Journal of Biophotonics最新文献

Hyperspectral Imaging Combined With Deep Learning for Precision Grading of Clear Cell Renal Cell Carcinoma 高光谱成像结合深度学习对透明细胞肾细胞癌的精确分级。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2025-06-23 DOI: 10.1002/jbio.202500180

Guoxia Zhang, Jing Zhang, Xulei Wang, Lv Haiyue, Mengqiu Zhang, Chunlei Wang, Xiaoqing Yang

引用次数: 0

Handheld Ultrasound and Photoacoustic Dual-Modal Imaging With Sound Speed Correction Guided by Ultrasound Image 超声图像引导声速校正的手持式超声光声双模成像。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2025-06-22 DOI: 10.1002/jbio.202500203

Yijie Huang, Lin Huang, Renbin Zhong

引用次数: 0

The Utility of Fourier Transform Infrared Spectroscopy (FTIR) for Detecting Exercise-Induced Changes in the Human Hand Epidermis 傅立叶变换红外光谱（FTIR）在检测人体手部表皮运动引起的变化中的应用。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2025-06-22 DOI: 10.1002/jbio.202500173

Paweł Król, Zbigniew Obmiński, Adam Reich, Wojciech Czarny, Józef Cebulski, Joanna Depciuch, Michał Zamorski, Katarzyna Stępień, Łukasz Rydzik

{"title":"The Utility of Fourier Transform Infrared Spectroscopy (FTIR) for Detecting Exercise-Induced Changes in the Human Hand Epidermis","authors":"Paweł Król, Zbigniew Obmiński, Adam Reich, Wojciech Czarny, Józef Cebulski, Joanna Depciuch, Michał Zamorski, Katarzyna Stępień, Łukasz Rydzik","doi":"10.1002/jbio.202500173","DOIUrl":"10.1002/jbio.202500173","url":null,"abstract":"<p>The literature lacks data on transient infrared spectral changes in the epidermis following physical exercise. This study tested the hypothesis that a single exercise session affects selected spectral bands (3270–1045 cm<sup>−1</sup>) in healthy individuals. Eight professional tennis players completed a 1.5-h moderate-intensity training session. Epidermal samples from the inner hand were collected before and after exercise, following cleaning with distilled water and 96% PA ethyl alcohol. Samples were analyzed using Fourier Transform Infrared Spectroscopy (FTIR). Absorbance values were recorded for 12 peaks. Significant correlations were observed for the 3270 cm<sup>−1</sup> (<i>r</i> = 0.976) and 1045 cm<sup>−1</sup> (<i>r</i> = 0.754) peaks. Notably, post-exercise increases were found at 1453 cm<sup>−1</sup> (lipids/proteins), 1078 cm<sup>−1</sup> (phospholipids), and 1045 cm<sup>−1</sup> (carbohydrates). No significant changes were observed for other peaks, though a general upward trend appeared. Inter-individual variability was high. FTIR may detect acute epidermal biochemical responses to exercise, especially in lipid- and phospholipid-related structures.</p>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jbio.202500173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144369899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing the Accuracy of Skin Lesion Diagnosis Using Hyperspectral Imaging and Deep Learning 利用高光谱成像和深度学习提高皮肤病变诊断的准确性。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2025-06-12 DOI: 10.1002/jbio.202500182

Huiwen Zheng, Yunqing Ren, Lijuan Yu, Zhenying Cai, Xin Xia, Guoqiang Qi, Jing Li, Chen Shen

{"title":"Enhancing the Accuracy of Skin Lesion Diagnosis Using Hyperspectral Imaging and Deep Learning","authors":"Huiwen Zheng, Yunqing Ren, Lijuan Yu, Zhenying Cai, Xin Xia, Guoqiang Qi, Jing Li, Chen Shen","doi":"10.1002/jbio.202500182","DOIUrl":"10.1002/jbio.202500182","url":null,"abstract":"<div>\u0000 \u0000 <p>This study presents a novel diagnostic approach that integrates hyperspectral imaging (HSI) with deep learning to discriminate among dermatitis, actinic keratosis (AK), and seborrheic keratosis (SK). We evaluated 60 intraoperative clinical specimens and achieved 93% accuracy, 91% sensitivity, and 95% specificity in three-class classification. A Savitzky–Golay filter was applied to the raw spectra to enhance the signal-to-noise ratio and data fidelity, while first-derivative spectral analysis enabled the model to capture subtle biochemical and morphological differences among lesions. Our results demonstrate that the combined HSI–deep-learning framework can accelerate dermatologic diagnosis and reduce error rates. This methodology not only provides a robust tool for clinical decision support in dermatology but also holds promise for wider adoption across medical imaging workflows. Future work will focus on scalability, cost–benefit optimization, and seamless integration with existing diagnostic platforms.</p>\u0000 </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 7","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on the Effect of the Energy Distribution of Dual-Beam Laser 980 and 1064 nm on Skin Tissue Soldering Performance 980和1064 nm双光束激光能量分布对皮肤组织焊接性能影响的研究。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2025-06-02 DOI: 10.1002/jbio.202500143

Jun Huang, Mintao Yan, Yanyu Li, Yuxin Chen, Kehong Wang

引用次数: 0

Hyperspectral Imaging for Predicting Bladder Cancer Grading: A Novel Diagnostic Approach 高光谱成像预测膀胱癌分级：一种新的诊断方法。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2025-06-02 DOI: 10.1002/jbio.202500161

Jinfeng Hu, Xiuqing Fu, Yanli Zhang, Mengqiu Zhang, Yihan Zhao, Xiaoqing Yang

引用次数: 0

Hyperspectral Imaging for Rapid Detection of Common Infected Bacteria Based on Fluorescence Effect 基于荧光效应的高光谱成像快速检测常见感染细菌。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2025-05-30 DOI: 10.1002/jbio.202500164

Lin Tao, Decheng Wu, Hao Tang, Wendan Liu, Xudong Fu, Zheng Hu, Dengchao Huang, Lianyang Zhang

{"title":"Hyperspectral Imaging for Rapid Detection of Common Infected Bacteria Based on Fluorescence Effect","authors":"Lin Tao, Decheng Wu, Hao Tang, Wendan Liu, Xudong Fu, Zheng Hu, Dengchao Huang, Lianyang Zhang","doi":"10.1002/jbio.202500164","DOIUrl":"10.1002/jbio.202500164","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid and accurate detection of bacterial infections in wounds is crucial for clinical diagnosis. Traditional methods, such as bacterial culture and polymerase chain reaction (PCR), are invasive and time-consuming. In this study, we propose a non-invasive detection method for common bacteria in wound infections, combining fluorescence hyperspectral imaging (FHSI) with deep learning algorithms. FHSI technology captures fluorescence data from culture plates for eight bacterial species, extracting spectral features within the 420–700 nm wavelength range. To manage the complex spatial and spectral data, we developed a Spatial-Spectral Multi-Scale Attention Network (SSMA-Net). Our method achieves an impressive 98.52% accuracy in bacterial classification under various growth conditions and 98.71% accuracy in species-level identification, with classification possible at bacterial concentrations as low as 10<sup>4</sup> CFU/mL. These results underscore the effectiveness of FHSI and deep learning for rapid, non-invasive bacterial typing, offering substantial potential for clinical applications.</p>\u0000 </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 7","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Empirical Mode Decomposition and Grassmann Manifold-Based Cervical Cancer Detection 基于经验模态分解和Grassmann流形的宫颈癌检测。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2025-05-28 DOI: 10.1002/jbio.202400584

Sidharthenee Nayak, Bhaswati Singha Deo, Mayukha Pal, Prasanta K. Panigrahi, Asima Pradhan

{"title":"Empirical Mode Decomposition and Grassmann Manifold-Based Cervical Cancer Detection","authors":"Sidharthenee Nayak, Bhaswati Singha Deo, Mayukha Pal, Prasanta K. Panigrahi, Asima Pradhan","doi":"10.1002/jbio.202400584","DOIUrl":"10.1002/jbio.202400584","url":null,"abstract":"<div>\u0000 \u0000 <p>Cervical cancer is a prevalent malignancy affecting the female reproductive system and is recognized as a prominent factor to female mortality on a global scale. Timely and precise detection of various stages of cervical cancer plays a crucial role in enhancing the chances of successful treatment and extending patient survival. Fluorescence spectroscopy stands out as a highly sensitive method for identifying biochemical alterations associated with cancer and numerous other pathological conditions. In our study, empirical mode decomposition (EMD) and Grassmann manifold (GM) learning are explored for reliable cancer detection using fluorescence spectral signals collected from 110 subjects representing various categories of the human cervix. Initially, EMD is used to decompose the signal into several multi-feature intrinsic mode functions (IMFs) on a spectral scale. Each IMF demonstrates uniqueness by capturing the inherent frequency characteristics within the signal, thus facilitating the extraction of signal features. The GM representation of IMFs is employed for investigating the non-linear subspace structure within spectral signals, which is subsequently followed by a low-rank representation to transform and analyze the spectral signals. The GM allows for the extraction of relevant information, reduction of dimensionality, and exploration of complex relationships within data, ultimately contributing to improved diagnosis. Mutual information is further used for feature selection to reduce the number of features and hence the computational cost. When the selected features were employed for classification, the Random Forest (RF) classifier attained a high five-fold validation accuracy of 99% and exhibited a minimal standard deviation of 0.02. Other state-of-the-art machine learning classifiers were also used and compared with the RF model.</p>\u0000 </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 7","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144164359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optical Method for the Detection of Viral RNA Using an Optical Fiber Sensor 利用光纤传感器检测病毒RNA的光学方法。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2025-05-27 DOI: 10.1002/jbio.202500063

Patryk Sokołowski, Paweł Wityk, Joanna Raczak-Gutknecht, Wiktoria Brzezińska, Michał Sobaszek, Paweł Kalinowski, Sebastian Garcia-Galan, Małgorzata Szczerska

引用次数: 0

Design and Analysis of a Novel Nanoscale 2D Photonic Crystal Structure for Enhanced Multi-Disorder Biosensing 一种新型纳米二维光子晶体结构的设计与分析，用于增强多无序生物传感。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2025-05-27 DOI: 10.1002/jbio.202500130

K. Bhuvaneshwari, B. Elizabeth Caroline, J. Vidhya, K. Sagadevan

引用次数: 0