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Zaki FR, Monroy GL, Shi J, Sudhir K, Boppart SA. Texture-based speciation of otitis media-related bacterial biofilms from optical coherence tomography images using supervised classification. JOURNAL OF BIOPHOTONICS 2024:e202400075. [PMID: 39103198 DOI: 10.1002/jbio.202400075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 07/06/2024] [Accepted: 07/07/2024] [Indexed: 08/07/2024]
Abstract
Otitis media (OM), a highly prevalent inflammatory middle-ear disease in children worldwide, is commonly caused by an infection, and can lead to antibiotic-resistant bacterial biofilms in recurrent/chronic OM cases. A biofilm related to OM typically contains one or multiple bacterial species. OCT has been used clinically to visualize the presence of bacterial biofilms in the middle ear. This study used OCT to compare microstructural image texture features from bacterial biofilms. The proposed method applied supervised machine-learning-based frameworks (SVM, random forest, and XGBoost) to classify multiple species bacterial biofilms from in vitro cultures and clinically-obtained in vivo images from human subjects. Our findings show that optimized SVM-RBF and XGBoost classifiers achieved more than 95% of AUC, detecting each biofilm class. These results demonstrate the potential for differentiating OM-causing bacterial biofilms through texture analysis of OCT images and a machine-learning framework, offering valuable insights for real-time in vivo characterization of ear infections.
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Affiliation(s)
- Farzana R Zaki
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Guillermo L Monroy
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Jindou Shi
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Kavya Sudhir
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Stephen A Boppart
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- NIH/NIBIB P41 Center for Label-free Imaging and Multiscale Biophotonics (CLIMB), University of Illinois Urbana-Champaign, Urbana, Illinois, USA
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Zhou Y, Zhou L, Yan J, Yan X, Chen Z. Using optical coherence tomography to assess luster of pearls: technique suitability and insights. Sci Rep 2024; 14:11126. [PMID: 38750292 PMCID: PMC11096156 DOI: 10.1038/s41598-024-62125-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 05/14/2024] [Indexed: 05/18/2024] Open
Abstract
Luster is one of the vital indexes in pearl grading. To find a fast, nondestructive, and low-cost grading method, optical coherence tomography (OCT) is introduced to predict the luster grade through the texture features. After background removal, flattening, and segmentation, the speckle pattern of the region of interest is described by seven kinds of feature textures, including center-symmetric auto-correlation (CSAC), fractal dimension (FD), Gabor, gray level co-occurrence matrix (GLCM), histogram of oriented gradients (HOG), laws texture energy (LAWS), and local binary patterns (LBP). To find the relations between speckle-derived texture features and luster grades, four Four groups of pearl samples were used in the experiment to detect texture differences based on support vector machines (SVMs) and random forest classifier (RFC)) for investigating the relations between speckle-derived texture features and luster grades. The precision, recall, F1-score, and accuracy are more significant than 0.9 in several simulations, even after dimension reduction. This demonstrates that the texture feature from OCT images can be applied to class the pearl luster based on speckle changes.
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Affiliation(s)
- Yang Zhou
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, Zhejiang, China.
- School of Innovation and Entrepreneurship, Zhejiang University of Science and Technology, Hangzhou, 310023, Zhejiang, China.
| | - Lifeng Zhou
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, Zhejiang, China
| | - Jun Yan
- Zhejiang Fangyuan Test Group Co., Ltd, Hangzhou, 310013, Zhejiang, China
| | - Xuejun Yan
- Zhejiang Fangyuan Test Group Co., Ltd, Hangzhou, 310013, Zhejiang, China
| | - Zhengwei Chen
- School of Innovation and Entrepreneurship, Zhejiang University of Science and Technology, Hangzhou, 310023, Zhejiang, China
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Salimi M, Roshanfar M, Tabatabaei N, Mosadegh B. Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine. J Pers Med 2023; 14:33. [PMID: 38248734 PMCID: PMC10817559 DOI: 10.3390/jpm14010033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/08/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.
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Affiliation(s)
| | - Majid Roshanfar
- Department of Mechanical Engineering, Concordia University, Montreal, QC H3G 1M8, Canada;
| | - Nima Tabatabaei
- Department of Mechanical Engineering, York University, Toronto, ON M3J 1P3, Canada;
| | - Bobak Mosadegh
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, Weill Cornell Medicine, New York, NY 10021, USA
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Barroso Á, Ketelhut S, Nettels-Hackert G, Heiduschka P, del Amor R, Naranjo V, Kemper B, Schnekenburger J. Durable 3D murine ex vivo retina glaucoma models for optical coherence tomography. BIOMEDICAL OPTICS EXPRESS 2023; 14:4421-4438. [PMID: 37791268 PMCID: PMC10545187 DOI: 10.1364/boe.494271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/07/2023] [Accepted: 07/08/2023] [Indexed: 10/05/2023]
Abstract
Durable and standardized phantoms with optical properties similar to native healthy and disease-like biological tissues are essential tools for the development, performance testing, calibration and comparison of label-free high-resolution optical coherence tomography (HR-OCT) systems. Available phantoms are based on artificial materials and reflect thus only partially ocular properties. To address this limitation, we have performed investigations on the establishment of durable tissue phantoms from ex vivo mouse retina for enhanced reproduction of in vivo structure and complexity. In a proof-of-concept study, we explored the establishment of durable 3D models from dissected mouse eyes that reproduce the properties of normal retina structures and tissue with glaucoma-like layer thickness alterations. We explored different sectioning and preparation procedures for embedding normal and N-methyl-D-aspartate (NMDA)-treated mouse retina in transparent gel matrices and epoxy resins, to generate durable three-dimensional tissue models. Sample quality and reproducibility were quantified by thickness determination of the generated layered structures utilizing computer-assisted segmentation of OCT B-scans that were acquired with a commercial HR-OCT system at a central wavelength of 905 nm and analyzed with custom build software. Our results show that the generated 3D models feature thin biological layers close to current OCT resolution limits and glaucoma-like tissue alterations that are suitable for reliable HR-OCT performance characterization. The comparison of data from resin-embedded tissue with native murine retina in gels demonstrates that by utilization of appropriate preparation protocols, highly stable samples with layered structures equivalent to native tissues can be fabricated. The experimental data demonstrate our concept as a promising approach toward the fabrication of durable biological 3D models suitable for high-resolution OCT system performance characterization supporting the development of optimized instruments for ophthalmology applications.
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Affiliation(s)
- Álvaro Barroso
- Biomedical Technology Center of the Medical Faculty, University of Muenster, Mendelstr. 17, D-48149 Muenster, Germany
| | - Steffi Ketelhut
- Biomedical Technology Center of the Medical Faculty, University of Muenster, Mendelstr. 17, D-48149 Muenster, Germany
| | - Gerburg Nettels-Hackert
- Department of Ophthalmology of the Medical Faculty, University of Muenster, Domagkstr. 15, D-48149 Muenster, Germany
| | - Peter Heiduschka
- Department of Ophthalmology of the Medical Faculty, University of Muenster, Domagkstr. 15, D-48149 Muenster, Germany
| | - Rocío del Amor
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain
| | - Valery Naranjo
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain
| | - Björn Kemper
- Biomedical Technology Center of the Medical Faculty, University of Muenster, Mendelstr. 17, D-48149 Muenster, Germany
| | - Jürgen Schnekenburger
- Biomedical Technology Center of the Medical Faculty, University of Muenster, Mendelstr. 17, D-48149 Muenster, Germany
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