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Peng C, Tian CX, Mu Y, Ma M, Zhang Z, Wan M, Liu J, Li Z, Zuo X, Li W, Li Y. Hyperspectral imaging facilitating resect-and-discard strategy through artificial intelligence-assisted diagnosis of colorectal polyps: A pilot study. Cancer Med 2024; 13:e70195. [PMID: 39320133 PMCID: PMC11423483 DOI: 10.1002/cam4.70195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 08/15/2024] [Accepted: 08/25/2024] [Indexed: 09/26/2024] Open
Abstract
BACKGROUND AND AIMS The resect-and-discard strategy for colorectal polyps based on accurate optical diagnosis remains challenges. Our aim was to investigate the feasibility of hyperspectral imaging (HSI) for identifying colorectal polyp properties and diagnosis of colorectal cancer in fresh tissues during colonoscopy. METHODS 144,900 two dimensional images generated from 161 hyperspectral images of colorectal polyp tissues were prospectively obtained from patients undergoing colonoscopy. A residual neural network model was trained with transfer learning to automatically differentiate colorectal polyps, validated by histopathologic diagnosis. The diagnostic performances of the HSI-AI model and endoscopists were calculated respectively, and the auxiliary efficiency of the model was evaluated after a 2-week interval. RESULTS Quantitative HSI revealed histological differences in colorectal polyps. The HSI-AI model showed considerable efficacy in differentiating nonneoplastic polyps, non-advanced adenomas, and advanced neoplasia in vitro, with sensitivities of 96.0%, 94.0%, and 99.0% and specificities of 99.0%, 99.0%, and 96.5%, respectively. With the assistance of the model, the median negative predictive value of neoplastic polyps increased from 50.0% to 88.2% (p = 0.013) in novices. CONCLUSION This study demonstrated the feasibility of using HSI as a diagnostic tool to differentiate neoplastic colorectal polyps in vitro and the potential of AI-assisted diagnosis synchronized with colonoscopy. The tool may improve the diagnostic performance of novices and facilitate the application of resect-and-discard strategy to decrease the cost.
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Affiliation(s)
- Cheng Peng
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | - Chong Xuan Tian
- Department of Biomedical Engineering Institute, School of Control Science and EngineeringShandong UniversityJinanChina
| | - Yijun Mu
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | - Mingjun Ma
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | - Zhenlei Zhang
- Department of Biomedical Engineering Institute, School of Control Science and EngineeringShandong UniversityJinanChina
| | - Meng Wan
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | - Jing Liu
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | - Xiuli Zuo
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
- Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
- Shandong Provincial Clinical Research Center for Digestive DiseaseJinanChina
| | - Wei Li
- Department of Biomedical Engineering Institute, School of Control Science and EngineeringShandong UniversityJinanChina
| | - Yanqing Li
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
- Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
- Shandong Provincial Clinical Research Center for Digestive DiseaseJinanChina
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Leung JH, Karmakar R, Mukundan A, Lin WS, Anwar F, Wang HC. Technological Frontiers in Brain Cancer: A Systematic Review and Meta-Analysis of Hyperspectral Imaging in Computer-Aided Diagnosis Systems. Diagnostics (Basel) 2024; 14:1888. [PMID: 39272675 PMCID: PMC11394276 DOI: 10.3390/diagnostics14171888] [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: 07/08/2024] [Revised: 08/19/2024] [Accepted: 08/23/2024] [Indexed: 09/15/2024] Open
Abstract
Brain cancer is a substantial factor in the mortality associated with cancer, presenting difficulties in the timely identification of the disease. The precision of diagnoses is significantly dependent on the proficiency of radiologists and neurologists. Although there is potential for early detection with computer-aided diagnosis (CAD) algorithms, the majority of current research is hindered by its modest sample sizes. This meta-analysis aims to comprehensively assess the diagnostic test accuracy (DTA) of computer-aided design (CAD) models specifically designed for the detection of brain cancer utilizing hyperspectral (HSI) technology. We employ Quadas-2 criteria to choose seven papers and classify the proposed methodologies according to the artificial intelligence method, cancer type, and publication year. In order to evaluate heterogeneity and diagnostic performance, we utilize Deeks' funnel plot, the forest plot, and accuracy charts. The results of our research suggest that there is no notable variation among the investigations. The CAD techniques that have been examined exhibit a notable level of precision in the automated detection of brain cancer. However, the absence of external validation hinders their potential implementation in real-time clinical settings. This highlights the necessity for additional studies in order to authenticate the CAD models for wider clinical applicability.
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Affiliation(s)
- Joseph-Hang Leung
- Department of Radiology, Ditmanson Medical Foundation Chia-yi Christian Hospital, Chia Yi 60002, Taiwan
| | - Riya Karmakar
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Arvind Mukundan
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Wen-Shou Lin
- Neurology Division, Department of Internal Medicine, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Lingya District, Kaohsiung City 80284, Taiwan
| | - Fathima Anwar
- Faculty of Allied Health Sciences, The University of Lahore, 1-Km Defense Road, Lahore 54590, Punjab, Pakistan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
- Department of Medical Research, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 2, Minsheng Road, Dalin, Chia Yi 62247, Taiwan
- Department of Technology Development, Hitspectra Intelligent Technology Co., Ltd., 8F.11-1, No. 25, Chenggong 2nd Rd., Qianzhen Dist., Kaohsiung City 80661, Taiwan
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Morales-Conde S, Navarro-Morales L, Moreno-Suero F, Balla A, Licardie E. Fluorescence and tracers in surgery: the coming future. Cir Esp 2024; 102 Suppl 1:S45-S60. [PMID: 38851317 DOI: 10.1016/j.cireng.2024.05.011] [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: 05/07/2024] [Accepted: 05/23/2024] [Indexed: 06/10/2024]
Abstract
The revolution that we are seeing in the world of surgery will determine the way we understand surgical approaches in coming years. Since the implementation of minimally invasive surgery, innovations have constantly been developed to allow the laparoscopic approach to go further and be applied to more and more procedures. In recent years, we have been in the middle of another revolutionary era, with robotic surgery, the application of artificial intelligence and image-guided surgery. The latter includes 3D reconstructions for surgical planning, virtual reality, holograms or tracer-guided surgery, where ICG-guided fluorescence has provided a different perspective on surgery. ICG has been used to identify anatomical structures, assess tissue perfusion, and identify tumors or tumor lymphatic drainage. But the most important thing is that this technology has come hand in hand with the potential to develop other types of tracers that will facilitate the identification of tumor cells and ureters, as well as different light beams to identify anatomical structures. These will lead to other types of systems to assess tissue perfusion without the use of tracers, such as hyperspectral imaging. Combined with the upcoming introduction of ICG quantification, these developments represent a real revolution in the surgical world. With the imminent implementation of these technological advances, a review of their clinical application in general surgery is timely, and this review serves that aim.
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Affiliation(s)
- Salvador Morales-Conde
- Servicio de Cirugía General y Digestiva, Hospital Universitario Virgen Macarena, Facultad de Medicina, Universidad de Sevilla, Sevilla, Spain; Servicio de Cirugía General y Digestiva, Hospital Quironsalud Sagrado Corazón, Sevilla, Spain.
| | - Laura Navarro-Morales
- Servicio de Cirugía General y Digestiva, Hospital Quironsalud Sagrado Corazón, Sevilla, Spain.
| | - Francisco Moreno-Suero
- Servicio de Cirugía General y Digestiva, Hospital Quironsalud Sagrado Corazón, Sevilla, Spain.
| | - Andrea Balla
- Servicio de Cirugía General y Digestiva, Hospital Universitario Virgen Macarena, Facultad de Medicina, Universidad de Sevilla, Sevilla, Spain; Servicio de Cirugía General y Digestiva, Hospital Quironsalud Sagrado Corazón, Sevilla, Spain.
| | - Eugenio Licardie
- Servicio de Cirugía General y Digestiva, Hospital Universitario Virgen Macarena, Facultad de Medicina, Universidad de Sevilla, Sevilla, Spain; Servicio de Cirugía General y Digestiva, Hospital Quironsalud Sagrado Corazón, Sevilla, Spain.
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Oswald W, Browning C, Yasmin R, Deal J, Rich TC, Leavesley SJ, Gong N. Fluorescence excitation-scanning hyperspectral imaging with scalable 2D-3D deep learning framework for colorectal cancer detection. Sci Rep 2024; 14:14790. [PMID: 38926431 PMCID: PMC11208566 DOI: 10.1038/s41598-024-64917-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians.
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Affiliation(s)
- Willaim Oswald
- Department of Electrical and Computer Engineering, University of South Alabama, Mobile Alabama, 36688, USA
- Department of Systems Engineering, University of South Alabama, Mobile, AL, 36688, USA
| | - Craig Browning
- Department of Systems Engineering, University of South Alabama, Mobile, AL, 36688, USA
- Department of Chemical and Biomolecular Engineering, University of South Alabama, Mobile, AL, 36688, USA
| | - Ruthba Yasmin
- Department of Electrical and Computer Engineering, University of South Alabama, Mobile Alabama, 36688, USA
| | | | - Thomas C Rich
- Department of Pharmacology, University of South Alabama, Mobile, AL, 36688, USA
- Center for Lung Biology, University of South Alabama, Mobile, AL, 36688, USA
| | - Silas J Leavesley
- Department of Systems Engineering, University of South Alabama, Mobile, AL, 36688, USA.
- Department of Chemical and Biomolecular Engineering, University of South Alabama, Mobile, AL, 36688, USA.
- Department of Pharmacology, University of South Alabama, Mobile, AL, 36688, USA.
- Center for Lung Biology, University of South Alabama, Mobile, AL, 36688, USA.
| | - Na Gong
- Department of Electrical and Computer Engineering, University of South Alabama, Mobile Alabama, 36688, USA.
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Burström G, Amini M, El-Hajj VG, Arfan A, Gharios M, Buwaider A, Losch MS, Manni F, Edström E, Elmi-Terander A. Optical Methods for Brain Tumor Detection: A Systematic Review. J Clin Med 2024; 13:2676. [PMID: 38731204 PMCID: PMC11084501 DOI: 10.3390/jcm13092676] [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: 04/11/2024] [Revised: 04/28/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
Background: In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. Methods: This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. Results: In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. Conclusions: This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue.
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Affiliation(s)
- Gustav Burström
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Misha Amini
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Victor Gabriel El-Hajj
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Arooj Arfan
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Maria Gharios
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Ali Buwaider
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Merle S. Losch
- Department of Biomechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, 2627 Delft, The Netherlands
| | - Francesca Manni
- Department of Electrical Engineering, Eindhoven University of Technology (TU/e), 5612 Eindhoven, The Netherlands;
| | - Erik Edström
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
- Capio Spine Center Stockholm, Löwenströmska Hospital, 194 80 Upplands-Väsby, Sweden
- Department of Medical Sciences, Örebro University, 701 85 Örebro, Sweden
| | - Adrian Elmi-Terander
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
- Capio Spine Center Stockholm, Löwenströmska Hospital, 194 80 Upplands-Väsby, Sweden
- Department of Medical Sciences, Örebro University, 701 85 Örebro, Sweden
- Department of Surgical Sciences, Uppsala University, 751 35 Uppsala, Sweden
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Anichini G, Leiloglou M, Hu Z, O'Neill K, Daniel Elson. Hyperspectral and multispectral imaging in neurosurgery: a systematic literature review and meta-analysis. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024:108293. [PMID: 38658267 DOI: 10.1016/j.ejso.2024.108293] [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: 10/20/2023] [Revised: 01/21/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
INTRODUCTION The neuro-surgical community is witnessing a rising interest for surgical application of multispectral/hyperspectral imaging. Several potential technical applications of this optical imaging are reported, but the set-up is variable and so are the processing methods. We present a systematic review of the relevant literature on the topic. MATERIALS AND METHODS A literature search based on the PRISMA principles was performed on PubMed, SCOPUS, and Web of Science, using MESH terms and Boolean operators. Papers regarding intra-operative in-vivo application of multispectral and/or hyperspectral imaging in humans during neurosurgical procedures were included. Papers reporting technologies related to radiological applications were excluded. A meta-analysis on the performance metrics was also conducted. RESULTS Our search string retrieved 20 papers. The main applications of optical imaging during neurosurgery concern tumour detection and improvement of the extent of resection (15 papers) or visualization of perfusion changes during neuro-oncology or neuro-vascular surgery (5 papers). All the retrieved articles were pilot studies, proof of concepts, or case reports, with limited number of patients recruited. Sensitivity, specificity, and accuracy were promising in most of the reports, but the metanalysis showed heterogeneous approaches and results among studies. CONCLUSIONS The present review shows that several approaches are currently being tested to integrate hyperspectral imaging in neurosurgery, but most of the studies reported a limited pool of patients, with different approaches to data collection and analysis. Further studies on larger cohorts of patients are therefore desirable to fully explore the potential of this imaging technique.
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Affiliation(s)
- Giulio Anichini
- Department of Brain Sciences, Imperial College of London, United Kingdom; Department of Neurosurgery, Neuroscience, Imperial College Healthcare NHS Trust, United Kingdom.
| | - Maria Leiloglou
- Department of Surgery and Cancer, Imperial College of London, United Kingdom; The Hamlyn Centre, Imperial College of London, United Kingdom
| | - Zepeng Hu
- Department of Surgery and Cancer, Imperial College of London, United Kingdom; The Hamlyn Centre, Imperial College of London, United Kingdom
| | - Kevin O'Neill
- Department of Brain Sciences, Imperial College of London, United Kingdom; Department of Neurosurgery, Neuroscience, Imperial College Healthcare NHS Trust, United Kingdom
| | - Daniel Elson
- Department of Surgery and Cancer, Imperial College of London, United Kingdom; The Hamlyn Centre, Imperial College of London, United Kingdom
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Aref MH, Korganbayev S, Aboughaleb IH, Hussein AA, Abbass MA, Abdlaty R, Sabry YM, Saccomandi P, Youssef ABM. Custom Hyperspectral Imaging System Reveals Unique Spectral Signatures of Heart, Kidney, and Liver Tissues. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123363. [PMID: 37776837 DOI: 10.1016/j.saa.2023.123363] [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: 05/18/2023] [Revised: 09/01/2023] [Accepted: 09/04/2023] [Indexed: 10/02/2023]
Abstract
The rapid advancement of diagnostic and therapeutic optical techniques for oncology demands a good understanding of the optical properties of biological tissues. This study explores the capabilities of hyperspectral (HS) cameras as a non-invasive and non-contact optical imaging system to distinguish and highlight spectral differences inbiological soft tissuesof three structures (kidney, heart, and liver) for use inendoscopic interventionoropen surgery. The study presents an optical system consisting of two individual setups, the transmission setup, and the reflection setup, both incorporating anHS camerawith apolychromatic light sourcewithin the range of 380 to 1050 nm to measure tissue's light transmission (Tr) and diffuse light reflectance (Rd), respectively. The optical system was calibrated with a customizedliquid optical phantom, then 30 samples from various organs were investigated fortissue characterizationby measuring both Tr and Rd at the visible and near infrared (VIS-NIR) band. We exploited the ANOVA test, subsequently by a Tukey's test on the created three independent clusters (kidney vs. heart: group I / kidney vs. liver: group II / heart vs. liver: group III) to identify the optimum wavelength for each tissue regarding their spectroscopic optical properties in the VIS-NIR spectrum. The optimum spectral span for the determined light Tr of the three groups was 640 ∼ 680 nm, and the ideal range was 720 ∼ 760 nm for the measured light Rd for mutual group I and group II. However, the group III range was different at a range of 520 ∼ 560 nm. Therefore, the investigation provides vital information concerning theoptimum spectral scalefor the computed light Tr and Rd of the investigatedbiological tissues(kidney, liver, and heart) to be employed in diagnostic andtherapeutic medical applications.
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Affiliation(s)
| | - Sanzhar Korganbayev
- Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy.
| | | | | | - Mohamed A Abbass
- Head of Biomedical Engineering Department, Military Technical College, Cairo, Egypt.
| | - Ramy Abdlaty
- Biomedical Engineering Department, Military Technical College, Cairo, Egypt.
| | - Yasser M Sabry
- Faculty of Engineering, Ain Shams University, Cairo, Egypt.
| | - Paola Saccomandi
- Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy.
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Agadi K, Dominari A, Tebha SS, Mohammadi A, Zahid S. Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review. J Korean Neurosurg Soc 2023; 66:632-641. [PMID: 35831137 PMCID: PMC10641423 DOI: 10.3340/jkns.2021.0213] [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: 08/23/2021] [Revised: 10/06/2021] [Accepted: 03/14/2022] [Indexed: 11/27/2022] Open
Abstract
Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.
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Affiliation(s)
- Kuchalambal Agadi
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
| | - Asimina Dominari
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
- Aristotle University of Thessaloniki School of Medicine, Thessaloniki, Greece
| | - Sameer Saleem Tebha
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
- Department of Neurosurgery and Neurology, Jinnah Medical and Dental College, Karachi, Pakistan
| | - Asma Mohammadi
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
| | - Samina Zahid
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
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MacCormac O, Noonan P, Janatka M, Horgan CC, Bahl A, Qiu J, Elliot M, Trotouin T, Jacobs J, Patel S, Bergholt MS, Ashkan K, Ourselin S, Ebner M, Vercauteren T, Shapey J. Lightfield hyperspectral imaging in neuro-oncology surgery: an IDEAL 0 and 1 study. Front Neurosci 2023; 17:1239764. [PMID: 37790587 PMCID: PMC10544348 DOI: 10.3389/fnins.2023.1239764] [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: 06/13/2023] [Accepted: 08/31/2023] [Indexed: 10/05/2023] Open
Abstract
Introduction Hyperspectral imaging (HSI) has shown promise in the field of intra-operative imaging and tissue differentiation as it carries the capability to provide real-time information invisible to the naked eye whilst remaining label free. Previous iterations of intra-operative HSI systems have shown limitations, either due to carrying a large footprint limiting ease of use within the confines of a neurosurgical theater environment, having a slow image acquisition time, or by compromising spatial/spectral resolution in favor of improvements to the surgical workflow. Lightfield hyperspectral imaging is a novel technique that has the potential to facilitate video rate image acquisition whilst maintaining a high spectral resolution. Our pre-clinical and first-in-human studies (IDEAL 0 and 1, respectively) demonstrate the necessary steps leading to the first in-vivo use of a real-time lightfield hyperspectral system in neuro-oncology surgery. Methods A lightfield hyperspectral camera (Cubert Ultris ×50) was integrated in a bespoke imaging system setup so that it could be safely adopted into the open neurosurgical workflow whilst maintaining sterility. Our system allowed the surgeon to capture in-vivo hyperspectral data (155 bands, 350-1,000 nm) at 1.5 Hz. Following successful implementation in a pre-clinical setup (IDEAL 0), our system was evaluated during brain tumor surgery in a single patient to remove a posterior fossa meningioma (IDEAL 1). Feedback from the theater team was analyzed and incorporated in a follow-up design aimed at implementing an IDEAL 2a study. Results Focusing on our IDEAL 1 study results, hyperspectral information was acquired from the cerebellum and associated meningioma with minimal disruption to the neurosurgical workflow. To the best of our knowledge, this is the first demonstration of HSI acquisition with 100+ spectral bands at a frame rate over 1Hz in surgery. Discussion This work demonstrated that a lightfield hyperspectral imaging system not only meets the design criteria and specifications outlined in an IDEAL-0 (pre-clinical) study, but also that it can translate into clinical practice as illustrated by a successful first in human study (IDEAL 1). This opens doors for further development and optimisation, given the increasing evidence that hyperspectral imaging can provide live, wide-field, and label-free intra-operative imaging and tissue differentiation.
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Affiliation(s)
- Oscar MacCormac
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
- Department of Neurosurgery, King's College Hospital, London, United Kingdom
| | - Philip Noonan
- Hypervision Surgical Limited, London, United Kingdom
| | - Mirek Janatka
- Hypervision Surgical Limited, London, United Kingdom
| | | | - Anisha Bahl
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
| | - Jianrong Qiu
- School of Craniofacial and Regenerative Biology, King's College London, London, United Kingdom
| | - Matthew Elliot
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
- Department of Neurosurgery, King's College Hospital, London, United Kingdom
| | - Théo Trotouin
- Hypervision Surgical Limited, London, United Kingdom
| | - Jaco Jacobs
- Hypervision Surgical Limited, London, United Kingdom
| | - Sabina Patel
- Department of Neurosurgery, King's College Hospital, London, United Kingdom
| | - Mads S. Bergholt
- School of Craniofacial and Regenerative Biology, King's College London, London, United Kingdom
| | - Keyoumars Ashkan
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
- Department of Neurosurgery, King's College Hospital, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
- Hypervision Surgical Limited, London, United Kingdom
| | - Michael Ebner
- Hypervision Surgical Limited, London, United Kingdom
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
- Hypervision Surgical Limited, London, United Kingdom
| | - Jonathan Shapey
- School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom
- Department of Neurosurgery, King's College Hospital, London, United Kingdom
- Hypervision Surgical Limited, London, United Kingdom
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10
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Planells H, Parmar V, Marcus HJ, Pandit AS. From theory to practice: what is the potential of artificial intelligence in the future of neurosurgery? Expert Rev Neurother 2023; 23:1041-1046. [PMID: 37997765 DOI: 10.1080/14737175.2023.2285432] [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: 09/05/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Affiliation(s)
- Hannah Planells
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Viraj Parmar
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Anand S Pandit
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- High-dimensional Neurology, Institute of Neurology, London, UK
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11
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Puustinen S, Vrzáková H, Hyttinen J, Rauramaa T, Fält P, Hauta-Kasari M, Bednarik R, Koivisto T, Rantala S, von Und Zu Fraunberg M, Jääskeläinen JE, Elomaa AP. Hyperspectral Imaging in Brain Tumor Surgery-Evidence of Machine Learning-Based Performance. World Neurosurg 2023; 175:e614-e635. [PMID: 37030483 DOI: 10.1016/j.wneu.2023.03.149] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 04/10/2023]
Abstract
BACKGROUND Hyperspectral imaging (HSI) has the potential to enhance surgical tissue detection and diagnostics. Definite utilization of intraoperative HSI guidance demands validated machine learning and public datasets that currently do not exist. Moreover, current imaging conventions are dispersed, and evidence-based paradigms for neurosurgical HSI have not been declared. METHODS We presented the rationale and a detailed clinical paradigm for establishing microneurosurgical HSI guidance. In addition, a systematic literature review was conducted to summarize the current indications and performance of neurosurgical HSI systems, with an emphasis on machine learning-based methods. RESULTS The published data comprised a few case series or case reports aiming to classify tissues during glioma operations. For a multitissue classification problem, the highest overall accuracy of 80% was obtained using deep learning. Our HSI system was capable of intraoperative data acquisition and visualization with minimal disturbance to glioma surgery. CONCLUSIONS In a limited number of publications, neurosurgical HSI has demonstrated unique capabilities in contrast to the established imaging techniques. Multidisciplinary work is required to establish communicable HSI standards and clinical impact. Our HSI paradigm endorses systematic intraoperative HSI data collection, which aims to facilitate the related standards, medical device regulations, and value-based medical imaging systems.
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Affiliation(s)
- Sami Puustinen
- University of Eastern Finland, Faculty of Health Sciences, School of Medicine, Kuopio, Finland; Kuopio University Hospital, Eastern Finland Microsurgery Center, Kuopio, Finland.
| | - Hana Vrzáková
- Kuopio University Hospital, Eastern Finland Microsurgery Center, Kuopio, Finland; University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Joni Hyttinen
- University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Tuomas Rauramaa
- Kuopio University Hospital, Department of Clinical Pathology, Kuopio, Finland
| | - Pauli Fält
- University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Markku Hauta-Kasari
- University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Roman Bednarik
- University of Eastern Finland, Faculty of Science and Forestry, School of Computing, Joensuu, Finland
| | - Timo Koivisto
- Kuopio University Hospital, Department of Neurosurgery, Kuopio, Finland
| | - Susanna Rantala
- Kuopio University Hospital, Department of Neurosurgery, Kuopio, Finland
| | - Mikael von Und Zu Fraunberg
- Oulu University Hospital, Department of Neurosurgery, Oulu, Finland; University of Oulu, Faculty of Medicine, Research Unit of Clinical Medicine, Oulu, Finland
| | | | - Antti-Pekka Elomaa
- University of Eastern Finland, Faculty of Health Sciences, School of Medicine, Kuopio, Finland; Kuopio University Hospital, Eastern Finland Microsurgery Center, Kuopio, Finland; Kuopio University Hospital, Department of Neurosurgery, Kuopio, Finland
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12
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Hill CS, Pandit AS. Moving towards a unified classification of glioblastomas utilizing artificial intelligence and deep machine learning integration. Front Oncol 2023; 13:1063937. [PMID: 37427111 PMCID: PMC10327552 DOI: 10.3389/fonc.2023.1063937] [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: 10/07/2022] [Accepted: 04/24/2023] [Indexed: 07/11/2023] Open
Abstract
Glioblastoma a deadly brain cancer that is nearly universally fatal. Accurate prognostication and the successful application of emerging precision medicine in glioblastoma relies upon the resolution and exactitude of classification. We discuss limitations of our current classification systems and their inability to capture the full heterogeneity of the disease. We review the various layers of data that are available to substratify glioblastoma and we discuss how artificial intelligence and machine learning tools provide the opportunity to organize and integrate this data in a nuanced way. In doing so there is the potential to generate clinically relevant disease sub-stratifications, which could help predict neuro-oncological patient outcomes with greater certainty. We discuss limitations of this approach and how these might be overcome. The development of a comprehensive unified classification of glioblastoma would be a major advance in the field. This will require the fusion of advances in understanding glioblastoma biology with technological innovation in data processing and organization.
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Affiliation(s)
- Ciaran Scott Hill
- Institute of Neurology, University College London, London, United Kingdom
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery (NHNN), London, United Kingdom
| | - Anand S. Pandit
- Institute of Neurology, University College London, London, United Kingdom
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery (NHNN), London, United Kingdom
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13
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In Vitro Antibody Quantification with Hyperspectral Imaging in a Large Field of View for Clinical Applications. Bioengineering (Basel) 2023; 10:bioengineering10030370. [PMID: 36978761 PMCID: PMC10045535 DOI: 10.3390/bioengineering10030370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 03/13/2023] [Indexed: 03/19/2023] Open
Abstract
Hyperspectral imaging (HSI) is a non-invasive, contrast-free optical-based tool that has recently been applied in medical and basic research fields. The opportunity to use HSI to identify exogenous tumor markers in a large field of view (LFOV) could increase precision in oncological diagnosis and surgical treatment. In this study, the anti-high mobility group B1 (HMGB1) labeled with Alexa fluorophore (647 nm) was used as the target molecule. This is the proof-of-concept of HSI’s ability to quantify antibodies via an in vitro setting. A first test was performed to understand whether the relative absorbance provided by the HSI camera was dependent on volume at a 1:1 concentration. A serial dilution of 1:1, 10, 100, 1000, and 10,000 with phosphatase-buffered saline (PBS) was then used to test the sensitivity of the camera at the minimum and maximum volumes. For the analysis, images at 640 nm were extracted from the hypercubes according to peak signals matching the specificities of the antibody manufacturer. The results showed a positive correlation between relative absorbance and volume (r = 0.9709, p = 0.0013). The correlation between concentration and relative absorbance at min (1 µL) and max (20 µL) volume showed r = 0.9925, p < 0.0001, and r = 0.9992, p < 0.0001, respectively. These results demonstrate the HSI potential in quantifying HMGB1, hence deserving further studies in ex vivo and in vivo settings.
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14
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Cui R, Yu H, Xu T, Xing X, Cao X, Yan K, Chen J. Deep Learning in Medical Hyperspectral Images: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249790. [PMID: 36560157 PMCID: PMC9784550 DOI: 10.3390/s22249790] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 06/13/2023]
Abstract
With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars.
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Affiliation(s)
- Rong Cui
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - He Yu
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
- Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement, Changchun University, Changchun 130022, China
| | - Tingfa Xu
- Image Engineering & Video Technology Lab, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China
| | - Xiaoxue Xing
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
- Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement, Changchun University, Changchun 130022, China
| | - Xiaorui Cao
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - Kang Yan
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - Jiexi Chen
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
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15
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Van Hese L, De Vleeschouwer S, Theys T, Rex S, Heeren RMA, Cuypers E. The diagnostic accuracy of intraoperative differentiation and delineation techniques in brain tumours. Discov Oncol 2022; 13:123. [PMID: 36355227 PMCID: PMC9649524 DOI: 10.1007/s12672-022-00585-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/22/2022] [Indexed: 11/11/2022] Open
Abstract
Brain tumour identification and delineation in a timeframe of seconds would significantly guide and support surgical decisions. Here, treatment is often complicated by the infiltration of gliomas in the surrounding brain parenchyma. Accurate delineation of the invasive margins is essential to increase the extent of resection and to avoid postoperative neurological deficits. Currently, histopathological annotation of brain biopsies and genetic phenotyping still define the first line treatment, where results become only available after surgery. Furthermore, adjuvant techniques to improve intraoperative visualisation of the tumour tissue have been developed and validated. In this review, we focused on the sensitivity and specificity of conventional techniques to characterise the tumour type and margin, specifically fluorescent-guided surgery, neuronavigation and intraoperative imaging as well as on more experimental techniques such as mass spectrometry-based diagnostics, Raman spectrometry and hyperspectral imaging. Based on our findings, all investigated methods had their advantages and limitations, guiding researchers towards the combined use of intraoperative imaging techniques. This can lead to an improved outcome in terms of extent of tumour resection and progression free survival while preserving neurological outcome of the patients.
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Affiliation(s)
- Laura Van Hese
- Division of Mass Spectrometry Imaging, Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands
- Department of Anaesthesiology, University Hospitals Leuven, 3000, Leuven, Belgium
- Department of Cardiovascular Sciences, KU Leuven, 3000, Leuven, Belgium
| | - Steven De Vleeschouwer
- Neurosurgery Department, University Hospitals Leuven, 3000, Leuven, Belgium
- Laboratory for Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, Leuven Brain Institute (LBI), 3000, Leuven, Belgium
| | - Tom Theys
- Neurosurgery Department, University Hospitals Leuven, 3000, Leuven, Belgium
- Laboratory for Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, Leuven Brain Institute (LBI), 3000, Leuven, Belgium
| | - Steffen Rex
- Department of Anaesthesiology, University Hospitals Leuven, 3000, Leuven, Belgium
- Department of Cardiovascular Sciences, KU Leuven, 3000, Leuven, Belgium
| | - Ron M A Heeren
- Division of Mass Spectrometry Imaging, Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands
| | - Eva Cuypers
- Division of Mass Spectrometry Imaging, Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands.
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16
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Massalimova A, Timmermans M, Esfandiari H, Carrillo F, Laux CJ, Farshad M, Denis K, Fürnstahl P. Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review. Front Surg 2022; 9:952539. [PMID: 35990097 PMCID: PMC9381957 DOI: 10.3389/fsurg.2022.952539] [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: 05/25/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques.
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Affiliation(s)
- Aidana Massalimova
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
- Correspondence: Aidana Massalimova
| | - Maikel Timmermans
- KU Leuven, Campus Group T, BioMechanics (BMe), Smart Instrumentation Group, Leuven, Belgium
| | - Hooman Esfandiari
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
| | - Fabio Carrillo
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
| | - Christoph J. Laux
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Mazda Farshad
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Kathleen Denis
- KU Leuven, Campus Group T, BioMechanics (BMe), Smart Instrumentation Group, Leuven, Belgium
| | - Philipp Fürnstahl
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
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17
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Xu J, Meng Y, Qiu K, Topatana W, Li S, Wei C, Chen T, Chen M, Ding Z, Niu G. Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges. Front Oncol 2022; 12:892056. [PMID: 35965542 PMCID: PMC9363668 DOI: 10.3389/fonc.2022.892056] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/22/2022] [Indexed: 12/24/2022] Open
Abstract
Glioma is one of the most fatal primary brain tumors, and it is well-known for its difficulty in diagnosis and management. Medical imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and spectral imaging can efficiently aid physicians in diagnosing, treating, and evaluating patients with gliomas. With the increasing clinical records and digital images, the application of artificial intelligence (AI) based on medical imaging has reduced the burden on physicians treating gliomas even further. This review will classify AI technologies and procedures used in medical imaging analysis. Additionally, we will discuss the applications of AI in glioma, including tumor segmentation and classification, prediction of genetic markers, and prediction of treatment response and prognosis, using MRI, PET, and spectral imaging. Despite the benefits of AI in clinical applications, several issues such as data management, incomprehension, safety, clinical efficacy evaluation, and ethical or legal considerations, remain to be solved. In the future, doctors and researchers should collaborate to solve these issues, with a particular emphasis on interdisciplinary teamwork.
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Affiliation(s)
- Jiaona Xu
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuting Meng
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kefan Qiu
- Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Win Topatana
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shijie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Wei
- Department of Neurology, Affiliated Ningbo First Hospital, Ningbo, China
| | - Tianwen Chen
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mingyu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
| | - Guozhong Niu
- Department of Neurology, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Mingyu Chen, ; Zhongxiang Ding, ; Guozhong Niu,
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18
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Blokker M, Hamer PCDW, Wesseling P, Groot ML, Veta M. Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning. Sci Rep 2022; 12:11334. [PMID: 35790792 PMCID: PMC9256596 DOI: 10.1038/s41598-022-15423-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 06/23/2022] [Indexed: 11/09/2022] Open
Abstract
Management of gliomas requires an invasive treatment strategy, including extensive surgical resection. The objective of the neurosurgeon is to maximize tumor removal while preserving healthy brain tissue. However, the lack of a clear tumor boundary hampers the neurosurgeon's ability to accurately detect and resect infiltrating tumor tissue. Nonlinear multiphoton microscopy, in particular higher harmonic generation, enables label-free imaging of excised brain tissue, revealing histological hallmarks within seconds. Here, we demonstrate a real-time deep learning-based pipeline for automated glioma image analysis, matching video-rate image acquisition. We used a custom noise detection scheme, and a fully-convolutional classification network, to achieve on average 79% binary accuracy, 0.77 AUC and 0.83 mean average precision compared to the consensus of three pathologists, on a preliminary dataset. We conclude that the combination of real-time imaging and image analysis shows great potential for intraoperative assessment of brain tissue during tumor surgery.
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Affiliation(s)
- Max Blokker
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Philip C de Witt Hamer
- Department of Neurosurgery, Amsterdam UMC location VU University Medical Center, Amsterdam, The Netherlands
| | - Pieter Wesseling
- Department of Pathology, Amsterdam UMC location VU University Medical Center, Amsterdam, The Netherlands
| | - Marie Louise Groot
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mitko Veta
- Medical Image Analysis Group (IMAG/e), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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19
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Wu Y, Xu Z, Yang W, Ning Z, Dong H. Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery. Front Bioeng Biotechnol 2022; 10:906728. [PMID: 35711634 PMCID: PMC9196632 DOI: 10.3389/fbioe.2022.906728] [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: 03/29/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
The study of brain science is vital to human health. The application of hyperspectral imaging in biomedical fields has grown dramatically in recent years due to their unique optical imaging method and multidimensional information acquisition. Hyperspectral imaging technology can acquire two-dimensional spatial information and one-dimensional spectral information of biological samples simultaneously, covering the ultraviolet, visible and infrared spectral ranges with high spectral resolution, which can provide diagnostic information about the physiological, morphological and biochemical components of tissues and organs. This technology also presents finer spectral features for brain imaging studies, and further provides more auxiliary information for cerebral disease research. This paper reviews the recent advance of hyperspectral imaging in cerebral diagnosis. Firstly, the experimental setup, image acquisition and pre-processing, and analysis methods of hyperspectral technology were introduced. Secondly, the latest research progress and applications of hyperspectral imaging in brain tissue metabolism, hemodynamics, and brain cancer diagnosis in recent years were summarized briefly. Finally, the limitations of the application of hyperspectral imaging in cerebral disease diagnosis field were analyzed, and the future development direction was proposed.
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Affiliation(s)
- Yue Wu
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, China
| | - Zhongyuan Xu
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, China
| | - Wenjian Yang
- Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, China
| | - Zhiqiang Ning
- Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (CAS), Hefei, China.,Science Island Branch, Graduate School of USTC, Hefei, China
| | - Hao Dong
- Research Center for Sensing Materials and Devices, Zhejiang Lab, Hangzhou, China
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20
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Abstract
AbstractMeasuring morphological and biochemical features of tissue is crucial for disease diagnosis and surgical guidance, providing clinically significant information related to pathophysiology. Hyperspectral imaging (HSI) techniques obtain both spatial and spectral features of tissue without labeling molecules such as fluorescent dyes, which provides rich information for improved disease diagnosis and treatment. Recent advances in HSI systems have demonstrated its potential for clinical applications, especially in disease diagnosis and image-guided surgery. This review summarizes the basic principle of HSI and optical systems, deep-learning-based image analysis, and clinical applications of HSI to provide insight into this rapidly growing field of research. In addition, the challenges facing the clinical implementation of HSI techniques are discussed.
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21
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Williams S, Layard Horsfall H, Funnell JP, Hanrahan JG, Khan DZ, Muirhead W, Stoyanov D, Marcus HJ. Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm. Cancers (Basel) 2021; 13:cancers13195010. [PMID: 34638495 PMCID: PMC8508169 DOI: 10.3390/cancers13195010] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/02/2021] [Accepted: 10/03/2021] [Indexed: 01/01/2023] Open
Abstract
Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.
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Affiliation(s)
- Simon Williams
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
- Correspondence:
| | - Hugo Layard Horsfall
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Jonathan P. Funnell
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - John G. Hanrahan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Danyal Z. Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - William Muirhead
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Danail Stoyanov
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
| | - Hani J. Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK; (H.L.H.); (J.P.F.); (J.G.H.); (D.Z.K.); (W.M.); (H.J.M.)
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London W1W 7TY, UK;
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Tariciotti L, Palmisciano P, Giordano M, Remoli G, Lacorte E, Bertani G, Locatelli M, Dimeco F, Caccavella VM, Prada F. Artificial intelligence-enhanced intraoperative neurosurgical workflow: state of the art and future perspectives. J Neurosurg Sci 2021; 66:139-150. [PMID: 34545735 DOI: 10.23736/s0390-5616.21.05483-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND Artificial Intelligence (AI) and Machine Learning (ML) augment decision-making processes and productivity by supporting surgeons over a range of clinical activities: from diagnosis and preoperative planning to intraoperative surgical assistance. We reviewed the literature to identify current AI platforms applied to neurosurgical perioperative and intraoperative settings and describe their role in multiple subspecialties. METHODS A systematic review of the literature was conducted following the PRISMA guidelines. PubMed, EMBASE, and Scopus databases were searched from inception to December 31, 2020. Original articles were included if they: presented AI platforms implemented in perioperative, intraoperative settings and reported ML models' performance metrics. Due to the heterogeneity in neurosurgical applications, a qualitative synthesis was deemed appropriate. The risk of bias and applicability of predicted outcomes were assessed using the PROBAST tool. RESULTS 41 articles were included. All studies evaluated a supervised learning algorithm. A total of 10 ML models were described; the most frequent were neural networks (n = 15) and tree-based models (n = 13). Overall, the risk of bias was medium-high, but applicability was considered positive for all studies. Articles were grouped into 4 categories according to the subspecialty of interest: neuro-oncology, spine, functional and other. For each category, different prediction tasks were identified. CONCLUSIONS In this review, we summarize the state-of-art applications of AI for the intraoperative augmentation of neurosurgical workflows across multiple subspecialties. ML models may boost surgical team performances by reducing human errors and providing patient-tailored surgical plans, but further and higher-quality studies need to be conducted.
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Affiliation(s)
- Leonardo Tariciotti
- Unit of Neurosurgery, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,NEVRALIS, Milan, Italy
| | - Paolo Palmisciano
- NEVRALIS, Milan, Italy.,Department of Neurosurgery, Trauma, Gamma Knife Center Cannizzaro Hospital, Catania, Italy
| | - Martina Giordano
- NEVRALIS, Milan, Italy.,Department of Neurosurgery, Fondazione Policlinico Universitario A Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giulia Remoli
- NEVRALIS, Milan, Italy.,National Center for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
| | - Eleonora Lacorte
- National Center for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
| | - Giulio Bertani
- Unit of Neurosurgery, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Marco Locatelli
- Unit of Neurosurgery, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.,Aldo Ravelli Research Center for Neurotechnology and Experimental Brain Therapeutics, University of Milan, Milan, Italy
| | - Francesco Dimeco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | - Valerio M Caccavella
- NEVRALIS, Milan, Italy - .,Department of Neurosurgery, Fondazione Policlinico Universitario A Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Francesco Prada
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy.,Department of Neurological Surgery, University of Virginia Health Science Center, Charlottesville, VA, USA
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23
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Zhang Y, Yu S, Zhu X, Ning X, Liu W, Wang C, Liu X, Zhao D, Zheng Y, Bao J. Explainable liver tumor delineation in surgical specimens using hyperspectral imaging and deep learning. BIOMEDICAL OPTICS EXPRESS 2021; 12:4510-4529. [PMID: 34457429 PMCID: PMC8367264 DOI: 10.1364/boe.432654] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 05/08/2023]
Abstract
Surgical removal is the primary treatment for liver cancer, but frequent recurrence caused by residual malignant tissue remains an important challenge, as recurrence leads to high mortality. It is unreliable to distinguish tumors from normal tissues merely under visual inspection. Hyperspectral imaging (HSI) has been proved to be a promising technology for intra-operative use by capturing the spatial and spectral information of tissue in a fast, non-contact and label-free manner. In this work, we investigated the feasibility of HSI for liver tumor delineation on surgical specimens using a multi-task U-Net framework. Measurements are performed on 19 patients and a dataset of 36 specimens was collected with corresponding pathological results serving as the ground truth. The developed framework can achieve an overall sensitivity of 94.48% and a specificity of 87.22%, outperforming the baseline SVM method by a large margin. In particular, we propose to add explanations on the well-trained model from the spatial and spectral dimensions to show the contribution of pixels and spectral channels explicitly. On that basis, a novel saliency-weighted channel selection method is further proposed to select a small subset of 5 spectral channels which provide essentially as much information as using all 224 channels. According to the dominant channels, the absorption difference of hemoglobin and bile content in the normal and malignant tissues seems to be promising markers that could be further exploited.
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Affiliation(s)
- Yating Zhang
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Si Yu
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Xueyu Zhu
- Department of Mathematics, University of Iowa, Iowa City, IA 52242, USA
| | - Xuefei Ning
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Wei Liu
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Chuting Wang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Xiaohu Liu
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
- Currently with the School of Biomedical Engineering, School of Ophthalmology & Optometry, Wenzhou Medical University. Xueyuan Road 270, Wenzhou 325027, China
| | - Ding Zhao
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Jie Bao
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
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24
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Javan AAK, Jafari M, Shoeibi A, Zare A, Khodatars M, Ghassemi N, Alizadehsani R, Gorriz JM. Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems. SENSORS (BASEL, SWITZERLAND) 2021; 21:3925. [PMID: 34200287 PMCID: PMC8200970 DOI: 10.3390/s21113925] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 01/03/2023]
Abstract
In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov's method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application.
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Affiliation(s)
- Ali Akbar Kekha Javan
- Faculty of Electrical Engineering, Zabol Branch, Islamic Azad University, Zabol 1939598616, Iran;
| | - Mahboobeh Jafari
- Electrical and Computer Engineering Faculty, Semnan University, Semnan 3513119111, Iran;
| | - Afshin Shoeibi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran 1631714191, Iran;
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran
| | - Marjane Khodatars
- Faculty of Engineering, Mashhad Branch, Islamic Azad University, Mashhad 91735413, Iran;
| | - Navid Ghassemi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia;
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, 52005 Granada, Spain;
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25
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Urbanos G, Martín A, Vázquez G, Villanueva M, Villa M, Jimenez-Roldan L, Chavarrías M, Lagares A, Juárez E, Sanz C. Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification. SENSORS 2021; 21:s21113827. [PMID: 34073145 PMCID: PMC8199064 DOI: 10.3390/s21113827] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 01/29/2023]
Abstract
Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.
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Affiliation(s)
- Gemma Urbanos
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), 28041 Madrid, Spain; (L.J.-R.); (A.L.)
| | - Alberto Martín
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - Guillermo Vázquez
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - Marta Villanueva
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - Manuel Villa
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - Luis Jimenez-Roldan
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), 28041 Madrid, Spain; (L.J.-R.); (A.L.)
| | - Miguel Chavarrías
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
- Correspondence:
| | - Alfonso Lagares
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), 28041 Madrid, Spain; (L.J.-R.); (A.L.)
| | - Eduardo Juárez
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - César Sanz
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
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