Zhang Y, Xing HY, Yan J. Development and prospect of near-infrared spectroscopy-assisted schizophrenia diagnosis based on bibliometrics. World J Psychiatry 2025; 15(1): 100791 [DOI: 10.5498/wjp.v15.i1.100791]
Corresponding Author of This Article
Juan Yan, BMed, Doctor, Chief Physician, Full Professor, Quality Control Office, The Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, No. 305 Tianmushan Road, Xihu District, Hangzhou 310013, Zhejiang Province, China. 294162939@qq.com
Research Domain of This Article
Psychology, Applied
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Psychiatry. Jan 19, 2025; 15(1): 100791 Published online Jan 19, 2025. doi: 10.5498/wjp.v15.i1.100791
Development and prospect of near-infrared spectroscopy-assisted schizophrenia diagnosis based on bibliometrics
Yan Zhang, Hao-Yu Xing, Juan Yan
Yan Zhang, Administration Office, Lishui Second People’s Hospital, Lishui 323060, Zhejiang Province, China
Hao-Yu Xing, Department of Medical Engineering, The Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou 310013, Zhejiang Province, China
Juan Yan, Quality Control Office, The Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou 310013, Zhejiang Province, China
Co-first authors: Yan Zhang and Hao-Yu Xing.
Author contributions: All authors contributed to the manuscript conception and design; Xing HY performed material preparation, data collection and analysis; Zhang Y and Xing HY wrote the first draft of the manuscript, and all authors commented on previous versions of the manuscript; All authors read and approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Juan Yan, BMed, Doctor, Chief Physician, Full Professor, Quality Control Office, The Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, No. 305 Tianmushan Road, Xihu District, Hangzhou 310013, Zhejiang Province, China. 294162939@qq.com
Received: August 26, 2024 Revised: November 4, 2024 Accepted: November 18, 2024 Published online: January 19, 2025 Processing time: 113 Days and 22.1 Hours
Abstract
In this editorial, we comment on the recent article by Fei et al exploring the field of near-infrared spectroscopy (NIRS) research in schizophrenia from a bibliometrics perspective. In recent years, NIRS has shown unique advantages in the auxiliary diagnosis of schizophrenia, and the introduction of bibliometrics has provided a macro perspective for research in this field. Despite the opportunities brought about by these technological developments, remaining challenges require multidisciplinary approach to devise a reliable and accurate diagnosis system for schizophrenia. Nonetheless, NIRS-assisted technology is expected to contribute to the division of methods for early intervention and treatment of schizophrenia.
Core Tip: This manuscript employs bibliometric analysis to investigate the application of near-infrared spectroscopy (NIRS) in the assisted diagnosis of schizophrenia. NIRS, a non-invasively user-friendly functional brain imaging modality, rapidly provides critical biomarker information via high-throughput data acquisition and advanced signal processing algorithms. The findings indicate deep learning and machine learning techniques, including random forests and support vector machines, enhance the accuracy and robustness of NIRS data analysis. Nonetheless, challenges remain in processing large datasets and integrating heterogeneous data sources. Future advancements in equipment, algorithms, and multi-center collaboration are anticipated to further elevate NIRS's clinical utility and reliability in schizophrenia diagnosis.
