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Nteziryayo D, Wang J, Qian H, An R, Baoyao G, Liu H, Liang M, Liu X, Li T, Uwiragiye J, Joseph P. Forensic significance of VOCs profiling in decayed ante- and post-mortem injuries by GC×GC-TOF/MS. Forensic Sci Med Pathol 2024:10.1007/s12024-024-00843-2. [PMID: 39002063 DOI: 10.1007/s12024-024-00843-2] [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] [Accepted: 05/27/2024] [Indexed: 07/15/2024]
Abstract
Accurately identifying and differentiating the types of injuries in decomposed corpses is a major challenge in forensic identification. Forensic investigations involving decomposed cadavers pose challenges in determining the cause of death. Traditional methods often lack conclusive evidence. However, the implementation of advanced analytical techniques, such as comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOF/MS), shows promise in overcoming these limitations, but the potential in this area remains limited. Therefore, this study aims to bridge this gap by exploring the potential of GC × GC-TOF/MS in the analysis of volatile organic compounds (VOCs) changes within decaying ante- and post-mortem injuries.The research emphasizes the forensic significance of VOCs changes in decomposed cadavers. We used GC × GC-TOF/MS analysis to identify the specific volatile compounds in putrefied corpse tissue samples from mice. The GC × GC-TOF/MS analysis results showed that under winter conditions, PC1 explained 57.16% of the variance, and PC2 explained 25.23% of the variance; while under summer conditions, PC1 explained 71.89% of the variance, and PC2 explained 24.49% of the variance. This demonstrates the potential of GC × GC-TOF/MS in identifying specific VOCs present in tissue samples that can serve as potential biomarkers for distinguishing between antemortem and postmortem injury. GC × GC-TOF/MS analysis revealed distinct VOC patterns in both conditions. Comprehensive use of GC × GC-TOF/MS analysis enhances accuracy in identifying and characterizing ante- and post-mortem injuries in decomposed cadavers. This study can significantly contribute to the field of forensic medicine and improve the accuracy of forensic investigations.
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Affiliation(s)
- Damascene Nteziryayo
- Institute of Forensic Injury, Institute of Forensic Bio‑Evidence, Western China Science and Technology Innovation Harbor, Xi'an Jiaotong University, Xi'an, People's Republic of China.
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Yanta Road W.76, Xi'an 710061, Shaanxi, People's Republic of China.
| | - Jing Wang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Yanta Road W.76, Xi'an 710061, Shaanxi, People's Republic of China
| | - Hongyan Qian
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Yanta Road W.76, Xi'an 710061, Shaanxi, People's Republic of China
| | - Ran An
- Institute of Forensic Injury, Institute of Forensic Bio‑Evidence, Western China Science and Technology Innovation Harbor, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Gao Baoyao
- Institute of Forensic Injury, Institute of Forensic Bio‑Evidence, Western China Science and Technology Innovation Harbor, Xi'an Jiaotong University, Xi'an, People's Republic of China
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Yanta Road W.76, Xi'an 710061, Shaanxi, People's Republic of China
| | - Hua Liu
- Key Laboratory of Forensic Toxicology, Ministry of Public Security, Beijing, People's Republic of China
| | - Min Liang
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Yanta Road W.76, Xi'an 710061, Shaanxi, People's Republic of China
| | - Xinshe Liu
- Institute of Forensic Injury, Institute of Forensic Bio‑Evidence, Western China Science and Technology Innovation Harbor, Xi'an Jiaotong University, Xi'an, People's Republic of China.
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Yanta Road W.76, Xi'an 710061, Shaanxi, People's Republic of China.
| | - Tao Li
- Institute of Forensic Injury, Institute of Forensic Bio‑Evidence, Western China Science and Technology Innovation Harbor, Xi'an Jiaotong University, Xi'an, People's Republic of China.
- Department of Forensic Pathology, College of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Yanta Road W.76, Xi'an 710061, Shaanxi, People's Republic of China.
| | - Jeannette Uwiragiye
- Department of Ecology, Key Laboratory of Plant Diversity and Evolution, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Phazha Joseph
- Institute of Forensic Injury, Institute of Forensic Bio‑Evidence, Western China Science and Technology Innovation Harbor, Xi'an Jiaotong University, Xi'an, People's Republic of China
- Faculty of Chemical and Forensic Sciences, Botswana International University of Science and Technology, Palapye, Botswana
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Liew YM, Ooi JH, Azman RR, Ganesan D, Zakaria MI, Mohd Khairuddin AS, Tan LK. Innovations in detecting skull fractures: A review of computer-aided techniques in CT imaging. Phys Med 2024; 124:103400. [PMID: 38996627 DOI: 10.1016/j.ejmp.2024.103400] [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: 12/11/2023] [Revised: 04/25/2024] [Accepted: 06/05/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND/INTRODUCTION Traumatic brain injury (TBI) remains a leading cause of disability and mortality, with skull fractures being a frequent and serious consequence. Accurate and rapid diagnosis of these fractures is crucial, yet current manual methods via cranial CT scans are time-consuming and prone to error. METHODS This review paper focuses on the evolution of computer-aided diagnosis (CAD) systems for detecting skull fractures in TBI patients. It critically assesses advancements from feature-based algorithms to modern machine learning and deep learning techniques. We examine current approaches to data acquisition, the use of public datasets, algorithmic strategies, and performance metrics RESULTS: The review highlights the potential of CAD systems to provide quick and reliable diagnostics, particularly outside regular clinical hours and in under-resourced settings. Our discussion encapsulates the challenges inherent in automated skull fracture assessment and suggests directions for future research to enhance diagnostic accuracy and patient care. CONCLUSION With CAD systems, we stand on the cusp of significantly improving TBI management, underscoring the need for continued innovation in this field.
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Affiliation(s)
- Yih Miin Liew
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
| | - Jia Hui Ooi
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia; Graduate School of Biomedical Engineering, UNSW Sydney, New South Wales, Australia
| | - Raja Rizal Azman
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Dharmendra Ganesan
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Mohd Idzwan Zakaria
- Academic Unit Emergency Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | | | - Li Kuo Tan
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
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Cheng CT, Ooyang CH, Kang SC, Liao CH. Applications of Deep Learning in Trauma Radiology: A Narrative Review. Biomed J 2024:100743. [PMID: 38679199 DOI: 10.1016/j.bj.2024.100743] [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: 11/13/2023] [Revised: 03/26/2024] [Accepted: 04/24/2024] [Indexed: 05/01/2024] Open
Abstract
Diagnostic imaging is essential in modern trauma care for initial evaluation and identifying injuries requiring intervention. Deep learning (DL) has become mainstream in medical image analysis and has shown promising efficacy for classification, segmentation, and lesion detection. This narrative review provides the fundamental concepts for developing DL algorithms in trauma imaging and presents an overview of current progress in each modality. DL has been applied to detect free fluid on Focused Assessment with Sonography for Trauma (FAST), traumatic findings on chest and pelvic X-rays, and computed tomography (CT) scans, identify intracranial hemorrhage on head CT, detect vertebral fractures, and identify injuries to organs like the spleen, liver, and lungs on abdominal and chest CT. Future directions involve expanding dataset size and diversity through federated learning, enhancing model explainability and transparency to build clinician trust, and integrating multimodal data to provide more meaningful insights into traumatic injuries. Though some commercial artificial intelligence products are Food and Drug Administration-approved for clinical use in the trauma field, adoption remains limited, highlighting the need for multi-disciplinary teams to engineer practical, real-world solutions. Overall, DL shows immense potential to improve the efficiency and accuracy of trauma imaging, but thoughtful development and validation are critical to ensure these technologies positively impact patient care.
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Affiliation(s)
- Chi-Tung Cheng
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan
| | - Chun-Hsiang Ooyang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan
| | - Shih-Ching Kang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan.
| | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan Taiwan
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Blackburn DC, Boyer DM, Gray JA, Winchester J, Bates JM, Baumgart SL, Braker E, Coldren D, Conway KW, Rabosky AD, de la Sancha N, Dillman CB, Dunnum JL, Early CM, Frable BW, Gage MW, Hanken J, Maisano JA, Marks BD, Maslenikov KP, McCormack JE, Nagesan RS, Pandelis GG, Prestridge HL, Rabosky DL, Randall ZS, Robbins MB, Scheinberg LA, Spencer CL, Summers AP, Tapanila L, Thompson CW, Tornabene L, Watkins-Colwell GJ, Welton LJ, Stanley EL. Increasing the impact of vertebrate scientific collections through 3D imaging: The openVertebrate (oVert) Thematic Collections Network. Bioscience 2024; 74:169-186. [PMID: 38560620 PMCID: PMC10977868 DOI: 10.1093/biosci/biad120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/08/2023] [Indexed: 04/04/2024] Open
Abstract
The impact of preserved museum specimens is transforming and increasing by three-dimensional (3D) imaging that creates high-fidelity online digital specimens. Through examples from the openVertebrate (oVert) Thematic Collections Network, we describe how we created a digitization community dedicated to the shared vision of making 3D data of specimens available and the impact of these data on a broad audience of scientists, students, teachers, artists, and more. High-fidelity digital 3D models allow people from multiple communities to simultaneously access and use scientific specimens. Based on our multiyear, multi-institution project, we identify significant technological and social hurdles that remain for fully realizing the potential impact of digital 3D specimens.
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Affiliation(s)
- David C Blackburn
- Florida Museum of Natural History (FLMNH), University of Florida, Gainesville, Florida, United States
- Blackburn served as the lead principal investigator for the oVert Thematic Collections Network
| | - Doug M Boyer
- Duke University, Durham, North Carolina, United States
| | - Jaimi A Gray
- Florida Museum of Natural History (FLMNH), University of Florida, Gainesville, Florida, United States
- Blackburn served as the lead principal investigator for the oVert Thematic Collections Network
| | | | - John M Bates
- Field Museum of Natural History, Chicago, Illinois, United States
| | - Stephanie L Baumgart
- University of Chicago and University of Florida, Gainesville, Florida, United States
| | - Emily Braker
- University of Colorado, Boulder, Colorado, United States
| | - Daryl Coldren
- Field Museum of Natural History, Chicago, Illinois, United States
| | - Kevin W Conway
- Texas A&M University, College Station, Texas, United States
| | | | - Noé de la Sancha
- Chicago State University DePaul University, Chicago, Illinois, United States
| | | | - Jonathan L Dunnum
- Museum of Southwestern Biology, University of New Mexico, Albuquerque, New Mexico, United States
| | - Catherine M Early
- FLMNH Science Museum of Minnesota, St. Paul, Minnesota, United States
| | - Benjamin W Frable
- Scripps Institute of Oceanography, University of California, San Diego, San Diego, California, United States
| | - Matt W Gage
- Harvard University, Cambridge, Massachusetts, United States
| | - James Hanken
- Harvard University, Cambridge, Massachusetts, United States
| | | | - Ben D Marks
- Field Museum of Natural History, Chicago, Illinois, United States
| | | | | | | | | | | | | | - Zachary S Randall
- Florida Museum of Natural History (FLMNH), University of Florida, Gainesville, Florida, United States
- Blackburn served as the lead principal investigator for the oVert Thematic Collections Network
| | | | | | - Carol L Spencer
- University of California, Berkeley, in Berkeley, California, United States
| | - Adam P Summers
- University of Washington, Seattle, Washington, United States
| | - Leif Tapanila
- Idaho State University, Pocatello, Idaho, United States
| | | | - Luke Tornabene
- University of Washington, Seattle, Washington, United States
| | | | - Luke J Welton
- University of Kansas, Lawrence, Kansas, United States
| | | | - Edward L Stanley
- Florida Museum of Natural History (FLMNH), University of Florida, Gainesville, Florida, United States
- Blackburn served as the lead principal investigator for the oVert Thematic Collections Network
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Green TR, Nguyen T, Dunker V, Ashton D, Ortiz JB, Murphy SM, Rowe RK. Blood-Brain Barrier Dysfunction Predicts Microglial Activation After Traumatic Brain Injury in Juvenile Rats. Neurotrauma Rep 2024; 5:95-116. [PMID: 38404523 PMCID: PMC10890961 DOI: 10.1089/neur.2023.0057] [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] [Indexed: 02/27/2024] Open
Abstract
Traumatic brain injury (TBI) disrupts the blood-brain barrier (BBB), which may exacerbate neuroinflammation post-injury. Few translational studies have examined BBB dysfunction and subsequent neuroinflammation post-TBI in juveniles. We hypothesized that BBB dysfunction positively predicts microglial activation and that vulnerability to BBB dysfunction and associated neuroinflammation are dependent on age at injury. Post-natal day (PND)17 and PND35 rats (n = 56) received midline fluid percussion injury or sham surgery, and immunoglobulin-G (IgG) stain was quantified as a marker of extravasated blood in the brain and BBB dysfunction. We investigated BBB dysfunction and the microglial response in the hippocampus, hypothalamus, and motor cortex relative to age at injury and days post-injury (DPI; 1, 7, and 25). We measured the morphologies of ionized calcium-binding adaptor molecule 1-labeled microglia using cell body area and perimeter, microglial branch number and length, end-points/microglial cell, and number of microglia. Data were analyzed using generalized hierarchical models. In PND17 rats, TBI increased levels of IgG compared to shams. Independent of age at injury, IgG in TBI rats was higher at 1 and 7 DPI, but resolved by 25 DPI. TBI activated microglia (more cells and fewer end-points) in PND35 rats compared to respective shams. Independent of age at injury, TBI induced morphological changes indicative of microglial activation, which resolved by 25 DPI. TBI rats had fewer cells and end-points per cell at 1 and 7 DPI than 25 DPI. Independent of TBI, PND17 rats had larger, more activated microglia than PND35 rats; PND17 TBI rats had larger cell body areas and perimeters than PND35 TBI rats. Importantly, we found support in both ages that IgG quantification predicted microglial activation after TBI. The number of microglia increased with increasing IgG, whereas branch length decreased with increasing IgG, which together indicate microglial activation. Our results suggest that stabilization of the BBB after pediatric TBI may be an important therapeutic strategy to limit neuroinflammation and promote recovery.
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Affiliation(s)
- Tabitha R.F. Green
- Department of Integrative Physiology, University of Colorado Boulder, Colorado, USA
| | - Tina Nguyen
- Department of Integrative Physiology, University of Colorado Boulder, Colorado, USA
| | - Veronika Dunker
- Department of Integrative Physiology, University of Colorado Boulder, Colorado, USA
| | - Danielle Ashton
- Department of Integrative Physiology, University of Colorado Boulder, Colorado, USA
| | - J. Bryce Ortiz
- Department of Child Health, University of Arizona College of Medicine–Phoenix, Arizona, USA
| | - Sean M. Murphy
- Cumberland Biological and Ecological Researchers, Longmont, Colorado, USA
| | - Rachel K. Rowe
- Department of Integrative Physiology, University of Colorado Boulder, Colorado, USA
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Petrov A, Kashevnik A, Haleev M, Ali A, Ivanov A, Samochernykh K, Rozhchenko L, Bobinov V. AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images. SENSORS (BASEL, SWITZERLAND) 2024; 24:721. [PMID: 38339438 PMCID: PMC10857356 DOI: 10.3390/s24030721] [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: 11/16/2023] [Revised: 01/03/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024]
Abstract
This paper presents a computer vision-based approach to chronic subdural hematoma segmentation that can be performed by one click. Chronic subdural hematoma is estimated to occur in 0.002-0.02% of the general population each year and the risk increases with age, with a high frequency of about 0.05-0.06% in people aged 70 years and above. In our research, we developed our own dataset, which includes 53 series of CT scans collected from 21 patients with one or two hematomas. Based on the dataset, we trained two neural network models based on U-Net architecture to automate the manual segmentation process. One of the models performed segmentation based only on the current frame, while the other additionally processed multiple adjacent images to provide context, a technique that is more similar to the behavior of a doctor. We used a 10-fold cross-validation technique to better estimate the developed models' efficiency. We used the Dice metric for segmentation accuracy estimation, which was 0.77. Also, for testing our approach, we used scans from five additional patients who did not form part of the dataset, and created a scenario in which three medical experts carried out a hematoma segmentation before we carried out segmentation using our best model. We developed the OsiriX DICOM Viewer plugin to implement our solution into the segmentation process. We compared the segmentation time, which was more than seven times faster using the one-click approach, and the experts agreed that the segmentation quality was acceptable for clinical usage.
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Affiliation(s)
- Andrey Petrov
- Polenov Russian Research Institute of Neurosurgery, Almazov National Medical Research Center, 191014 St. Petersburg, Russia; (A.P.); (A.I.); (K.S.); (L.R.); (V.B.)
| | - Alexey Kashevnik
- St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia;
| | - Mikhail Haleev
- St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia;
| | - Ammar Ali
- Information Technologies and Programming Faculty, ITMO University, 197101 St. Petersburg, Russia;
| | - Arkady Ivanov
- Polenov Russian Research Institute of Neurosurgery, Almazov National Medical Research Center, 191014 St. Petersburg, Russia; (A.P.); (A.I.); (K.S.); (L.R.); (V.B.)
| | - Konstantin Samochernykh
- Polenov Russian Research Institute of Neurosurgery, Almazov National Medical Research Center, 191014 St. Petersburg, Russia; (A.P.); (A.I.); (K.S.); (L.R.); (V.B.)
| | - Larisa Rozhchenko
- Polenov Russian Research Institute of Neurosurgery, Almazov National Medical Research Center, 191014 St. Petersburg, Russia; (A.P.); (A.I.); (K.S.); (L.R.); (V.B.)
| | - Vasiliy Bobinov
- Polenov Russian Research Institute of Neurosurgery, Almazov National Medical Research Center, 191014 St. Petersburg, Russia; (A.P.); (A.I.); (K.S.); (L.R.); (V.B.)
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Liu Z, Zheng L, Gu L, Yang S, Zhong Z, Zhang G. InstrumentNet: An integrated model for real-time segmentation of intracranial surgical instruments. Comput Biol Med 2023; 166:107565. [PMID: 37839219 DOI: 10.1016/j.compbiomed.2023.107565] [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: 06/12/2023] [Revised: 09/13/2023] [Accepted: 10/10/2023] [Indexed: 10/17/2023]
Abstract
In robot-assisted surgery, precise surgical instrument segmentation technology can provide accurate location and pose data for surgeons, helping them perform a series of surgical operations efficiently and safely. However, there are still some interfering factors, such as surgical instruments being covered by tissue, multiple surgical instruments interlacing with each other, and instrument shaking during surgery. To better address these issues, an effective surgical instrument segmentation network called InstrumentNet is proposed, which adopts YOLOv7 as the object detection framework to achieve a real-time detection solution. Specifically, a multiscale feature fusion network is constructed, which aims to avoid problems such as feature redundancy and feature loss and enhance the generalization ability. Furthermore, an adaptive feature-weighted fusion mechanism is introduced to regulate network learning and convergence. Finally, a semantic segmentation head is introduced to integrate the detection and segmentation functions, and a multitask learning loss function is specifically designed to optimize the surgical instrument segmentation performance. The proposed segmentation model is validated on a dataset of intracranial surgical instruments provided by seven experts from Beijing Tiantan Hospital and achieved an mAP score of 93.5 %, Dice score of 82.49 %, and MIoU score of 85.48 %, demonstrating its universality and superiority. The experimental results demonstrate that the proposed model achieves good segmentation performance on surgical instruments compared to other advanced models and can provide a reference for developing intelligent medical robots.
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Affiliation(s)
- Zhenzhong Liu
- Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, 300384, China; National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology), China
| | - Laiwang Zheng
- Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, 300384, China; National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology), China
| | - Lin Gu
- RIkagaku KENkyusho, Tokyo, Japan; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Shubin Yang
- Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, 300384, China; National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology), China
| | - Zichen Zhong
- Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, 300384, China; National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology), China
| | - Guobin Zhang
- Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, 300384, China; National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology), China.
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Jiang B, Ozkara BB, Creeden S, Zhu G, Ding VY, Chen H, Lanzman B, Wolman D, Shams S, Trinh A, Li Y, Khalaf A, Parker JJ, Halpern CH, Wintermark M. Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement. Neuroradiology 2023; 65:1605-1617. [PMID: 37269414 DOI: 10.1007/s00234-023-03170-5] [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: 04/03/2023] [Accepted: 05/21/2023] [Indexed: 06/05/2023]
Abstract
PURPOSE This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast computed tomography (NCCT) scans of patients with suspicion of traumatic brain injury (TBI). METHODS This retrospective and multi-reader study included patients with TBI suspicion who were transported to the emergency department and underwent NCCT scans. Eight reviewers, with varying levels of training and experience (two neuroradiology attendings, two neuroradiology fellows, two neuroradiology residents, one neurosurgery attending, and one neurosurgery resident), independently evaluated NCCT head scans. The same scans were evaluated using the version 5.0 of the DL model icobrain tbi. The establishment of the ground truth involved a thorough assessment of all accessible clinical and laboratory data, as well as follow-up imaging studies, including NCCT and magnetic resonance imaging, as a consensus amongst the study reviewers. The outcomes of interest included neuroimaging radiological interpretation system (NIRIS) scores, the presence of midline shift, mass effect, hemorrhagic lesions, hydrocephalus, and severe hydrocephalus, as well as measurements of midline shift and volumes of hemorrhagic lesions. Comparisons using weighted Cohen's kappa coefficient were made. The McNemar test was used to compare the diagnostic performance. Bland-Altman plots were used to compare measurements. RESULTS One hundred patients were included, with the DL model successfully categorizing 77 scans. The median age for the total group was 48, with the omitted group having a median age of 44.5 and the included group having a median age of 48. The DL model demonstrated moderate agreement with the ground truth, trainees, and attendings. With the DL model's assistance, trainees' agreement with the ground truth improved. The DL model showed high specificity (0.88) and positive predictive value (0.96) in classifying NIRIS scores as 0-2 or 3-4. Trainees and attendings had the highest accuracy (0.95). The DL model's performance in classifying various TBI CT imaging common data elements was comparable to that of trainees and attendings. The average difference for the DL model in quantifying the volume of hemorrhagic lesions was 6.0 mL with a wide 95% confidence interval (CI) of - 68.32 to 80.22, and for midline shift, the average difference was 1.4 mm with a 95% CI of - 3.4 to 6.2. CONCLUSION While the DL model outperformed trainees in some aspects, attendings' assessments remained superior in most instances. Using the DL model as an assistive tool benefited trainees, improving their NIRIS score agreement with the ground truth. Although the DL model showed high potential in classifying some TBI CT imaging common data elements, further refinement and optimization are necessary to enhance its clinical utility.
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Affiliation(s)
- Bin Jiang
- Department of Radiology, Neuroradiology Division, Stanford University, Stanford, CA, USA
| | | | - Sean Creeden
- Deparment of Neuroradiology, University of Illinois College of Medicine Peoria, Peoria, IL, USA
| | - Guangming Zhu
- Department of Neurology, The University of Arizona, Tucson, AZ, USA
| | - Victoria Y Ding
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Hui Chen
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, USA
| | - Bryan Lanzman
- Department of Radiology, Neuroradiology Division, Stanford University, Stanford, CA, USA
| | - Dylan Wolman
- Department of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, USA
| | - Sara Shams
- Department of Radiology, Neuroradiology Division, Stanford University, Stanford, CA, USA
- Department of Radiology, Karolinska University Hospital, Stockholm, Sweden
- Institution for Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Austin Trinh
- Department of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, USA
| | - Ying Li
- Department of Radiology, Neuroradiology Division, Stanford University, Stanford, CA, USA
| | - Alexander Khalaf
- Department of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, USA
| | - Jonathon J Parker
- Device-Based Neuroelectronics Laboratory, Mayo Clinic, Phoenix, AZ, USA
- Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, USA
| | - Casey H Halpern
- Department of Neurosurgery, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Max Wintermark
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, USA.
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Chaidee S, Inkeaw P, Makee T, Khamyod K, Angkurawaranon S, Traisathit P, Vaniyapong T, Chitapanarux I. Comparative analysis between different volumetric methods on measuring intracranial hemorrhage incorporating roundness index. PLoS One 2023; 18:e0292092. [PMID: 37788246 PMCID: PMC10547167 DOI: 10.1371/journal.pone.0292092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/12/2023] [Indexed: 10/05/2023] Open
Abstract
Intracranial hematoma (ICH) volume is considered a predictor of clinical outcome and mortality rate in ICH patients with traumatic brain injury (TBI). The ABC/2 method for ICH volume is the standard method used to date, however, its level of accuracy has been questioned in some studies. This study compared the performance of the ABC/2 method with planimetry and truncated pyramidal methods to highlight the potential of the planimetry method applied with automatic segmentation for evaluation of epidural hematoma (EDH) and intraparenchymal hematoma (IPH) volume. Six different phantoms were designed to evaluate the accuracy of volume estimation methods. 221 hematoma regions extracted from CT scans of 125 patients with head injury were also used to analyze the efficiency. The roundness index was utilized for the quantification of the ellipsoid-like shape. Regions of EDH and IPH on the CT scans were annotated by radiologists. The estimation errors for each method were statistically analyzed and compared. In addition, the relationship between the errors and roundness index was examined. The planimetry method showed the lowest relative error on phantom data. In the case of the CT scan data, the truncated pyramidal method resulted in the underestimation of the volumes of EDH and IPH. Meanwhile, the ABC/2, through principal component analysis (PCA) in the two-dimensional and PCA in the three-dimensional methods, resulted in a significant overestimation. In addition, both these approaches produced relative errors that showed a correlation with the roundness indexes for IPH. In comparison to other methods, the planimetry method had the lowest level of error with regards to calculation of the volume and it was also independent of the hematoma shape. The planimetry method, therefore, has the potential to serve as a useful tool for the assessment of ICH volume in TBI patients by using a deep learning system.
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Affiliation(s)
- Supanut Chaidee
- Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
| | - Papangkorn Inkeaw
- Data Science Research Center, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Global Health and Chronic Conditions Research Center, Chiang Mai University, Chiang Mai, Thailand
| | - Thampaphon Makee
- Data Science Research Center, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
| | - Kamoltip Khamyod
- Department of Radiology, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Salita Angkurawaranon
- Global Health and Chronic Conditions Research Center, Chiang Mai University, Chiang Mai, Thailand
- Department of Radiology, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Patrinee Traisathit
- Data Science Research Center, Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
| | - Tanat Vaniyapong
- Neurosurgery Division, Department of Surgery, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Imjai Chitapanarux
- Department of Radiology, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
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10
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Abdollahifard S, Farrokhi A, Mowla A. Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis. J Neurointerv Surg 2023; 15:995-1000. [PMID: 36418163 DOI: 10.1136/jnis-2022-019627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 11/09/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND This study aimed to investigate the application of deep learning (DL) models for the detection of subdural hematoma (SDH). METHODS We conducted a comprehensive search using relevant keywords. Articles extracted were original studies in which sensitivity and/or specificity were reported. Two different approaches of frequentist and Bayesian inference were applied. For quality and risk of bias assessment we used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). RESULTS We analyzed 22 articles that included 1,997,749 patients. In the first step, the frequentist method showed a pooled sensitivity of 88.8% (95% confidence interval (CI): 83.9% to 92.4%) and a specificity of 97.2% (95% CI 94.6% to 98.6%). In the second step, using Bayesian methods including 11 studies that reported sensitivity and specificity, a sensitivity rate of 86.8% (95% CI: 77.6% to 92.9%) at a specificity level of 86.9% (95% CI: 60.9% to 97.2%) was achieved. The risk of bias assessment was not remarkable using QUADAS-2. CONCLUSION DL models might be an appropriate tool for detecting SDHs with a reasonably high sensitivity and specificity.
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Affiliation(s)
- Saeed Abdollahifard
- Medical School, Shiraz University of Medical Sciences, Shiraz, Iran
- Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Amirmohammad Farrokhi
- Medical School, Shiraz University of Medical Sciences, Shiraz, Iran
- Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ashkan Mowla
- Neurological Surgery, University of Southern California, Los Angeles, California, USA
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11
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Agarwal S, Wood DA, Modat M, Booth TC. Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis. J Neurointerv Surg 2023; 15:1056-1057. [PMID: 37258226 DOI: 10.1136/jnis-2023-020218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 04/17/2023] [Indexed: 06/02/2023]
Affiliation(s)
- Siddharth Agarwal
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - David A Wood
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Thomas C Booth
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, King's College Hospital NHS Foundation Trust, London, UK
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12
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Xu B, Fan Y, Liu J, Zhang G, Wang Z, Li Z, Guo W, Tang X. CHSNet: Automatic lesion segmentation network guided by CT image features for acute cerebral hemorrhage. Comput Biol Med 2023; 164:107334. [PMID: 37573720 DOI: 10.1016/j.compbiomed.2023.107334] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/28/2023] [Accepted: 08/07/2023] [Indexed: 08/15/2023]
Abstract
Stroke is a cerebrovascular disease that can lead to severe sequelae such as hemiplegia and mental retardation with a mortality rate of up to 40%. In this paper, we proposed an automatic segmentation network (CHSNet) to segment the lesions in cranial CT images based on the characteristics of acute cerebral hemorrhage images, such as high density, multi-scale, and variable location, and realized the three-dimensional (3D) visualization and localization of the cranial lesions after the segmentation was completed. To enhance the feature representation of high-density regions, and capture multi-scale and up-down information on the target location, we constructed a convolutional neural network with encoding-decoding backbone, Res-RCL module, Atrous Spatial Pyramid Pooling, and Attention Gate. We collected images of 203 patients with acute cerebral hemorrhage, constructed a dataset containing 5998 cranial CT slices, and conducted comparative and ablation experiments on the dataset to verify the effectiveness of our model. Our model achieved the best results on both test sets with different segmentation difficulties, test1: Dice = 0.918, IoU = 0.853, ASD = 0.476, RVE = 0.113; test2: Dice = 0.716, IoU = 0.604, ASD = 5.402, RVE = 1.079. Based on the segmentation results, we achieved 3D visualization and localization of hemorrhage in CT images of stroke patients. The study has important implications for clinical adjuvant diagnosis.
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Affiliation(s)
- Bohao Xu
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Jingming Liu
- Emergency Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Guobin Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Zhiping Wang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China
| | - Zhili Li
- BECHOICE (Beijing) Science and Technology Development Ltd., Beijing, 100050, China
| | - Wei Guo
- Emergency Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
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13
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Angkurawaranon S, Sanorsieng N, Unsrisong K, Inkeaw P, Sripan P, Khumrin P, Angkurawaranon C, Vaniyapong T, Chitapanarux I. A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage. Sci Rep 2023; 13:9975. [PMID: 37340038 DOI: 10.1038/s41598-023-37114-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/15/2023] [Indexed: 06/22/2023] Open
Abstract
Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investigation and recognition by physicians. Computed tomography (CT) scanning is the investigation of choice for TBI and has become increasingly utilized under the shortage of trained radiology personnel. It is anticipated that deep learning models will be a promising solution for the generation of timely and accurate radiology reports. Our study examines the diagnostic performance of a deep learning model and compares the performance of that with detection, localization and classification of traumatic ICHs involving radiology, emergency medicine, and neurosurgery residents. Our results demonstrate that the high level of accuracy achieved by the deep learning model, (0.89), outperforms the residents with regard to sensitivity (0.82) but still lacks behind in specificity (0.90). Overall, our study suggests that the deep learning model may serve as a potential screening tool aiding the interpretation of head CT scans among traumatic brain injury patients.
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Affiliation(s)
- Salita Angkurawaranon
- Department of Radiology, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
- Global Health and Chronic Conditions Research Group, Chiang Mai, 50200, Thailand
| | - Nonn Sanorsieng
- Department of Radiology, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Kittisak Unsrisong
- Department of Radiology, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Papangkorn Inkeaw
- Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Patumrat Sripan
- Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Piyapong Khumrin
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Chaisiri Angkurawaranon
- Global Health and Chronic Conditions Research Group, Chiang Mai, 50200, Thailand
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Tanat Vaniyapong
- Neurosurgery Division, Department of Surgery, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Imjai Chitapanarux
- Department of Radiology, Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
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14
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Street JS, Pandit AS, Toma AK. Predicting vasospasm risk using first presentation aneurysmal subarachnoid hemorrhage volume: A semi-automated CT image segmentation analysis using ITK-SNAP. PLoS One 2023; 18:e0286485. [PMID: 37262041 DOI: 10.1371/journal.pone.0286485] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/17/2023] [Indexed: 06/03/2023] Open
Abstract
PURPOSE Cerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is a significant complication associated with poor neurological outcomes. We present a novel, semi-automated pipeline, implemented in the open-source medical imaging analysis software ITK-SNAP, to segment subarachnoid blood volume from initial CT head (CTH) scans and use this to predict future radiological vasospasm. METHODS 42 patients were admitted between February 2020 and December 2021 to our tertiary neurosciences center, and whose initial referral CTH scan was used for this retrospective cohort study. Blood load was segmented using a semi-automated random forest classifier and active contour evolution implemented in ITK-SNAP. Clinical data were extracted from electronic healthcare records in order to fit models aimed at predicting radiological vasospasm risk. RESULTS Semi-automated segmentations demonstrated excellent agreement with manual, expert-derived volumes (mean Dice coefficient = 0.92). Total normalized blood volume, extracted from CTH images at first presentation, was significantly associated with greater odds of later radiological vasospasm, increasing by approximately 7% for each additional cm3 of blood (OR = 1.069, 95% CI: 1.021-1.120; p < .005). Greater blood volume was also significantly associated with vasospasm of a higher Lindegaard ratio, of longer duration, and a greater number of discrete episodes. Total blood volume predicted radiological vasospasm with a greater accuracy as compared to the modified Fisher scale (AUC = 0.86 vs 0.70), and was of independent predictive value. CONCLUSION Semi-automated methods provide a plausible pipeline for the segmentation of blood from CT head images in aSAH, and total blood volume is a robust, extendable predictor of radiological vasospasm, outperforming the modified Fisher scale. Greater subarachnoid blood volume significantly increases the odds of subsequent vasospasm, its time course and its severity.
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Affiliation(s)
- James S Street
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, United Kingdom
| | - Anand S Pandit
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
- High-Dimensional Neurology, Institute of Neurology, University College London, London, United Kingdom
| | - Ahmed K Toma
- Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
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15
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Rajaei F, Cheng S, Williamson CA, Wittrup E, Najarian K. AI-Based Decision Support System for Traumatic Brain Injury: A Survey. Diagnostics (Basel) 2023; 13:diagnostics13091640. [PMID: 37175031 PMCID: PMC10177859 DOI: 10.3390/diagnostics13091640] [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: 03/28/2023] [Revised: 04/22/2023] [Accepted: 04/29/2023] [Indexed: 05/15/2023] Open
Abstract
Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.
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Affiliation(s)
- Flora Rajaei
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Shuyang Cheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Craig A Williamson
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, USA
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA
| | - Emily Wittrup
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Data-Driven Drug Development and Treatment Assessment (DATA), University of Michigan, Ann Arbor, MI 48109, USA
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16
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Bennett A, Garner R, Morris MD, La Rocca M, Barisano G, Cua R, Loon J, Alba C, Carbone P, Gao S, Pantoja A, Khan A, Nouaili N, Vespa P, Toga AW, Duncan D. Manual lesion segmentations for traumatic brain injury characterization. FRONTIERS IN NEUROIMAGING 2023; 2:1068591. [PMID: 37554636 PMCID: PMC10406209 DOI: 10.3389/fnimg.2023.1068591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/17/2023] [Indexed: 08/10/2023]
Abstract
Traumatic brain injury (TBI) often results in heterogenous lesions that can be visualized through various neuroimaging techniques, such as magnetic resonance imaging (MRI). However, injury burden varies greatly between patients and structural deformations often impact usability of available analytic algorithms. Therefore, it is difficult to segment lesions automatically and accurately in TBI cohorts. Mislabeled lesions will ultimately lead to inaccurate findings regarding imaging biomarkers. Therefore, manual segmentation is currently considered the gold standard as this produces more accurate masks than existing automated algorithms. These masks can provide important lesion phenotype data including location, volume, and intensity, among others. There has been a recent push to investigate the correlation between these characteristics and the onset of post traumatic epilepsy (PTE), a disabling consequence of TBI. One motivation of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is to identify reliable imaging biomarkers of PTE. Here, we report the protocol and importance of our manual segmentation process in patients with moderate-severe TBI enrolled in EpiBioS4Rx. Through these methods, we have generated a dataset of 127 validated lesion segmentation masks for TBI patients. These ground-truths can be used for robust PTE biomarker analyses, including optimization of multimodal MRI analysis via inclusion of lesioned tissue labels. Moreover, our protocol allows for analysis of the refinement process. Though tedious, the methods reported in this work are necessary to create reliable data for effective training of future machine-learning based lesion segmentation methods in TBI patients and subsequent PTE analyses.
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Affiliation(s)
- Alexis Bennett
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Rachael Garner
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Michael D. Morris
- David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Marianna La Rocca
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli studi di Bari “A. Moro”, Bari, Italy
| | - Giuseppe Barisano
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Ruskin Cua
- USC Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Jordan Loon
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Celina Alba
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Patrick Carbone
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Shawn Gao
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Asenat Pantoja
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Azrin Khan
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Noor Nouaili
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Paul Vespa
- David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Arthur W. Toga
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Dominique Duncan
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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17
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Smorchkova AK, Khoruzhaya AN, Kremneva EI, Petryaikin AV. [Machine learning technologies in CT-based diagnostics and classification of intracranial hemorrhages]. ZHURNAL VOPROSY NEIROKHIRURGII IMENI N. N. BURDENKO 2023; 87:85-91. [PMID: 37011333 DOI: 10.17116/neiro20238702185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Abstract
This review discusses pooled experience of creation, implementation and effectiveness of machine learning technologies in CT-based diagnosis of intracranial hemorrhages. The authors analyzed 21 original articles between 2015 and 2022 using the following keywords: «intracranial hemorrhage», «machine learning», «deep learning», «artificial intelligence». The review contains general data on basic concepts of machine learning and also considers in more detail such aspects as technical characteristics of data sets used for creation of AI algorithms for certain type of clinical task, their possible impact on effectiveness and clinical experience.
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Affiliation(s)
- A K Smorchkova
- Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow, Russia
| | - A N Khoruzhaya
- Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow, Russia
| | - E I Kremneva
- Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow, Russia
- Neurology Research Center, Moscow, Russia
| | - A V Petryaikin
- Moscow Research Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow, Russia
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18
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Hibi A, Jaberipour M, Cusimano MD, Bilbily A, Krishnan RG, Aviv RI, Tyrrell PN. Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet? Medicine (Baltimore) 2022; 101:e31848. [PMID: 36451512 PMCID: PMC9704869 DOI: 10.1097/md.0000000000031848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND The purpose of this study was to conduct a systematic review for understanding the availability and limitations of artificial intelligence (AI) approaches that could automatically identify and quantify computed tomography (CT) findings in traumatic brain injury (TBI). METHODS Systematic review, in accordance with PRISMA 2020 and SPIRIT-AI extension guidelines, with a search of 4 databases (Medline, Embase, IEEE Xplore, and Web of Science) was performed to find AI studies that automated the clinical tasks for identifying and quantifying CT findings of TBI-related abnormalities. RESULTS A total of 531 unique publications were reviewed, which resulted in 66 articles that met our inclusion criteria. The following components for identification and quantification regarding TBI were covered and automated by existing AI studies: identification of TBI-related abnormalities; classification of intracranial hemorrhage types; slice-, pixel-, and voxel-level localization of hemorrhage; measurement of midline shift; and measurement of hematoma volume. Automated identification of obliterated basal cisterns was not investigated in the existing AI studies. Most of the AI algorithms were based on deep neural networks that were trained on 2- or 3-dimensional CT imaging datasets. CONCLUSION We identified several important TBI-related CT findings that can be automatically identified and quantified with AI. A combination of these techniques may provide useful tools to enhance reproducibility of TBI identification and quantification by supporting radiologists and clinicians in their TBI assessments and reducing subjective human factors.
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Affiliation(s)
- Atsuhiro Hibi
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Majid Jaberipour
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Michael D Cusimano
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Division of Neurosurgery, St Michael's Hospital, University of Toronto, Toronto, Canada
| | - Alexander Bilbily
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Rahul G Krishnan
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Richard I Aviv
- Department of Radiology, Radiation Oncology and Medical Physics, University of Ottawa, Ottawa, Ontario, Canada
| | - Pascal N Tyrrell
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
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19
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Nijiati M, Tuersun A, Zhang Y, Yuan Q, Gong P, Abulizi A, Tuoheti A, Abulaiti A, Zou X. A symmetric prior knowledge based deep learning model for intracerebral hemorrhage lesion segmentation. Front Physiol 2022; 13:977427. [PMID: 36505076 PMCID: PMC9727183 DOI: 10.3389/fphys.2022.977427] [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: 07/05/2022] [Accepted: 11/11/2022] [Indexed: 11/24/2022] Open
Abstract
Background: Accurate localization and classification of intracerebral hemorrhage (ICH) lesions are of great significance for the treatment and prognosis of patients with ICH. The purpose of this study is to develop a symmetric prior knowledge based deep learning model to segment ICH lesions in computed tomography (CT). Methods: A novel symmetric Transformer network (Sym-TransNet) is designed to segment ICH lesions in CT images. A cohort of 1,157 patients diagnosed with ICH is established to train (n = 857), validate (n = 100), and test (n = 200) the Sym-TransNet. A healthy cohort of 200 subjects is added, establishing a test set with balanced positive and negative cases (n = 400), to further evaluate the accuracy, sensitivity, and specificity of the diagnosis of ICH. The segmentation results are obtained after data pre-processing and Sym-TransNet. The DICE coefficient is used to evaluate the similarity between the segmentation results and the segmentation gold standard. Furthermore, some recent deep learning methods are reproduced to compare with Sym-TransNet, and statistical analysis is performed to prove the statistical significance of the proposed method. Ablation experiments are conducted to prove that each component in Sym-TransNet could effectively improve the DICE coefficient of ICH lesions. Results: For the segmentation of ICH lesions, the DICE coefficient of Sym-TransNet is 0.716 ± 0.031 in the test set which contains 200 CT images of ICH. The DICE coefficients of five subtypes of ICH, including intraparenchymal hemorrhage (IPH), intraventricular hemorrhage (IVH), extradural hemorrhage (EDH), subdural hemorrhage (SDH), and subarachnoid hemorrhage (SAH), are 0.784 ± 0.039, 0.680 ± 0.049, 0.359 ± 0.186, 0.534 ± 0.455, and 0.337 ± 0.044, respectively. Statistical results show that the proposed Sym-TransNet can significantly improve the DICE coefficient of ICH lesions in most cases. In addition, the accuracy, sensitivity, and specificity of Sym-TransNet in the diagnosis of ICH in 400 CT images are 91.25%, 98.50%, and 84.00%, respectively. Conclusion: Compared with recent mainstream deep learning methods, the proposed Sym-TransNet can segment and identify different types of lesions from CT images of ICH patients more effectively. Moreover, the Sym-TransNet can diagnose ICH more stably and efficiently, which has clinical application prospects.
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Affiliation(s)
- Mayidili Nijiati
- Department of Radiology, The First People’s Hospital of Kashi Prefecture, Kashi, China
| | - Abudouresuli Tuersun
- Department of Radiology, The First People’s Hospital of Kashi Prefecture, Kashi, China
| | | | - Qing Yuan
- Department of Radiology, The First People’s Hospital of Kashi Prefecture, Kashi, China
| | | | | | - Awanisa Tuoheti
- Department of Radiology, The First People’s Hospital of Kashi Prefecture, Kashi, China
| | - Adili Abulaiti
- Department of Radiology, The First People’s Hospital of Kashi Prefecture, Kashi, China,*Correspondence: Adili Abulaiti, ; Xiaoguang Zou,
| | - Xiaoguang Zou
- Clinical Medical Research Center, The First People’s Hospital of Kashi Prefecture, Kashi, China,*Correspondence: Adili Abulaiti, ; Xiaoguang Zou,
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Wu S, Wang Y, Yang H, Wang P. Improved Faster R-CNN for the Detection Method of Industrial Control Logic Graph Recognition. Front Bioeng Biotechnol 2022; 10:944944. [PMID: 35992353 PMCID: PMC9386596 DOI: 10.3389/fbioe.2022.944944] [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: 05/16/2022] [Accepted: 06/08/2022] [Indexed: 11/30/2022] Open
Abstract
In the process of developing the industrial control SAMA logic diagram commonly used in the industrial process control system, there are some problems, that is, the size of logic diagram elements is small, the shape is various, similar element recognition is easily confused, and the detection accuracy is low. In this study, the faster R-CNN network has been improved. The original VGG16 network has been replaced by the ResNet101 network, and the residual value module was introduced to ensure the detailed features of the deep network. Then the industrial control logic diagram dataset was analyzed to improve the anchor size ratio through the K-means clustering algorithm. The candidate box screening problem was optimized by improving the non-maximum suppression algorithm. The elements were distinguished via the combination of the candidate box location and the inherent text, which improved the recognition accuracy of similar elements. An experimental platform was built using the TensorFlow framework based on the Windows system, and the improved method was compared with the original one by the control variable. The results showed that the performance of similar element recognition has been greatly enhanced through an improved faster R-CNN network.
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Affiliation(s)
- Shilin Wu
- Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, China
- School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, China
- *Correspondence: Shilin Wu,
| | - Yan Wang
- School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, China
| | - Huayu Yang
- School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, China
| | - Pingfeng Wang
- School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, China
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Shih YJ, Liu YL, Chen JH, Ho CH, Yang CC, Chen TY, Wu TC, Ko CC, Zhou JT, Zhang Y, Su MY. Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters. Diagnostics (Basel) 2022; 12:diagnostics12071677. [PMID: 35885581 PMCID: PMC9320220 DOI: 10.3390/diagnostics12071677] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/04/2022] [Accepted: 07/08/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Radiomics analysis of spontaneous intracerebral hemorrhages on computed tomography (CT) images has been proven effective in predicting hematoma expansion and poor neurologic outcome. In contrast, there is limited evidence on its predictive abilities for traumatic intraparenchymal hemorrhage (IPH). (2) Methods: A retrospective analysis of 107 traumatic IPH patients was conducted. Among them, 45 patients (42.1%) showed hemorrhagic progression of contusion (HPC) and 51 patients (47.7%) had poor neurological outcome. The IPH on the initial CT was manually segmented for radiomics analysis. After feature extraction, selection and repeatability evaluation, several machine learning algorithms were used to derive radiomics scores (R-scores) for the prediction of HPC and poor neurologic outcome. (3) Results: The AUCs for R-scores alone to predict HPC and poor neurologic outcome were 0.76 and 0.81, respectively. Clinical parameters were used to build comparison models. For HPC prediction, variables including age, multiple IPH, subdural hemorrhage, Injury Severity Score (ISS), international normalized ratio (INR) and IPH volume taken together yielded an AUC of 0.74, which was significantly (p = 0.022) increased to 0.83 after incorporation of the R-score in a combined model. For poor neurologic outcome prediction, clinical variables of age, Glasgow Coma Scale, ISS, INR and IPH volume showed high predictability with an AUC of 0.92, and further incorporation of the R-score did not improve the AUC. (4) Conclusion: The results suggest that radiomics analysis of IPH lesions on initial CT images has the potential to predict HPC and poor neurologic outcome in traumatic IPH patients. The clinical and R-score combined model further improves the performance of HPC prediction.
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Affiliation(s)
- Yun-Ju Shih
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-J.S.); (C.-C.Y.); (T.-Y.C.); (T.-C.W.); (C.-C.K.)
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, Irvine, CA 92868, USA; (Y.-L.L.); (J.T.Z.); (Y.Z.); (M.-Y.S.)
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, CA 92868, USA; (Y.-L.L.); (J.T.Z.); (Y.Z.); (M.-Y.S.)
- Department of Radiology, E-Da Hospital/I-Shou University, Kaohsiung 824, Taiwan
- Correspondence:
| | - Chung-Han Ho
- Department of Medical Research, Chi Mei Medical Center, Tainan 710, Taiwan;
- Department of Information Management, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan
| | - Cheng-Chun Yang
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-J.S.); (C.-C.Y.); (T.-Y.C.); (T.-C.W.); (C.-C.K.)
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-J.S.); (C.-C.Y.); (T.-Y.C.); (T.-C.W.); (C.-C.K.)
- Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan 711, Taiwan
| | - Te-Chang Wu
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-J.S.); (C.-C.Y.); (T.-Y.C.); (T.-C.W.); (C.-C.K.)
- Department of Medical Sciences Industry, Chang Jung Christian University, Tainan 711, Taiwan
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-J.S.); (C.-C.Y.); (T.-Y.C.); (T.-C.W.); (C.-C.K.)
- Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan 717, Taiwan
| | - Jonathan T. Zhou
- Department of Radiological Sciences, University of California, Irvine, CA 92868, USA; (Y.-L.L.); (J.T.Z.); (Y.Z.); (M.-Y.S.)
| | - Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, CA 92868, USA; (Y.-L.L.); (J.T.Z.); (Y.Z.); (M.-Y.S.)
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ 08903, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, CA 92868, USA; (Y.-L.L.); (J.T.Z.); (Y.Z.); (M.-Y.S.)
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung 807, Taiwan
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