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Ding H, Wu C, Su Z, Wang T, Zhuang S, Li C, Li Y. Current landscape and future trends in salivary gland oncology research-a bibliometric evaluation. Gland Surg 2024; 13:969-986. [PMID: 39015723 PMCID: PMC11247595 DOI: 10.21037/gs-24-94] [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: 03/20/2024] [Accepted: 06/11/2024] [Indexed: 07/18/2024]
Abstract
Background The salivary glands are susceptible to both endogenous and exogenous influences, potentially resulting in the development of oncology. With the wide application of various technologies, research in this area has experienced rapid growth. Therefore, researchers must identify and characterize the current research hot topics to grasp the forefront of developments in the dynamic field of salivary gland oncology. The objective of this study was to thoroughly assess the current status and identify potential future research directions in salivary gland oncology. Methods The relevant salivary gland oncology dataset was obtained from the Web of Science Core Collection (WOSCC) database. Subsequently, VoSviewer and CiteSpace were employed for further evaluation. Results A total of 9,695 manuscripts were extracted and downloaded from the WOSCC database. Our findings revealed a substantial surge in research volume over the past 12 years. The researchers' analysis revealed that Abbas Agami showed unparalleled dedication, with over 180 publications, and that RH Spiro had the highest cocitation count, confirming its status as a key figure in the field. The detection of bursts in secretory carcinoma and the integration of artificial intelligence in salivary oncology have attracted increasing interest. Notably, there is a discernible trend towards increased research engagement in the study of salivary gland malignancies. Conclusions This study not only evaluated the current research landscape in salivary gland oncology but also anticipates future trends. These insights could contribute to the advancement of knowledge and policymaking in salivary gland oncology.
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Affiliation(s)
- Haoran Ding
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Chenzhou Wu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Zhifei Su
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Tianyi Wang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Shiyong Zhuang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Chunjie Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yi Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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Pfahl A, Polat ST, Köhler H, Gockel I, Melzer A, Chalopin C. Switchable LED-based laparoscopic multispectral system for rapid high-resolution perfusion imaging. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:126002. [PMID: 38094710 PMCID: PMC10718192 DOI: 10.1117/1.jbo.28.12.126002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 11/03/2023] [Accepted: 11/08/2023] [Indexed: 12/18/2023]
Abstract
Significance Multispectral imaging (MSI) is an approach for real-time, quantitative, and non-invasive tissue perfusion measurements. Current laparoscopic systems based on mosaic sensors or filter wheels lack high spatial resolution or acceptable frame rates. Aim To develop a laparoscopic system for MSI-based color video and tissue perfusion imaging during gastrointestinal surgery without compromising spatial or temporal resolution. Approach The system was built with 14 switchable light-emitting diodes in the visible and near-infrared spectral range, a 4K image sensor, and a 10 mm laparoscope. Illumination patterns were created for tissue oxygenation and hemoglobin content monitoring. The system was calibrated to a clinically approved laparoscopic hyperspectral system using linear regression models and evaluated in an occlusion study with 36 volunteers. Results The root mean squared errors between the MSI and reference system were 0.073 for hemoglobin content, 0.039 for oxygenation in deeper tissue layers, and 0.093 for superficial oxygenation. The spatial resolution at a working distance of 45 mm was 156 μ m . The effective frame rate was 20 fps. Conclusions High-resolution perfusion monitoring was successfully achieved. Hardware optimizations will increase the frame rate. Parameter optimizations through alternative illumination patterns, regression, or assumed tissue models are planned. Intraoperative measurements must confirm the suitability during surgery.
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Affiliation(s)
- Annekatrin Pfahl
- Leipzig University, Faculty of Medicine, Innovation Center Computer Assisted Surgery, Leipzig, Germany
| | - Süleyman T. Polat
- Leipzig University, Faculty of Medicine, Innovation Center Computer Assisted Surgery, Leipzig, Germany
| | - Hannes Köhler
- Leipzig University, Faculty of Medicine, Innovation Center Computer Assisted Surgery, Leipzig, Germany
| | - Ines Gockel
- University Hospital of Leipzig, Department of Visceral, Transplant, Thoracic, and Vascular Surgery, Leipzig, Germany
| | - Andreas Melzer
- Leipzig University, Faculty of Medicine, Innovation Center Computer Assisted Surgery, Leipzig, Germany
- University of Dundee, School of Medicine, Institute for Medical Science and Technology, Dundee, United Kingdom
| | - Claire Chalopin
- Leipzig University, Faculty of Medicine, Innovation Center Computer Assisted Surgery, Leipzig, Germany
- University of Applied Sciences and Arts, Faculty of Engineering and Health, Göttingen, Germany
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Liu GS, Hodges JM, Yu J, Sung CK, Erickson‐DiRenzo E, Doyle PC. End-to-end deep learning classification of vocal pathology using stacked vowels. Laryngoscope Investig Otolaryngol 2023; 8:1312-1318. [PMID: 37899847 PMCID: PMC10601590 DOI: 10.1002/lio2.1144] [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: 08/06/2023] [Accepted: 08/13/2023] [Indexed: 10/31/2023] Open
Abstract
Objectives Advances in artificial intelligence (AI) technology have increased the feasibility of classifying voice disorders using voice recordings as a screening tool. This work develops upon previous models that take in single vowel recordings by analyzing multiple vowel recordings simultaneously to enhance prediction of vocal pathology. Methods Voice samples from the Saarbruecken Voice Database, including three sustained vowels (/a/, /i/, /u/) from 687 healthy human participants and 334 dysphonic patients, were used to train 1-dimensional convolutional neural network models for multiclass classification of healthy, hyperfunctional dysphonia, and laryngitis voice recordings. Three models were trained: (1) a baseline model that analyzed individual vowels in isolation, (2) a stacked vowel model that analyzed three vowels (/a/, /i/, /u/) in the neutral pitch simultaneously, and (3) a stacked pitch model that analyzed the /a/ vowel in three pitches (low, neutral, and high) simultaneously. Results For multiclass classification of healthy, hyperfunctional dysphonia, and laryngitis voice recordings, the stacked vowel model demonstrated higher performance compared with the baseline and stacked pitch models (F1 score 0.81 vs. 0.77 and 0.78, respectively). Specifically, the stacked vowel model achieved higher performance for class-specific classification of hyperfunctional dysphonia voice samples compared with the baseline and stacked pitch models (F1 score 0.56 vs. 0.49 and 0.50, respectively). Conclusions This study demonstrates the feasibility and potential of analyzing multiple sustained vowel recordings simultaneously to improve AI-driven screening and classification of vocal pathology. The stacked vowel model architecture in particular offers promise to enhance such an approach. Lay Summary AI analysis of multiple vowel recordings can improve classification of voice pathologies compared with models using a single sustained vowel and offer a strategy to enhance AI-driven screening of voice disorders. Level of Evidence 3.
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Affiliation(s)
- George S. Liu
- Department of Otolaryngology Head and Neck SurgeryStanford University School of Medicine, Stanford UniversityStanfordCaliforniaUSA
- Division of LaryngologyStanford University School of Medicine, Stanford UniversityStanfordCaliforniaUSA
| | - Jordan M. Hodges
- Computer Science DepartmentSchool of Engineering, Stanford UniversityStanfordCaliforniaUSA
| | - Jingzhi Yu
- Biomedical Informatics, Department of Biomedical Data ScienceStanford University School of MedicineStanfordCaliforniaUSA
| | - C. Kwang Sung
- Department of Otolaryngology Head and Neck SurgeryStanford University School of Medicine, Stanford UniversityStanfordCaliforniaUSA
- Division of LaryngologyStanford University School of Medicine, Stanford UniversityStanfordCaliforniaUSA
| | - Elizabeth Erickson‐DiRenzo
- Department of Otolaryngology Head and Neck SurgeryStanford University School of Medicine, Stanford UniversityStanfordCaliforniaUSA
- Division of LaryngologyStanford University School of Medicine, Stanford UniversityStanfordCaliforniaUSA
| | - Philip C. Doyle
- Department of Otolaryngology Head and Neck SurgeryStanford University School of Medicine, Stanford UniversityStanfordCaliforniaUSA
- Division of LaryngologyStanford University School of Medicine, Stanford UniversityStanfordCaliforniaUSA
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Jiang S, Ma D, Tan X, Yang M, Jiao Q, Xu L. Bibliometric analysis of the current status and trends on medical hyperspectral imaging. Front Med (Lausanne) 2023; 10:1235955. [PMID: 37795419 PMCID: PMC10545955 DOI: 10.3389/fmed.2023.1235955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/30/2023] [Indexed: 10/06/2023] Open
Abstract
Hyperspectral imaging (HSI) is a promising technology that can provide valuable support for the advancement of the medical field. Bibliometrics can analyze a vast number of publications on both macroscopic and microscopic levels, providing scholars with essential foundations to shape future directions. The purpose of this study is to comprehensively review the existing literature on medical hyperspectral imaging (MHSI). Based on the Web of Science (WOS) database, this study systematically combs through literature using bibliometric methods and visualization software such as VOSviewer and CiteSpace to draw scientific conclusions. The analysis yielded 2,274 articles from 73 countries/regions, involving 7,401 authors, 2,037 institutions, 1,038 journals/conferences, and a total of 7,522 keywords. The field of MHSI is currently in a positive stage of development and has conducted extensive research worldwide. This research encompasses not only HSI technology but also its application to diverse medical research subjects, such as skin, cancer, tumors, etc., covering a wide range of hardware constructions and software algorithms. In addition to advancements in hardware, the future should focus on the development of algorithm standards for specific medical research targets and cultivate medical professionals of managing vast amounts of technical information.
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Affiliation(s)
| | | | | | | | | | - Liang Xu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin,China
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Wisotzky EL, Rosenthal JC, Meij S, van den Dobblesteen J, Arens P, Hilsmann A, Eisert P, Uecker FC, Schneider A. Telepresence for surgical assistance and training using eXtended reality during and after pandemic periods. J Telemed Telecare 2023:1357633X231166226. [PMID: 37093788 DOI: 10.1177/1357633x231166226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Existing challenges in surgical education (See one, do one, teach one) as well as the COVID-19 pandemic make it necessary to develop new ways for surgical training. Therefore, this work describes the implementation of a scalable remote solution called "TeleSTAR" using immersive, interactive and augmented reality elements which enhances surgical training in the operating room. The system uses a full digital surgical microscope in the context of Ear-Nose-Throat surgery. The microscope is equipped with a modular software augmented reality interface consisting an interactive annotation mode to mark anatomical landmarks using a touch device, an experimental intraoperative image-based stereo-spectral algorithm unit to measure anatomical details and highlight tissue characteristics. The new educational tool was evaluated and tested during the broadcast of three live XR-based three-dimensional cochlear implant surgeries. The system was able to scale to five different remote locations in parallel with low latency and offering a separate two-dimensional YouTube stream with a higher latency. In total more than 150 persons were trained including healthcare professionals, biomedical engineers and medical students.
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Affiliation(s)
- Eric L Wisotzky
- Fraunhofer Heinrich-Hertz-Institute HHI, Berlin, Germany
- Klinik und Poliklinik für Hals-Nasen-Ohrenheilkunde, Kopf- und Halschirurgie "Otto Körner", Rostock University Medical Center, Rostock, Germany
- Department of Informatics, Humboldt University, Berlin, Germany
| | - Jean-Claude Rosenthal
- Fraunhofer Heinrich-Hertz-Institute HHI, Berlin, Germany
- Klinik und Poliklinik für Hals-Nasen-Ohrenheilkunde, Kopf- und Halschirurgie "Otto Körner", Rostock University Medical Center, Rostock, Germany
| | - Senna Meij
- Delft University of Technology, Faculty of Mechanical Engineering, BioMechanical Engineering, Delft, The Netherlands
| | - John van den Dobblesteen
- Delft University of Technology, Faculty of Mechanical Engineering, BioMechanical Engineering, Delft, The Netherlands
| | - Philipp Arens
- Department of Otorhinolaryngology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Anna Hilsmann
- Fraunhofer Heinrich-Hertz-Institute HHI, Berlin, Germany
| | - Peter Eisert
- Fraunhofer Heinrich-Hertz-Institute HHI, Berlin, Germany
- Department of Informatics, Humboldt University, Berlin, Germany
| | | | - Armin Schneider
- Munich Surgical Imaging, Munich, Germany
- Department of Engineering Sciences, Jade Hochschule, Wilhelmshaven, Germany
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