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Bansal RK. Endodontic radiography: Are handheld X-ray devices safer than wall-mounted machines? JOURNAL OF CONSERVATIVE DENTISTRY AND ENDODONTICS 2024; 27:662-663. [PMID: 38989500 PMCID: PMC11232755 DOI: 10.4103/jcde.jcde_231_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 07/12/2024]
Affiliation(s)
- Rajinder Kumar Bansal
- Department of Conservative Dentistry and Endodontics, Guru Nanak Dev Dental College and Research Institute, Sunam, Punjab, India
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152
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Xi Y, Li Y, Wang H, Sun A, Deng X, Chen Z, Fan Y. Effect of veno-arterial extracorporeal membrane oxygenation lower-extremity cannulation on intra-arterial flow characteristics, oxygen content, and thrombosis risk. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 251:108204. [PMID: 38728829 DOI: 10.1016/j.cmpb.2024.108204] [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: 01/14/2024] [Revised: 04/22/2024] [Accepted: 04/24/2024] [Indexed: 05/12/2024]
Abstract
PURPOSE This study aimed to investigate the effects of lower-extremity cannulation on the intra-arterial hemodynamic environment, oxygen content, blood damage, and thrombosis risk under different levels of veno-arterial (V-A) ECMO support. METHODS Computational fluid dynamics methods were used to investigate the effects of different levels of ECMO support (ECMO flow ratios supplying oxygen-rich blood 100-40 %). Flow rates and oxygen content in each arterial branch were used to determine organ perfusion. A new thrombosis model considering platelet activation and deposition was proposed to determine the platelet activation and thrombosis risk at different levels of ECMO support. A red blood cell damage model was used to explore the risk of hemolysis. RESULTS Our study found that partial recovery of cardiac function improved the intra-arterial hemodynamic environment, with reduced impingement of the intra-arterial flow field by high-velocity blood flow from the cannula, a flow rate per unit time into each arterial branch closer to physiological levels, and improved perfusion in the lower extremities. Partial recovery of cardiac function helps reduce intra-arterial high shear stress and residence time, thereby reducing blood damage. The overall level of hemolysis and platelet activation in the aorta decreased with the gradual recovery of cardiac contraction function. The areas at high risk of thrombosis under V-A ECMO femoral cannulation support were the aortic root and the area distal to the cannula, which moved to the descending aorta when cardiac function recovered to 40-60 %. However, with the recovery of cardiac contraction function, hypoxic blood pumped by the heart is insufficient in supplying oxygen to the front of the aortic arch, which may result in upper extremity hypoxia. CONCLUSION We developed a thrombosis risk prediction model applicable to ECMO cannulation and validated the model accuracy using clinical data. Partial recovery of cardiac function contributed to an improvement in the aortic hemodynamic environment and a reduction in the risk of blood damage; however, there is a potential risk of insufficient perfusion of oxygen-rich blood to organs.
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Affiliation(s)
- Yifeng Xi
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Yuan Li
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Hongyu Wang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Anqiang Sun
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Xiaoyan Deng
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Zengsheng Chen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China.
| | - Yubo Fan
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China.
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153
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Fujiwara Y, Kamihoriuchi Y, Higuchi F, Nakayama S, Ohyama Y, Sasaki T, Watanabe S, Masuda T. Evaluation of overexposure risk when there is a space between the subject and the couch in computed tomography: a phantom study. Radiol Phys Technol 2024; 17:561-568. [PMID: 38668938 DOI: 10.1007/s12194-024-00804-y] [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: 12/12/2023] [Revised: 04/02/2024] [Accepted: 04/14/2024] [Indexed: 05/27/2024]
Abstract
The purpose of this study was to investigate the risk of overexposure associated with automatic tube current modulation (ATCM) and automatic couch height positioning compensation mechanism (AHC) in computed tomography (CT) systems, particularly in scenarios involving a gap between the subject and the couch. Results revealed that when AHC was enabled, CT dose index volume (CTDIvol) increased by approximately 10% at 2.5 cm, 20% at 5.0 cm, and 40% at 10.0 cm gaps compared to close contact conditions. While the AHC function ensures consistent exposure doses and image quality regardless of subject positioning relative to the CT gantry isocenter, the study highlights a potential risk of overexposure when a gap exists between the subject and the couch. These findings offer valuable insights for optimizing CT imaging protocols and underscore the importance of carefully considering subject positioning in clinical practice.
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Affiliation(s)
- Yuta Fujiwara
- Division of Clinical Radiology Service, Okayama Central Hospital, 6-3, Ishimakitamachi, Kitaku, Okayama, 700-0017, Japan.
| | - Yoshiki Kamihoriuchi
- Division of Clinical Radiology Service, Okayama Central Hospital, 6-3, Ishimakitamachi, Kitaku, Okayama, 700-0017, Japan
| | - Fumie Higuchi
- Division of Clinical Radiology Service, Okayama Central Hospital, 6-3, Ishimakitamachi, Kitaku, Okayama, 700-0017, Japan
| | - Shinichi Nakayama
- Division of Clinical Radiology Service, Okayama Central Hospital, 6-3, Ishimakitamachi, Kitaku, Okayama, 700-0017, Japan
| | - Yutako Ohyama
- Division of Clinical Radiology Service, Okayama Central Hospital, 6-3, Ishimakitamachi, Kitaku, Okayama, 700-0017, Japan
| | - Tomoko Sasaki
- Division of Clinical Radiology Service, Okayama Central Hospital, 6-3, Ishimakitamachi, Kitaku, Okayama, 700-0017, Japan
| | - Shinsaku Watanabe
- Division of Clinical Radiology Service, Okayama Central Hospital, 6-3, Ishimakitamachi, Kitaku, Okayama, 700-0017, Japan
| | - Takanori Masuda
- Department of Radiological Technology, Faculty of Health Science and Technology, Kawasaki University of Medical Welfare, Matsushima, Kurashiki, Okayama, 288701-0193, Japan
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154
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Seoni S, Shahini A, Meiburger KM, Marzola F, Rotunno G, Acharya UR, Molinari F, Salvi M. All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108200. [PMID: 38677080 DOI: 10.1016/j.cmpb.2024.108200] [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: 01/27/2024] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND AND OBJECTIVES Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols and equipment can introduce inconsistencies that hamper the effective pooling of multi-source datasets. This systematic review evaluates strategies for image harmonization, which standardizes appearances to enable reliable AI analysis of multi-source medical imaging. METHODS A literature search using PRISMA guidelines was conducted to identify relevant papers published between 2013 and 2023 analyzing multi-centric and multi-device medical imaging studies that utilized image harmonization approaches. RESULTS Common image harmonization techniques included grayscale normalization (improving classification accuracy by up to 24.42 %), resampling (increasing the percentage of robust radiomics features from 59.5 % to 89.25 %), and color normalization (enhancing AUC by up to 0.25 in external test sets). Initially, mathematical and statistical methods dominated, but machine and deep learning adoption has risen recently. Color imaging modalities like digital pathology and dermatology have remained prominent application areas, though harmonization efforts have expanded to diverse fields including radiology, nuclear medicine, and ultrasound imaging. In all the modalities covered by this review, image harmonization improved AI performance, with increasing of up to 24.42 % in classification accuracy and 47 % in segmentation Dice scores. CONCLUSIONS Continued progress in image harmonization represents a promising strategy for advancing healthcare by enabling large-scale, reliable analysis of integrated multi-source datasets using AI. Standardizing imaging data across clinical settings can help realize personalized, evidence-based care supported by data-driven technologies while mitigating biases associated with specific populations or acquisition protocols.
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Affiliation(s)
- Silvia Seoni
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Alen Shahini
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Kristen M Meiburger
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Francesco Marzola
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Giulia Rotunno
- 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; Centre for Health Research, University of Southern Queensland, Australia
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Massimo Salvi
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
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155
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Li Y, Yu R, Chang H, Yan W, Wang D, Li F, Cui Y, Wang Y, Wang X, Yan Q, Liu X, Jia W, Zeng Q. Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:976-987. [PMID: 38347392 PMCID: PMC11169103 DOI: 10.1007/s10278-024-00988-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 06/13/2024]
Abstract
The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.
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Affiliation(s)
- Yuting Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ruize Yu
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Huan Chang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Wanying Yan
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Dawei Wang
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Fuyan Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yi Cui
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Yong Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xiao Wang
- Department of Radiology, Jining No. 1 People's Hospital, Jining, China
| | - Qingqing Yan
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Xinhui Liu
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Wenjing Jia
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China.
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156
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Li L, Zhang J, Zhe X, Tang M, Zhang L, Lei X, Zhang X. Prediction of histopathologic grades of bladder cancer with radiomics based on MRI: Comparison with traditional MRI. Urol Oncol 2024; 42:176.e9-176.e20. [PMID: 38556403 DOI: 10.1016/j.urolonc.2024.02.008] [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: 09/16/2023] [Revised: 02/01/2024] [Accepted: 02/20/2024] [Indexed: 04/02/2024]
Abstract
PURPOSE To compare biparametric magnetic resonance imaging (bp-MRI) radiomics signatures and traditional MRI model for the preoperative prediction of bladder cancer (BCa) grade. MATERIALS AND METHODS This retrospective study included 255 consecutive patients with pathologically confirmed 113 low-grade and 142 high-grade BCa. The traditional MRI nomogram model was developed using univariate and multivariate logistic regression by the mean apparent diffusion coefficient (ADC), vesical imaging reporting and data system, tumor size, and the number of tumors. Volumes of interest were manually drawn on T2-weighted imaging (T2WI) and ADC maps by 2 radiologists. Using one-way analysis of variance, correlation, and least absolute shrinkage and selection operator methods to select features. Then, a logistic regression classifier was used to develop the radiomics signatures. Receiver operating characteristic (ROC) analysis was used to compare the diagnostic abilities of the radiomics and traditional MRI models by the DeLong test. Finally, decision curve analysis was performed by estimating the clinical usefulness of the 2 models. RESULTS The area under the ROC curves (AUCs) of the traditional MRI model were 0.841 in the training cohort and 0.806 in the validation cohort. The AUCs of the 3 groups of radiomics model [ADC, T2WI, bp-MRI (ADC and T2WI)] were 0.888, 0.875, and 0.899 in the training cohort and 0.863, 0.805, and 0.867 in the validation cohort, respectively. The combined radiomics model achieved higher AUCs than the traditional MRI model. decision curve analysis indicated that the radiomics model had higher net benefits than the traditional MRI model. CONCLUSION The bp-MRI radiomics model may help distinguish high-grade and low-grade BCa and outperforming the traditional MRI model. Multicenter validation is needed to acquire high-level evidence for its clinical application.
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Affiliation(s)
- Longchao Li
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China
| | - Jing Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China
| | - Xia Zhe
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China
| | - Min Tang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China
| | - Li Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
| | - Xiaoyan Lei
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
| | - Xiaoling Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China
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157
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Chen JX, Shen YC, Peng SL, Chen YW, Fang HY, Lan JL, Shih CT. Pattern classification of interstitial lung diseases from computed tomography images using a ResNet-based network with a split-transform-merge strategy and split attention. Phys Eng Sci Med 2024; 47:755-767. [PMID: 38436886 DOI: 10.1007/s13246-024-01404-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 02/09/2024] [Indexed: 03/05/2024]
Abstract
In patients with interstitial lung disease (ILD), accurate pattern assessment from their computed tomography (CT) images could help track lung abnormalities and evaluate treatment efficacy. Based on excellent image classification performance, convolutional neural networks (CNNs) have been massively investigated for classifying and labeling pathological patterns in the CT images of ILD patients. However, previous studies rarely considered the three-dimensional (3D) structure of the pathological patterns of ILD and used two-dimensional network input. In addition, ResNet-based networks such as SE-ResNet and ResNeXt with high classification performance have not been used for pattern classification of ILD. This study proposed a SE-ResNeXt-SA-18 for classifying pathological patterns of ILD. The SE-ResNeXt-SA-18 integrated the multipath design of the ResNeXt and the feature weighting of the squeeze-and-excitation network with split attention. The classification performance of the SE-ResNeXt-SA-18 was compared with the ResNet-18 and SE-ResNeXt-18. The influence of the input patch size on classification performance was also evaluated. Results show that the classification accuracy was increased with the increase of the patch size. With a 32 × 32 × 16 input, the SE-ResNeXt-SA-18 presented the highest performance with average accuracy, sensitivity, and specificity of 0.991, 0.979, and 0.994. High-weight regions in the class activation maps of the SE-ResNeXt-SA-18 also matched the specific pattern features. In comparison, the performance of the SE-ResNeXt-SA-18 is superior to the previously reported CNNs in classifying the ILD patterns. We concluded that the SE-ResNeXt-SA-18 could help track or monitor the progress of ILD through accuracy pattern classification.
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Affiliation(s)
- Jian-Xun Chen
- Department of Thoracic Surgery, China Medical University Hospital, Taichung, Taiwan
| | - Yu-Cheng Shen
- Department of Thoracic Surgery, China Medical University Hospital, Taichung, Taiwan
| | - Shin-Lei Peng
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan
| | - Yi-Wen Chen
- x-Dimension Center for Medical Research and Translation, China Medical University Hospital, Taichung, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Hsin-Yuan Fang
- x-Dimension Center for Medical Research and Translation, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| | - Joung-Liang Lan
- School of Medicine, China Medical University, Taichung, Taiwan
- Rheumatology and Immunology Center, China Medical University Hospital, Taichung, Taiwan
| | - Cheng-Ting Shih
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan.
- x-Dimension Center for Medical Research and Translation, China Medical University Hospital, Taichung, Taiwan.
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158
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Rodgers S, Atkinson J, Cryer D, Storm C, Nezich R, Ebert MA, Rowshanfarzad P. Construction and validation of an infant chest phantom for paediatric computed tomography. Phys Eng Sci Med 2024; 47:491-501. [PMID: 38315414 PMCID: PMC11166826 DOI: 10.1007/s13246-023-01379-5] [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: 11/13/2022] [Accepted: 12/21/2023] [Indexed: 02/07/2024]
Abstract
Paediatric imaging protocols should be carefully optimised to maintain the desired image quality while minimising the delivered patient dose. A paediatric chest phantom was designed, constructed and evaluated to optimise chest CT examinations for infants. The phantom was designed to enable dosimetry and image quality measurements within the anthropomorphic structure. It was constructed using tissue equivalent materials to mimic thoracic structures of infants, aged 0-6 months. The phantom materials were validated across a range of diagnostic tube voltages with resulting CT numbers found equivalent to paediatric tissues observed via a survey of clinical paediatric chest studies. The phantom has been successfully used to measure radiation dose and evaluate various image quality parameters for paediatric specific protocols.
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Affiliation(s)
- Seonaid Rodgers
- Department of Medical Technology and Physics, Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, WA, 6009, Australia.
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, WA, Australia.
| | - Janette Atkinson
- Department of Medical Technology and Physics, Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, WA, 6009, Australia
| | - David Cryer
- Department of Medical Technology and Physics, Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, WA, 6009, Australia
| | - Cameron Storm
- Department of Medical Technology and Physics, Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, WA, 6009, Australia
| | - Rikki Nezich
- Department of Medical Technology and Physics, Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, WA, 6009, Australia
| | - Martin A Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, WA, Australia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, WA, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, WA, Australia
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, WA, Australia
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159
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Amstutz F, D'Almeida PG, Wu X, Albertini F, Bachtiary B, Weber DC, Unkelbach J, Lomax AJ, Zhang Y. Quantification of deformable image registration uncertainties for dose accumulation on head and neck cancer proton treatments. Phys Med 2024; 122:103386. [PMID: 38805762 DOI: 10.1016/j.ejmp.2024.103386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 03/11/2024] [Accepted: 05/21/2024] [Indexed: 05/30/2024] Open
Abstract
PURPOSE Head and neck cancer (HNC) patients in radiotherapy require adaptive treatment plans due to anatomical changes. Deformable image registration (DIR) is used in adaptive radiotherapy, e.g. for deformable dose accumulation (DDA). However, DIR's ill-posedness necessitates addressing uncertainties, often overlooked in clinical implementations. DIR's further clinical implementation is hindered by missing quantitative commissioning and quality assurance tools. This study evaluates one pathway for more quantitative DDA uncertainties. METHODS For five HNC patients, each with multiple repeated CTs acquired during treatment, a simultaneous-integrated boost (SIB) plan was optimized. Recalculated doses were warped individually using multiple DIRs from repeated to reference CTs, and voxel-by-voxel dose ranges determined an error-bar for DDA. Followed by evaluating, a previously proposed early-stage DDA uncertainty estimation method tested for lung cancer, which combines geometric DIR uncertainties, dose gradients and their directional dependence, in the context of HNC. RESULTS Applying multiple DIRs show dose differences, pronounced in high dose gradient regions. The patient with largest anatomical changes (-13.1 % in ROI body volume), exhibited 33 % maximum uncertainty in contralateral parotid, with 54 % of voxels presenting an uncertainty >5 %. Accumulation over multiple CTs partially mitigated uncertainties. The estimation approach predicted 92.6 % of voxels within ±5 % to the reference dose uncertainty across all patients. CONCLUSIONS DIR variations impact accumulated doses, emphasizing DDA uncertainty quantification's importance for HNC patients. Multiple DIR dose warping aids in quantifying DDA uncertainties. An estimation approach previously described for lung cancer was successfully validated for HNC, for SIB plans, presenting different dose gradients, and for accumulated treatments.
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Affiliation(s)
- Florian Amstutz
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | - Peter G D'Almeida
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Information Technology & Electrical Engineering, ETH Zurich, Switzerland
| | - Xin Wu
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Information Technology & Electrical Engineering, ETH Zurich, Switzerland
| | | | | | - Damien C Weber
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Radiation Oncology, University Hospital Zurich, Switzerland; Department of Radiation Oncology, University Hospital Bern, Switzerland
| | - Jan Unkelbach
- Department of Radiation Oncology, University Hospital Zurich, Switzerland
| | - Antony J Lomax
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland.
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160
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He H, Liu J, Li C, Guo Y, Liang K, Du J, Xue J, Liang Y, Chen P, Liu L, Cui M, Wang J, Liu Y, Tian S, Deng Y. Predicting Hematoma Expansion and Prognosis in Cerebral Contusions: A Radiomics-Clinical Approach. J Neurotrauma 2024; 41:1337-1352. [PMID: 38326935 DOI: 10.1089/neu.2023.0410] [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] [Indexed: 02/09/2024] Open
Abstract
Hemorrhagic progression of contusion (HPC) often occurs early in cerebral contusions (CC) patients, significantly impacting their prognosis. It is vital to promptly assess HPC and predict outcomes for effective tailored interventions, thereby enhancing prognosis in CC patients. We utilized the Attention-3DUNet neural network to semi-automatically segment hematomas from computed tomography (CT) images of 452 CC patients, incorporating 695 hematomas. Subsequently, 1502 radiomic features were extracted from 358 hematomas in 261 patients. After a selection process, these features were used to calculate the radiomic signature (Radscore). The Radscore, along with clinical features such as medical history, physical examinations, laboratory results, and radiological findings, was employed to develop predictive models. For prognosis (discharge Glasgow Outcome Scale score), radiomic features of each hematoma were augmented and fused for correlation. We employed various machine learning methodologies to create both a combined model, integrating radiomics and clinical features, and a clinical-only model. Nomograms based on logistic regression were constructed to visually represent the predictive procedure, and external validation was performed on 170 patients from three additional centers. The results showed that for HPC, the combined model, incorporating hemoglobin levels, Rotterdam CT score of 3, multi-hematoma fuzzy sign, concurrent subdural hemorrhage, international normalized ratio, and Radscore, achieved area under the receiver operating characteristic curve (AUC) values of 0.848 and 0.836 in the test and external validation cohorts, respectively. The clinical model predicting prognosis, utilizing age, Abbreviated Injury Scale for the head, Glasgow Coma Scale Motor component, Glasgow Coma Scale Verbal component, albumin, and Radscore, attained AUC values of 0.846 and 0.803 in the test and external validation cohorts, respectively. Selected radiomic features indicated that irregularly shaped and highly heterogeneous hematomas increased the likelihood of HPC, while larger weighted axial lengths and lower densities of hematomas were associated with a higher risk of poor prognosis. Predictive models that combine radiomic and clinical features exhibit robust performance in forecasting HPC and the risk of poor prognosis in CC patients. Radiomic features complement clinical features in predicting HPC, although their ability to enhance the predictive accuracy of the clinical model for adverse prognosis is limited.
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Affiliation(s)
- Haoyue He
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
- Bioengineering College, Chongqing University, Chongqing, China
| | - Jinxin Liu
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
- School of Medicine, Chongqing University, Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Yi Guo
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Kaixin Liang
- Department of Neurosurgery, Yubei District Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Jun Du
- Department of Neurosurgery, Chongqing Qianjiang Central Hospital, Chongqing University Qianjiang Hospital, Chongqing, China
| | - Jun Xue
- Department of Neurosurgery, Bishan Hospital of Chongqing, Bishan Hospital of Chongqing Medical University, Chongqing, China
| | - Yidan Liang
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Peng Chen
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Liu Liu
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Min Cui
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Jia Wang
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Ye Liu
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
- School of Medicine, Chongqing University, Chongqing, China
| | - Shanshan Tian
- Department of Prehospital Emergency, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Yongbing Deng
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
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161
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Wang X, Wang J, Fei N, Duanmu D, Feng B, Li X, IP WY, Hu Y. Alternative muscle synergy patterns of upper limb amputees. Cogn Neurodyn 2024; 18:1119-1133. [PMID: 38826662 PMCID: PMC11143172 DOI: 10.1007/s11571-023-09969-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 03/29/2023] [Accepted: 04/11/2023] [Indexed: 06/04/2024] Open
Abstract
Myoelectric hand prostheses are effective tools for upper limb amputees to regain hand functions. Much progress has been made with pattern recognition algorithms to recognize surface electromyography (sEMG) patterns, but few attentions was placed on the amputees' motor learning process. Many potential myoelectric prostheses users could not fully master the control or had declined performance over time. It is possible that learning to produce distinct and consistent muscle activation patterns with the residual limb could help amputees better control the myoelectric prosthesis. In this study, we observed longitudinal effect of motor skill learning with 2 amputees who have developed alternative muscle activation patterns in response to the same set of target prosthetic actions. During a 10-week program, amputee participants were trained to produce distinct and constant muscle activations with visual feedback of live sEMG and without interaction with prosthesis. At the end, their sEMG patterns were different from each other and from non-amputee control groups. For certain intended hand motion, gradually reducing root mean square (RMS) variance was observed. The learning effect was also assessed with a CNN-LSTM mixture classifier designed for mobile sEMG pattern recognition. The classification accuracy had a rising trend over time, implicating potential performance improvement of myoelectric prosthesis control. A follow-up session took place 6 months after the program and showed lasting effect of the motor skill learning in terms of sEMG pattern classification accuracy. The results indicated that with proper feedback training, amputees could learn unique muscle activation patterns that allow them to trigger intended prosthesis functions, and the original motor control scheme is updated. The effect of such motor skill learning could help to improve myoelectric prosthetic control performance.
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Affiliation(s)
- Xiaojun Wang
- Department of Orthopedics and Traumatology, LKS Faculty of Medicine, The University of Hong Kong, 999077 Hong Kong, China
| | - Junlin Wang
- Department of Orthopedics and Traumatology, LKS Faculty of Medicine, The University of Hong Kong, 999077 Hong Kong, China
- Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, 518000 China
| | - Ningbo Fei
- Department of Orthopedics and Traumatology, LKS Faculty of Medicine, The University of Hong Kong, 999077 Hong Kong, China
| | - Dehao Duanmu
- Department of Orthopedics and Traumatology, LKS Faculty of Medicine, The University of Hong Kong, 999077 Hong Kong, China
| | - Beibei Feng
- Department of Orthopedics and Traumatology, LKS Faculty of Medicine, The University of Hong Kong, 999077 Hong Kong, China
| | - Xiaodong Li
- Department of Orthopedics and Traumatology, LKS Faculty of Medicine, The University of Hong Kong, 999077 Hong Kong, China
- Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, 518000 China
| | - Wing-Yuk IP
- Department of Orthopedics and Traumatology, LKS Faculty of Medicine, The University of Hong Kong, 999077 Hong Kong, China
| | - Yong Hu
- Department of Orthopedics and Traumatology, LKS Faculty of Medicine, The University of Hong Kong, 999077 Hong Kong, China
- Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, 518000 China
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162
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Miyama K, Akiyama T, Bise R, Nakamura S, Nakashima Y, Uchida S. Development of an automatic surgical planning system for high tibial osteotomy using artificial intelligence. Knee 2024; 48:128-137. [PMID: 38599029 DOI: 10.1016/j.knee.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/06/2024] [Accepted: 03/19/2024] [Indexed: 04/12/2024]
Abstract
BACKGROUND This study proposed an automatic surgical planning system for high tibial osteotomy (HTO) using deep learning-based artificial intelligence and validated its accuracy. The system simulates osteotomy and measures lower-limb alignment parameters in pre- and post-osteotomy simulations. METHODS A total of 107 whole-leg standing radiographs were obtained from 107 patients who underwent HTO. First, the system detected anatomical landmarks on radiographs. Then, it simulated osteotomy and automatically measured five parameters in pre- and post-osteotomy simulation (hip knee angle [HKA], weight-bearing line ratio [WBL ratio], mechanical lateral distal femoral angle [mLDFA], mechanical medial proximal tibial angle [mMPTA], and mechanical lateral distal tibial angle [mLDTA]). The accuracy of the measured parameters was validated by comparing them with the ground truth (GT) values given by two orthopaedic surgeons. RESULTS All absolute errors of the system were within 1.5° or 1.5%. All inter-rater correlation confidence (ICC) values between the system and GT showed good reliability (>0.80). Excellent reliability was observed in the HKA (0.99) and WBL ratios (>0.99) for the pre-osteotomy simulation. The intra-rater difference of the system exhibited excellent reliability with an ICC value of 1.00 for all lower-limb alignment parameters in pre- and post-osteotomy simulations. In addition, the measurement time per radiograph (0.24 s) was considerably shorter than that of an orthopaedic surgeon (118 s). CONCLUSION The proposed system is practically applicable because it can measure lower-limb alignment parameters accurately and quickly in pre- and post-osteotomy simulations. The system has potential applications in surgical planning systems.
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Affiliation(s)
- Kazuki Miyama
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka 812-8582, Japan; Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-Ku, Fukuoka 819-0395, Japan; Akiyama Clinic, 2-28-39, Noke, Sawaraku, Fukuoka City, Fukuoka 814-0171, Japan.
| | - Takenori Akiyama
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka 812-8582, Japan; Akiyama Clinic, 2-28-39, Noke, Sawaraku, Fukuoka City, Fukuoka 814-0171, Japan
| | - Ryoma Bise
- Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-Ku, Fukuoka 819-0395, Japan
| | - Shunsuke Nakamura
- Akiyama Clinic, 2-28-39, Noke, Sawaraku, Fukuoka City, Fukuoka 814-0171, Japan
| | - Yasuharu Nakashima
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka 812-8582, Japan
| | - Seiichi Uchida
- Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-Ku, Fukuoka 819-0395, Japan
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163
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Yousif YAM, Daniel J, Healy B, Hill R. A study of polarity effect for various ionization chambers in kilovoltage x-ray beams. Med Phys 2024; 51:4513-4523. [PMID: 38669346 DOI: 10.1002/mp.17096] [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: 09/28/2023] [Revised: 03/01/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Ionization chambers play an essential role in dosimetry measurements for kilovoltage (kV) x-ray beams. Despite their widespread use, there is limited data on the absolute values for the polarity correction factors across a range of commonly employed ionization chambers. PURPOSE This study aimed to investigate the polarity effects for five different ionization chambers in kV x-ray beams. METHODS Two plane-parallel chambers being the Advanced Markus and Roos and three cylindrical chambers; 3D PinPoint, Semiflex and Farmer chamber (PTW, Freiburg, Germany), were employed to measure the polarity correction factors. The kV x-ray beams were produced from an Xstrahl 300 unit (Xstrahl Ltd., UK). All measurements were acquired at 2 cm depth in a PTW-MP1 water tank for beams between 60 kVp (HVL 1.29 mm Al) and 300 kVp (HVL 3.08 mm Cu), and field sizes of 2-10 cm diameter for 30 cm focus-source distance (FSD) and 4 × 4 cm2 - 20 × 20 cm2 for 50 cm FSD. The ionization chambers were connected to a PTW-UNIDOS electrometer, and the polarity effect was determined using the AAPM TG-61 code of practice methodology. RESULTS The study revealed significant polarity effects in ionization chambers, especially in those with smaller volumes. For the plane-parallel chambers, the Advanced Markus chamber exhibited a maximum polarity effect of 2.5%, whereas the Roos chamber showed 0.3% at 150 KVp with the 10 cm circular diameter open-ended applicator. Among the cylindrical chambers at the same beam energy and applicator, the Pinpoint chamber exhibited a 3% polarity effect, followed by Semiflex with 1.7%, and Farmer with 0.4%. However, as the beam energy increased to 300 kVp, the polarity effect significantly increased reaching 8.5% for the Advanced Markus chamber and 13.5% for the PinPoint chamber at a 20 × 20 cm2 field size. Notably, the magnitude of the polarity effect increased with both the field size and beam energy, and was significantly influenced by the size of the chamber's sensitive volume. CONCLUSIONS The findings demonstrate that ionization chambers can exhibit substantial polarity effects in kV x-ray beams, particularly for those chambers with smaller volumes. Therefore, it is important to account for polarity corrections when conducting relative dose measurements in kV x-ray beams to enhance the dosimetry accuracy and improve patient dose calculations.
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Affiliation(s)
- Yousif A M Yousif
- Crown Princess Mary Cancer Centre, Westmead Hospital, Wentworthville, New South Wales, Australia
- North West Cancer Centre, Tamworth Hospital, Tamworth, New South Wales, Australia
| | - John Daniel
- North West Cancer Centre, Tamworth Hospital, Tamworth, New South Wales, Australia
- Department of Radiation Oncology, Calvary Mater Newcastle, Newcastle, New South Wales, Australia
| | - Brendan Healy
- Australian Clinical Dosimetry Service (ACDS), Yallambie, Victoria, Australia
| | - Robin Hill
- Department of Radiation Oncology, Chris O'Brien Lifehouse, Camperdown, New South Wales, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Camperdown, New South Wales, Australia
- Arto Hardy Family Biomedical Innovation Hub, Chris O'Brien Lifehouse, Camperdown, New South Wales, Australia
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164
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Kumar R, Aggarwal Y, Nigam VK, Sinha RK. Time-domain heart rate dynamics in the prognosis of progressive atherosclerosis. Nutr Metab Cardiovasc Dis 2024; 34:1389-1398. [PMID: 38403487 DOI: 10.1016/j.numecd.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/07/2023] [Accepted: 01/09/2024] [Indexed: 02/27/2024]
Abstract
BACKGROUND AND AIM The regular uptake of a high-fat diet (HFD) with changing lifestyle causes atherosclerosis leading to cardiovascular diseases and autonomic dysfunction. Therefore, the current study aimed to investigate the correlation of autonomic activity to lipid and atherosclerosis markers. Further, the study proposes a support vector machine (SVM) based model in the prediction of atherosclerosis severity. METHODS AND RESULTS The Lead-II electrocardiogram and blood markers were measured from both the control and the experiment subjects each week for nine consecutive weeks. The time-domain heart rate variability (HRV) parameters were derived, and the significance level was tested using a one-way Analysis of Variance. The correlation analysis was performed to determine the relation between autonomic parameters and lipid and atherosclerosis markers. The statistically significant time-domain values were used as features of the SVM. The observed results demonstrated the reduced time domain HRV parameters with the increase in lipid and atherosclerosis index markers with the progressive atherosclerosis severity. The correlation analysis revealed a negative association between time-domain HRV parameters with lipid and atherosclerosis parameters. The percentage accuracy increases from 86.58% to 98.71% with the increase in atherosclerosis severity with regular consumption of HFD. CONCLUSIONS Atherosclerosis causes autonomic dysfunction with reduced HRV. The negative correlation between autonomic parameters and lipid profile and atherosclerosis indexes marker revealed the potential role of vagal activity in the prognosis of atherosclerosis progression. The support vector machine presented a respectable accuracy in the prediction of atherosclerosis severity from the control group.
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Affiliation(s)
- Rahul Kumar
- Department of Bioengineering and Biotechnology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
| | - Yogender Aggarwal
- Department of Bioengineering and Biotechnology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
| | - Vinod Kumar Nigam
- Department of Bioengineering and Biotechnology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
| | - Rakesh Kumar Sinha
- Department of Bioengineering and Biotechnology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
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165
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Mukwada G, Hirst A, Rowshanfarzad P, Ebert MA. Development of a 3D printed phantom for commissioning and quality assurance of multiple brain targets stereotactic radiosurgery. Phys Eng Sci Med 2024; 47:455-463. [PMID: 38285271 PMCID: PMC11166808 DOI: 10.1007/s13246-023-01374-w] [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: 07/13/2023] [Accepted: 12/18/2023] [Indexed: 01/30/2024]
Abstract
Single plan techniques for multiple brain targets (MBT) stereotactic radiosurgery (SRS) are now routine. Patient specific quality assurance (QA) for MBT poses challenges due to the limited capabilities of existing QA tools which necessitates several plan redeliveries. This study sought to develop an SRS QA phantom that enables flexible MBT patient specific QA in a single delivery, along with complex SRS commissioning. PLA marble and PLA StoneFil materials were selected based on the literature and previous research conducted in our department. The HU numbers were investigated to determine the appropriate percentage infill for skull and soft-tissue equivalence. A Prusa MK3S printer in conjunction with the above-mentioned filaments were used to print the SRS QA phantom. Quality control (QC) was performed on the printed skull, film inserts and plugs for point dose measurements. EBT3 film and point dose measurements were performed using a CC04 ionisation chamber. QC demonstrated that the SRS QA phantom transverse, coronal and sagittal film planes were orthogonal within 0.5°. HU numbers for the skull, film inserts and plugs were 858 ± 20 and 35 ± 12 respectively. Point and EBT3 film dose measurements were within 2.5% and 3%/2 mm 95% gamma pass rate, respectively except one Gross Tumour Volume (GTV) that had a slightly lower gamma pass rate. Dose distributions to five GTVs were measured with EBT3 film in a single plan delivery on CyberKnife. In conclusion, an SRS QA phantom was designed, and 3D printed and its use for performing complex MBT patient specific QA in a single delivery was demonstrated.
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Affiliation(s)
- Godfrey Mukwada
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Hospital Ave, Nedlands, WA, Australia.
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, WA, Australia.
| | - Andrew Hirst
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Hospital Ave, Nedlands, WA, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, WA, Australia
| | - Martin A Ebert
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Hospital Ave, Nedlands, WA, Australia
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, WA, Australia
- Medical School, Australian Centre for Quantitative Imaging, University of Western Australia, Crawley, WA, Australia
- School of Medicine and Population Health, University of Wisconsin, Madison, WI, USA
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166
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Karami Z, Yazdanfar SA, Kashefpour M, Khosrowabadi R. Brain waves and landscape settings: emotional responses to attractiveness. Exp Brain Res 2024; 242:1291-1300. [PMID: 38548893 DOI: 10.1007/s00221-024-06812-z] [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: 05/11/2023] [Accepted: 02/20/2024] [Indexed: 05/23/2024]
Abstract
Neuro-architecture is a specific branch of architecture that studies how the physical environment can change our mental processes and influence our behaviors. One of the main purposes of this field is to use changes in brain activities as a measure to quantify attractiveness of the landscapes. In this study, we investigated how changes in elements of attractiveness influence ones' emotional perception and present the related pattern of changes in brain activities. Therefore, we implied five elements of attractiveness including mystery, visual openness, landscape or greenness, walkability, and social interaction using the Delphi method. Then, we made changes in each element separately to make the landscape more attractive and assessed their effects on a group of young adults. We used the self-assessment manikin questionnaire to measure the participants' emotional perception while the participants' brain activities were recorded using a 32-channel EEG while exposed to the landscape images. The results showed that changes in attractive elements of the landscape could significantly improve ones' emotional perception of the landscape. In addition, these changes are perceived by changing the oscillatory pattern of brain activities. We hope these findings could shed a light to use of neural markers in measurement of place attractiveness.
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Affiliation(s)
- Zahra Karami
- School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran, Iran
| | - Seyed-Abbas Yazdanfar
- School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran, Iran
| | - Maryam Kashefpour
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Evin Sq., Tehran, 19839-63113, Iran
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Evin Sq., Tehran, 19839-63113, Iran.
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167
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Tokur ME, Alkan S. Bibliometric Analysis of Scientific Output Growth in the Field of Lung Transplantation. Thorac Cardiovasc Surg 2024; 72:300-310. [PMID: 37640062 DOI: 10.1055/a-2161-0420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
BACKGROUND Lung transplantation (LT) has recently emerged as a scientifically validated curative therapeutic modality for patients afflicted with end-stage lung disease. This study aimed to conduct a global bibliometric analysis of research articles on LT between 1983 and 2021. METHODS Employing the Web of Science database, a bibliometric analysis was conducted to assess the expansion of scientific output within the field of LT. We searched specific bibliometric characteristics such as language, and year of publication, first author, institutional affiliation, main publishing journals, and highly cited articles. Additionally, we made comparisons of the most productive countries. The VOSviewer program and the open-source visualization software Biblioshiny (version 2.0) were used to perform the bibliometric analysis. RESULTS We identified 10,467 articles on LT published between 1983 and 2021, of which 94.898% were published in the Science Citation Index Expanded. The articles were from 101 different research areas. The publications were from 81 different countries globally, and mostly from the United States (41.196%), Germany (7.118%), and Canada (6.372%). The Journal of Heart and Lung Transplantation was the most published journal. Four thousand seven hundred and ninety three of the publications were published in the last 10 years with a 78,781 citation number in total. The highest number of publications and citations was in 2021. CONCLUSION The majority of cutting-edge research findings are focused on only a few developed nations, and exchanges with emerging nations are still in their infancy. The United States has a strong, commanding position among the active countries in LT.
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Affiliation(s)
- Murat Emre Tokur
- Department of Chest Diseases, Department of Intensive Care, Ege University Faculty of Medicine, İzmir, Turkey
| | - Sevil Alkan
- Department of Infectious Diseases and Clinical Microbiology, Çanakkale Onsekiz Mart University Faculty of Medicine, Çanakkale, Turkey
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168
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Yamazawa Y, Osaka A, Fujii Y, Nakayama T, Nishioka K, Tanabe Y. Evaluation of the effect of sagging correction calibration errors in radiotherapy software on image matching. Phys Eng Sci Med 2024; 47:589-596. [PMID: 38372942 PMCID: PMC11166816 DOI: 10.1007/s13246-024-01388-y] [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: 07/17/2023] [Accepted: 01/08/2024] [Indexed: 02/20/2024]
Abstract
To investigate the impact of sagging correction calibration errors in radiotherapy software on image matching. Three software applications were used, with and without a polymethyl methacrylate rod supporting the ball bearings (BB). The calibration error for sagging correction across nine flex maps (FMs) was determined by shifting the BB positions along the Left-Right (LR), Gun-Target (GT), and Up-Down (UD) directions from the reference point. Lucy and pelvic phantom cone-beam computed tomography (CBCT) images underwent auto-matching after modifying each FM. Image deformation was assessed in orthogonal CBCT planes, and the correlations among BB shift magnitude, deformation vector value, and differences in auto-matching were analyzed. The average difference in analysis results among the three softwares for the Winston-Lutz test was within 0.1 mm. The determination coefficients (R2) between the BB shift amount and Lucy phantom matching error in each FM were 0.99, 0.99, and 1.00 in the LR-, GT-, and UD-directions, respectively. The pelvis phantom demonstrated no cross-correlation in the GT direction during auto-matching error evaluation using each FM. The correlation coefficient (r) between the BB shift and the deformation vector value was 0.95 on average for all image planes. Slight differences were observed among software in the evaluation of the Winston-Lutz test. The sagging correction calibration error in the radiotherapy imaging system was caused by an auto-matching error of the phantom and deformation of CBCT images.
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Affiliation(s)
- Yumi Yamazawa
- Department of Radiology, Niigata Prefectural Central Hospital, 205, Shin-minamimachi, Niigata, 205943-0192, Japan
| | - Akitane Osaka
- Department of Radiology, Niigata Prefectural Central Hospital, 205, Shin-minamimachi, Niigata, 205943-0192, Japan
| | - Yasushi Fujii
- Department of Radiology, Chugoku Central Hospital of the Mutual Aid Association of Public School Teachers, 148-13, Miyuki, Fukuyama, Hiroshima, 720-2121, Japan
| | - Takahiro Nakayama
- Department of Radiology, Chugoku Central Hospital of the Mutual Aid Association of Public School Teachers, 148-13, Miyuki, Fukuyama, Hiroshima, 720-2121, Japan
| | - Kunio Nishioka
- Department of Radiology, Tokuyama Central Hospital, 1-1 Kodacho, Shunan, Yamaguchi, 745-8522, Japan
| | - Yoshinori Tanabe
- Faculty of Medicine, Graduate School of Health Sciences, Okayama University, 2-5-1, Shikata, Kita, Okayama, 700-8525, Japan.
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169
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Ichikawa K, Kawashima H, Takata T. An image-based metal artifact reduction technique utilizing forward projection in computed tomography. Radiol Phys Technol 2024; 17:402-411. [PMID: 38546970 PMCID: PMC11128408 DOI: 10.1007/s12194-024-00790-1] [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: 01/06/2024] [Revised: 01/26/2024] [Accepted: 02/13/2024] [Indexed: 05/27/2024]
Abstract
The projection data generated via the forward projection of a computed tomography (CT) image (FP-data) have useful potentials in cases where only image data are available. However, there is a question of whether the FP-data generated from an image severely corrupted by metal artifacts can be used for the metal artifact reduction (MAR). The aim of this study was to investigate the feasibility of a MAR technique using FP-data by comparing its performance with that of a conventional robust MAR using projection data normalization (NMARconv). The NMARconv was modified to make use of FP-data (FPNMAR). A graphics processing unit was used to reduce the time required to generate FP-data and subsequent processes. The performances of FPNMAR and NMARconv were quantitatively compared using a normalized artifact index (AIn) for two cases each of hip prosthesis and dental fillings. Several clinical CT images with metal artifacts were processed by FPNMAR. The AIn values of FPNMAR and NMARconv were not significantly different from each other, showing almost the same performance between these two techniques. For all the clinical cases tested, FPNMAR significantly reduced the metal artifacts; thereby, the images of the soft tissues and bones obscured by the artifacts were notably recovered. The computation time per image was ~ 56 ms. FPNMAR, which can be applied to CT images without accessing the projection data, exhibited almost the same performance as that of NMARconv, while consuming significantly shorter processing time. This capability testifies the potential of FPNMAR for wider use in clinical settings.
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Affiliation(s)
- Katsuhiro Ichikawa
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan.
| | - Hiroki Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan
| | - Tadanori Takata
- Department of Diagnostic Radiology, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, 920-8641, Japan
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170
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Zhao K, Zhang H, Lin J, Xu S, Liu J, Qian X, Gu Y, Ren G, Lu X, Chen B, Chen D, Yan J, Ma J, Wei W, Wang Y. Radiomic Prediction of CCND1 Expression Levels and Prognosis in Low-grade Glioma Based on Magnetic Resonance Imaging. Acad Radiol 2024:S1076-6332(24)00196-X. [PMID: 38824087 DOI: 10.1016/j.acra.2024.03.031] [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: 10/27/2023] [Revised: 03/24/2024] [Accepted: 03/24/2024] [Indexed: 06/03/2024]
Abstract
OJECTIVES Low-grade glioma (LGG) is associated with increased mortality owing to recrudescence and the tendency for malignant transformation. Therefore, it is imperative to discover novel prognostic biomarkers as existing traditional prognostic biomarkers of glioma, including clinicopathological features and imaging examinations, are unable to meet the clinical demand for precision medicine. Accordingly, we aimed to evaluate the prognostic value of cyclin D1 (CCND1) expression levels and construct radiomic models to predict these levels in patients with LGG MATERIALS AND METHODS: A total of 412 LGG cases from The Cancer Genome Atlas (TCGA) were used for gene-based prognostic analysis. Using magnetic resonance imaging (MRI) images stored in The Cancer Imaging Archive with genomic data from TCGA, 149 cases were selected for radiomics feature extraction and model construction. After feature extraction, the radiomic signature was constructed using logistic regression (LR) and support vector machine (SVM) analyses. RESULTS CCND1 was identified as a prognosis-related gene with differential expression in tumor and normal samples and plays a role in regulating both the cell cycle and immune response. Landmark analysis revealed that high-expression levels of CCND1 were beneficial for survival (P < 0.05) in advanced LGG. Four optimal radiomics features were selected to construct radiomics models. The performance of LR and SVM achieved areas under the curve of 0.703 and 0.705, as well as 0.724 and 0.726 in the training and validation sets, respectively. CONCLUSION Elevated levels of CCND1 expression could impact the prognosis of patients with LGG. MRI-based radiomics, especially the AUC values, can serve as a novel tool for predicting CCND1 expression and understanding the correlation between elevated CCND1 expression and prognosis. AVAILABILITY OF DATA AND MATERIALS The datasets analyzed during the current study are available in the TCGA, TCIA, UCSC XENA and GTEx repository, https://portal.gdc.cancer.gov/, https://www.cancerimagingarchive.net/, https://xenabrowser.net/datapages/, https://www.gtexportal.org/home/.
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Affiliation(s)
- Kun Zhao
- Department of Neurology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (K.Z., S.X., J.L.); Department of Cell Biology, Institute of Bioengineering, School of Medicine, Soochow University, Suzhou, Jiangsu, China (K.Z., W.W.); Suzhou Niumag Analytical Instrument Corporation, Suzhou, Jiangsu, China (K.Z., D.C., J.Y.)
| | - Hui Zhang
- Fujian Center for Safety Evaluation of New Drug, Fujian Medical University, Fuzhou, Fujian, China (H.Z.)
| | - Jianyang Lin
- Department of General Surgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (J.L.)
| | - Shoucheng Xu
- Department of Neurology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (K.Z., S.X., J.L.)
| | - Jianzhi Liu
- Department of Neurology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (K.Z., S.X., J.L.)
| | - Xianjing Qian
- Medical College, Jiangsu University, Zhenjiang, Jiangsu, China (X.Q.)
| | - Yongbing Gu
- Medical Imaging Department, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (Y.G., G.R.)
| | - Guoqiang Ren
- Medical Imaging Department, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (Y.G., G.R.)
| | - Xinyu Lu
- Department of Neurosurgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (X.L., B.C.)
| | - Baomin Chen
- Department of Neurosurgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (X.L., B.C.)
| | - Deng Chen
- Suzhou Niumag Analytical Instrument Corporation, Suzhou, Jiangsu, China (K.Z., D.C., J.Y.)
| | - Jun Yan
- Suzhou Niumag Analytical Instrument Corporation, Suzhou, Jiangsu, China (K.Z., D.C., J.Y.)
| | - Jichun Ma
- Laboratory Center, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China (J.M.)
| | - Wenxiang Wei
- Department of Cell Biology, Institute of Bioengineering, School of Medicine, Soochow University, Suzhou, Jiangsu, China (K.Z., W.W.)
| | - Yuanwei Wang
- Department of Neurology, Shuyang Hospital, Shuyang Hospital Affiliated to Xuzhou Medical University, Shuyang, Jiangsu, China (Y.W.).
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Vijayvargiya A, Sinha A, Gehlot N, Jena A, Kumar R, Moran K. S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality. PLoS One 2024; 19:e0301263. [PMID: 38820390 PMCID: PMC11142505 DOI: 10.1371/journal.pone.0301263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/13/2024] [Indexed: 06/02/2024] Open
Abstract
The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the collection, while the Synthetic Minority Oversampling Technique (SMOTE) is considered to balance the data by increasing the minority class samples during the training of machine learning techniques. The findings indicate that the hybrid WD-EEMD with SMOTE oversampling technique enhances the efficacy of the examined classifiers when employed on the imbalanced sEMG data. The F-Score of the Extra Tree Classifier, when utilizing WD-EEMD signal processing with SMOTE oversampling, is 98.4%, whereas, without the SMOTE oversampling technique, it is 95.1%.
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Affiliation(s)
- Ankit Vijayvargiya
- Insight Science Foundation Ireland Research Centre for Data Analytics, School of Human and Health Performance, Dublin City University, Dublin, Ireland
- Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, Rajasthan, India
| | - Aparna Sinha
- Department of Information Technology, Bansthali Vidyapeeth, Radha Kishnpura, Rajasthan, India
| | - Naveen Gehlot
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Ashutosh Jena
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Rajesh Kumar
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Kieran Moran
- Insight Science Foundation Ireland Research Centre for Data Analytics, School of Human and Health Performance, Dublin City University, Dublin, Ireland
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Jamal N, Krisanachinda A, Tsapaki V, Islam MR, Pawiro S, Al Omari M, Yeong CH, Myint TT, Kakakhel MB, Kharita MH, Lee CLJ, Ismail A, Nguyen TB, Knoll P, Ciraj-Bjelac O, Malek M. Strengthening education and training programmes for medical physics in Asia and the Pacific: the IAEA non-agreement technical cooperation (TC) regional RAS6088 project. Phys Eng Sci Med 2024:10.1007/s13246-024-01437-6. [PMID: 38807011 DOI: 10.1007/s13246-024-01437-6] [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: 12/29/2023] [Accepted: 04/30/2024] [Indexed: 05/30/2024]
Abstract
This article documents the work conducted in implementing the IAEA non-agreement TC regional RAS6088 project "Strengthening Education and Training Programmes for Medical Physics". Necessary information on the project was collected from the project counterparts via emails for a period of one month, starting from 21st September 2023, and verified at the Final Regional Coordination Meeting in Bangkok, Thailand from 30th October 2023 to 3rd November 2023. Sixty-three participants were trained in 5 Regional Training Courses (RTCs), with 48%, 32% and 20% in radiation therapy, diagnostic radiology, and nuclear medicine, respectively. One RTC was successfully organised to introduce molecular biology as an academic module to participants. Three participating Member States, namely United Arab Emirates (UAE), Nepal and Afghanistan have initiated processes to start the postgraduate master medical physics education programmes by coursework, adopting the IAEA TCS56 Guidelines. UAE has succeeded in completing the process while Nepal and Afghanistan have yet to initiate the programme. The postgraduate master medical physics programmes by coursework were strengthened in Indonesia, Jordan, Malaysia, Pakistan, Syria, and Thailand, along with the national registration of medical physicists. In particular, Thailand has revised 6 postgraduate master medical physics programmes by coursework during the tenure of this project. Home Based Assignment and RTCs have resulted in two publications. In conclusion, the RAS6088 project was found to have achieved its planned outcomes despite challenges faced due to the COVID-19 pandemic. It is proposed that a follow up project be implemented to increase the number of Member States who are better prepared to improve medical physics education and training in the region.
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Affiliation(s)
- Noriah Jamal
- Platinum Radiation Sciences Consultancy Sdn. Bhd, Kuala Lumpur, Malaysia.
| | | | - Virginia Tsapaki
- International Atomic Energy Agency, Vienna International Centre, Vienna, Austria
| | - Md Rafiqul Islam
- Institute of Nuclear Medical Physics, Bangladesh Atomic Energy Commission, Baipayl, Bangladesh
| | - Supriyanto Pawiro
- Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia
| | - Muhammad Al Omari
- Department of Radiology and Nuclear Medicine, King Abdullah University Hospital Al-Ramtha IRBID, Ar-Ramtha, Jordan
| | - Chai Hong Yeong
- Faculty of Health and Medical Sciences, Taylor's University, Selangor, Malaysia
| | - Thinn Thinn Myint
- Department of Nuclear Medicine, Yangon General Hospital, Yangon, Myanmar
| | - Muhammad Basim Kakakhel
- Department of Physics and Applied Mathematics, Institute of Engineering and Applied Sciences, Islamabad, Pakistan
| | | | - Cheow Lei James Lee
- Division of Radiation Oncology, National Cancer Center Singapore, Singapore, Singapore
| | - Anas Ismail
- Department of Protection and Safety, Atomic Energy Commission of Syria, Damascus, Syrian Arab Republic
| | | | - Peter Knoll
- International Atomic Energy Agency, Vienna International Centre, Vienna, Austria
| | - Olivera Ciraj-Bjelac
- International Atomic Energy Agency, Vienna International Centre, Vienna, Austria
| | - Massoud Malek
- International Atomic Energy Agency, Vienna International Centre, Vienna, Austria
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Claridge Mackonis E, Stensmyr R, Poldy R, White P, Moutrie Z, Gorjiara T, Seymour E, Erven T, Hardcastle N, Haworth A. Improving motion management in radiation therapy: findings from a workshop and survey in Australia and New Zealand. Phys Eng Sci Med 2024:10.1007/s13246-024-01405-0. [PMID: 38805104 DOI: 10.1007/s13246-024-01405-0] [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: 10/27/2023] [Accepted: 02/09/2024] [Indexed: 05/29/2024]
Abstract
Motion management has become an integral part of radiation therapy. Multiple approaches to motion management have been reported in the literature. To allow the sharing of experiences on current practice and emerging technology, the University of Sydney and the New South Wales/Australian Capital Territory branch of the Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) held a two-day motion management workshop. To inform the workshop program, participants were invited to complete a survey prior to the workshop on current use of motion management techniques and their opinion on the effectiveness of each approach. A post-workshop survey was also conducted, designed to capture changes in opinion as a result of workshop participation. The online workshop was the most well attended ever hosted by the ACPSEM, with over 300 participants and a response to the pre-workshop survey was received from at least 60% of the radiation therapy centres in Australia and New Zealand. Motion management is extensively used in the region with use of deep inspiration breath-hold (DIBH) reported by 98% of centres for left-sided breast treatments and 91% for at least some right-sided breast treatments. Surface guided radiation therapy (SGRT) was the most popular session at the workshop and survey results showed that the use of SGRT is likely to increase. The workshop provided an excellent opportunity for the exchange of knowledge and experience, with most survey respondents indicating that their participation would lead to improvements in the quality of delivery of treatments at their centres.
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Affiliation(s)
| | | | - Rachel Poldy
- Canberra Region Cancer Centre, Canberra, Australia
| | - Paul White
- South Eastern Sydney LHD, Randwick, Australia
| | - Zoë Moutrie
- South Western Sydney Cancer Services, Sydney, NSW, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- South Western Sydney Clinical School, University of NSW, Liverpool, NSW, Australia
| | | | | | - Tania Erven
- South Western Sydney Cancer Services, Sydney, NSW, Australia
| | - Nicholas Hardcastle
- Peter MacCallum Cancer Centres, Melbourne, Australia
- Institute of Medical Physics, University of Sydney, Camperdown, Australia
| | - Annette Haworth
- Institute of Medical Physics, University of Sydney, Camperdown, Australia
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174
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Benzaid A, Djemili R, Arbateni K. Seizure detection using nonlinear measures over EEG frequency bands and deep learning classifiers. Comput Methods Biomech Biomed Engin 2024:1-17. [PMID: 38803055 DOI: 10.1080/10255842.2024.2356634] [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: 01/10/2024] [Accepted: 05/10/2024] [Indexed: 05/29/2024]
Abstract
Epilepsy is a brain disorder that causes patients to suffer from convulsions, which affects their behavior and way of life. Epilepsy can be detected with electroencephalograms (EEGs), which record brain neural activity. Traditional approaches for detecting epileptic seizures from an EEG signal are time-consuming and annoying. To supersede these traditional methods, a myriad of automated seizure detection frameworks based on machine learning techniques have recently been developed. Feature extraction and classification are the two essential phases for seizure detection. The classifier assigns the proper class label after feature extraction lowers the input pattern space while maintaining useful features. This paper proposes a new feature extraction method based on calculating nonlinear features from the most relevant EEG frequency bands. The EEG signal is first decomposed into smaller time segments from which a vector of nonlinear features is computed and supplied to machine learning (ML) and deep learning (DL) classifiers. Experiments on the Bonn dataset reveals an accuracy of 99.7% reached in classifying normal and ictal EEG signals; and an accuracy of 98.8% in the discrimination of ictal and interictal EEG signals. Furthermore, a performance of 100% is achieved on the Hauz Khas dataset. The classification results of the proposed approach were compared to those from published state of the art techniques. Our results are equivalent to or better than some recent studies appeared in the literature.
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Affiliation(s)
- Amel Benzaid
- LRES Lab, Universite 20 Aout 1955 Skikda Faculte de Technologie, Skikda, Algeria
| | - Rafik Djemili
- LRES Lab, Universite 20 Aout 1955 Skikda Faculte de Technologie, Skikda, Algeria
| | - Khaled Arbateni
- LRES Lab, Universite 20 Aout 1955 Skikda Faculte de Technologie, Skikda, Algeria
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175
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Summerfield N, Morris E, Banerjee S, He Q, Ghanem AI, Zhu S, Zhao J, Dong M, Glide-Hurst C. Enhancing Precision in Cardiac Segmentation for Magnetic Resonance-Guided Radiation Therapy Through Deep Learning. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00671-0. [PMID: 38797498 DOI: 10.1016/j.ijrobp.2024.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/25/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024]
Abstract
PURPOSE Cardiac substructure dose metrics are more strongly linked to late cardiac morbidities than to whole-heart metrics. Magnetic resonance (MR)-guided radiation therapy (MRgRT) enables substructure visualization during daily localization, allowing potential for enhanced cardiac sparing. We extend a publicly available state-of-the-art deep learning framework, "No New" U-Net, to incorporate self-distillation (nnU-Net.wSD) for substructure segmentation for MRgRT. METHODS AND MATERIALS Eighteen (institute A) patients who underwent thoracic or abdominal radiation therapy on a 0.35 T MR-guided linear accelerator were retrospectively evaluated. On each image, 1 of 2 radiation oncologists delineated reference contours of 12 cardiac substructures (chambers, great vessels, and coronary arteries) used to train (n = 10), validate (n = 3), and test (n = 5) nnU-Net.wSD by leveraging a teacher-student network and comparing it to standard 3-dimensional U-Net. The impact of using simulation data or including 3 to 4 daily images for augmentation during training was evaluated for nnU-Net.wSD. Geometric metrics (Dice similarity coefficient, mean distance to agreement, and 95% Hausdorff distance), visual inspection, and clinical dose-volume histograms were evaluated. To determine generalizability, institute A's model was tested on an unlabeled data set from institute B (n = 22) and evaluated via consensus scoring and volume comparisons. RESULTS nnU-Net.wSD yielded a Dice similarity coefficient (reported mean ± SD) of 0.65 ± 0.25 across the 12 substructures (chambers, 0.85 ± 0.05; great vessels, 0.67 ± 0.19; and coronary arteries, 0.33 ± 0.16; mean distance to agreement, <3 mm; mean 95% Hausdorff distance, <9 mm) while outperforming the 3-dimensional U-Net (0.583 ± 0.28; P <.01). Leveraging fractionated data for augmentation improved over a single MR simulation time point (0.579 ± 0.29; P <.01). Predicted contours yielded dose-volume histograms that closely matched those of the clinical treatment plans where mean and maximum (ie, dose to 0.03 cc) doses deviated by 0.32 ± 0.5 Gy and 1.42 ± 2.6 Gy, respectively. There were no statistically significant differences between institute A and B volumes (P >.05) for 11 of 12 substructures, with larger volumes requiring minor changes and coronary arteries exhibiting more variability. CONCLUSIONS This work is a critical step toward rapid and reliable cardiac substructure segmentation to improve cardiac sparing in low-field MRgRT.
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Affiliation(s)
- Nicholas Summerfield
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin; Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Eric Morris
- Department of Radiation Oncology, Washington University of Medicine in St. Louis, St. Louis, Missouri
| | - Soumyanil Banerjee
- Department of Computer Science, Wayne State University, Detroit, Michigan
| | - Qisheng He
- Department of Computer Science, Wayne State University, Detroit, Michigan
| | - Ahmed I Ghanem
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan; Alexandria Department of Clinical Oncology, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Simeng Zhu
- Department of Radiation Oncology, The Ohio State University, Columbus, Ohio
| | - Jiwei Zhao
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Ming Dong
- Department of Computer Science, Wayne State University, Detroit, Michigan
| | - Carri Glide-Hurst
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin; Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin.
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176
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Wang B, Xu Y, Peng S, Wang H, Li F. Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks. SENSORS (BASEL, SWITZERLAND) 2024; 24:3360. [PMID: 38894151 PMCID: PMC11174829 DOI: 10.3390/s24113360] [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: 04/30/2024] [Revised: 05/17/2024] [Accepted: 05/18/2024] [Indexed: 06/21/2024]
Abstract
Epilepsy is a common neurological disorder, and its diagnosis mainly relies on the analysis of electroencephalogram (EEG) signals. However, the raw EEG signals contain limited recognizable features, and in order to increase the recognizable features in the input of the network, the differential features of the signals, the amplitude spectrum and the phase spectrum in the frequency domain are extracted to form a two-dimensional feature vector. In order to solve the problem of recognizing multimodal features, a neural network model based on a multimodal dual-stream network is proposed, which uses a mixture of one-dimensional convolution, two-dimensional convolution and LSTM neural networks to extract the spatial features of the EEG two-dimensional vectors and the temporal features of the signals, respectively, and combines the advantages of the two networks, using the hybrid neural network to extract both the temporal and spatial features of the signals at the same time. In addition, a channel attention module was used to focus the model on features related to seizures. Finally, multiple sets of experiments were conducted on the Bonn and New Delhi data sets, and the highest accuracy rates of 99.69% and 97.5% were obtained on the test set, respectively, verifying the superiority of the proposed model in the task of epileptic seizure detection.
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Affiliation(s)
- Baiyang Wang
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China; (B.W.)
| | - Yidong Xu
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China; (B.W.)
| | - Siyu Peng
- School of Information Engineering, Changji University, Changji Hui Autonomous Prefecture, Changji 831100, China
| | - Hongjun Wang
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China; (B.W.)
| | - Fang Li
- School of Information Engineering, Changji University, Changji Hui Autonomous Prefecture, Changji 831100, China
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Costa IM, Firth G, Kim J, Banu A, Pham TT, Sunassee K, Langdon S, De Santis V, Vass L, Schettino G, Fruhwirth GO, Terry SYA. In Vitro and Preclinical Systematic Dose-Effect Studies of Auger Electron- and β Particle-Emitting Radionuclides and External Beam Radiation for Cancer Treatment. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00672-2. [PMID: 38797497 DOI: 10.1016/j.ijrobp.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 04/09/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024]
Abstract
PURPOSE Despite a rise in clinical use of radiopharmaceutical therapies, the biological effects of radionuclides and their relationship with absorbed radiation dose are poorly understood. Here, we set out to define this relationship for Auger electron emitters [99mTc]TcO4- and [123I]I- and β--particle emitter [188Re]ReO4-. Studies were carried out using genetically modified cells that permitted direct radionuclide comparisons. METHODS AND MATERIALS Triple-negative MDA-MB-231 breast cancer cells expressing the human sodium iodide symporter (hNIS) and green fluorescent protein (GFP; MDA-MB-231.hNIS-GFP) were used. In vitro radiotoxicity of [99mTc]TcO4-, [123I]I-, and [188Re]ReO4- was determined using clonogenic assays. Radionuclide uptake, efflux, and subcellular location were used to calculate nuclear absorbed doses using the Medical Internal Radiation Dose (MIRD) formalism. In vivo studies were performed using female NSG mice bearing orthotopic MDA-MB-231.hNIS-GFP tumors and compared with X-ray-treated (12.6-15 Gy) and untreated cohorts. Absorbed dose per unit activity in tumors and sodium iodide symporter-expressing organs was extrapolated to reference human adult models using OLINDA/EXM. RESULTS [99mTc]TcO4- and [123I]I- reduced the survival fraction only in hNIS-expressing cells, whereas [188Re]ReO4- reduced survival fraction in hNIS-expressing and parental cells. [123I]I- required 2.4- and 1.5-fold lower decays/cell to achieve 37% survival compared with [99mTc]TcO4- and [188Re]ReO4-, respectively, after 72 hours of incubation. Additionally, [99mTc]TcO4-, [123I]I-, and [188Re]ReO4- had superior cell killing effectiveness in vitro compared with X-rays. In vivo, X-ray led to a greater median survival compared with [188Re]ReO4- and [123I]I- (54 days vs 45 and 43 days, respectively). Unlike the X-ray cohort, no metastases were visualized in the radionuclide-treated cohorts. Extrapolated human absorbed doses of [188Re]ReO4- to a 1 g tumor were 13.8- and 11.2-fold greater than for [123I]I- in female and male models, respectively. CONCLUSIONS This work reports reference dose-effect data using cell and tumor models for [99mTc]TcO4-, [123I]I-, and [188Re]ReO4- for the first time. We further demonstrate the tumor-controlling effects of [123I]I- and [188Re]ReO4- in comparison with external beam radiation therapy.
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Affiliation(s)
- Ines M Costa
- Department of Imaging Chemistry and Biology, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Imaging Therapies and Cancer Group, Comprehensive Cancer Centre, School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom
| | - George Firth
- Department of Imaging Chemistry and Biology, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Kim
- Department of Imaging Chemistry and Biology, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Arshiya Banu
- Department of Imaging Chemistry and Biology, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Truc T Pham
- Department of Imaging Chemistry and Biology, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Kavitha Sunassee
- Department of Imaging Chemistry and Biology, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Sophie Langdon
- Department of Imaging Chemistry and Biology, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Vittorio De Santis
- Department of Imaging Chemistry and Biology, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Laurence Vass
- Department of Imaging Chemistry and Biology, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Giuseppe Schettino
- Medical Radiation Science Group, National Physical Laboratory, Teddington, United Kingdom.
| | - Gilbert O Fruhwirth
- Imaging Therapies and Cancer Group, Comprehensive Cancer Centre, School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom.
| | - Samantha Y A Terry
- Department of Imaging Chemistry and Biology, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
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Alsulimani A, Akhter N, Jameela F, Ashgar RI, Jawed A, Hassani MA, Dar SA. The Impact of Artificial Intelligence on Microbial Diagnosis. Microorganisms 2024; 12:1051. [PMID: 38930432 PMCID: PMC11205376 DOI: 10.3390/microorganisms12061051] [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: 05/08/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.
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Affiliation(s)
- Ahmad Alsulimani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Naseem Akhter
- Department of Biology, Arizona State University, Lake Havasu City, AZ 86403, USA;
| | - Fatima Jameela
- Modern American Dental Clinic, West Warren Avenue, Dearborn, MI 48126, USA;
| | - Rnda I. Ashgar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Arshad Jawed
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Mohammed Ahmed Hassani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Sajad Ahmad Dar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
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179
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Matsuo M, Higuchi T, Ichibakase T, Suyama H, Takahara R, Nakamura M. Differences in Electroencephalography Power Levels between Poor and Good Performance in Attentional Tasks. Brain Sci 2024; 14:527. [PMID: 38928528 PMCID: PMC11202263 DOI: 10.3390/brainsci14060527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/09/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024] Open
Abstract
Decreased attentional function causes problems in daily life. However, a quick and easy evaluation method of attentional function has not yet been developed. Therefore, we are searching for a method to evaluate attentional function easily and quickly. This study aimed to collect basic data on the features of electroencephalography (EEG) during attention tasks to develop a new method for evaluating attentional function using EEG. Twenty healthy young adults participated; we examined cerebral activity during a Clinical Assessment for Attention using portable EEG devices. The Mann-Whitney U test was performed to assess differences in power levels of EEG during tasks between the low- and high-attention groups. The findings revealed that the high-attention group showed significantly higher EEG power levels in the δ wave of L-temporal and bilateral parietal lobes, as well as in the β and γ waves of the R-occipital lobe, than did the low-attention group during digit-forward, whereas the high-attention group showed significantly higher EEG power levels in the θ wave of R-frontal and the α wave of bilateral frontal lobes during digit-backward. Notably, lower θ, α, and β bands of the right hemisphere found in the low-attention group may be key elements to detect attentional deficit.
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Affiliation(s)
- Moemi Matsuo
- Faculty of Rehabilitation Sciences, Nishikyushu University, Kanzaki 842-8585, Saga, Japan (M.N.)
| | - Takashi Higuchi
- Department of Physical Therapy, Osaka University of Human Sciences, Settsu 566-8501, Osaka, Japan;
| | - Taiyo Ichibakase
- Faculty of Rehabilitation Sciences, Nishikyushu University, Kanzaki 842-8585, Saga, Japan (M.N.)
| | - Hikaru Suyama
- Faculty of Rehabilitation Sciences, Nishikyushu University, Kanzaki 842-8585, Saga, Japan (M.N.)
| | - Runa Takahara
- Faculty of Rehabilitation Sciences, Nishikyushu University, Kanzaki 842-8585, Saga, Japan (M.N.)
| | - Masatoshi Nakamura
- Faculty of Rehabilitation Sciences, Nishikyushu University, Kanzaki 842-8585, Saga, Japan (M.N.)
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180
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Stocker D, Sommer C, Gueng S, Stäuble J, Özden I, Griessinger J, Weyland MS, Lutters G, Scheidegger S. Probabilistic U-Net model observer for the DDC method in CT scan protocol optimization. Phys Med Biol 2024; 69:115026. [PMID: 38657639 DOI: 10.1088/1361-6560/ad4302] [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: 10/25/2023] [Accepted: 04/24/2024] [Indexed: 04/26/2024]
Abstract
Optimizing complex imaging procedures within Computed Tomography, considering both dose and image quality, presents significant challenges amidst rapid technological advancements and the adoption of machine learning (ML) methods. A crucial metric in this context is the Difference-Detailed Curve, which relies on human observer studies. However, these studies are labor-intensive and prone to both inter- and intra-observer variability. To tackle these issues, a ML-based model observer utilizing the U-Net architecture and a Bayesian methodology is proposed. In order to train a model observer unaffected by the spatial arrangement of low-contrast objects, the image preprocessing incorporates a Gaussian Process-based noise model. Additionally, gradient-weighted class activation mapping is utilized to gain insights into the model observer's decision-making process. By training on data from a diverse group of observers, well-calibrated probabilistic predictions that quantify observer variability are achieved. Leveraging the principles of Beta regression, the Bayesian methodology is used to derive a model observer performance metric, effectively gauging the model observer's strength in terms of an 'effective number of observers'. Ultimately, this framework enables to predict the DDC distribution by applying thresholds to the inferred probabilities (Part of this work has been presented at: Stocker D, Sommer C, Gueng S, Stäuble J, Özden I, Griessinger J, Weyland M S, Lutters G, Scheidegger S (2023). Probabilistic U-Net Model Observer for the DDC Method in CT Scan Protocol Optimization. The 56th SSRMP Annual Meeting 2023, November 30. - December 1., 2023, Luzern, Switzerland).
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Affiliation(s)
- David Stocker
- ZHAW School of Engineering, 8401 Winterthur, Switzerland
| | | | - Sarah Gueng
- ZHAW School of Engineering, 8401 Winterthur, Switzerland
| | - Jason Stäuble
- ZHAW School of Engineering, 8401 Winterthur, Switzerland
| | - Ismail Özden
- Fachstelle Strahlenschutz und Medizinphysik, Kantonsspital Aarau, 5000 Aarau, Switzerland
| | - Jennifer Griessinger
- Fachstelle Strahlenschutz und Medizinphysik, Kantonsspital Aarau, 5000 Aarau, Switzerland
| | | | - Gerd Lutters
- Fachstelle Strahlenschutz und Medizinphysik, Kantonsspital Aarau, 5000 Aarau, Switzerland
| | - Stephan Scheidegger
- ZHAW School of Engineering, 8401 Winterthur, Switzerland
- Fachstelle Strahlenschutz und Medizinphysik, Kantonsspital Aarau, 5000 Aarau, Switzerland
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181
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Khan AQ, Sun G, Khalid M, Imran A, Bilal A, Azam M, Sarwar R. A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification. PLoS One 2024; 19:e0303094. [PMID: 38768222 PMCID: PMC11147523 DOI: 10.1371/journal.pone.0303094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 04/18/2024] [Indexed: 05/22/2024] Open
Abstract
In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant contributor to vision loss globally, through a pioneering approach. We propose a novel integration of a Genetic Grey Wolf Optimization (G-GWO) algorithm with a Fully Convolutional Encoder-Decoder Network (FCEDN), further enhanced by a Kernel Extreme Learning Machine (KELM) for refined image segmentation and disease classification. This innovative combination leverages the genetic algorithm and grey wolf optimization to boost the FCEDN's efficiency, enabling precise detection of DED stages and differentiation among disease types. Tested across diverse datasets, including IDRiD, DR-HAGIS, and ODIR, our model showcased superior performance, achieving classification accuracies between 98.5% to 98.8%, surpassing existing methods. This advancement sets a new standard in DED detection and offers significant potential for automating fundus image analysis, reducing reliance on manual examination, and improving patient care efficiency. Our findings are crucial to enhancing diagnostic accuracy and patient outcomes in DED management.
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Affiliation(s)
- Abdul Qadir Khan
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Guangmin Sun
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Majdi Khalid
- Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Azhar Imran
- Department of Creative Technologies, Air University, Islamabad, Pakistan
| | - Anas Bilal
- College of Information Science and Technology, Hainan Normal University, Haikou, China
- Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou, China
| | - Muhammad Azam
- Department of Computer Science, Superior University, Lahore, Pakistan
| | - Raheem Sarwar
- OTEHM, Manchester Metropolitan University, Manchester, United Kingdom
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182
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Modlińska S, Czogalik Ł, Rojek M, Dudek P, Janik M, Mielcarska S, Kufel J. Digital Subtraction Angiography of Cerebral Arteries: Influence of Cranial Dimensions on X-ray Tube Performance. J Clin Med 2024; 13:3002. [PMID: 38792543 PMCID: PMC11122296 DOI: 10.3390/jcm13103002] [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: 04/16/2024] [Revised: 05/13/2024] [Accepted: 05/17/2024] [Indexed: 05/26/2024] Open
Abstract
(1) Background. Digital subtraction angiography (DSA) is indispensable for diagnosing cerebral aneurysms due to its superior imaging precision. However, optimizing X-ray parameters is crucial for accurate diagnosis, with X-ray tube settings significantly influencing image quality. Understanding the relationship between skull dimensions and X-ray parameters is pivotal for tailoring imaging protocols to individual patients. (2) Methods. A retrospective analysis of DSA data from a single center was conducted, involving 251 patients. Cephalometric measurements and statistical analyses were performed to assess correlations between skull dimensions and X-ray tube parameters (voltage and current). (3) Results. The study revealed significant correlations between skull dimensions and X-ray tube parameters, highlighting the importance of considering individual anatomical variations. Gender-based differences in X-ray parameters were observed, emphasizing the need for personalized imaging protocols. (4) Conclusions. Personalized approaches to DSA imaging, integrating individual anatomical variations and gender-specific differences, are essential for optimizing diagnostic outcomes. While this study provides valuable insights, further research across multiple centers and diverse imaging equipment is warranted to validate these findings.
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Affiliation(s)
- Sandra Modlińska
- Department of Radiodiagnostics, Invasive Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-055 Katowice, Poland
- Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-055 Katowice, Poland
| | - Łukasz Czogalik
- Students’ Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-055 Katowice, Poland
| | - Marcin Rojek
- Students’ Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-055 Katowice, Poland
| | - Piotr Dudek
- Students’ Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-055 Katowice, Poland
| | - Michał Janik
- Students’ Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-055 Katowice, Poland
| | - Sylwia Mielcarska
- Department of Medical and Molecular Biology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland
| | - Jakub Kufel
- Department of Radiodiagnostics, Invasive Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-055 Katowice, Poland
- Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-055 Katowice, Poland
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland
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183
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Finnegan RN, Quinn A, Booth J, Belous G, Hardcastle N, Stewart M, Griffiths B, Carroll S, Thwaites DI. Cardiac substructure delineation in radiation therapy - A state-of-the-art review. J Med Imaging Radiat Oncol 2024. [PMID: 38757728 DOI: 10.1111/1754-9485.13668] [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: 01/24/2024] [Accepted: 04/29/2024] [Indexed: 05/18/2024]
Abstract
Delineation of cardiac substructures is crucial for a better understanding of radiation-related cardiotoxicities and to facilitate accurate and precise cardiac dose calculation for developing and applying risk models. This review examines recent advancements in cardiac substructure delineation in the radiation therapy (RT) context, aiming to provide a comprehensive overview of the current level of knowledge, challenges and future directions in this evolving field. Imaging used for RT planning presents challenges in reliably visualising cardiac anatomy. Although cardiac atlases and contouring guidelines aid in standardisation and reduction of variability, significant uncertainties remain in defining cardiac anatomy. Coupled with the inherent complexity of the heart, this necessitates auto-contouring for consistent large-scale data analysis and improved efficiency in prospective applications. Auto-contouring models, developed primarily for breast and lung cancer RT, have demonstrated performance comparable to manual contouring, marking a significant milestone in the evolution of cardiac delineation practices. Nevertheless, several key concerns require further investigation. There is an unmet need for expanding cardiac auto-contouring models to encompass a broader range of cancer sites. A shift in focus is needed from ensuring accuracy to enhancing the robustness and accessibility of auto-contouring models. Addressing these challenges is paramount for the integration of cardiac substructure delineation and associated risk models into routine clinical practice, thereby improving the safety of RT for future cancer patients.
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Affiliation(s)
- Robert N Finnegan
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Alexandra Quinn
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Jeremy Booth
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Gregg Belous
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Queensland, Australia
| | - Nicholas Hardcastle
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Maegan Stewart
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Brooke Griffiths
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Susan Carroll
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - David I Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
- Radiotherapy Research Group, Leeds Institute of Medical Research, St James's Hospital and University of Leeds, Leeds, UK
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184
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Uddin AH, Chen YL, Akter MR, Ku CS, Yang J, Por LY. Colon and lung cancer classification from multi-modal images using resilient and efficient neural network architectures. Heliyon 2024; 10:e30625. [PMID: 38742084 PMCID: PMC11089372 DOI: 10.1016/j.heliyon.2024.e30625] [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: 02/03/2024] [Revised: 04/02/2024] [Accepted: 04/30/2024] [Indexed: 05/16/2024] Open
Abstract
Automatic classification of colon and lung cancer images is crucial for early detection and accurate diagnostics. However, there is room for improvement to enhance accuracy, ensuring better diagnostic precision. This study introduces two novel dense architectures (D1 and D2) and emphasizes their effectiveness in classifying colon and lung cancer from diverse images. It also highlights their resilience, efficiency, and superior performance across multiple datasets. These architectures were tested on various types of datasets, including NCT-CRC-HE-100K (set of 100,000 non-overlapping image patches from hematoxylin and eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue), CRC-VAL-HE-7K (set of 7180 image patches from N = 50 patients with colorectal adenocarcinoma, no overlap with patients in NCT-CRC-HE-100K), LC25000 (Lung and Colon Cancer Histopathological Image), and IQ-OTHNCCD (Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases), showcasing their effectiveness in classifying colon and lung cancers from histopathological and Computed Tomography (CT) scan images. This underscores the multi-modal image classification capability of the proposed models. Moreover, the study addresses imbalanced datasets, particularly in CRC-VAL-HE-7K and IQ-OTHNCCD, with a specific focus on model resilience and robustness. To assess overall performance, the study conducted experiments in different scenarios. The D1 model achieved an impressive 99.80 % accuracy on the NCT-CRC-HE-100K dataset, with a Jaccard Index (J) of 0.8371, a Matthew's Correlation Coefficient (MCC) of 0.9073, a Cohen's Kappa (Kp) of 0.9057, and a Critical Success Index (CSI) of 0.8213. When subjected to 10-fold cross-validation on LC25000, the D1 model averaged (avg) 99.96 % accuracy (avg J, MCC, Kp, and CSI of 0.9993, 0.9987, 0.9853, and 0.9990), surpassing recent reported performances. Furthermore, the ensemble of D1 and D2 reached 93 % accuracy (J, MCC, Kp, and CSI of 0.7556, 0.8839, 0.8796, and 0.7140) on the IQ-OTHNCCD dataset, exceeding recent benchmarks and aligning with other reported results. Efficiency evaluations were conducted in various scenarios. For instance, training on only 10 % of LC25000 resulted in high accuracy rates of 99.19 % (J, MCC, Kp, and CSI of 0.9840, 0.9898, 0.9898, and 0.9837) (D1) and 99.30 % (J, MCC, Kp, and CSI of 0.9863, 0.9913, 0.9913, and 0.9861) (D2). In NCT-CRC-HE-100K, D2 achieved an impressive 99.53 % accuracy (J, MCC, Kp, and CSI of 0.9906, 0.9946, 0.9946, and 0.9906) with training on only 30 % of the dataset and testing on the remaining 70 %. When tested on CRC-VAL-HE-7K, D1 and D2 achieved 95 % accuracy (J, MCC, Kp, and CSI of 0.8845, 0.9455, 0.9452, and 0.8745) and 96 % accuracy (J, MCC, Kp, and CSI of 0.8926, 0.9504, 0.9503, and 0.8798), respectively, outperforming previously reported results and aligning closely with others. Lastly, training D2 on just 10 % of NCT-CRC-HE-100K and testing on CRC-VAL-HE-7K resulted in significant outperformance of InceptionV3, Xception, and DenseNet201 benchmarks, achieving an accuracy rate of 82.98 % (J, MCC, Kp, and CSI of 0.7227, 0.8095, 0.8081, and 0.6671). Finally, using explainable AI algorithms such as Grad-CAM, Grad-CAM++, Score-CAM, and Faster Score-CAM, along with their emphasized versions, we visualized the features from the last layer of DenseNet201 for histopathological as well as CT-scan image samples. The proposed dense models, with their multi-modality, robustness, and efficiency in cancer image classification, hold the promise of significant advancements in medical diagnostics. They have the potential to revolutionize early cancer detection and improve healthcare accessibility worldwide.
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Affiliation(s)
- A. Hasib Uddin
- Department of Computer Science and Engineering, Khwaja Yunus Ali University, Enayetpur, Chouhali, Sirajganj, 6751, Bangladesh
| | - Yen-Lin Chen
- Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, 106344, Taiwan
| | - Miss Rokeya Akter
- Department of Computer Science and Engineering, Khwaja Yunus Ali University, Enayetpur, Chouhali, Sirajganj, 6751, Bangladesh
| | - Chin Soon Ku
- Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar, 31900, Malaysia
| | - Jing Yang
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Lip Yee Por
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
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185
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Bennour A, Ben Aoun N, Khalaf OI, Ghabban F, Wong WK, Algburi S. Contribution to pulmonary diseases diagnostic from X-ray images using innovative deep learning models. Heliyon 2024; 10:e30308. [PMID: 38707425 PMCID: PMC11068804 DOI: 10.1016/j.heliyon.2024.e30308] [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: 02/15/2024] [Revised: 04/09/2024] [Accepted: 04/23/2024] [Indexed: 05/07/2024] Open
Abstract
Pulmonary disease identification and characterization are among the most intriguing research topics of recent years since they require an accurate and prompt diagnosis. Although pulmonary radiography has helped in lung disease diagnosis, the interpretation of the radiographic image has always been a major concern for doctors and radiologists to reduce diagnosis errors. Due to their success in image classification and segmentation tasks, cutting-edge artificial intelligence techniques like machine learning (ML) and deep learning (DL) are widely encouraged to be applied in the field of diagnosing lung disorders and identifying them using medical images, particularly radiographic ones. For this end, the researchers are concurring to build systems based on these techniques in particular deep learning ones. In this paper, we proposed three deep-learning models that were trained to identify the presence of certain lung diseases using thoracic radiography. The first model, named "CovCXR-Net", identifies the COVID-19 disease (two cases: COVID-19 or normal). The second model, named "MDCXR3-Net", identifies the COVID-19 and pneumonia diseases (three cases: COVID-19, pneumonia, or normal), and the last model, named "MDCXR4-Net", is destined to identify the COVID-19, pneumonia and the pulmonary opacity diseases (4 cases: COVID-19, pneumonia, pulmonary opacity or normal). These models have proven their superiority in comparison with the state-of-the-art models and reached an accuracy of 99,09 %, 97.74 %, and 90,37 % respectively with three benchmarks.
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Affiliation(s)
- Akram Bennour
- LAMIS Laboratiry, Echahid Cheikh Larbi Tebessi University, Tebessa, Algeria
| | - Najib Ben Aoun
- College of Computer Science and Information Technology, Al-Baha University, Al Baha, Saudi Arabia
- REGIM-Lab: Research Groups in Intelligent Machines, National School of Engineers of Sfax (ENIS), University of Sfax, Tunisia
| | - Osamah Ibrahim Khalaf
- Department of Solar, Al-Nahrain Research Center for Renewable Energy, Al-Nahrain University, Jadriya, Baghdad, Iraq
| | - Fahad Ghabban
- College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
| | | | - Sameer Algburi
- Al-Kitab University, College of Engineering Techniques, Kirkuk, Iraq
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186
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Monsef A, Sheikhzadeh P, Steiner JR, Sadeghi F, Yazdani M, Ghafarian P. Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study. Biomed Phys Eng Express 2024; 10:045019. [PMID: 38608316 DOI: 10.1088/2057-1976/ad3e00] [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: 10/16/2023] [Accepted: 04/12/2024] [Indexed: 04/14/2024]
Abstract
Objectives: The aim of this study was to evaluate Cu-64 PET phantom image quality using Bayesian Penalized Likelihood (BPL) and Ordered Subset Expectation Maximum with point-spread function modeling (OSEM-PSF) reconstruction algorithms. In the BPL, the regularization parameterβwas varied to identify the optimum value for image quality. In the OSEM-PSF, the effect of acquisition time was evaluated to assess the feasibility of shortened scan duration.Methods: A NEMA IEC PET body phantom was filled with known activities of water soluble Cu-64. The phantom was imaged on a PET/CT scanner and was reconstructed using BPL and OSEM-PSF algorithms. For the BPL reconstruction, variousβvalues (150, 250, 350, 450, and 550) were evaluated. For the OSEM-PSF algorithm, reconstructions were performed using list-mode data intervals ranging from 7.5 to 240 s. Image quality was assessed by evaluating the signal to noise ratio (SNR), contrast to noise ratio (CNR), and background variability (BV).Results: The SNR and CNR were higher in images reconstructed with BPL compared to OSEM-PSF. Both the SNR and CNR increased with increasingβ, peaking atβ= 550. The CNR for allβ, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images withβ= 550 demonstrated the highest improvement in image quality. For OSEM-PSF reconstructed images with list-mode data duration ≥ 120 s, the noise level and CNR were not significantly different from the baseline 240 s list-mode data duration.Conclusions: BPL reconstruction improved Cu-64 PET phantom image quality by increasing SNR and CNR relative to OSEM-PSF reconstruction. Additionally, this study demonstrated scan time can be reduced from 240 to 120 s when using OSEM-PSF reconstruction while maintaining similar image quality. This study provides baseline data that may guide future studies aimed to improve clinical Cu-64 imaging.
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Affiliation(s)
- Abbas Monsef
- Department of Radiation Oncology, University of Minnesota Medical School, Minneapolis, United States of America
- Department of Radiology, University of Minnesota Medical School, Minneapolis, United States of America
| | - Peyman Sheikhzadeh
- Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | - Joseph R Steiner
- Department of Radiology, University of Minnesota Medical School, Minneapolis, United States of America
| | - Fatemeh Sadeghi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Pardis Ghafarian
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
- PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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187
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Chow JCL, Ruda HE. Mechanisms of Action in FLASH Radiotherapy: A Comprehensive Review of Physicochemical and Biological Processes on Cancerous and Normal Cells. Cells 2024; 13:835. [PMID: 38786057 PMCID: PMC11120005 DOI: 10.3390/cells13100835] [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: 04/09/2024] [Revised: 05/09/2024] [Accepted: 05/11/2024] [Indexed: 05/25/2024] Open
Abstract
The advent of FLASH radiotherapy (FLASH-RT) has brought forth a paradigm shift in cancer treatment, showcasing remarkable normal cell sparing effects with ultra-high dose rates (>40 Gy/s). This review delves into the multifaceted mechanisms underpinning the efficacy of FLASH effect, examining both physicochemical and biological hypotheses in cell biophysics. The physicochemical process encompasses oxygen depletion, reactive oxygen species, and free radical recombination. In parallel, the biological process explores the FLASH effect on the immune system and on blood vessels in treatment sites such as the brain, lung, gastrointestinal tract, skin, and subcutaneous tissue. This review investigated the selective targeting of cancer cells and the modulation of the tumor microenvironment through FLASH-RT. Examining these mechanisms, we explore the implications and challenges of integrating FLASH-RT into cancer treatment. The potential to spare normal cells, boost the immune response, and modify the tumor vasculature offers new therapeutic strategies. Despite progress in understanding FLASH-RT, this review highlights knowledge gaps, emphasizing the need for further research to optimize its clinical applications. The synthesis of physicochemical and biological insights serves as a comprehensive resource for cell biology, molecular biology, and biophysics researchers and clinicians navigating the evolution of FLASH-RT in cancer therapy.
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Affiliation(s)
- James C. L. Chow
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1X6, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Harry E. Ruda
- Centre of Advance Nanotechnology, Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada;
- Department of Materials Science and Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada
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188
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Smyth L, Alves A, Collins K, Beveridge S. Gafchromic EBT3 film provides equivalent dosimetric performance to EBT-XD film for stereotactic radiosurgery dosimetry. Phys Eng Sci Med 2024:10.1007/s13246-024-01430-z. [PMID: 38739345 DOI: 10.1007/s13246-024-01430-z] [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: 08/14/2023] [Accepted: 04/18/2024] [Indexed: 05/14/2024]
Abstract
The accurate assessment of film results is highly dependent on the methodology and techniques used to process film. This study aims to compare the performance of EBT3 and EBT-XD film for SRS dosimetry using two different film processing methods. Experiments were performed in a solid water slab and an anthropomorphic head phantom. For each experiment, the net optical density of the film was calculated using two different methods; taking the background (initial) optical density from 1) an unirradiated film from the same film lot as the irradiated film (stock to stock (S-S) method), and 2) a scan of the same piece of film taken prior to irradiation (film to film (F-F) method). EBT3 and EBT-XD performed similarly across the suite of experiments when using the green channel only or with triple channel RGB dosimetry. The dosimetric performance of EBT-XD was improved across all colour channels by using an F-F method, particularly for the blue channel. In contrast, EBT3 performed similarly well regardless of the net optical density method used. Across 21 SRS treatment plans, the average per-pixel agreement between EBT3 and EBT-XD films, normalised to the 20 Gy prescription dose, was within 2% and 4% for the non-target (2-10 Gy) and target (> 10 Gy) regions, respectively, when using the F-F method. At doses relevant to SRS, EBT3 provides comparable dosimetric performance to EBT-XD. In addition, an S-S dosimetry method is suitable for EBT3 while an F-F method should be adopted if using EBT-XD.
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Affiliation(s)
- Lloyd Smyth
- Australian Radiation Protection And Nuclear Safety Agency, Australian Clinical Dosimetry Service, Yallambie, VIC, Australia
| | - Andrew Alves
- Australian Radiation Protection And Nuclear Safety Agency, Australian Clinical Dosimetry Service, Yallambie, VIC, Australia
| | - Katherine Collins
- Australian Radiation Protection And Nuclear Safety Agency, Australian Clinical Dosimetry Service, Yallambie, VIC, Australia
| | - Sabeena Beveridge
- Australian Radiation Protection And Nuclear Safety Agency, Australian Clinical Dosimetry Service, Yallambie, VIC, Australia.
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189
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Risqiwati D, Wibawa AD, Pane ES, Yuniarno EM, Islamiyah WR, Purnomo MH. Effective relax acquisition: a novel approach to classify relaxed state in alpha band EEG-based transformation. Brain Inform 2024; 11:12. [PMID: 38740660 DOI: 10.1186/s40708-024-00225-y] [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: 04/17/2023] [Accepted: 04/17/2024] [Indexed: 05/16/2024] Open
Abstract
A relaxed state is essential for effective hypnotherapy, a crucial component of mental health treatments. During hypnotherapy sessions, neurologists rely on the patient's relaxed state to introduce positive suggestions. While EEG is a widely recognized method for detecting human emotions, analyzing EEG data presents challenges due to its multi-channel, multi-band nature, leading to high-dimensional data. Furthermore, determining the onset of relaxation remains challenging for neurologists. This paper presents the Effective Relax Acquisition (ERA) method designed to identify the beginning of a relaxed state. ERA employs sub-band sampling within the Alpha band for the frequency domain and segments the data into four-period groups for the time domain analysis. Data enhancement strategies include using Window Length (WL) and Overlapping Shifting Windows (OSW) scenarios. Dimensionality reduction is achieved through Principal Component Analysis (PCA) by prioritizing the most significant eigenvector values. Our experimental results indicate that the relaxed state is predominantly observable in the high Alpha sub-band, particularly within the fourth period group. The ERA demonstrates high accuracy with a WL of 3 s and OSW of 0.25 s using the KNN classifier (90.63%). These findings validate the effectiveness of ERA in accurately identifying relaxed states while managing the complexity of EEG data.
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Affiliation(s)
- Diah Risqiwati
- Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
- Departement of Informatics, Universitas Muhammadiyah Malang, Tlogomas, Malang, 65144, Indonesia
| | - Adhi Dharma Wibawa
- Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
- Medical Technology, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
| | - Evi Septiana Pane
- Industrial Training and Education of Surabaya, Ministry of Industry RI, Gayungan, Surabaya, 60235, Indonesia
| | - Eko Mulyanto Yuniarno
- Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
- Departement of Computer Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
| | - Wardah Rahmatul Islamiyah
- Neurology Department, Faculty of Medicine, Universitas Airlangga, Gubeng, Surabaya, 60131, Indonesia
| | - Mauridhi Hery Purnomo
- Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia.
- Departement of Computer Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia.
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190
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Durán-Santos M, Salazar-Varas R, Etcheverry G. Modeling the cortical response elicited by wrist manipulation via a nonlinear delay differential embedding. Phys Eng Sci Med 2024:10.1007/s13246-024-01427-8. [PMID: 38739346 DOI: 10.1007/s13246-024-01427-8] [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: 08/01/2023] [Accepted: 04/16/2024] [Indexed: 05/14/2024]
Abstract
Regarding motor processes, modeling healthy people's brains is essential to understand the brain activity in people with motor impairments. However, little research has been undertaken when external forces disturb limbs, having limited information on physiological pathways. Therefore, in this paper, a nonlinear delay differential embedding model is used to estimate the brain response elicited by externally controlled wrist movement in healthy individuals. The aim is to improve the understanding of the relationship between a controlled wrist movement and the generated cortical activity of healthy people, helping to disclose the underlying mechanisms and physiological relationships involved in the motor event. To evaluate the model, a public database from the Delft University of Technology is used, which contains electroencephalographic recordings of ten healthy subjects while wrist movement was externally provoked by a robotic system. In this work, the cortical response related to movement is identified via Independent Component Analysis and estimated based on a nonlinear delay differential embedding model. After a cross-validation analysis, the model performance reaches 90.21% ± 4.46% Variance Accounted For, and Correlation 95.14% ± 2.31%. The proposed methodology allows to select the model degree, to estimate a general predominant operation mode of the cortical response elicited by wrist movement. The obtained results revealed two facts that had not previously been reported: the movement's acceleration affects the cortical response, and a common delayed activity is shared among subjects. Going forward, identifying biomarkers related to motor tasks could aid in the evaluation of rehabilitation treatments for patients with upper limbs motor impairments.
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Affiliation(s)
- Martín Durán-Santos
- Department of Computing, Electronics and Mechatronics, Universidad de las Americas Puebla (UDLAP), Ex Hacienda Sta. Catarina Mártir S/N, C.P. 72810, San Andrés Cholula, Puebla, Mexico.
| | - R Salazar-Varas
- Department of Computing, Electronics and Mechatronics, Universidad de las Americas Puebla (UDLAP), Ex Hacienda Sta. Catarina Mártir S/N, C.P. 72810, San Andrés Cholula, Puebla, Mexico
| | - Gibran Etcheverry
- Department of Mathematics, Tiffin University, 155 Miami St, Tiffin, OH, 44883, USA
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191
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Mercier M, Pepi C, Carfi-Pavia G, De Benedictis A, Espagnet MCR, Pirani G, Vigevano F, Marras CE, Specchio N, De Palma L. The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach. Sci Rep 2024; 14:10887. [PMID: 38740844 PMCID: PMC11091060 DOI: 10.1038/s41598-024-60622-5] [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: 10/06/2023] [Accepted: 04/25/2024] [Indexed: 05/16/2024] Open
Abstract
Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.
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Affiliation(s)
- Mattia Mercier
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
- Department of Physiology, Behavioural Neuroscience PhD Program, Sapienza University, Rome, Italy
| | - Chiara Pepi
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Giusy Carfi-Pavia
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | | | - Greta Pirani
- Department of Mechanical and Aerospace Engineering - DIMA, Sapienza University of Rome, Rome, Italy
| | - Federico Vigevano
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Carlo Efisio Marras
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Nicola Specchio
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy.
| | - Luca De Palma
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
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192
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Li Z, Gan G, Guo J, Zhan W, Chen L. Accurate object localization facilitates automatic esophagus segmentation in deep learning. Radiat Oncol 2024; 19:55. [PMID: 38735947 PMCID: PMC11088757 DOI: 10.1186/s13014-024-02448-z] [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: 10/20/2023] [Accepted: 05/01/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Currently, automatic esophagus segmentation remains a challenging task due to its small size, low contrast, and large shape variation. We aimed to improve the performance of esophagus segmentation in deep learning by applying a strategy that involves locating the object first and then performing the segmentation task. METHODS A total of 100 cases with thoracic computed tomography scans from two publicly available datasets were used in this study. A modified CenterNet, an object location network, was employed to locate the center of the esophagus for each slice. Subsequently, the 3D U-net and 2D U-net_coarse models were trained to segment the esophagus based on the predicted object center. A 2D U-net_fine model was trained based on the updated object center according to the 3D U-net model. The dice similarity coefficient and the 95% Hausdorff distance were used as quantitative evaluation indexes for the delineation performance. The characteristics of the automatically delineated esophageal contours by the 2D U-net and 3D U-net models were summarized. Additionally, the impact of the accuracy of object localization on the delineation performance was analyzed. Finally, the delineation performance in different segments of the esophagus was also summarized. RESULTS The mean dice coefficient of the 3D U-net, 2D U-net_coarse, and 2D U-net_fine models were 0.77, 0.81, and 0.82, respectively. The 95% Hausdorff distance for the above models was 6.55, 3.57, and 3.76, respectively. Compared with the 2D U-net, the 3D U-net has a lower incidence of delineating wrong objects and a higher incidence of missing objects. After using the fine object center, the average dice coefficient was improved by 5.5% in the cases with a dice coefficient less than 0.75, while that value was only 0.3% in the cases with a dice coefficient greater than 0.75. The dice coefficients were lower for the esophagus between the orifice of the inferior and the pulmonary bifurcation compared with the other regions. CONCLUSION The 3D U-net model tended to delineate fewer incorrect objects but also miss more objects. Two-stage strategy with accurate object location could enhance the robustness of the segmentation model and significantly improve the esophageal delineation performance, especially for cases with poor delineation results.
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Affiliation(s)
- Zhibin Li
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Guanghui Gan
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jian Guo
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Wei Zhan
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Long Chen
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China.
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193
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Choi DH, Ahn SH, Kim DW, Choi SH, Ahn WS, Kim J, Kim JS. Development of shielding evaluation and management program for O-ring type linear accelerators. Sci Rep 2024; 14:10719. [PMID: 38729975 PMCID: PMC11087655 DOI: 10.1038/s41598-024-60362-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 04/22/2024] [Indexed: 05/12/2024] Open
Abstract
The shielding parameters can vary depending on the geometrical structure of the linear accelerators (LINAC), treatment techniques, and beam energies. Recently, the introduction of O-ring type linear accelerators is increasing. The objective of this study is to evaluate the shielding parameters of new type of linac using a dedicated program developed by us named ORSE (O-ring type Radiation therapy equipment Shielding Evaluation). The shielding evaluation was conducted for a total of four treatment rooms including Elekta Unity, Varian Halcyon, and Accuray Tomotherapy. The developed program possesses the capability to calculate transmitted dose, maximum treatable patient capacity, and shielding wall thickness based on patient data. The doses were measured for five days using glass dosimeters to compare with the results of program. The IMRT factors and use factors obtained from patient data showed differences of up to 65.0% and 33.8%, respectively, compared to safety management report. The shielding evaluation conducted in each treatment room showed that the transmitted dose at every location was below 1% of the dose limit. The results of program and measurements showed a maximum difference of 0.003 mSv/week in transmitted dose. The ORSE program allows for the shielding evaluation results to the clinical environment of each institution based on patient data.
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Affiliation(s)
- Dong Hyeok Choi
- Department of Medicine, Yonsei University College of Medicine, Seoul, Korea
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - So Hyun Ahn
- Ewha Medical Research Institute, School of Medicine, Ewha Womans University, Seoul, South Korea.
| | - Dong Wook Kim
- Department of Medicine, Yonsei University College of Medicine, Seoul, Korea.
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea.
| | - Sang Hyoun Choi
- Department of Radiation Oncology, Institute of Radiological and Medical Sciences, Seoul, Republic of Korea
| | - Woo Sang Ahn
- Department of Radiation Oncology, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Republic of Korea
| | - Jihun Kim
- Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jin Sung Kim
- Department of Medicine, Yonsei University College of Medicine, Seoul, Korea
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
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194
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Efe E, Yavsan E. AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5863-5880. [PMID: 38872562 DOI: 10.3934/mbe.2024259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Within the domain of cardiovascular diseases, arrhythmia is one of the leading anomalies causing sudden deaths. These anomalies, including arrhythmia, are detectable through the electrocardiogram, a pivotal component in the analysis of heart diseases. However, conventional methods like electrocardiography encounter challenges such as subjective analysis and limited monitoring duration. In this work, a novel hybrid model, AttBiLFNet, was proposed for precise arrhythmia detection in ECG signals, including imbalanced class distributions. AttBiLFNet integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with a convolutional neural network (CNN) and incorporates an attention mechanism using the focal loss function. This architecture is capable of autonomously extracting features by harnessing BiLSTM's bidirectional information flow, which proves advantageous in capturing long-range dependencies. The attention mechanism enhances the model's focus on pertinent segments of the input sequence, which is particularly beneficial in class imbalance classification scenarios where minority class samples tend to be overshadowed. The focal loss function effectively addresses the impact of class imbalance, thereby improving overall classification performance. The proposed AttBiLFNet model achieved 99.55% accuracy and 98.52% precision. Moreover, performance metrics such as MF1, K score, and sensitivity were calculated, and the model was compared with various methods in the literature. Empirical evidence showed that AttBiLFNet outperformed other methods in terms of both accuracy and computational efficiency. The introduced model serves as a reliable tool for the timely identification of arrhythmias.
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Affiliation(s)
- Enes Efe
- Department of Electrical and Electronics Engineering, Hitit University, Corum 19030, Turkey
| | - Emrehan Yavsan
- Department of Electronics and Automation, Tekirdag Namik Kemal University, Tekirdag 59030, Turkey
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195
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Daniyal M, Qureshi M, Marzo RR, Aljuaid M, Shahid D. Exploring clinical specialists' perspectives on the future role of AI: evaluating replacement perceptions, benefits, and drawbacks. BMC Health Serv Res 2024; 24:587. [PMID: 38725039 PMCID: PMC11080164 DOI: 10.1186/s12913-024-10928-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 03/29/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND OF STUDY Over the past few decades, the utilization of Artificial Intelligence (AI) has surged in popularity, and its application in the medical field is witnessing a global increase. Nevertheless, the implementation of AI-based healthcare solutions has been slow in developing nations like Pakistan. This unique study aims to assess the opinion of clinical specialists on the future replacement of AI, its associated benefits, and its drawbacks in form southern region of Pakistan. MATERIAL AND METHODS A cross-sectional selective study was conducted from 140 clinical specialists (Surgery = 24, Pathology = 31, Radiology = 35, Gynecology = 35, Pediatric = 17) from the neglected southern Punjab region of Pakistan. The study was analyzed using χ2 - the test of association and the nexus between different factors was examined by multinomial logistic regression. RESULTS Out of 140 respondents, 34 (24.3%) believed hospitals were ready for AI, while 81 (57.9%) disagreed. Additionally, 42(30.0%) were concerned about privacy violations, and 70(50%) feared AI could lead to unemployment. Specialists with less than 6 years of experience are more likely to embrace AI (p = 0.0327, OR = 3.184, 95% C.I; 0.262, 3.556) and those who firmly believe that AI knowledge will not replace their future tasks exhibit a lower likelihood of accepting AI (p = 0.015, OR = 0.235, 95% C.I: (0.073, 0.758). Clinical specialists who perceive AI as a technology that encompasses both drawbacks and benefits demonstrated a higher likelihood of accepting its adoption (p = 0.084, OR = 2.969, 95% C.I; 0.865, 5.187). CONCLUSION Clinical specialists have embraced AI as the future of the medical field while acknowledging concerns about privacy and unemployment.
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Affiliation(s)
- Muhammad Daniyal
- Department of Statistics, Faculty of Computing, Islamia University of Bahawalpur, Bahawalpur, Pakistan.
| | - Moiz Qureshi
- Government Degree College, TandoJam, Hyderabad, Sindh, Pakistan
| | - Roy Rillera Marzo
- Faculty of Humanities and Health Sciences, Curtin University, Malaysia, , Miri, Sarawak, Malaysia
- Jeffrey Cheah School of Medicine and Health Sciences, Global Public Health, Monash University Malaysia, Subang Jaya, Selangor, Malaysia
| | - Mohammed Aljuaid
- Department of Health Administration, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
| | - Duaa Shahid
- Hult International Business School, 02141, Cambridge, MA, USA
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196
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Ye P, Zhao W, Shimomura T, Li KW, Haga A, Geng LS. Pixel-by-pixel correction of beam hardening artifacts by bowtie filter in fan-beam CT. Phys Med Biol 2024; 69:105020. [PMID: 38640915 DOI: 10.1088/1361-6560/ad40fa] [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: 01/04/2024] [Accepted: 04/19/2024] [Indexed: 04/21/2024]
Abstract
Objective. Beam hardening (BH) artifacts in computed tomography (CT) images originate from the polychromatic nature of x-ray photons. In a CT system with a bowtie filter, residual BH artifacts remain when polynomial fits are used. These artifacts lead to worse visuals, reduced contrast, and inaccurate CT numbers. This work proposes a pixel-by-pixel correction (PPC) method to reduce the residual BH artifacts caused by a bowtie filter.Approach. The energy spectrum for each pixel at the detector after the photons pass through the bowtie filter was calculated. Then, the spectrum was filtered through a series of water slabs with different thicknesses. The polychromatic projection corresponding to the thickness of the water slab for each detector pixel could be obtained. Next, we carried out a water slab experiment with a mono energyE= 69 keV to get the monochromatic projection. The polychromatic and monochromatic projections were then fitted with a 2nd-order polynomial. The proposed method was evaluated on digital phantoms in a virtual CT system and phantoms in a real CT machine.Main results. In the case of a virtual CT system, the standard deviation of the line profile was reduced by 23.8%, 37.3%, and 14.3%, respectively, in the water phantom with different shapes. The difference of the linear attenuation coefficients (LAC) in the central and peripheral areas of an image was reduced from 0.010 to 0.003cm-1and 0.007cm-1to 0 in the biological tissue phantom and human phantom, respectively. The method was also validated using CT projection data obtained from Activion16 (Canon Medical Systems, Japan). The difference in the LAC in the central and peripheral areas can be reduced by a factor of two.Significance. The proposed PPC method can successfully remove the cupping artifacts in both virtual and authentic CT images. The scanned object's shapes and materials do not affect the technique.
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Affiliation(s)
- Ping Ye
- School of Physics, Beihang University, Beijing 102206, People's Republic of China
| | - Wei Zhao
- School of Physics, Beihang University, Beijing 102206, People's Republic of China
- Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, People's Republic of China
| | - Taisei Shimomura
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Kai-Wen Li
- Research and Development Department, CAS Ion Medical Technology Co., Ltd, Beijing 100190, People's Republic of China
| | - Akihiro Haga
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Li-Sheng Geng
- School of Physics, Beihang University, Beijing 102206, People's Republic of China
- Beijing Key Laboratory of Advanced Nuclear Materials and Physics, Beihang University, Beijing 102206, People's Republic of China
- Peng Huanwu Collaborative Center for Research and Education, Beihang University, Beijing 100191, People's Republic of China
- Southern Center for Nuclear-Science Theory (SCNT), Institute of Modern Physics, Chinese Academy of Sciences, Huizhou 516000, People's Republic of China
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197
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Khan NU, Ullah S, Khan FU, Merla A. Development of 2400-2450 MHz Frequency Band RF Energy Harvesting System for Low-Power Device Operation. SENSORS (BASEL, SWITZERLAND) 2024; 24:2986. [PMID: 38793841 PMCID: PMC11125279 DOI: 10.3390/s24102986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/02/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024]
Abstract
Recently, there has been an increasing fascination for employing radio frequency (RF) energy harvesting techniques to energize various low-power devices by harnessing the ambient RF energy in the surroundings. This work outlines a novel advancement in RF energy harvesting (RFEH) technology, intending to power portable gadgets with minimal operating power demands. A high-gain receiver microstrip patch antenna was designed and tested to capture ambient RF residue, operating at 2450 MHz. Similarly, a two-stage Dickson voltage booster was developed and employed with the RFEH to transform the received RF signals into useful DC voltage signals. Additionally, an LC series circuit was utilized to ensure impedance matching between the antenna and rectifier, facilitating the extraction of maximum power from the developed prototype. The findings indicate that the developed rectifier attained a peak power conversion efficiency (PCE) of 64% when operating at an input power level of 0 dBm. During experimentation, the voltage booster demonstrated its capability to rectify a minimum input AC signal of only 50 mV, yielding a corresponding 180 mV output DC signal. Moreover, the maximum power of 4.60 µW was achieved when subjected to an input AC signal of 1500 mV with a load resistance of 470 kΩ. Finally, the devised RFEH was also tested in an open environment, receiving signals from Wi-Fi modems positioned at varying distances for evaluation.
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Affiliation(s)
- Nasir Ullah Khan
- Department of Engineering and Geology, Università degli Studi “G. d’Annunzio” Chieti—Pescara, 65127 Pescara, Italy;
| | - Sana Ullah
- Department of Electrical and Information Engineering, Politecnico di Bari, 70126 Bari, Italy
| | - Farid Ullah Khan
- Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
| | - Arcangelo Merla
- Department of Engineering and Geology, Università degli Studi “G. d’Annunzio” Chieti—Pescara, 65127 Pescara, Italy;
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198
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Feng M, Xu J. Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children. Brain Sci 2024; 14:469. [PMID: 38790448 PMCID: PMC11118831 DOI: 10.3390/brainsci14050469] [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: 04/09/2024] [Revised: 04/29/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental disorder that affects approximately 5-10% of school-aged children worldwide. Early diagnosis and intervention are essential to improve the quality of life of patients and their families. In this study, we propose ConvMixer-ECA, a novel deep learning architecture that combines ConvMixer with efficient channel attention (ECA) blocks for the accurate diagnosis of ADHD using electroencephalogram (EEG) signals. The model was trained and evaluated using EEG recordings from 60 healthy children and 61 children with ADHD. A series of experiments were conducted to evaluate the performance of the ConvMixer-ECA. The results showed that the ConvMixer-ECA performed well in ADHD recognition with 94.52% accuracy. The incorporation of attentional mechanisms, in particular ECA, improved the performance of ConvMixer; it outperformed other attention-based variants. In addition, ConvMixer-ECA outperformed state-of-the-art deep learning models including EEGNet, CNN, RNN, LSTM, and GRU. t-SNE visualization of the output of this model layer validated the effectiveness of ConvMixer-ECA in capturing the underlying patterns and features that separate ADHD from typically developing individuals through hierarchical feature learning. These outcomes demonstrate the potential of ConvMixer-ECA as a valuable tool to assist clinicians in the early diagnosis and intervention of ADHD in children.
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Affiliation(s)
- Min Feng
- Nanjing Rehabilitation Medical Center, The Affiliated Brain Hospital, Nanjing Medical University, Nanjing 210029, China
- School of Chinese Language and Literature, Nanjing Normal University, Nanjing 210024, China
| | - Juncai Xu
- School of Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
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199
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Cha J, Kim C, Choi SH. Extrinsic Laryngeal Muscle Activity With Different Diameters and Water Depths in a Semi-Occluded Vocal Tract Exercise. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2024; 67:1324-1338. [PMID: 38592964 DOI: 10.1044/2024_jslhr-23-00194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
PURPOSE Surface electromyography (sEMG) has been used to evaluate extrinsic laryngeal muscle activity during swallowing and phonation. In the current study, sEMG amplitudes were measured from the infrahyoid and suprahyoid muscles during phonation through a tube submerged in water. METHOD The sEMG amplitude values measured from the extrinsic laryngeal muscles and the electroglottographic contact quotient (CQ) were obtained simultaneously from 62 healthy participants (31 men, 31 women) during phonation through a tube at six different depths (2, 4, 7, 10, 15, and 20 cm) while using two tubes with different diameters (1 and 0.5 cm). RESULTS With increasing depth, the sEMG amplitude for the suprahyoid muscles increased in men and women. However, sEMG amplitudes for the infrahyoid muscles increased significantly only in men. Tube diameter had a significant effect on the suprahyoid sEMG amplitudes only for men, with higher sEMG amplitudes when phonating with a 1.0-cm tube. CQ values increased with submerged depth for both men and women. Tube diameter affected results such than CQ values were higher for men when using the wider tube and for women with the narrower tube. CONCLUSIONS Vocal fold vibratory patterns changed with the depth of tube submersion in water for both men and women, but the patterns of muscle activation differed between the sexes. This suggests that men and women use different strategies when confronted with increased intraoral pressure during semi-occluded vocal tract exercises. In this study, sEMG provided insight into the mechanism for differences between vocally normal individuals and could help detect compensatory muscle activation during tube phonation in water for people with voice disorders.
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Affiliation(s)
- Junseo Cha
- Department of Audiology and Speech-Language Pathology, Research Institute of Biomimetic Sensory Control, Catholic Hearing Voice Speech Center, Daegu Catholic University, Gyeongsan, South Korea
| | - Chaehyun Kim
- Department of Audiology and Speech-Language Pathology, Research Institute of Biomimetic Sensory Control, Catholic Hearing Voice Speech Center, Daegu Catholic University, Gyeongsan, South Korea
| | - Seong Hee Choi
- Department of Audiology and Speech-Language Pathology, Research Institute of Biomimetic Sensory Control, Catholic Hearing Voice Speech Center, Daegu Catholic University, Gyeongsan, South Korea
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Jebreen K, Radwan E, Kammoun-Rebai W, Alattar E, Radwan A, Safi W, Radwan W, Alajez M. Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine. BMC MEDICAL EDUCATION 2024; 24:507. [PMID: 38714993 PMCID: PMC11077786 DOI: 10.1186/s12909-024-05465-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 04/24/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula. METHODS This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively. RESULTS From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields. CONCLUSION Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.
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Affiliation(s)
- Kamel Jebreen
- Department of Mathematics, Palestine Technical University - Kadoorie, Hebron, Palestine
- Department of Mathematics, An-Najah National University, Nablus, Palestine
- Unité de Recherche Clinique Saint-Louis Fernand-Widal Lariboisière, APHP, Paris, France
| | - Eqbal Radwan
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine.
| | | | - Etimad Alattar
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Afnan Radwan
- Faculty of Education, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Safi
- Department of Biotechnology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Radwan
- University College of Applied Sciences - Gaza, Gaza, Palestine
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