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Cao J, Ball IK, Cassidy B, Rae CD. Functional conductivity imaging: quantitative mapping of brain activity. Phys Eng Sci Med 2024:10.1007/s13246-024-01484-z. [PMID: 39259483 DOI: 10.1007/s13246-024-01484-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: 07/31/2023] [Accepted: 08/28/2024] [Indexed: 09/13/2024]
Abstract
Theory and modelling suggest that detection of neuronal activity may be feasible using phase sensitive MRI methods. Successful detection of neuronal activity both in vitro and in vivo has been described while others have reported negative results. Magnetic resonance electrical properties tomography may be a route by which signal changes can be identified. Here, we report successful and repeatable detection at 3 Tesla of human brain activation in response to visual and somatosensory stimuli using a functional version of tissue conductivity imaging (funCI). This detects activation in both white and grey matter with apparent tissue conductivity changes of 0.1 S/m (17-20%, depending on the tissue baseline conductivity measure) allowing visualization of complete system circuitry. The degree of activation scales with the degree of the stimulus (duration or contrast). The conductivity response functions show a distinct timecourse from that of traditional fMRI haemodynamic (BOLD or Blood Oxygenation Level Dependent) response functions, peaking within milliseconds of stimulus cessation and returning to baseline within 3-4 s. We demonstrate the utility of the funCI approach by showing robust activation of the lateral somatosensory circuitry on stimulation of an index finger, on stimulation of a big toe or of noxious (heat) stimulation of the face as well as activation of visual circuitry on visual stimulation in up to five different individuals. The sensitivity and repeatability of this approach provides further evidence that magnetic resonance imaging approaches can detect brain activation beyond changes in blood supply.
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Affiliation(s)
- Jun Cao
- Neuroscience Research Australia, 139 Barker St, Randwick, NSW, 2031, Australia
| | - Iain K Ball
- Philips Australia & New Zealand, North Ryde, NSW, 2113, Australia
| | - Benjamin Cassidy
- Neuroscience Research Australia, 139 Barker St, Randwick, NSW, 2031, Australia
- Pathfinder Exploration LLC, Tonopah, NV, USA
| | - Caroline D Rae
- Neuroscience Research Australia, 139 Barker St, Randwick, NSW, 2031, Australia.
- School of Psychology, The University of New South Wales, Sydney, NSW, 2052, Australia.
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Khorasani A, Moghim S, Wagemans J, Lavigne R, Mirzaei A. Antibiotic profile classification of Proteus mirabilis using machine learning: An investigation into multidimensional radiomics features. Comput Biol Med 2024; 182:109131. [PMID: 39260045 DOI: 10.1016/j.compbiomed.2024.109131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 08/23/2024] [Accepted: 09/06/2024] [Indexed: 09/13/2024]
Abstract
Antimicrobial resistance (AMR) presents a significant threat to global healthcare. Proteus mirabilis causes catheter-associated urinary tract infections (CAUTIs) and exhibits increased antibiotic resistance. Traditional diagnostics still rely on culture-based approaches, which remain time-consuming. Here, we study the use of machine learning (ML) to classify bacterial resistance profiles using straightforward microscopic imaging of P. mirabilis for resistance classification integrated with radiomics feature analysis and ML models. From 150 P. mirabilis strains isolated from catheters of patients diagnosed with a CAUTI, 30 % displayed multidrug resistance using the standardized disk diffusion method, and 60 % showed strong biofilm activity in microtiter plate assays. As a more rapid alternative, we introduce wavelet-based and regular microscopy imaging with feature extraction/selection, following image preprocessing steps (image denoising, normalization, and mask creation). These features enable training and testing different ML models with 5-fold cross-validation for P. mirabilis resistance classification. From these models, the Random Forest (RF) algorithm exhibited the highest performance with ACC = 0.95, specificity = 0.97, sensitivity = 0.88, and AUC = 0.98 among the other ML algorithms considered in this study for P. mirabilis resistance classification. This successful application of wavelet-based feature Radiomics analysis with RF model represents a crucial step towards a precise, rapid, and cost-effective method to distinguish antibiotic resistant P. mirabilis strains.
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Affiliation(s)
- Amir Khorasani
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Department of Bioimaging, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Sharareh Moghim
- Department of Bacteriology and Virology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Rob Lavigne
- Department of Biosystems, KU Leuven, Leuven, Belgium
| | - Arezoo Mirzaei
- Department of Bacteriology and Virology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
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Kron T, Offer K. In response to topical debate: In Australia professional registration for qualified medical physicists should be mandated through the Australian Health Practitioner Regulation Agency (AHPRA). Phys Eng Sci Med 2024:10.1007/s13246-024-01483-0. [PMID: 39254822 DOI: 10.1007/s13246-024-01483-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2024] [Accepted: 08/28/2024] [Indexed: 09/11/2024]
Affiliation(s)
- Tomas Kron
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia.
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia.
| | - Keith Offer
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia
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Zhao Z, Ran X, Wang J, Lv S, Qiu M, Niu Y, Wang C, Xu Y, Gao Z, Ren W, Zhou X, Fan X, Song J, Yu Y. Common and differential EEG microstate of major depressive disorder patients with and without response to rTMS treatment. J Affect Disord 2024; 367:777-787. [PMID: 39265862 DOI: 10.1016/j.jad.2024.09.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Revised: 08/31/2024] [Accepted: 09/08/2024] [Indexed: 09/14/2024]
Abstract
OBJECTIVE Repetitive transcranial magnetic stimulation (rTMS) has recently emerged as a novel treatment option for patients with major depressive disorder (MDD), but clinical observations reveal variability in patient's responses to rTMS. Therefore, it is clinically significant to investigate the baseline neuroimaging differences between patients with (Responder) and without (NonResponder) response to rTMS treatment and predict rTMS treatment outcomes based on baseline neuroimaging data. METHOD Baseline resting-state EEG data and Beck Depression Inventory (BDI) were collected from 74 rTMS Responder, 43 NonResponder, and 47 matched healthy controls (HC). EEG microstate analysis was applied to analyze common and differential microstate characteristics of Responder and NonResponder. In addition, the microstate temporal parameters were sent to four machine learning models to classify Responder from NonResponder. RESULT There exists some common and differential EEG microstate characteristics for Responder and NonResponder. Specifically, compared to the HC group, both Responder and NonResponder exhibited a significant increase in the occurrence of microstate A. Only Responder showed an increase in the coverage of microstate A, occurrence of microstate D, transition probability (TP) from A to D, D to A, and C to A, and a decrease in the duration of microstates B and E, TP from A to B and C to B compared to HC. Only NonResponder exhibited a significant decrease in the duration of microstate D, TP from C to D, and an increase in the occurrence of microstate E, TP from C to E compared to HC. The primary differences between the Responder and NonResponder are that Responder had higher parameters for microstate D, TP from other microstates to D, and lower parameters for microstate E, TP from other microstates to E compared to NonResponder. Baseline parameters of microstate D showed significant correlation with Beck Depression Inventory (BDI) reduction rate. Additionally, these microstate features were sent to four machine learning models to predict rTMS treatment response and classification results indicate that an excellent predicting performance (accuracy = 97.35 %, precision = 96.31 %, recall = 100 %, F1 score = 98.06 %) was obtained when using AdaBoost model. These results suggest that baseline resting-state EEG microstate parameters could serve as robust indicators for predicting the effectiveness of rTMS treatment. CONCLUSION This study reveals significant baseline EEG microstate differences between rTMS Responder, NonResponder, and healthy controls. Microstates D and E in baseline EEG can serve as potential biomarkers for predicting rTMS treatment outcomes in MDD patients. These findings may aid in identifying patients likely to respond to rTMS, optimizing treatment plans and reducing trial-and-error approaches in therapy selection.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; The Second Affiliated Hospital of Xinxiang Medical University, Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China; Henan Engineering Research Center of Physical Diagnostics and Treatment Technology for the Mental and Neurological Diseases, People's Republic of China.
| | - Xiangying Ran
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Junming Wang
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Shiyang Lv
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Mengyue Qiu
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Yanxiang Niu
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, People's Republic of China
| | - Chang Wang
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Yongtao Xu
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Zhixian Gao
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Wu Ren
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Xuezhi Zhou
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Xiaofeng Fan
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China
| | - Jinggui Song
- Henan Engineering Research Center of Physical Diagnostics and Treatment Technology for the Mental and Neurological Diseases, People's Republic of China
| | - Yi Yu
- School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, People's Republic of China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, People's Republic of China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, People's Republic of China; Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, People's Republic of China.
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Barraclough B, Labby ZE, Frigo SP. Portability of IMRT QA between matched linear accelerators. J Appl Clin Med Phys 2024:e14492. [PMID: 39250771 DOI: 10.1002/acm2.14492] [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: 01/23/2024] [Revised: 04/29/2024] [Accepted: 07/27/2024] [Indexed: 09/11/2024] Open
Abstract
PURPOSE To determine if patient-specific IMRT quality assurance can be measured on any matched treatment delivery system (TDS) for patient treatment delivery on another. METHODS Three VMAT plans of varying complexity were created for each available energy for head and neck, SBRT lung, and right chestwall anatomical sites. Each plan was delivered on three matched Varian TrueBeam TDSs to the same Scandidos Delta4 Phantom+ diode array with only energy-specific device calibrations. Dose distributions were corrected for TDS output and then compared to TPS calculations using gamma analysis. Round-robin comparisons between measurements from each TDS were also performed using point-by-point dose difference, median dose difference, and the percent of point dose differences within 2% of the mean metrics. RESULTS All plans had more than 95% of points passing a gamma analysis using 3%/3 mm criteria with global normalization and a 20% threshold when comparing measurements to calculations. The tightest gamma analysis criteria where a plan still passed > 95% were similar across delivery systems-within 0.5%/0.5 mm for all but three plan/energy combinations. Median dose deviations in measurement-to-measurement comparisons were within 0.7% and 1.0% for global and local normalization, respectively. More than 90% of the point differences were within 2%. CONCLUSION A set of plans spanning available energies and complexity levels were delivered by three matched TDSs. Comparisons to calculations and between measurements showed dose distributions delivered by each TDS using the same DICOM RT-plan file meet tolerances much smaller than typical clinical IMRT QA criteria. This demonstrates each TDS is modeled to a similar accuracy by a common class (shared) beam model. Additionally, it demonstrates that dose distributions from one TDS show small differences in median dose to the others. This is an important validation component of the common beam model approach, allowing for operational improvements in the clinic.
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Affiliation(s)
- Brendan Barraclough
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
| | - Zacariah E Labby
- Department of Human Oncology, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Sean P Frigo
- Department of Human Oncology, University of Wisconsin - Madison, Madison, Wisconsin, USA
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Metin SZ, Uyulan Ç, Farhad S, Ergüzel TT, Türk Ö, Metin B, Çerezci Ö, Tarhan N. Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy. Clin EEG Neurosci 2024:15500594241273181. [PMID: 39251228 DOI: 10.1177/15500594241273181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Background: Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Identifying the TRD population is crucial in terms of saving time and resources in depression treatment. Recently several studies employed various methods on EEG datasets for automatic depression detection or treatment outcome prediction. However, no previous study has used the deep learning (DL) approach and EEG signals for detecting treatment resistance. Method: 77 patients with TRD, 43 patients with non-TRD, and 40 healthy controls were compared using GoogleNet convolutional neural network and DL on EEG data. Additionally, Class Activation Maps (CAMs) acquired from the TRD and non-TRD groups were used to obtain distinctive regions for classification. Results: GoogleNet classified the healthy controls and non-TRD group with 88.43%, the healthy controls and TRD subjects with 89.73%, and the TRD and non-TRD group with 90.05% accuracy. The external validation accuracy for the TRD-non-TRD classification was 73.33%. Finally, the CAM analysis revealed that the TRD group contained dominant features in class detection of deep learning architecture in almost all electrodes. Limitations: Our study is limited by the moderate sample size of clinical groups and the retrospective nature of the study. Conclusion: These findings suggest that EEG-based deep learning can be used to classify treatment resistance in depression and may in the future prove to be a useful tool in psychiatry practice to identify patients who need more vigorous intervention.
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Affiliation(s)
| | - Çağlar Uyulan
- Department of Mechanical Engineering, Katip Çelebi University, İzmir, Turkey
| | - Shams Farhad
- Department of Neuroscience, Uskudar University, Istanbul, Turkey
| | - Türker Tekin Ergüzel
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey
| | - Ömer Türk
- Department of Computer Technologies, Artuklu University, Mardin, Turkey
| | - Barış Metin
- Neurology Department, Medical Faculty, Uskudar University, Istanbul, Turkey
| | - Önder Çerezci
- Department of Physioterapy and Rehabilitation, Faculty of Health SciencesUskudar University, Istanbul, Turkey
| | - Nevzat Tarhan
- Department of Psychiatry, Uskudar University, Istanbul, Turkey
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Yang D, Miao Y, Liu C, Zhang N, Zhang D, Guo Q, Gao S, Li L, Wang J, Liang S, Li P, Bai X, Zhang K. Advances in artificial intelligence applications in the field of lung cancer. Front Oncol 2024; 14:1449068. [PMID: 39309740 PMCID: PMC11412794 DOI: 10.3389/fonc.2024.1449068] [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: 06/14/2024] [Accepted: 08/19/2024] [Indexed: 09/25/2024] Open
Abstract
Lung cancer remains a leading cause of cancer-related deaths globally, with its incidence steadily rising each year, representing a significant threat to human health. Early detection, diagnosis, and timely treatment play a crucial role in improving survival rates and reducing mortality. In recent years, significant and rapid advancements in artificial intelligence (AI) technology have found successful applications in various clinical areas, especially in the diagnosis and treatment of lung cancer. AI not only improves the efficiency and accuracy of physician diagnosis but also aids in patient treatment and management. This comprehensive review presents an overview of fundamental AI-related algorithms and highlights their clinical applications in lung nodule detection, lung cancer pathology classification, gene mutation prediction, treatment strategies, and prognosis. Additionally, the rapidly advancing field of AI-based three-dimensional (3D) reconstruction in lung cancer surgical resection is discussed. Lastly, the limitations of AI and future prospects are addressed.
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Affiliation(s)
- Di Yang
- Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Yafei Miao
- Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Changjiang Liu
- Thoracic Surgery Department, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Nan Zhang
- Thoracic Surgery Department, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Duo Zhang
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Qiang Guo
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Shuo Gao
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Information center, Affiliated Hospital of Hebei University, Baoding, China
| | - Linqian Li
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
- 3D Image and 3D Printing Center, Affiliated Hospital of Hebei University, Baoding, China
| | - Jianing Wang
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
| | - Si Liang
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Peng Li
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Xuan Bai
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Ke Zhang
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
- 3D Image and 3D Printing Center, Affiliated Hospital of Hebei University, Baoding, China
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Huang SX, Yang SH, Zeng B, Li XH. Optimization of sub-arc collimator angles in volumetric modulated arc therapy: a heatmap-based blocking index approach for multiple brain metastases. Phys Eng Sci Med 2024:10.1007/s13246-024-01477-y. [PMID: 39235667 DOI: 10.1007/s13246-024-01477-y] [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: 05/02/2024] [Accepted: 08/07/2024] [Indexed: 09/06/2024]
Abstract
To develop and assess an automated Sub-arc Collimator Angle Optimization (SACAO) algorithm and Cumulative Blocking Index Ratio (CBIR) metrics for single-isocenter coplanar volumetric modulated arc therapy (VMAT) to treat multiple brain metastases. This study included 31 patients with multiple brain metastases, each having 2 to 8 targets. Initially, for each control point, the MLC blocking index was calculated at different collimator angles, resulting in a two-dimensional heatmap. Optimal sub-arc segmentation and collimator angle optimization were achieved using an interval dynamic programming algorithm. Subsequently, VMAT plans were designed using two approaches: SACAO and the conventional Full-Arc Fixed Collimator Angle. CBIR was calculated as the ratio of the cumulative blocking index between the two plan approaches. Finally, dosimetric and planning parameters of both plans were compared. Normal brain tissue, brainstem, and eyes received better protection in the SACAO group (P < 0.05).Query Notable reductions in the SACAO group included 11.47% in gradient index (GI), 15.03% in monitor units (MU), 15.73% in mean control point Jaw area (AJaw,mean), and 19.14% in mean control point Jaw-X width (WJaw-X,mean), all statistically significant (P < 0.001). Furthermore, CBIR showed a strong negative correlation with the degree of plan improvement. The SACAO method enhanced protection of normal organs while improving transmission efficiency and optimization performance of VMAT. In particular, the CBIR metrics show promise in quantifying the differences specifically in the 'island blocking problem' between SACAO and conventional VMAT, and in guiding the enhanced application of the SACAO algorithm.
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Affiliation(s)
- Shi-Xiong Huang
- School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, 410013, Hunan, People's Republic of China
| | - Song-Hua Yang
- Department of Clinical Pharmaceutical Research Institution,Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, Hunan, People's Republic of China
| | - Biao Zeng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, 410013, Hunan, People's Republic of China.
| | - Xiao-Hua Li
- School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China.
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Zhang F, Ye J, Zhu J, Qian W, Wang H, Luo C. Key Cell-in-Cell Related Genes are Identified by Bioinformatics and Experiments in Glioblastoma. Cancer Manag Res 2024; 16:1109-1130. [PMID: 39253064 PMCID: PMC11382672 DOI: 10.2147/cmar.s475513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 08/27/2024] [Indexed: 09/11/2024] Open
Abstract
Purpose This study aimed to explore the roles of cell-in-cell (CIC)-related genes in glioblastoma (GBM) using bioinformatics and experimental strategies. Patients and Methods The ssGSEA algorithm was used to calculate the CIC score for each patient. Subsequently, differentially expressed genes (DEGs) between the CIClow and CIChigh groups and between the tumor and control samples were screened using the limma R package. Key CIC-related genes (CICRGs) were further filtered using univariate Cox and LASSO analyses, followed by the construction of a CIC-related risk score model. The performance of the risk score model in predicting GBM prognosis was evaluated using ROC curves and an external validation cohort. Moreover, their location and differentiation trajectory in GBM were analyzed at the single-cell level using the Seurat R package. Finally, the expression of key CICRGs in clinical samples was examined by qPCR. Results In the current study, we found that CIC scorelow group had a significantly better survival in the TCGA-GBM cohort, supporting the important role of CICRGs in GBM. Using univariate Cox and LASSO analyses, PTX3, TIMP1, IGFBP2, SNCAIP, LOXL1, SLC47A2, and LGALS3 were identified as key CICRGs. Based on this data, a CIC-related prognostic risk score model was built using the TCGA-GBM cohort and validated in the CGGA-GBM cohort. Further mechanistic analyses showed that the CIC-related risk score is closely related to immune and inflammatory responses. Interestingly, at the single-cell level, key CICRGs were expressed in the neurons and myeloids of tumor tissues and exhibited unique temporal dynamics of expression changes. Finally, the expression of key CICRGs was validated by qPCR using clinical samples from GBM patients. Conclusion We identified novel CIC-related genes and built a reliable prognostic prediction model for GBM, which will provide further basic clues for studying the exact molecular mechanisms of GBM pathogenesis from a CIC perspective.
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Affiliation(s)
- Fenglin Zhang
- Department of Neurosurgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Jingliang Ye
- Department of Neurosurgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Junle Zhu
- Department of Neurosurgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Wenbo Qian
- Department of Neurosurgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Haoheng Wang
- Department of Neurosurgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Chun Luo
- Department of Neurosurgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
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Seerangan K, Nandagopal M, Nair RR, Periyasamy S, Jhaveri RH, Balusamy B, Selvarajan S. ERABiLNet: enhanced residual attention with bidirectional long short-term memory. Sci Rep 2024; 14:20622. [PMID: 39232053 PMCID: PMC11374906 DOI: 10.1038/s41598-024-71299-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 08/27/2024] [Indexed: 09/06/2024] Open
Abstract
Alzheimer's Disease (AD) causes slow death in brain cells due to shrinkage of brain cells which is more prevalent in older people. In most cases, the symptoms of AD are mistaken as age-related stresses. The most widely utilized method to detect AD is Magnetic Resonance Imaging (MRI). Along with Artificial Intelligence (AI) techniques, the efficacy of identifying diseases related to the brain has become easier. But, the identical phenotype makes it challenging to identify the disease from the neuro-images. Hence, a deep learning method to detect AD at the beginning stage is suggested in this work. The newly implemented "Enhanced Residual Attention with Bi-directional Long Short-Term Memory (Bi-LSTM) (ERABi-LNet)" is used in the detection phase to identify the AD from the MRI images. This model is used for enhancing the performance of the Alzheimer's detection in scale of 2-5%, minimizing the error rates, increasing the balance of the model, so that the multi-class problems are supported. At first, MRI images are given to "Residual Attention Network (RAN)", which is specially developed with three convolutional layers, namely atrous, dilated and Depth-Wise Separable (DWS), to obtain the relevant attributes. The most appropriate attributes are determined by these layers, and subjected to target-based fusion. Then the fused attributes are fed into the "Attention-based Bi-LSTM". The final outcome is obtained from this unit. The detection efficiency based on median is 26.37% and accuracy is 97.367% obtained by tuning the parameters in the ERABi-LNet with the help of Modified Search and Rescue Operations (MCDMR-SRO). The obtained results are compared with ROA-ERABi-LNet, EOO-ERABi-LNet, GTBO-ERABi-LNet and SRO-ERABi-LNet respectively. The ERABi_LNet thus provides enhanced accuracy and other performance metrics compared to such deep learning models. The proposed method has the better sensitivity, specificity, F1-Score and False Positive Rate compared with all the above mentioned competing models with values such as 97.49%.97.84%,97.74% and 2.616 respective;y. This ensures that the model has better learning capabilities and provides lesser false positives with balanced prediction.
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Affiliation(s)
| | - Malarvizhi Nandagopal
- Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, 600062, India
| | - Resmi R Nair
- Department of Electronics and Communication Engineering, Saveetha Engineering College (Autonomous), Chennai, Tamil Nadu, 602105, India
| | - Sakthivel Periyasamy
- Department of Electronics and Communication Engineering, Anna University, Chennai, Tamil Nadu, 600025, India
| | - Rutvij H Jhaveri
- Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India
| | - Balamurugan Balusamy
- Shiv Nadar (Institution of Eminence Deemed to Be University), Noida, Uttar Pradesh, 201314, India
| | - Shitharth Selvarajan
- Department of Computer Science, Kebri Dehar University, 250, Kebri Dehar, Ethiopia.
- School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS6 3QS, United Kingdom.
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61
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Kamalakannan N, Macharla SR, Kanimozhi M, Sudhakar MS. Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection. Comput Biol Med 2024; 182:109093. [PMID: 39232407 DOI: 10.1016/j.compbiomed.2024.109093] [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: 01/09/2024] [Revised: 08/25/2024] [Accepted: 08/29/2024] [Indexed: 09/06/2024]
Abstract
The heightened prevalence of respiratory disorders, particularly exacerbated by a significant upswing in fatalities due to the novel coronavirus, underscores the critical need for early detection and timely intervention. This imperative is paramount, possessing the potential to profoundly impact and safeguard numerous lives. Medically, chest radiography stands out as an essential and economically viable medical imaging approach for diagnosing and assessing the severity of diverse Respiratory Disorders. However, their detection in Chest X-Rays is a cumbersome task even for well-trained radiologists owing to low contrast issues, overlapping of the tissue structures, subjective variability, and the presence of noise. To address these issues, a novel analytical model termed Exponential Pixelating Integral is introduced for the automatic detection of infections in Chest X-Rays in this work. Initially, the presented Exponential Pixelating Integral enhances the pixel intensities to overcome the low-contrast issues that are then polar-transformed followed by their representation using the locally invariant Mandelbrot and Julia fractal geometries for effective distinction of structural features. The collated features labeled Exponential Pixelating Integral with dually characterized fractal features are then classified by the non-parametric multivariate adaptive regression splines to establish an ensemble model between each pair of classes for effective diagnosis of diverse diseases. Rigorous analysis of the proposed classification framework on large medical benchmarked datasets showcases its superiority over its peers by registering a higher classification accuracy and F1 scores ranging from 98.46 to 99.45 % and 96.53-98.10 % respectively, making it a precise and interpretable automated system for diagnosing respiratory disorders.
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Affiliation(s)
| | | | - M Kanimozhi
- School of Electrical & Electronics, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India
| | - M S Sudhakar
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India.
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Talib MA, Moufti MA, Nasir Q, Kabbani Y, Aljaghber D, Afadar Y. Transfer Learning-Based Classifier to Automate the Extraction of False X-Ray Images From Hospital's Database. Int Dent J 2024:S0020-6539(24)01413-8. [PMID: 39232939 DOI: 10.1016/j.identj.2024.08.002] [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: 04/22/2024] [Revised: 07/11/2024] [Accepted: 08/02/2024] [Indexed: 09/06/2024] Open
Abstract
BACKGROUND During preclinical training, dental students take radiographs of acrylic (plastic) blocks containing extracted patient teeth. With the digitisation of medical records, a central archiving system was created to store and retrieve all x-ray images, regardless of whether they were images of teeth on acrylic blocks, or those from patients. In the early stage of the digitisation process, and due to the immaturity of the data management system, numerous images were mixed up and stored in random locations within a unified archiving system, including patient record files. Filtering out and expunging the undesired training images is imperative as manual searching for such images is problematic. Hence the aim of this stidy was to differentiate intraoral images from artificial images on acrylic blocks. METHODS An artificial intelligence (AI) solution to automatically differentiate between intraoral radiographs taken of patients and those taken of acrylic blocks was utilised in this study. The concept of transfer learning was applied to a dataset provided by a Dental Hospital. RESULTS An accuracy score, F1 score, and a recall score of 98.8%, 99.2%, and 100%, respectively, were achieved using a VGG16 pre-trained model. These results were more sensitive compared to those obtained initally using a baseline model with 96.5%, 97.5%, and 98.9% accuracy score, F1 score, and a recall score respectively. CONCLUSIONS The proposed system using transfer learning was able to accurately identify "fake" radiographs images and distinguish them from the real intraoral images.
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Affiliation(s)
- Manar Abu Talib
- Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates
| | - Mohammad Adel Moufti
- Department of Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates.
| | - Qassim Nasir
- Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates
| | - Yousuf Kabbani
- Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates
| | - Dana Aljaghber
- Department of Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Yaman Afadar
- Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates
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Kukker A, Sharma R, Pandey G, Faseehuddin M. Fuzzy lattices assisted EJAYA Q-learning for automated pulmonary diseases classification. Biomed Phys Eng Express 2024; 10:065001. [PMID: 39178885 DOI: 10.1088/2057-1976/ad72f8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 08/23/2024] [Indexed: 08/26/2024]
Abstract
This work proposes a novel technique called Enhanced JAYA (EJAYA) assisted Q-Learning for the classification of pulmonary diseases, such as pneumonia and tuberculosis (TB) sub-classes using chest x-ray images. The work introduces Fuzzy lattices formation to handle real time (non-linear and non-stationary) data based feature extraction using Schrödinger equation. Features based adaptive classification is made possible through the Q-learning algorithm wherein optimal Q-values selection is done via EJAYA optimization algorithm. Fuzzy lattice is formed using x-ray image pixels and lattice Kinetic Energy (K.E.) is calculated using the Schrödinger equation. Feature vector lattices having highest K.E. have been used as an input features for the classifier. The classifier has been employed for pneumonia classification (normal, mild and severe) and Tuberculosis detection (presence or absence). A total of 3000 images have been used for pneumonia classification yielding an accuracy, sensitivity, specificity, precision and F-scores of 97.90%, 98.43%, 97.25%, 97.78% and 98.10%, respectively. For Tuberculosis 600 samples have been used. The achived accuracy, sensitivity, specificity, precision and F-score are 95.50%, 96.39%, 94.40% 95.52% and 95.95%, respectively. Computational time are 40.96 and 39.98 s for pneumonia and TB classification. Classifier learning rate (training accuracy) for pneumonia classes (normal, mild and severe) are 97.907%, 95.375% and 96.391%, respectively and for tuberculosis (present and absent) are 96.928% and 95.905%, respectively. The results have been compared with contemporary classification techniques which shows superiority of the proposed approach in terms of accuracy and speed of classification. The technique could serve as a fast and accurate tool for automated pneumonia and tuberculosis classification.
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Affiliation(s)
- Amit Kukker
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi-NCR Campus, Modinagar, Ghaziabad, U.P., 201204, India
| | - Rajneesh Sharma
- ICE Division, Netaji Subhas University of Technology, Delhi, 110078, India
| | - Gaurav Pandey
- ICE Division, Netaji Subhas University of Technology, Delhi, 110078, India
| | - Mohammad Faseehuddin
- Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India
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Wang L, Fatemi M, Alizad A. Artificial intelligence techniques in liver cancer. Front Oncol 2024; 14:1415859. [PMID: 39290245 PMCID: PMC11405163 DOI: 10.3389/fonc.2024.1415859] [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: 04/11/2024] [Accepted: 08/15/2024] [Indexed: 09/19/2024] Open
Abstract
Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant contributor to worldwide cancer-related deaths. Various medical imaging techniques, including computed tomography, magnetic resonance imaging, and ultrasound, play a crucial role in accurately evaluating HCC and formulating effective treatment plans. Artificial Intelligence (AI) technologies have demonstrated potential in supporting physicians by providing more accurate and consistent medical diagnoses. Recent advancements have led to the development of AI-based multi-modal prediction systems. These systems integrate medical imaging with other modalities, such as electronic health record reports and clinical parameters, to enhance the accuracy of predicting biological characteristics and prognosis, including those associated with HCC. These multi-modal prediction systems pave the way for predicting the response to transarterial chemoembolization and microvascular invasion treatments and can assist clinicians in identifying the optimal patients with HCC who could benefit from interventional therapy. This paper provides an overview of the latest AI-based medical imaging models developed for diagnosing and predicting HCC. It also explores the challenges and potential future directions related to the clinical application of AI techniques.
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Affiliation(s)
- Lulu Wang
- Department of Engineering, School of Technology, Reykjavık University, Reykjavík, Iceland
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
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Maraqah HH, Aboubechara JP, Abu-Asab MS, Lee HS, Aboud O. Excessive lipid production shapes glioma tumor microenvironment. Ultrastruct Pathol 2024; 48:367-377. [PMID: 39157967 DOI: 10.1080/01913123.2024.2392728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 05/27/2024] [Accepted: 08/12/2024] [Indexed: 08/20/2024]
Abstract
Disrupted lipid metabolism is a characteristic of gliomas. This study utilizes an ultrastructural approach to characterize the prevalence and distribution of lipids within gliomas. This study made use of tissue from IDH1 wild type (IDH1-wt) glioblastoma (n = 18) and IDH1 mutant (IDH1-mt) astrocytoma (n = 12) tumors. We uncover a prevalent and intriguing surplus of lipids. The bulk of the lipids manifested as sizable cytoplasmic inclusions and extracellular deposits in the tumor microenvironment (TME); in some tumors the lipids were stored in the classical membraneless spheroidal lipid droplets (LDs). Frequently, lipids accumulated inside mitochondria, suggesting possible dysfunction of the beta-oxidation pathway. Additionally, the tumor vasculature have lipid deposits in their lumen and vessel walls; this lipid could have shifted in from the tumor microenvironment or have been produced by the vessel-invading tumor cells. Lipid excess in gliomas stems from disrupted beta-oxidation and dysfunctional oxidative phosphorylation pathways. The implications of this lipid-driven environment include structural support for the tumor cells and protection against immune responses, non-lipophilic drugs, and free radicals.
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Affiliation(s)
- Haitham H Maraqah
- Medicine & Health Science Faculty, School of Meidicine, An-Najah National University, Nablus, Palestine
| | - John Paul Aboubechara
- Department of Neurology, University of California Davis, Sacramento, CA, USA
- Comprehensive Cancer Center, University of California, Davis, Sacramento, CA, USA
| | - Mones S Abu-Asab
- Electron Microscopy Lab, Biological Imaging Core, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Han Sung Lee
- Department of Pathology and Laboratory Medicine, UC Davis Comprehensive Cancer Center, University of California Davis, Sacramento, CA, USA
| | - Orwa Aboud
- Department of Neurology, University of California Davis, Sacramento, CA, USA
- Comprehensive Cancer Center, University of California, Davis, Sacramento, CA, USA
- Department of Neurosurgery, UC Davis Comprehensive Cancer Center, University of California Davis, Sacramento, CA, USA
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66
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Wang JG, Huang YT. Reconstruction residual network with a fused spatial-channel attention mechanism for automatically classifying diabetic foot ulcer. Phys Eng Sci Med 2024:10.1007/s13246-024-01472-3. [PMID: 39222215 DOI: 10.1007/s13246-024-01472-3] [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: 01/02/2024] [Accepted: 08/01/2024] [Indexed: 09/04/2024]
Abstract
Diabetic foot ulcer (DFU) is a common chronic complication of diabetes. This complication is characterized by the formation of ulcers that are difficult to heal on the skin of the foot. Ulcers can negatively affect patients' quality of life, and improperly treated lesions can result in amputation and even death. Traditionally, the severity and type of foot ulcers are determined by doctors through visual observations and on the basis of their clinical experience; however, this subjective evaluation can lead to misjudgments. In addition, quantitative methods have been developed for classifying and scoring are therefore time-consuming and labor-intensive. In this paper, we propose a reconstruction residual network with a fused spatial-channel attention mechanism (FARRNet) for automatically classifying DFUs. The use of pseudo-labeling and Data augmentation as a pre-processing technique can overcome problems caused by data imbalance and small sample size. The developed model's attention was enhanced using a spatial channel attention (SPCA) module that incorporates spatial and channel attention mechanisms. A reconstruction mechanism was incorporated into the developed residual network to improve its feature extraction ability for achieving better classification. The performance of the proposed model was compared with that of state-of-the-art models and those in the DFUC Grand Challenge. When applied to the DFUC Grand Challenge, the proposed method outperforms other state-of-the-art schemes in terms of accuracy, as evaluated using 5-fold cross-validation and the following metrics: macro-average F1-score, AUC, Recall, and Precision. FARRNet achieved the F1-score of 60.81%, AUC of 87.37%, Recall of 61.04%, and Precision of 61.56%. Therefore, the proposed model is more suitable for use in medical diagnosis environments with embedded devices and limited computing resources. The proposed model can assist patients in initial identifications of ulcer wounds, thereby helping them to obtain timely treatment.
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Affiliation(s)
- Jyun-Guo Wang
- The Department of Medical Informatics, Tzu Chi University, Hualien County, Taiwan.
| | - Yu-Ting Huang
- The Department of Medical Informatics, Tzu Chi University, Hualien County, Taiwan.
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AbdelAziz NM, Said W, AbdelHafeez MM, Ali AH. Advanced interpretable diagnosis of Alzheimer's disease using SECNN-RF framework with explainable AI. Front Artif Intell 2024; 7:1456069. [PMID: 39286548 PMCID: PMC11402894 DOI: 10.3389/frai.2024.1456069] [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: 07/01/2024] [Accepted: 08/12/2024] [Indexed: 09/19/2024] Open
Abstract
Early detection of Alzheimer's disease (AD) is vital for effective treatment, as interventions are most successful in the disease's early stages. Combining Magnetic Resonance Imaging (MRI) with artificial intelligence (AI) offers significant potential for enhancing AD diagnosis. However, traditional AI models often lack transparency in their decision-making processes. Explainable Artificial Intelligence (XAI) is an evolving field that aims to make AI decisions understandable to humans, providing transparency and insight into AI systems. This research introduces the Squeeze-and-Excitation Convolutional Neural Network with Random Forest (SECNN-RF) framework for early AD detection using MRI scans. The SECNN-RF integrates Squeeze-and-Excitation (SE) blocks into a Convolutional Neural Network (CNN) to focus on crucial features and uses Dropout layers to prevent overfitting. It then employs a Random Forest classifier to accurately categorize the extracted features. The SECNN-RF demonstrates high accuracy (99.89%) and offers an explainable analysis, enhancing the model's interpretability. Further exploration of the SECNN framework involved substituting the Random Forest classifier with other machine learning algorithms like Decision Tree, XGBoost, Support Vector Machine, and Gradient Boosting. While all these classifiers improved model performance, Random Forest achieved the highest accuracy, followed closely by XGBoost, Gradient Boosting, Support Vector Machine, and Decision Tree which achieved lower accuracy.
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Affiliation(s)
- Nabil M AbdelAziz
- Information System Department, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt
| | - Wael Said
- Computer Science Department, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt
| | - Mohamed M AbdelHafeez
- Information System Department, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt
| | - Asmaa H Ali
- Information System Department, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt
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DA Silva DG, DA Silva DG, Angleri V, Scarpelli MC, Bergamasco JGA, Nóbrega SR, Damas F, Chaves TS, Camargo HDEA, Ugrinowitsch C, Libardi CA. Application of Artificial Intelligence to Automate the Reconstruction of Muscle Cross-Sectional Area Obtained by Ultrasound. Med Sci Sports Exerc 2024; 56:1840-1848. [PMID: 38637954 DOI: 10.1249/mss.0000000000003456] [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: 04/20/2024]
Abstract
PURPOSE Manual reconstruction (MR) of the vastus lateralis (VL) muscle cross-sectional area (CSA) from sequential ultrasound (US) images is accessible, is reproducible, and has concurrent validity with magnetic resonance imaging. However, this technique requires numerous controls and procedures during image acquisition and reconstruction, making it laborious and time-consuming. The aim of this study was to determine the concurrent validity of VL CSA assessments between MR and computer vision-based automated reconstruction (AR) of CSA from sequential images of the VL obtained by US. METHODS The images from each sequence were manually rotated to align the fascia between images and thus visualize the VL CSA. For the AR, an artificial neural network model was utilized to segment areas of interest in the image, such as skin, fascia, deep aponeurosis, and femur. This segmentation was crucial to impose necessary constraints for the main assembly phase. At this stage, an image registration application, combined with differential evolution, was employed to achieve appropriate adjustments between the images. Next, the VL CSA obtained from the MR ( n = 488) and AR ( n = 488) techniques was used to determine their concurrent validity. RESULTS Our findings demonstrated a low coefficient of variation (CV) (1.51%) for AR compared with MR. The Bland-Altman plot showed low bias and close limits of agreement (+1.18 cm 2 , -1.19 cm 2 ), containing more than 95% of the data points. CONCLUSIONS The AR technique is valid compared with MR when measuring VL CSA in a heterogeneous sample.
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Affiliation(s)
- Deivid Gomes DA Silva
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - Diego Gomes DA Silva
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - Vitor Angleri
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - Maíra Camargo Scarpelli
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - João Guilherme Almeida Bergamasco
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - Sanmy Rocha Nóbrega
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - Felipe Damas
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | - Talisson Santos Chaves
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
| | | | | | - Cleiton Augusto Libardi
- MUSCULAB-Laboratory of Neuromuscular Adaptations to Resistance Training, Department of Physical Education, Federal University of São Carlos (UFSCar), São Carlos, BRAZIL
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Iramina H, Tsuneda M, Okamoto H, Kadoya N, Mukumoto N, Toyota M, Fukunaga J, Fujita Y, Tohyama N, Onishi H, Nakamura M. Multi-institutional questionnaire-based survey on online adaptive radiotherapy performed using commercial systems in Japan in 2023. Radiol Phys Technol 2024; 17:581-595. [PMID: 39028438 DOI: 10.1007/s12194-024-00828-4] [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: 05/27/2024] [Revised: 07/12/2024] [Accepted: 07/13/2024] [Indexed: 07/20/2024]
Abstract
In this study, we aimed to conduct a survey on the current clinical practice of, staffing for, commissioning of, and staff training for online adaptive radiotherapy (oART) in the institutions that installed commercial oART systems in Japan, and to share the information with institutions that will implement oART systems in future. A web-based questionnaire, containing 107 questions, was distributed to nine institutions in Japan. Data were collected from November to December 2023. Three institutions each with the MRIdian (ViewRay, Oakwood Village, OH, USA), Unity (Elekta AB, Stockholm, Sweden), and Ethos (Varian Medical Systems, Palo Alto, CA, USA) systems completed the questionnaire. One institution (MRIdian) had not performed oART by the response deadline. Each institution had installed only one oART system. Hypofractionation, and moderate hypofractionation or conventional fractionation were employed in the MRIdian/Unity and Ethos systems, respectively. The elapsed time for the oART process was faster with the Ethos than with the other systems. All institutions added additional staff for oART. Commissioning periods differed among the oART systems owing to provision of beam data from the vendors. Chambers used during commissioning measurements differed among the institutions. Institutional training was provided by all nine institutions. To the best of our knowledge, this was the first survey about oART performed using commercial systems in Japan. We believe that this study will provide useful information to institutions that installed, are installing, or are planning to install oART systems.
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Affiliation(s)
- Hiraku Iramina
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto-Shi, Kyoto, 606-8507, Japan
| | - Masato Tsuneda
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Department of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8670, Japan
| | - Hiroyuki Okamoto
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Radiation Safety and Quality Assurance Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Noriyuki Kadoya
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-Machi, Aoba-Ku, Sendai-Shi, Miyagi, 980-8574, Japan
| | - Nobutaka Mukumoto
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Department of Radiation Oncology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-Ku, Osaka-Shi, Osaka, 545-8585, Japan
| | - Masahiko Toyota
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Division of Radiology, Department of Clinical Technology, Kagoshima University Hospital, 8-35-1 Sakuragaoka, Kagoshima-Shi, Kagoshima, 890-8520, Japan
| | - Junichi Fukunaga
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-Ku, Fukuoka-Shi, Fukuoka, 812-8582, Japan
| | - Yukio Fujita
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Department of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8670, Japan
- Department of Radiological Sciences, Komazawa University, 1-23-1 Komazawa, Setagaya-Ku, Tokyo, 154-8525, Japan
| | - Naoki Tohyama
- Department of Radiological Sciences, Komazawa University, 1-23-1 Komazawa, Setagaya-Ku, Tokyo, 154-8525, Japan
| | - Hiroshi Onishi
- Department of Radiology, University of Yamanashi, 1110 Shimokato, Chuo-Shi, Yamanashi, 409-3898, Japan
| | - Mitsuhiro Nakamura
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan.
- Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, 53 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto-Shi, Kyoto, 606-8507, Japan.
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McCallum-Hee BI, Mukwada G. Navigating the 2021 ACPSEM ROMP workforce model: insights from a single institution. Phys Eng Sci Med 2024; 47:1259-1265. [PMID: 38421582 PMCID: PMC11408395 DOI: 10.1007/s13246-024-01406-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: 08/17/2023] [Accepted: 02/09/2024] [Indexed: 03/02/2024]
Abstract
Workforce modelling for Radiation Oncology Medical Physicists (ROMPs) is evolving and challenging, prompting the development of the 2021 Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) ROMP Workforce (ARW) Model. In the exploration of this model at Sir Charles Gairdner Hospital, a comprehensive productivity exercise was conducted to obtain a detailed breakdown of ROMP time at a granular level. The results provide valuable insights into ROMP activities and enabled an evaluation of ARW Model calculations. The findings also capture the changing ROMP role as evidenced by an increasing involvement in consultation and advisory tasks with other professionals in the field. They also suggest that CyberKnife QA time requirements in the data utilised by the model may need to be revised. This study emphasises features inherent in the model, that need to be understood if the model is to be applied correctly.
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Affiliation(s)
- Broderick Ivan McCallum-Hee
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, 6009, Nedlands, WA, Australia.
- School of Physics, Mathematics and Computing, The University of Western Australia, 6009, Crawley, WA, Australia.
| | - Godfrey Mukwada
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, 6009, Nedlands, WA, Australia
- School of Physics, Mathematics and Computing, The University of Western Australia, 6009, Crawley, WA, Australia
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71
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Dimitriadis SI. ℛSCZ: A Riemannian schizophrenia diagnosis framework based on the multiplexity of EEG-based dynamic functional connectivity patterns. Comput Biol Med 2024; 180:108862. [PMID: 39068901 DOI: 10.1016/j.compbiomed.2024.108862] [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: 02/11/2024] [Revised: 06/30/2024] [Accepted: 07/06/2024] [Indexed: 07/30/2024]
Abstract
Abnormal electrophysiological (EEG) activity has been largely reported in schizophrenia (SCZ). In the last decade, research has focused to the automatic diagnosis of SCZ via the investigation of an EEG aberrant activity and connectivity linked to this mental disorder. These studies followed various preprocessing steps of EEG activity focusing on frequency-dependent functional connectivity brain network (FCBN) construction disregarding the topological dependency among edges. FCBN belongs to a family of symmetric positive definite (SPD) matrices forming the Riemannian manifold. Due to its unique geometric properties, the whole analysis of FCBN can be performed on the Riemannian geometry of the SPD space. The advantage of the analysis of FCBN on the SPD space is that it takes into account all the pairwise interdependencies as a whole. However, only a few studies have adopted a FCBN analysis on the SPD manifold, while no study exists on the analysis of dynamic FCBN (dFCBN) tailored to SCZ. In the present study, I analyzed two open EEG-SCZ datasets under a Riemannian geometry of SPD matrices for the dFCBN analysis proposing also a multiplexity index that quantifies the associations of multi-frequency brainwave patterns. I adopted a machine learning procedure employing a leave-one-subject-out cross-validation (LOSO-CV) using snapshots of dFCBN from (N-1) subjects to train a battery of classifiers. Each classifier operated in the inter-subject dFCBN distances of sample covariance matrices (SCMs) following a rhythm-dependent decision and a multiplex-dependent one. The proposed ℛSCZ decoder supported both the Riemannian geometry of SPD and the multiplexity index DC reaching an absolute accuracy (100 %) in both datasets in the virtual default mode network (DMN) source space.
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Affiliation(s)
- Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Passeig Vall D'Hebron 171, 08035, Barcelona, Spain; Institut de Neurociencies, University of Barcelona, Municipality of Horta-Guinardó, 08035, Barcelona, Spain; Integrative Neuroimaging Lab, Thessaloniki, 55133, Makedonia, Greece; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Rd, CF24 4HQ, Cardiff, Wales, United Kingdom.
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Pellegrino S, Origlia D, Di Donna E, Lamagna M, Della Pepa R, Pane F, Del Vecchio S, Fonti R. Coefficient of variation and texture analysis of 18F-FDG PET/CT images for the prediction of outcome in patients with multiple myeloma. Ann Hematol 2024; 103:3713-3721. [PMID: 39046513 PMCID: PMC11358233 DOI: 10.1007/s00277-024-05905-7] [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: 02/28/2024] [Accepted: 07/18/2024] [Indexed: 07/25/2024]
Abstract
In multiple myeloma (MM) bone marrow infiltration by monoclonal plasma cells can occur in both focal and diffuse manner, making staging and prognosis rather difficult. The aim of our study was to test whether texture analysis of 18 F-2-deoxy-d-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) images can predict survival in MM patients. Forty-six patients underwent 18 F-FDG-PET/CT before treatment. We used an automated contouring program for segmenting the hottest focal lesion (FL) and a lumbar vertebra for assessing diffuse bone marrow involvement (DI). Maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean) and texture features such as Coefficient of variation (CoV), were obtained from 46 FL and 46 DI. After a mean follow-up of 51 months, 24 patients died of myeloma and were compared to the 22 survivors. At univariate analysis, FL SUVmax (p = 0.0453), FL SUVmean (p = 0.0463), FL CoV (p = 0.0211) and DI SUVmax (p = 0.0538) predicted overall survival (OS). At multivariate analysis only FL CoV and DI SUVmax were retained in the model (p = 0.0154). By Kaplan-Meier method and log-rank testing, patients with FL CoV below the cut-off had significantly better OS than those with FL CoV above the cut-off (p = 0.0003), as well as patients with DI SUVmax below the threshold versus those with DI SUVmax above the threshold (p = 0.0006). Combining FL CoV and DI SUVmax by using their respective cut-off values, a statistically significant difference was found between the resulting four survival curves (p = 0.0001). Indeed, patients with both FL CoV and DI SUVmax below their respective cut-off values showed the best prognosis. Conventional and texture parameters derived from 18F-FDG PET/CT analysis can predict survival in MM patients by assessing the heterogeneity and aggressiveness of both focal and diffuse infiltration.
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Affiliation(s)
- Sara Pellegrino
- Department of Advanced Biomedical Sciences, University Federico II, Via Sergio Pansini 5, Naples, 80131, Italy
| | - Davide Origlia
- Department of Advanced Biomedical Sciences, University Federico II, Via Sergio Pansini 5, Naples, 80131, Italy
| | - Erica Di Donna
- Department of Advanced Biomedical Sciences, University Federico II, Via Sergio Pansini 5, Naples, 80131, Italy
| | - Martina Lamagna
- Department of Clinical Medicine and Surgery, University Federico II, Naples, Italy
| | - Roberta Della Pepa
- Department of Clinical Medicine and Surgery, University Federico II, Naples, Italy
| | - Fabrizio Pane
- Department of Clinical Medicine and Surgery, University Federico II, Naples, Italy
| | - Silvana Del Vecchio
- Department of Advanced Biomedical Sciences, University Federico II, Via Sergio Pansini 5, Naples, 80131, Italy
| | - Rosa Fonti
- Department of Advanced Biomedical Sciences, University Federico II, Via Sergio Pansini 5, Naples, 80131, Italy.
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73
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Chen G, Zou L, Ji Z. A review: Blood pressure monitoring based on PPG and circadian rhythm. APL Bioeng 2024; 8:031501. [PMID: 39049850 PMCID: PMC11268918 DOI: 10.1063/5.0206980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 06/26/2024] [Indexed: 07/27/2024] Open
Abstract
The demand for ambulatory blood pressure monitoring (ABPM) is increasing due to the global rise in cardiovascular disease patients. However, conventional ABPM methods are discontinuous and can disrupt daily activities and sleep patterns. Photoplethysmography (PPG) is gaining attention from researchers due to its simplicity, portability, affordability, and ease of signal acquisition. This paper critically examines the advancements achieved in the technology of PPG-guided noninvasive blood pressure (BP) monitoring and explores future opportunities. We have performed a literature search using the Web of Science and PubMed search engines, from January 2018 to October 2023, for PPG signal quality assessment (SQA), cuffless BP estimation using single PPG, and associations between circadian rhythm and BP. Based on this foundation, we first examine the impact of PPG signal quality on blood pressure estimation results while focusing on methods for assessing PPG signal quality. Subsequently, the methods documented for estimating cuff-free BP from PPG signals are summarized. Furthermore, the study examines how individual differences affect the accuracy of BP estimation, incorporating the factors that influence arterial blood pressure (ABP) and elucidating the impact of circadian rhythm on blood pressure. Finally, there will be a summary of the study's findings and suggestions for future research directions.
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Affiliation(s)
- Gang Chen
- College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Linglin Zou
- Department of oncology, Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Zhong Ji
- Author to whom correspondence should be addressed:
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Alzubaidi L, Al-Dulaimi K, Salhi A, Alammar Z, Fadhel MA, Albahri AS, Alamoodi AH, Albahri OS, Hasan AF, Bai J, Gilliland L, Peng J, Branni M, Shuker T, Cutbush K, Santamaría J, Moreira C, Ouyang C, Duan Y, Manoufali M, Jomaa M, Gupta A, Abbosh A, Gu Y. Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion. Artif Intell Med 2024; 155:102935. [PMID: 39079201 DOI: 10.1016/j.artmed.2024.102935] [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: 06/01/2023] [Revised: 03/18/2024] [Accepted: 07/22/2024] [Indexed: 08/24/2024]
Abstract
Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.
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Affiliation(s)
- Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia.
| | - Khamael Al-Dulaimi
- Computer Science Department, College of Science, Al-Nahrain University, Baghdad, Baghdad 10011, Iraq; School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Asma Salhi
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Zaenab Alammar
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Mohammed A Fadhel
- Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - A S Albahri
- Technical College, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq
| | - A H Alamoodi
- Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang 43000, Malaysia
| | - O S Albahri
- Australian Technical and Management College, Melbourne, Australia
| | - Amjad F Hasan
- Faculty of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jinshuai Bai
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Luke Gilliland
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Jing Peng
- Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Marco Branni
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Tristan Shuker
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Kenneth Cutbush
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Jose Santamaría
- Department of Computer Science, University of Jaén, Jaén 23071, Spain
| | - Catarina Moreira
- Data Science Institute, University of Technology Sydney, Australia
| | - Chun Ouyang
- School of Information Systems, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Ye Duan
- School of Computing, Clemson University, Clemson, 29631, SC, USA
| | - Mohamed Manoufali
- CSIRO, Kensington, WA 6151, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia
| | - Mohammad Jomaa
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Ashish Gupta
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Amin Abbosh
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia
| | - Yuantong Gu
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
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75
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Ruitenbeek HC, Oei EHG, Visser JJ, Kijowski R. Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade. Skeletal Radiol 2024; 53:1849-1868. [PMID: 38902420 DOI: 10.1007/s00256-024-04684-6] [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: 02/01/2024] [Revised: 04/06/2024] [Accepted: 04/15/2024] [Indexed: 06/22/2024]
Abstract
This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.
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Affiliation(s)
- Huibert C Ruitenbeek
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Edwin H G Oei
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Richard Kijowski
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA.
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76
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Cai T, Zhao G, Zang J, Zong C, Zhang Z, Xue C. Quantifying instability in neurological disorders EEG based on phase space DTM function. Comput Biol Med 2024; 180:108951. [PMID: 39094326 DOI: 10.1016/j.compbiomed.2024.108951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 08/04/2024]
Abstract
Classifying individuals with neurological disorders and healthy subjects using EEG is a crucial area of research. The current feature extraction approach focuses on the frequency domain features in each of the EEG frequency bands and functional brain networks. In recent years, researchers have discovered and extensively studied stability differences in the electroencephalograms (EEG) of patients with neurological disorders. Based on this, this paper proposes a feature descriptor to characterize EEG instability. The proposed method starts by forming a signal point cloud through Phase Space Reconstruction (PSR). Subsequently, a pseudo-metric space is constructed, and pseudo-distances are calculated based on the consistent measure of the point cloud. Finally, Distance to Measure (DTM) Function are generated to replace the distance function in the original metric space. We calculated the relative distances in the point cloud by measuring signal similarity and, based on this, summarized the point cloud structures formed by EEG with different stabilities after PSR. This process demonstrated that Multivariate Kernel Density Estimation (MKDE) based on a Gaussian kernel can effectively separate the mappings of different stable components within the signal in the phase space. The two average DTM values are then proposed as feature descriptors for EEG instability.In the validation phase, the proposed feature descriptor is tested on three typical neurological disorders: epilepsy, Alzheimer's disease, and Parkinson's disease, using the Bonn dataset, CHB-MIT, the Florida State University dataset, and the Iowa State University dataset. DTM values are used as feature inputs for four different machine learning classifiers, and The results show that the best classification accuracy of the proposed method reaches 98.00 %, 96.25 %, 96.71 % and 95.34 % respectively, outperforming commonly used nonlinear descriptors. Finally, the proposed method is tested and analyzed using noisy signals, demonstrating its robustness compared to other methods.
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Affiliation(s)
- Tianming Cai
- Shanxi College of Technology, No.11 Changning Street, Development Zone, Shuozhou, Shanxi, 036000, China; North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China
| | - Guoying Zhao
- Shanxi College of Technology, No.11 Changning Street, Development Zone, Shuozhou, Shanxi, 036000, China; North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China
| | - Junbin Zang
- Shanxi College of Technology, No.11 Changning Street, Development Zone, Shuozhou, Shanxi, 036000, China; North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China.
| | - Chen Zong
- The Second Hospital of Shanxi Medical University, No.382 Wuyi Road, Taiyuan, Shanxi, 030001, China
| | - Zhidong Zhang
- North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China
| | - Chenyang Xue
- North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China
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Tanner IL, Ye K, Moore MS, Rechenmacher AJ, Ramirez MM, George SZ, Bolognesi MP, Horn ME. Developing a Computer Vision Model to Automate Quantitative Measurement of Hip-Knee-Ankle Angle in Total Hip and Knee Arthroplasty Patients. J Arthroplasty 2024; 39:2225-2233. [PMID: 38679347 DOI: 10.1016/j.arth.2024.04.062] [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: 09/25/2023] [Revised: 04/19/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024] Open
Abstract
BACKGROUND Increasing deformity of the lower extremities, as measured by the hip-knee-ankle angle (HKAA), is associated with poor patient outcomes after total hip and knee arthroplasty (THA, TKA). Automated calculation of HKAA is imperative to reduce the burden on orthopaedic surgeons. We proposed a detection-based deep learning (DL) model to calculate HKAA in THA and TKA patients and assessed the agreement between DL-derived HKAAs and manual measurement. METHODS We retrospectively identified 1,379 long-leg radiographs (LLRs) from patients scheduled for THA or TKA within an academic medical center. There were 1,221 LLRs used to develop the model (randomly split into 70% training, 20% validation, and 10% held-out test sets); 158 LLRs were considered "difficult," as the femoral head was difficult to distinguish from surrounding tissue. There were 2 raters who annotated the HKAA of both lower extremities, and inter-rater reliability was calculated to compare the DL-derived HKAAs with manual measurement within the test set. RESULTS The DL model achieved a mean average precision of 0.985 on the test set. The average HKAA of the operative leg was 173.05 ± 4.54°; the nonoperative leg was 175.55 ± 3.56°. The inter-rater reliability between manual and DL-derived HKAA measurements on the operative leg and nonoperative leg indicated excellent reliability (intraclass correlation (2,k) = 0.987 [0.96, 0.99], intraclass correlation (2, k) = 0.987 [0.98, 0.99, respectively]). The standard error of measurement for the DL-derived HKAA for the operative and nonoperative legs was 0.515° and 0.403°, respectively. CONCLUSIONS A detection-based DL algorithm can calculate the HKAA in LLRs and is comparable to that calculated by manual measurement. The algorithm can detect the bilateral femoral head, knee, and ankle joints with high precision, even in patients where the femoral head is difficult to visualize.
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Affiliation(s)
- Irene L Tanner
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - Ken Ye
- Trinity College of Arts & Sciences, Duke University, Durham, North Carolina
| | - Miles S Moore
- Physical Therapy Division, Duke University School of Medicine, Durham, North Carolina
| | - Albert J Rechenmacher
- Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, North Carolina
| | - Michelle M Ramirez
- Department of Population Health Sciences, Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, North Carolina
| | - Steven Z George
- Department of Orthopaedic Surgery, Department of Population Health Sciences, Duke Clinical Research Institute, Duke University, Durham, North Carolina
| | | | - Maggie E Horn
- Department of Population Health Sciences, Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, North Carolina
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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; 47:1095-1106. [PMID: 38739345 PMCID: PMC11408406 DOI: 10.1007/s13246-024-01430-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: 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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Guo L, Shi L, Wang W, Wang X. Neural Network Classification Algorithm Based on Self-attention Mechanism and Ensemble Learning for MASLD Ultrasound Images. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1361-1371. [PMID: 38910034 DOI: 10.1016/j.ultrasmedbio.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 04/11/2024] [Accepted: 05/10/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND Ultrasound image examination has become the preferred choice for diagnosing metabolic dysfunction-associated steatotic liver disease (MASLD) due to its non-invasive nature. Computer-aided diagnosis (CAD) technology can assist doctors in avoiding deviations in the detection and classification of MASLD. METHOD We propose a hybrid model that integrates the pre-trained VGG16 network with an attention mechanism and a stacking ensemble learning model, which is capable of multi-scale feature aggregation based on the self-attention mechanism and multi-classification model fusion (Logistic regression, random forest, support vector machine) based on stacking ensemble learning. The proposed hybrid method achieves four classifications of normal, mild, moderate, and severe fatty liver based on ultrasound images. RESULT AND CONCLUSION Our proposed hybrid model reaches an accuracy of 91.34% and exhibits superior robustness against interference, which is better than traditional neural network algorithms. Experimental results show that, compared with the pre-trained VGG16 model, adding the self-attention mechanism improves the accuracy by 3.02%. Using the stacking ensemble learning model as a classifier further increases the accuracy to 91.34%, exceeding any single classifier such as LR (89.86%) and SVM (90.34%) and RF (90.73%). The proposed hybrid method can effectively improve the efficiency and accuracy of MASLD ultrasound image detection.
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Affiliation(s)
- Lijuan Guo
- Children's Hospital of Shanxi & Women Health Center of Shanxi, Taiyuan, China.
| | - Liling Shi
- Children's Hospital of Shanxi & Women Health Center of Shanxi, Taiyuan, China.
| | - Wenjuan Wang
- Shanxi International Travel Health Care Center, Taiyuan, China
| | - Xiaotong Wang
- Children's Hospital of Shanxi & Women Health Center of Shanxi, Taiyuan, China
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80
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Effeney B, Pullar A, Burbery J, Hargrave C, Brady C. Dose to organs at risk for total body irradiation: Single-institution data using the modulated arc total body irradiation technique. Pediatr Blood Cancer 2024; 71:e31164. [PMID: 38953144 DOI: 10.1002/pbc.31164] [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: 03/04/2024] [Revised: 06/12/2024] [Accepted: 06/12/2024] [Indexed: 07/03/2024]
Abstract
BACKGROUND Organs at risk (OAR) dose reporting for total body irradiation (TBI) patients is limited, and standardly reported only as mean doses to the lungs and kidneys. Consequently, dose received and effects on other OAR remain unexplored. To remedy this gap, this study reports dose data on an extensive list of OAR for patients treated at a single institution using the modulated arc total body irradiation (MATBI) technique. METHOD An audit was undertaken of all patients treated with MATBI between January 2015 and March 2021 who had completed their course of treatment. OAR were contoured on MATBI patient treatment plans, with 12 Gy in six fraction prescription. OAR dose statistics and dose volume histogram data are reported for the whole body, lungs, kidneys, bones, brain, lens, heart, liver and bowel bag. RESULTS The OAR dose data for 29 patients are reported. Mean dose results are body 11.77 Gy, lungs 9.86 Gy, kidneys 11.84 Gy, bones 12.03 Gy, brain 12.12 Gy, right lens 12.31 Gy, left lens 12.64 Gy, heart 11.07 Gy, liver 11.81 Gy and bowel bag 12.06 Gy. Dose statistics at 1-Gy intervals of V6-V13 for lungs and V10-V13 for kidneys are also included. CONCLUSION This is the first time an extensive list of OAR data has been reported for any TBI technique. Due to the paucity of reporting, this information could be used by centres implementing the MATBI technique, in addition to aiding comparison between TBI techniques, with the potential for greater understanding of the relationship between dose volume data and toxicity.
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Affiliation(s)
- Beth Effeney
- Radiation Oncology Princess Alexandra Hospital - Raymond Terrace, South Brisbane, Queensland, Australia
| | - Andrew Pullar
- Radiation Oncology Princess Alexandra Hospital - Raymond Terrace, South Brisbane, Queensland, Australia
| | - Julie Burbery
- School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Cathy Hargrave
- Radiation Oncology Princess Alexandra Hospital - Raymond Terrace, South Brisbane, Queensland, Australia
- School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Carole Brady
- Radiation Oncology Princess Alexandra Hospital - Raymond Terrace, South Brisbane, Queensland, Australia
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81
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Latreche I, Slatnia S, Kazar O, Harous S, Khelili MA. Identification and diagnosis of schizophrenia based on multichannel EEG and CNN deep learning model. Schizophr Res 2024; 271:28-35. [PMID: 39002527 DOI: 10.1016/j.schres.2024.07.015] [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: 09/02/2023] [Revised: 07/06/2024] [Accepted: 07/07/2024] [Indexed: 07/15/2024]
Abstract
This paper proposes a high-accuracy EEG-based schizophrenia (SZ) detection approach. Unlike comparable literature studies employing conventional machine learning algorithms, our method autonomously extracts the necessary features for network training from EEG recordings. The proposed model is a ten-layered CNN that contains a max pooling layer, a Global Average Pooling layer, four convolution layers, two dropout layers for overfitting prevention, and two fully connected layers. The efficiency of the suggested method was assessed using the ten-fold-cross validation technique and the EEG records of 14 healthy subjects and 14 SZ patients. The obtained mean accuracy score was 99.18 %. To confirm the high mean accuracy attained, we tested the model on unseen data with a near-perfect accuracy score (almost 100 %). In addition, the results we obtained outperform numerous other comparable works.
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Affiliation(s)
- Imene Latreche
- Department of Computer Science, University of Biskra, Biskra, Algeria.
| | - Sihem Slatnia
- Department of Computer Science, University of Biskra, Biskra, Algeria.
| | - Okba Kazar
- College of Arts, Sciences & Information Technology, University of Kalba, Sharjah, United Arab Emirates
| | - Saad Harous
- College of Computing and Informatics, Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates.
| | - Mohamed Akram Khelili
- Department of Computer Science, University of Biskra, Biskra, Algeria; Numidia Institute of Technology, Algiers, Algeria.
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Zhao R, Wang G, Li F, Wang J, Zhang Y, Li D, Liu S, Li J, Song J, Wei F, Wang C. Developing Machine Learning-Based Predictive Models for Hallux Valgus Recurrence Based on Measurements From Radiographs. Foot Ankle Int 2024; 45:1000-1008. [PMID: 38872342 DOI: 10.1177/10711007241256648] [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] [Indexed: 06/15/2024]
Abstract
BACKGROUND Machine learning (ML) is increasingly used to predict the prognosis of numerous diseases. This retrospective analysis aimed to develop a prediction model using ML algorithms and to identify predictors associated with the recurrence of hallux valgus (HV) following surgery. METHODS A total of 198 symptomatic feet that underwent chevron osteotomy combined with a distal soft tissue procedure were enrolled and analyzed from 2 independent medical centers. The feet were grouped according to nonrecurrence or recurrence based on 1-year follow-up outcomes. Preoperative weightbearing radiographs and immediate postoperative nonweightbearing radiographs were obtained for each HV foot. Radiographic measurements (eg, HV angle and intermetatarsal angle) were acquired and used for ML model training. A total of 9 commonly used ML models were trained on the data obtained from one institute (108 feet), and tested on the other data set from another independent institute (90 feet) for external validation. Optimal feature sets for each model were identified based on a 2000-resample bootstrap-based internal validation via an exhaustive search. The performance of each model was then tested on the external validation set. The area under the curve (AUC), classification accuracy, sensitivity, and specificity of each model were calculated to evaluate the performance of each model. RESULTS The support vector machine (SVM) model showed the highest predictive accuracy compared to other methods, with an AUC of 0.88 and an accuracy of 75.6%. Preoperative hallux valgus angle, tibial sesamoid position, postoperative intermetatarsal angle, and postoperative tibial sesamoid position were identified as the most selected features by several ML models. CONCLUSION ML classifiers such as SVM could predict the recurrence of HV (an HVA >20 degrees) at a 1-year follow-up while identifying associated predictors in a multivariate manner. This study holds the potential for foot and ankle surgeons to effectively identify individuals at higher risk of HV recurrence postsurgery.
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Affiliation(s)
- Rui Zhao
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Guobin Wang
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Fengtan Li
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jinchan Wang
- Department of Dermatology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yuan Zhang
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Dong Li
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Shen Liu
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Jie Li
- Graduate School, Tianjin Medical University, Tianjin, China
| | - Jiajun Song
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Fangyuan Wei
- Department of Hand and Foot Surgery, Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China
- Engineering Research Center of Chinese Orthopaedic and Sports Rehabilitation Artificial Intelligent, Ministry of Education, Beijing, China
| | - Chenguang Wang
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
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83
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Quigley KS, Gianaros PJ, Norman GJ, Jennings JR, Berntson GG, de Geus EJC. Publication guidelines for human heart rate and heart rate variability studies in psychophysiology-Part 1: Physiological underpinnings and foundations of measurement. Psychophysiology 2024; 61:e14604. [PMID: 38873876 DOI: 10.1111/psyp.14604] [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: 05/11/2022] [Revised: 12/22/2023] [Accepted: 04/04/2024] [Indexed: 06/15/2024]
Abstract
This Committee Report provides methodological, interpretive, and reporting guidance for researchers who use measures of heart rate (HR) and heart rate variability (HRV) in psychophysiological research. We provide brief summaries of best practices in measuring HR and HRV via electrocardiographic and photoplethysmographic signals in laboratory, field (ambulatory), and brain-imaging contexts to address research questions incorporating measures of HR and HRV. The Report emphasizes evidence for the strengths and weaknesses of different recording and derivation methods for measures of HR and HRV. Along with this guidance, the Report reviews what is known about the origin of the heartbeat and its neural control, including factors that produce and influence HRV metrics. The Report concludes with checklists to guide authors in study design and analysis considerations, as well as guidance on the reporting of key methodological details and characteristics of the samples under study. It is expected that rigorous and transparent recording and reporting of HR and HRV measures will strengthen inferences across the many applications of these metrics in psychophysiology. The prior Committee Reports on HR and HRV are several decades old. Since their appearance, technologies for human cardiac and vascular monitoring in laboratory and daily life (i.e., ambulatory) contexts have greatly expanded. This Committee Report was prepared for the Society for Psychophysiological Research to provide updated methodological and interpretive guidance, as well as to summarize best practices for reporting HR and HRV studies in humans.
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Affiliation(s)
- Karen S Quigley
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA
| | - Peter J Gianaros
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Greg J Norman
- Department of Psychology, The University of Chicago, Chicago, Illinois, USA
| | - J Richard Jennings
- Department of Psychiatry & Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Gary G Berntson
- Department of Psychology & Psychiatry, The Ohio State University, Columbus, Ohio, USA
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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84
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Mitchell J, McLaren DB, Burns Pollock D, Wright J, Killean A, Trainer M, Adamson S, McKernan L, Nailon WH. Clinical implementation of real time motion management for prostate SBRT: A radiation therapist's perspective. Tech Innov Patient Support Radiat Oncol 2024; 31:100267. [PMID: 39220550 PMCID: PMC11363481 DOI: 10.1016/j.tipsro.2024.100267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 07/10/2024] [Accepted: 08/03/2024] [Indexed: 09/04/2024] Open
Abstract
Background and purpose The adoption of hypo-fractionated stereotactic body radiotherapy (SBRT) for treating prostate cancer has led to an increase in specialised techniques for monitoring prostate motion. The aim of this study was to comprehensively review a radiation therapist (RTT) led treatment process in which two such systems were utilised, and present initial findings on their use within a SBRT prostate clinical trial. Materials and Methods 18 patients were investigated, nine were fitted with the Micropos RayPilotTM (RP) system (Micropos Medical, Gothenburg, SE) and nine were fitted with the Micropos Raypilot Hypocath TM (HC) system. 36.25 Gray (Gy) was delivered in 5 fractions over 7 days with daily pre- and post-treatment cone beam computed tomography (CBCT) images acquired. Acute toxicity was reported on completion of treatment at six- and 12-weeks post-treatment, using the Radiation Therapy Oncology Group (RTOG) grading system and vertical (Vrt), longitudinal (Lng) and lateral (Lat) transmitter displacements recorded. Results A significant difference was found in the Lat displacement between devices (P=0.003). A more consistent bladder volume was reported in the HC group (68.03 cc to 483.7 cc RP, 196.11 cc to 313.85 cc HC). No significant difference was observed in mean dose to the bladder, rectum and bladder dose maximum between the groups. Comparison of the rectal dose maximum between the groups reported a significant result (P=0.09). Comparing displacements with toxicity endpoints identified two significant correlations: Grade 2 Genitourinary (GU) at 6 weeks, P=0.029; and no toxicity, Gastrointestinal (GI) at 12 weeks P=0.013. Conclusion Both the directly implanted RP device and the urinary catheter-based HC device are capable of real time motion monitoring. Here, the HC system was advantageous in the SBRT prostate workflow.
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Affiliation(s)
- Joanne Mitchell
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
- Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
- College of Medicine and Veterinary Medicine, the University of Edinburgh, UK
| | - Duncan B. McLaren
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
- Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Donna Burns Pollock
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Joella Wright
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Angus Killean
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Michael Trainer
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Susan Adamson
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Laura McKernan
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - William H. Nailon
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
- Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
- School of Engineering, the University of Edinburgh, the King’s Buildings, Mayfield Road, Edinburgh EH9 3JL, UK
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85
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Al Fahoum A, Zyout A. Wavelet Transform, Reconstructed Phase Space, and Deep Learning Neural Networks for EEG-Based Schizophrenia Detection. Int J Neural Syst 2024; 34:2450046. [PMID: 39010724 DOI: 10.1142/s0129065724500461] [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: 07/17/2024]
Abstract
This study proposes an innovative expert system that uses exclusively EEG signals to diagnose schizophrenia in its early stages. For diagnosing psychiatric/neurological disorders, electroencephalogram (EEG) testing is considered a financially viable, safe, and reliable alternative. Using the reconstructed phase space (RPS) and the continuous wavelet transform, the researchers maximized the differences between the EEG nonstationary signals of normal and schizophrenia individuals, which cannot be observed in the time, frequency, or time-frequency domains. This reveals significant information, highlighting more distinguishable features. Then, a deep learning network was trained to enhance the accuracy of the resulting image classification. The algorithm's efficacy was confirmed through three distinct methods: employing 70% of the dataset for training, 15% for validation, and the remaining 15% for testing. This was followed by a 5-fold cross-validation technique and a leave-one-out classification approach. Each method was iterated 100 times to ascertain the algorithm's robustness. The performance metrics derived from these tests - accuracy, precision, sensitivity, F1 score, Matthews correlation coefficient, and Kappa - indicated remarkable outcomes. The algorithm demonstrated steady performance across all evaluation strategies, underscoring its relevance and reliability. The outcomes validate the system's accuracy, precision, sensitivity, and robustness by showcasing its capability to autonomously differentiate individuals diagnosed with schizophrenia from those in a state of normal health.
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Affiliation(s)
- Amjed Al Fahoum
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid 21163, Jordan
| | - Ala'a Zyout
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid 21163, Jordan
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86
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van der Graaf JW, van Hooff ML, van Ginneken B, Huisman M, Rutten M, Lamers D, Lessmann N, de Kleuver M. Development and validation of AI-based automatic measurement of coronal Cobb angles in degenerative scoliosis using sagittal lumbar MRI. Eur Radiol 2024; 34:5748-5757. [PMID: 38383922 PMCID: PMC11364572 DOI: 10.1007/s00330-024-10616-8] [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: 10/09/2023] [Revised: 12/19/2023] [Accepted: 12/23/2023] [Indexed: 02/23/2024]
Abstract
OBJECTIVES Severity of degenerative scoliosis (DS) is assessed by measuring the Cobb angle on anteroposterior radiographs. However, MRI images are often available to study the degenerative spine. This retrospective study aims to develop and evaluate the reliability of a novel automatic method that measures coronal Cobb angles on lumbar MRI in DS patients. MATERIALS AND METHODS Vertebrae and intervertebral discs were automatically segmented using a 3D AI algorithm, trained on 447 lumbar MRI series. The segmentations were used to calculate all possible angles between the vertebral endplates, with the largest being the Cobb angle. The results were validated with 50 high-resolution sagittal lumbar MRI scans of DS patients, in which three experienced readers measured the Cobb angle. Reliability was determined using the intraclass correlation coefficient (ICC). RESULTS The ICCs between the readers ranged from 0.90 (95% CI 0.83-0.94) to 0.93 (95% CI 0.88-0.96). The ICC between the maximum angle found by the algorithm and the average manually measured Cobb angles was 0.83 (95% CI 0.71-0.90). In 9 out of the 50 cases (18%), all readers agreed on both vertebral levels for Cobb angle measurement. When using the algorithm to extract the angles at the vertebral levels chosen by the readers, the ICCs ranged from 0.92 (95% CI 0.87-0.96) to 0.97 (95% CI 0.94-0.98). CONCLUSION The Cobb angle can be accurately measured on MRI using the newly developed algorithm in patients with DS. The readers failed to consistently choose the same vertebral level for Cobb angle measurement, whereas the automatic approach ensures the maximum angle is consistently measured. CLINICAL RELEVANCE STATEMENT Our AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients, potentially reducing the reliance on conventional radiographs, ensuring consistent assessments, and therefore improving patient care. KEY POINTS • While often available, MRI images are rarely utilized to determine the severity of degenerative scoliosis. • The presented MRI Cobb angle algorithm is more reliable than humans in patients with degenerative scoliosis. • Radiographic imaging for Cobb angle measurements is mitigated when lumbar MRI images are available.
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Affiliation(s)
- Jasper W van der Graaf
- Diagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands.
- Department of Orthopedics, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands.
| | - Miranda L van Hooff
- Department of Orthopedics, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
- Department of Research, Sint Maartenskliniek, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Merel Huisman
- Department of Medical Imaging, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Matthieu Rutten
- Diagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands
- Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - Dominique Lamers
- Department of Orthopedics, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Nikolas Lessmann
- Diagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Marinus de Kleuver
- Department of Orthopedics, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
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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; 47:1-14. [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] [MESH Headings] [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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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; 47:1167-1176. [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] [MESH Headings] [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; 47:813-820. [PMID: 38805104 PMCID: PMC11408578 DOI: 10.1007/s13246-024-01405-0] [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: 10/27/2023] [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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90
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Haghayegh F, Norouziazad A, Haghani E, Feygin AA, Rahimi RH, Ghavamabadi HA, Sadighbayan D, Madhoun F, Papagelis M, Felfeli T, Salahandish R. Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400595. [PMID: 38958517 PMCID: PMC11423253 DOI: 10.1002/advs.202400595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/19/2024] [Indexed: 07/04/2024]
Abstract
Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.
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Affiliation(s)
- Fatemeh Haghayegh
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Alireza Norouziazad
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Elnaz Haghani
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Ariel Avraham Feygin
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Reza Hamed Rahimi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Hamidreza Akbari Ghavamabadi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Deniz Sadighbayan
- Department of BiologyFaculty of ScienceYork UniversityTorontoONM3J 1P3Canada
| | - Faress Madhoun
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Manos Papagelis
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Tina Felfeli
- Department of Ophthalmology and Vision SciencesUniversity of TorontoOntarioM5T 3A9Canada
- Institute of Health PolicyManagement and EvaluationUniversity of TorontoOntarioM5T 3M6Canada
| | - Razieh Salahandish
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
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91
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Ando Y, Okada M, Matsumoto N, Ikuhiro K, Ishihara S, Kiriu H, Tanabe Y. Evaluation of output factors of different radiotherapy planning systems using Exradin W2 plastic scintillator detector. Phys Eng Sci Med 2024; 47:1177-1189. [PMID: 38753285 DOI: 10.1007/s13246-024-01438-5] [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: 12/23/2022] [Accepted: 05/02/2024] [Indexed: 09/18/2024]
Abstract
This study aims to evaluate the output factors (OPF) of different radiation therapy planning systems (TPSs) using a plastic scintillator detector (PSD). The validation results for determining a practical field size for clinical use were verified. The implemented validation system was an Exradin W2 PSD. The focus was to validate the OPFs of the small irradiation fields of two modeled radiation TPSs using RayStation version 10.0.1 and Monaco version 5.51.10. The linear accelerator used for irradiation was a TrueBeam with three energies: 4, 6, and 10 MV. RayStation calculations showed that when the irradiation field size was reduced from 10 × 10 to 0.5 × 0.5 cm2, the results were within 2.0% of the measured values for all energies. Similarly, the values calculated using Monaco were within approximately 2.0% of the measured values for irradiation field sizes between 10 × 10 and 1.5 × 1.5 cm2 for all beam energies of interest. Thus, PSDs are effective validation tools for OPF calculations in TPS. A TPS modeled with the same source data has different minimum irradiation field sizes that can be calculated. These findings could aid in verification of equipment accuracy for treatment planning requiring highly accurate dose calculations and for third-party evaluation of OPF calculations for TPS.
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Affiliation(s)
| | - Masahiro Okada
- Hiroshima City North Medical Center Asa Citizens Hospital, Hiroshima, Japan
| | - Natsuko Matsumoto
- Hiroshima City North Medical Center Asa Citizens Hospital, Hiroshima, Japan
| | - Kawasaki Ikuhiro
- Hiroshima City North Medical Center Asa Citizens Hospital, Hiroshima, Japan
| | | | | | - Yoshinori Tanabe
- Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, 5-1 Shikata-cho, 2-chome, Kita-ku, Okayama, 700-8558, Japan.
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92
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Baumann AN, Trager RJ, Anaspure OS, Floccari L, Li Y, Baldwin KD. The Schroth Method for Pediatric Scoliosis: A Systematic and Critical Analysis Review. JBJS Rev 2024; 12:01874474-202409000-00014. [PMID: 39348476 DOI: 10.2106/jbjs.rvw.24.00096] [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: 10/02/2024]
Abstract
BACKGROUND The Schroth method is the most commonly used patient scoliosis-specific exercise paradigm for treating pediatric scoliosis. The aim of this study is to systematically and critically examine the evidence for the Schroth method for pediatric scoliosis. METHODS PubMed, MEDLINE, CINAHL, and Web of Science were searched through April 5, 2024, for articles examining the Schroth method for pediatric scoliosis (<18 years old). Thirteen review questions were created spanning the study aim. Each included article was independently assessed for the level of evidence (I-IV). Research questions were given a grade of recommendation (A, B, C, and I [insufficient]). RESULTS A total of 29 articles (41.4% Level I, 31.0% Level II, 13.8% Level II, and 13.8% Level IV) met inclusion criteria out of 845 initially retrieved, describing 1,555 patients with scoliosis aged 4 to 18 years. There was grade A evidence that the Schroth method is most commonly used for adolescent idiopathic scoliosis (AIS), can improve the angle of trunk rotation, and is safe; grade B evidence for improvement in posture; and grade I evidence for improvement in Cobb angle, cosmetic deformity, quality of life, ideal treatment parameters, economic value, utility in delaying/preventing surgery, effectiveness in relation to patient characteristics (e.g., skeletal maturity or curve size), and comparative effectiveness to other conservative interventions. CONCLUSION While there is good evidence that the Schroth method is commonly and safely used in AIS and can minimally improve the angle of trunk rotation and fair evidence of improvement in posture, there is insufficient evidence regarding multiple important clinical and economic outcomes, such as comparative effectiveness to other conservative interventions and improvement of Cobb angle. Although clinicians may consider the Schroth method as 1 option of several conservative strategies, clinical benefit may be limited, and further high-quality research is needed to evaluate its performance in areas of insufficient evidence.
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Affiliation(s)
- Anthony N Baumann
- College of Medicine, Northeast Ohio Medical University, Rootstown, Ohio
- Department of Rehabilitation Services, University Hospitals, Cleveland, Ohio
| | - Robert J Trager
- Connor Whole Health, University Hospitals Cleveland Medical Center, Cleveland, Ohio
- Department of Family Medicine and Community Health, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Omkar S Anaspure
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Lorena Floccari
- Department of Orthopedic Surgery, Akron Children's Hospital, Akron, Ohio
| | - Ying Li
- Department of Orthopedic Surgery, University of Michigan, Ann Arbor, Michigan
| | - Keith D Baldwin
- Department of Orthopedic Surgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
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93
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Dai Y, Liu P, Hou W, Kadier K, Mu Z, Lu Z, Chen P, Ma X, Dai J. Deep learning fusion framework for automated coronary artery disease detection using raw heart sound signals. Heliyon 2024; 10:e35631. [PMID: 39262986 PMCID: PMC11388508 DOI: 10.1016/j.heliyon.2024.e35631] [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: 05/12/2024] [Revised: 06/21/2024] [Accepted: 08/01/2024] [Indexed: 09/11/2024] Open
Abstract
One of the most common cardiovascular diseases is coronary artery disease (CAD). Thus, it is crucial for early CAD diagnosis to control disease progression. Computer-aided CAD detection often converts heart sounds into graphics for analysis. However, this method relies heavily on the subjective experience of experts. Therefore, in this study, we proposed a method for CAD detection using raw heart sound signals by constructing a fusion framework with two CAD detection models: a multidomain feature model and a medical multidomain feature fusion model. We collected heart sound signal datasets from 400 participants, extracting 206 multidomain features and 126 medical multidomain features. The designed framework fused the same one-dimensional deep learning features with different multidomain features for CAD detection. The experimental results showed that the multidomain feature model and the medical multidomain feature fusion model achieved areas under the curve (AUC) of 94.7 % and 92.7 %, respectively, demonstrating the effectiveness of the fusion framework in integrating one-dimensional and cross-domain heart sound features through deep learning algorithms, providing an effective solution for noninvasive CAD detection.
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Affiliation(s)
- YunFei Dai
- College of Information Science and Technology, Shihezi University, Shihezi, Xinjiang, 832000, China
| | - PengFei Liu
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830000, China
| | - WenQing Hou
- School of Information Network Security, Xinjiang University of Political Science and Law, Tumushuke, Xinjiang, 843900, China
| | - Kaisaierjiang Kadier
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830000, China
| | - ZhengYang Mu
- College of Information Science and Technology, Shihezi University, Shihezi, Xinjiang, 832000, China
| | - Zang Lu
- College of Information Science and Technology, Shihezi University, Shihezi, Xinjiang, 832000, China
| | - PeiPei Chen
- College of Information Science and Technology, Shihezi University, Shihezi, Xinjiang, 832000, China
| | - Xiang Ma
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830000, China
| | - JianGuo Dai
- College of Information Science and Technology, Shihezi University, Shihezi, Xinjiang, 832000, China
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94
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Nanni C, Deroose CM, Balogova S, Lapa C, Withofs N, Subesinghe M, Jamet B, Zamagni E, Ippolito D, Delforge M, Kraeber-Bodéré F. EANM guidelines on the use of [ 18F]FDG PET/CT in diagnosis, staging, prognostication, therapy assessment, and restaging of plasma cell disorders. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06858-9. [PMID: 39207486 DOI: 10.1007/s00259-024-06858-9] [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: 04/24/2024] [Accepted: 07/21/2024] [Indexed: 09/04/2024]
Abstract
We provide updated guidance and standards for the indication, acquisition, and interpretation of [18F]FDG PET/CT for plasma cell disorders. Procedures and characteristics are reported and different scenarios for the clinical use of [18F]FDG PET/CT are discussed. This document provides clinicians and technicians with the best available evidence to support the implementation of [18F]FDG PET/CT imaging in routine practice and future research.
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Affiliation(s)
- Cristina Nanni
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Christophe M Deroose
- Nuclear Medicine, University Hospitals (UZ) Leuven, 3000, Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Sona Balogova
- Nuclear Medicine, Comenius University, Bratislava, Slovakia
- Médecine Nucléaire, Hôpital Tenon, GH AP.SU, Paris, France
| | - Constantin Lapa
- Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Nadia Withofs
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liege, Liege, Belgium
- GIGA-CRC in Vivo Imaging, University of Liege, Liege, Belgium
| | - Manil Subesinghe
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Bastien Jamet
- Médecine Nucléaire, CHU Nantes, F-44000, Nantes, France
| | - Elena Zamagni
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia "Seràgnoli", Bologna, Italy.
- Dipartimento di Scienze Mediche e Chirurgiche, Università di Bologna, Bologna, Italy.
| | - Davide Ippolito
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900, Monza, Italy
- University of Milano-Bicocca, School of Medicine, Via Cadore 33, 20090, Monza, Italy
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95
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Won H, Lee HS, Youn D, Park D, Eo T, Kim W, Hwang D. Deep Learning-Based Joint Effusion Classification in Adult Knee Radiographs: A Multi-Center Prospective Study. Diagnostics (Basel) 2024; 14:1900. [PMID: 39272685 PMCID: PMC11394442 DOI: 10.3390/diagnostics14171900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Revised: 08/09/2024] [Accepted: 08/23/2024] [Indexed: 09/15/2024] Open
Abstract
Knee effusion, a common and important indicator of joint diseases such as osteoarthritis, is typically more discernible on magnetic resonance imaging (MRI) scans compared to radiographs. However, the use of radiographs for the early detection of knee effusion remains promising due to their cost-effectiveness and accessibility. This multi-center prospective study collected a total of 1413 radiographs from four hospitals between February 2022 to March 2023, of which 1281 were analyzed after exclusions. To automatically detect knee effusion on radiographs, we utilized a state-of-the-art (SOTA) deep learning-based classification model with a novel preprocessing technique to optimize images for diagnosing knee effusion. The diagnostic performance of the proposed method was significantly higher than that of the baseline model, achieving an area under the receiver operating characteristic curve (AUC) of 0.892, accuracy of 0.803, sensitivity of 0.820, and specificity of 0.785. Moreover, the proposed method significantly outperformed two non-orthopedic physicians. Coupled with an explainable artificial intelligence method for visualization, this approach not only improved diagnostic performance but also interpretability, highlighting areas of effusion. These results demonstrate that the proposed method enables the early and accurate classification of knee effusions on radiographs, thereby reducing healthcare costs and improving patient outcomes through timely interventions.
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Affiliation(s)
- Hyeyeon Won
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
- Probe Medical Inc., 61, Yonsei-ro 2na-gil, Seodaemun-gu, Seoul 03777, Republic of Korea
| | - Hye Sang Lee
- Independent Researcher, Seoul 06295, Republic of Korea
| | - Daemyung Youn
- School of Management of Technology, Yonsei University, Seoul 03722, Republic of Korea
| | - Doohyun Park
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Taejoon Eo
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
- Probe Medical Inc., 61, Yonsei-ro 2na-gil, Seodaemun-gu, Seoul 03777, Republic of Korea
| | - Wooju Kim
- Department of Industrial Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Dosik Hwang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
- Probe Medical Inc., 61, Yonsei-ro 2na-gil, Seodaemun-gu, Seoul 03777, Republic of Korea
- Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Republic of Korea
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul 03722, Republic of Korea
- Department of Radiology, Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medical, Seoul 03722, Republic of Korea
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96
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Wen C, Bai X, Yang J, Li S, Wang X, Yang D. Deep learning based approach: automated gingival inflammation grading model using gingival removal strategy. Sci Rep 2024; 14:19780. [PMID: 39187553 PMCID: PMC11347620 DOI: 10.1038/s41598-024-70311-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: 06/11/2024] [Accepted: 08/14/2024] [Indexed: 08/28/2024] Open
Abstract
Gingival inflammation grade serves as a well-established index in periodontitis. The aim of this study was to develop a deep learning network utilizing a novel feature extraction method for the automatic assessment of gingival inflammation. T-distributed Stochastic Neighbor Embedding (t-SNE) was utilized for dimensionality reduction. A convolutional neural network (CNN) model based on DenseNet was developed for the identification and evaluation of gingival inflammation. To enhance the performance of the deep learning (DL) model, a novel teeth removal algorithm was implemented. Additionally, a Grad-CAM + + encoder was applied to generate heatmaps for computer visual attention analysis. The mean Intersection over Union (MIoU) for the identification of gingivitis was 0.727 ± 0.117. The accuracy rates for the five inflammatory degrees were 77.09%, 77.25%, 74.38%, 73.68% and 79.22%. The Area Under the Receiver Operating Characteristic (AUROC) values were 0.83, 0.80, 0.81, 0.81 and 0.84, respectively. The attention ratio towards gingival tissue increased from 37.73% to 62.20%, and within 8 mm of the gingival margin, it rose from 21.11% to 38.23%. On the gingiva, the overall attention ratio increased from 51.82% to 78.21%. The proposed DL model with novel feature extraction method provides high accuracy and sensitivity for identifying and grading gingival inflammation.
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Affiliation(s)
- Chang Wen
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, #237 Luoyu Road, Hongshan District, Wuhan, China
- Center for Orthodontics and Pediatric Dentistry at Optics Valley Branch, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Xueying Bai
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, #237 Luoyu Road, Hongshan District, Wuhan, China
| | - Jiaxin Yang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, #237 Luoyu Road, Hongshan District, Wuhan, China
| | - Sihong Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, #237 Luoyu Road, Hongshan District, Wuhan, China
| | - Xiaoxuan Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, #237 Luoyu Road, Hongshan District, Wuhan, China
- Department of Periodontology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Dong Yang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, #237 Luoyu Road, Hongshan District, Wuhan, China.
- Department of Periodontology, School & Hospital of Stomatology, Wuhan University, Wuhan, China.
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97
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Xu M, Chen Y, Liu D, Wang L, Wu M. Clinical utility of multi-row spiral CT in diagnosing hepatic nodular lesions, gastric cancer, and Crohn's disease: a comprehensive meta-analysis. AMERICAN JOURNAL OF CLINICAL AND EXPERIMENTAL IMMUNOLOGY 2024; 13:165-176. [PMID: 39310125 PMCID: PMC11411159 DOI: 10.62347/srej4505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Accepted: 08/17/2024] [Indexed: 09/25/2024]
Abstract
A retrieval of relevant literature on hepatic nodular lesions, gastric cancer (GC), and Crohn's disease (CD) was conducted from Chinese and English databases. Meta-analysis was performed using Review Manager 5.4 software and the MIDAS package in Stata 18.0. Results from 11 studies comprising 1847 patients were synthesized. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio with 95% confidence intervals were: 0.91 (0.84-0.95), 0.73 (0.65-0.79), 3.30 (2.60-4.30), 0.13 (0.07-0.23), and 26.00 (12.00-53.00), respectively. Significant statistical heterogeneity was found in sensitivity and specificity (P<0.05), with specificity heterogeneity originating from n, type, and mode (P<0.05). Sensitivity and specificity for n, type, object, and mode were non-heterogeneous (P>0.05). The combined AUC from SROC curve analysis of the 11 studies was 0.85. Deeks' funnel plot asymmetry test yielded a p-value of 0.01, indicating potential bias across studies in the diagnostic odds ratio funnel plot. Fagan's nomogram demonstrated that using CT for diagnostic modeling increased the post-test probability of correctly diagnosing hepatic nodular lesions, GC, and CD from 50.00% to 77.00%. Overall, multi-detector CT shows good diagnostic value for hepatic nodular lesions, GC, and CD, supporting its clinical flexibility based on patient-specific considerations.
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Affiliation(s)
- Ming Xu
- Department of Gastroenterology Medicine, Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal UniversityChangsha 410016, Hunan, China
| | - Yinyun Chen
- Department of Gastroenterology Medicine, Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal UniversityChangsha 410016, Hunan, China
| | - Dan Liu
- Department of Gastroenterology Medicine, Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal UniversityChangsha 410016, Hunan, China
| | - Lile Wang
- Department of Respiratory Medicine, Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal UniversityChangsha 410016, Hunan, China
| | - Minghao Wu
- Department of Gastroenterology Medicine, Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal UniversityChangsha 410016, Hunan, China
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98
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Liu Y, Gan K, Li J, Sun D, Qiu H, Liu D. [Study on automatic and rapid diagnosis of distal radius fracture by X-ray]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2024; 41:798-806. [PMID: 39218607 PMCID: PMC11366454 DOI: 10.7507/1001-5515.202309050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
This article aims to combine deep learning with image analysis technology and propose an effective classification method for distal radius fracture types. Firstly, an extended U-Net three-layer cascaded segmentation network was used to accurately segment the most important joint surface and non joint surface areas for identifying fractures. Then, the images of the joint surface area and non joint surface area separately were classified and trained to distinguish fractures. Finally, based on the classification results of the two images, the normal or ABC fracture classification results could be comprehensively determined. The accuracy rates of normal, A-type, B-type, and C-type fracture on the test set were 0.99, 0.92, 0.91, and 0.82, respectively. For orthopedic medical experts, the average recognition accuracy rates were 0.98, 0.90, 0.87, and 0.81, respectively. The proposed automatic recognition method is generally better than experts, and can be used for preliminary auxiliary diagnosis of distal radius fractures in scenarios without expert participation.
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Affiliation(s)
- Yunpeng Liu
- Information and Computing Science Department, International Exchange College, Ningbo University of Technology, Ningbo, Zhejiang 315000, P. R. China
| | - Kaifeng Gan
- Orthopedics, Lihuili Hospital Affiliated to Ningbo University, Ningbo, Zhejiang 3151000, P. R. China
| | - Jin Li
- Zhejiang Wanli University, Ningbo, Zhejiang 315000, P. R. China
| | - Dechao Sun
- College of Digital Technology and Engineering, Ningbo University of Finance & Economics, Ningbo, Zhejiang 315000, P. R. China
| | - Hong Qiu
- Zhejiang Wanli University, Ningbo, Zhejiang 315000, P. R. China
| | - Dongquan Liu
- Radiology Department, Ninghai First Hospital, Ningbo, Zhejiang 315000, P. R. China
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Neagu AI, Poalelungi DG, Fulga A, Neagu M, Fulga I, Nechita A. Enhanced Immunohistochemistry Interpretation with a Machine Learning-Based Expert System. Diagnostics (Basel) 2024; 14:1853. [PMID: 39272638 PMCID: PMC11394116 DOI: 10.3390/diagnostics14171853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 07/26/2024] [Accepted: 08/22/2024] [Indexed: 09/15/2024] Open
Abstract
BACKGROUND In recent decades, machine-learning (ML) technologies have advanced the management of high-dimensional and complex cancer data by developing reliable and user-friendly automated diagnostic tools for clinical applications. Immunohistochemistry (IHC) is an essential staining method that enables the identification of cellular origins by analyzing the expression of specific antigens within tissue samples. The aim of this study was to identify a model that could predict histopathological diagnoses based on specific immunohistochemical markers. METHODS The XGBoost learning model was applied, where the input variable (target variable) was the histopathological diagnosis and the predictors (independent variables influencing the target variable) were the immunohistochemical markers. RESULTS Our study demonstrated a precision rate of 85.97% within the dataset, indicating a high level of performance and suggesting that the model is generally reliable in producing accurate predictions. CONCLUSIONS This study demonstrated the feasibility and clinical efficacy of utilizing the probabilistic decision tree algorithm to differentiate tumor diagnoses according to immunohistochemistry profiles.
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Affiliation(s)
- Anca Iulia Neagu
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania
- Saint John Clinical Emergency Hospital for Children, 800487 Galati, Romania
| | - Diana Gina Poalelungi
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei St., 800578 Galati, Romania
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei St., 800578 Galati, Romania
| | - Marius Neagu
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei St., 800578 Galati, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei St., 800578 Galati, Romania
| | - Aurel Nechita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania
- Saint John Clinical Emergency Hospital for Children, 800487 Galati, Romania
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Nechita LC, Nechita A, Voipan AE, Voipan D, Debita M, Fulga A, Fulga I, Musat CL. AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions. Diagnostics (Basel) 2024; 14:1839. [PMID: 39272624 PMCID: PMC11394310 DOI: 10.3390/diagnostics14171839] [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: 07/15/2024] [Revised: 08/17/2024] [Accepted: 08/22/2024] [Indexed: 09/15/2024] Open
Abstract
The application of artificial intelligence (AI) in electrocardiography is revolutionizing cardiology and providing essential insights into the consequences of the COVID-19 pandemic. This comprehensive review explores AI-enhanced ECG (AI-ECG) applications in risk prediction and diagnosis of heart diseases, with a dedicated chapter on COVID-19-related complications. Introductory concepts on AI and machine learning (ML) are explained to provide a foundational understanding for those seeking knowledge, supported by examples from the literature and current practices. We analyze AI and ML methods for arrhythmias, heart failure, pulmonary hypertension, mortality prediction, cardiomyopathy, mitral regurgitation, hypertension, pulmonary embolism, and myocardial infarction, comparing their effectiveness from both medical and AI perspectives. Special emphasis is placed on AI applications in COVID-19 and cardiology, including detailed comparisons of different methods, identifying the most suitable AI approaches for specific medical applications and analyzing their strengths, weaknesses, accuracy, clinical relevance, and key findings. Additionally, we explore AI's role in the emerging field of cardio-oncology, particularly in managing chemotherapy-induced cardiotoxicity and detecting cardiac masses. This comprehensive review serves as both an insightful guide and a call to action for further research and collaboration in the integration of AI in cardiology, aiming to enhance precision medicine and optimize clinical decision-making.
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Affiliation(s)
- Luiza Camelia Nechita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Aurel Nechita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Andreea Elena Voipan
- Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Daniel Voipan
- Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Mihaela Debita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
| | - Carmina Liana Musat
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania
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