical University of Moldova, 168 Stefan cel Mare Av., MD-2004 Chisinau, Moldova
- Department of Materials Science, Chair for Multicomponent Materials, Faculty of Engineering, Kiel University, Kaiserstraße 2, D-24143 Kiel, Germany
- Department of Materials Science, Chair for Functional Nanomaterials, Faculty of Engineering, Kiel University, Kaiserstraße 2, D-24143 Kiel, Germany
- Institut de Recherche de Chimie Paris-IRCP, Chimie ParisTech, PSL Université, 11 rue Pierre et Marie Curie, 75231 Paris, Cedex 05, France
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17
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Mortada MHDJ, Tomassini S, Anbar H, Morettini M, Burattini L, Sbrollini A. Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning. Diagnostics (Basel) 2023; 13:1683. [PMID: 37238168 PMCID: PMC10217142 DOI: 10.3390/diagnostics13101683] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/19/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
Knowledge about the anatomical structures of the left heart, specifically the atrium (LA) and ventricle (i.e., endocardium-Vendo-and epicardium-LVepi) is essential for the evaluation of cardiac functionality. Manual segmentation of cardiac structures from echocardiography is the baseline reference, but results are user-dependent and time-consuming. With the aim of supporting clinical practice, this paper presents a new deep-learning (DL)-based tool for segmenting anatomical structures of the left heart from echocardiographic images. Specifically, it was designed as a combination of two convolutional neural networks, the YOLOv7 algorithm and a U-Net, and it aims to automatically segment an echocardiographic image into LVendo, LVepi and LA. The DL-based tool was trained and tested on the Cardiac Acquisitions for Multi-Structure Ultrasound Segmentation (CAMUS) dataset of the University Hospital of St. Etienne, which consists of echocardiographic images from 450 patients. For each patient, apical two- and four-chamber views at end-systole and end-diastole were acquired and annotated by clinicians. Globally, our DL-based tool was able to segment LVendo, LVepi and LA, providing Dice similarity coefficients equal to 92.63%, 85.59%, and 87.57%, respectively. In conclusion, the presented DL-based tool proved to be reliable in automatically segmenting the anatomical structures of the left heart and supporting the cardiological clinical practice.
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Affiliation(s)
| | | | | | | | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche, 60121 Ancona, Italy; (M.J.M.); (S.T.); (H.A.); (M.M.); (A.S.)
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18
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Rostamian A, Fallah K, Rostamiyan Y. Reduction of rupture risk in ICA aneurysms by endovascular techniques of coiling and stent: numerical study. Sci Rep 2023; 13:7216. [PMID: 37137951 PMCID: PMC10156732 DOI: 10.1038/s41598-023-34228-2] [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: 03/14/2023] [Accepted: 04/26/2023] [Indexed: 05/05/2023] Open
Abstract
The initiation, growth, and rupture of cerebral aneurysms are directly associated with Hemodynamic factors. This report tries to disclose effects of endovascular technique (coiling and stenting) on the quantitative intra-aneurysmal hemodynamic and the rupture of cerebral aneurysms. In this paper, Computational Fluid Dynamic are done to investigate and compare blood hemodynamic inside aneurysm under effects of deformation (due to stent) and coiling of aneurysm. The blood stream inside the sac of aneurysm as well as pressure and OSI distribution on the aneurysm wall are compared in nine cases and results of two distinctive cases are compared and reported. Obtained results specifies that the mean WSS is reduced up to 20% via coiling of the aneurysm while the deformation of the aneurysm (applying stent) could reduce the mean WSS up to 71%. In addition, comparison of the blood hemodynamic shows that the blood bifurcation occurs in the dome of aneurysm when endovascular technique for the treatment is not applied. It is found that the bifurcation occurs at ostium section when ICA aneurysm is deformed by the application of stent. The impacts of coiling are mainly limited since the blood flow entrance is not limited in this technique and WSS is not reduced substantial. However, usage of stent deforms the aneurysm angle with the orientation of parent vessel and this reduces blood velocity at entrance of the ostium and consequently, WSS is decreased when deformation of the aneurysm fully occurs. These qualitative procedures provide a preliminary idea for more profound quantitative examination intended for assigning aneurysm risk of upcoming rupture.
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Affiliation(s)
- Ali Rostamian
- Department of Mechanical Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Keivan Fallah
- Department of Mechanical Engineering, Sari Branch, Islamic Azad University, Sari, Iran.
| | - Yasser Rostamiyan
- Department of Mechanical Engineering, Sari Branch, Islamic Azad University, Sari, Iran
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19
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He J, Wang J, Han Z, Ma J, Wang C, Qi M. An interpretable transformer network for the retinal disease classification using optical coherence tomography. Sci Rep 2023; 13:3637. [PMID: 36869160 PMCID: PMC9984386 DOI: 10.1038/s41598-023-30853-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 03/02/2023] [Indexed: 03/05/2023] Open
Abstract
Retinal illnesses such as age-related macular degeneration and diabetic macular edema will lead to irreversible blindness. With optical coherence tomography (OCT), doctors are able to see cross-sections of the retinal layers and provide patients with a diagnosis. Manual reading of OCT images is time-consuming, labor-intensive and even error-prone. Computer-aided diagnosis algorithms improve efficiency by automatically analyzing and diagnosing retinal OCT images. However, the accuracy and interpretability of these algorithms can be further improved through effective feature extraction, loss optimization and visualization analysis. In this paper, we propose an interpretable Swin-Poly Transformer network for performing automatically retinal OCT image classification. By shifting the window partition, the Swin-Poly Transformer constructs connections between neighboring non-overlapping windows in the previous layer and thus has the flexibility to model multi-scale features. Besides, the Swin-Poly Transformer modifies the importance of polynomial bases to refine cross entropy for better retinal OCT image classification. In addition, the proposed method also provides confidence score maps, assisting medical practitioners to understand the models' decision-making process. Experiments in OCT2017 and OCT-C8 reveal that the proposed method outperforms both the convolutional neural network approach and ViT, with an accuracy of 99.80% and an AUC of 99.99%.
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Affiliation(s)
- Jingzhen He
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China.
| | - Junxia Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Zeyu Han
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, China
| | - Jun Ma
- School of Cyber Science and Engineering, Southeast University, Nanjing, 211189, China
| | - Chongjing Wang
- China Academy of Information and Communications Technology, Beijing, 100191, China
| | - Meng Qi
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China.
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