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Zhou Y, Zhang L, Huang D, Zhang Y, Zhu L, Chen X, Cui G, Chen Q, Chen X, Ali S. Hyperspectral imaging combined with blood oxygen saturation for in vivo analysis of small intestinal necrosis tissue. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124298. [PMID: 38642522 DOI: 10.1016/j.saa.2024.124298] [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: 11/10/2023] [Revised: 04/14/2024] [Accepted: 04/14/2024] [Indexed: 04/22/2024]
Abstract
Acute mesenteric ischemia (AMI) is a clinically significant vascular and gastrointestinal condition, which is closely related to the blood supply of the small intestine. Unfortunately, it is still challenging to properly discriminate small intestinal tissues with different degrees of ischemia. In this study, hyperspectral imaging (HSI) was used to construct pseudo-color images of oxygen saturation about small intestinal tissues and to discriminate different degrees of ischemia. First, several small intestine tissue models of New Zealand white rabbits were prepared and collected their hyperspectral data. Then, a set of isosbestic points were used to linearly transform the measurement data twice to match the reference spectra of oxyhemoglobin and deoxyhemoglobin, respectively. The oxygen saturation was measured at the characteristic peak band of oxyhemoglobin (560 nm). Ultimately, using the oxygenated hemoglobin reflectance spectrum as the benchmark, we obtained the relative amount of median oxygen saturation in normal tissues was 70.0 %, the IQR was 10.1 %, the relative amount of median oxygen saturation in ischemic tissues was 49.6 %, and the IQR was 14.6 %. The results demonstrate that HSI combined with the oxygen saturation computation method can efficiently differentiate between normal and ischemic regions of the small intestinal tissues. This technique provides a powerful support for internist to discriminate small bowel tissues with different degrees of ischemia, and also provides a new way of thinking for the diagnosis of AMI.
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Affiliation(s)
- Yao Zhou
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130000, China; Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan 528400, China
| | - LeChao Zhang
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130000, China; Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan 528400, China
| | - DanFei Huang
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130000, China; Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan 528400, China.
| | - Yong Zhang
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130000, China; Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan 528400, China
| | - LiBin Zhu
- Pediatric General Surgery, The Second Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Xiaoqing Chen
- Pediatric General Surgery, The Second Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Guihua Cui
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325000, China
| | - Qifan Chen
- Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan 528400, China
| | - XiaoJing Chen
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325000, China.
| | - Shujat Ali
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325000, China
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Bannone E, Collins T, Esposito A, Cinelli L, De Pastena M, Pessaux P, Felli E, Andreotti E, Okamoto N, Barberio M, Felli E, Montorsi RM, Ingaglio N, Rodríguez-Luna MR, Nkusi R, Marescaux J, Hostettler A, Salvia R, Diana M. Surgical optomics: hyperspectral imaging and deep learning towards precision intraoperative automatic tissue recognition-results from the EX-MACHYNA trial. Surg Endosc 2024:10.1007/s00464-024-10880-1. [PMID: 38789623 DOI: 10.1007/s00464-024-10880-1] [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: 02/03/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND Hyperspectral imaging (HSI), combined with machine learning, can help to identify characteristic tissue signatures enabling automatic tissue recognition during surgery. This study aims to develop the first HSI-based automatic abdominal tissue recognition with human data in a prospective bi-center setting. METHODS Data were collected from patients undergoing elective open abdominal surgery at two international tertiary referral hospitals from September 2020 to June 2021. HS images were captured at various time points throughout the surgical procedure. Resulting RGB images were annotated with 13 distinct organ labels. Convolutional Neural Networks (CNNs) were employed for the analysis, with both external and internal validation settings utilized. RESULTS A total of 169 patients were included, 73 (43.2%) from Strasbourg and 96 (56.8%) from Verona. The internal validation within centers combined patients from both centers into a single cohort, randomly allocated to the training (127 patients, 75.1%, 585 images) and test sets (42 patients, 24.9%, 181 images). This validation setting showed the best performance. The highest true positive rate was achieved for the skin (100%) and the liver (97%). Misclassifications included tissues with a similar embryological origin (omentum and mesentery: 32%) or with overlaying boundaries (liver and hepatic ligament: 22%). The median DICE score for ten tissue classes exceeded 80%. CONCLUSION To improve automatic surgical scene segmentation and to drive clinical translation, multicenter accurate HSI datasets are essential, but further work is needed to quantify the clinical value of HSI. HSI might be included in a new omics science, namely surgical optomics, which uses light to extract quantifiable tissue features during surgery.
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Affiliation(s)
- Elisa Bannone
- Research Institute Against Digestive Cancer (IRCAD), 67000, Strasbourg, France.
- Department of General and Pancreatic Surgery, The Pancreas Institute, University of Verona Hospital Trust, P.Le Scuro 10, 37134, Verona, Italy.
| | - Toby Collins
- Research Institute Against Digestive Cancer (IRCAD), 67000, Strasbourg, France
| | - Alessandro Esposito
- Department of General and Pancreatic Surgery, The Pancreas Institute, University of Verona Hospital Trust, P.Le Scuro 10, 37134, Verona, Italy
| | - Lorenzo Cinelli
- Research Institute Against Digestive Cancer (IRCAD), 67000, Strasbourg, France
- Department of Gastrointestinal Surgery, San Raffaele Hospital IRCCS, Milan, Italy
| | - Matteo De Pastena
- Department of General and Pancreatic Surgery, The Pancreas Institute, University of Verona Hospital Trust, P.Le Scuro 10, 37134, Verona, Italy
| | - Patrick Pessaux
- Research Institute Against Digestive Cancer (IRCAD), 67000, Strasbourg, France
- Department of General, Digestive, and Endocrine Surgery, University Hospital of Strasbourg, Strasbourg, France
- Institut of Viral and Liver Disease, Inserm U1110, University of Strasbourg, Strasbourg, France
| | - Emanuele Felli
- Research Institute Against Digestive Cancer (IRCAD), 67000, Strasbourg, France
- Department of General, Digestive, and Endocrine Surgery, University Hospital of Strasbourg, Strasbourg, France
- Institut of Viral and Liver Disease, Inserm U1110, University of Strasbourg, Strasbourg, France
| | - Elena Andreotti
- Department of General and Pancreatic Surgery, The Pancreas Institute, University of Verona Hospital Trust, P.Le Scuro 10, 37134, Verona, Italy
| | - Nariaki Okamoto
- Research Institute Against Digestive Cancer (IRCAD), 67000, Strasbourg, France
- Photonics Instrumentation for Health, iCube Laboratory, University of Strasbourg, Strasbourg, France
| | - Manuel Barberio
- Research Institute Against Digestive Cancer (IRCAD), 67000, Strasbourg, France
- General Surgery Department, Ospedale Cardinale G. Panico, Tricase, Italy
| | - Eric Felli
- Research Institute Against Digestive Cancer (IRCAD), 67000, Strasbourg, France
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Roberto Maria Montorsi
- Department of General and Pancreatic Surgery, The Pancreas Institute, University of Verona Hospital Trust, P.Le Scuro 10, 37134, Verona, Italy
| | - Naomi Ingaglio
- Department of General and Pancreatic Surgery, The Pancreas Institute, University of Verona Hospital Trust, P.Le Scuro 10, 37134, Verona, Italy
| | - María Rita Rodríguez-Luna
- Research Institute Against Digestive Cancer (IRCAD), 67000, Strasbourg, France
- Photonics Instrumentation for Health, iCube Laboratory, University of Strasbourg, Strasbourg, France
| | - Richard Nkusi
- Research Institute Against Digestive Cancer (IRCAD), 67000, Strasbourg, France
| | - Jacque Marescaux
- Research Institute Against Digestive Cancer (IRCAD), 67000, Strasbourg, France
| | | | - Roberto Salvia
- Department of General and Pancreatic Surgery, The Pancreas Institute, University of Verona Hospital Trust, P.Le Scuro 10, 37134, Verona, Italy
| | - Michele Diana
- Photonics Instrumentation for Health, iCube Laboratory, University of Strasbourg, Strasbourg, France
- Department of Surgery, University Hospital of Geneva, Geneva, Switzerland
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Zhao SQ, Liu WT. Progress in artificial intelligence assisted digestive endoscopy diagnosis of digestive system diseases. WORLD CHINESE JOURNAL OF DIGESTOLOGY 2024; 32:171-181. [DOI: 10.11569/wcjd.v32.i3.171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2024]
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Sutton PA, van Dam MA, Cahill RA, Mieog S, Polom K, Vahrmeijer AL, van der Vorst J. Fluorescence-guided surgery: comprehensive review. BJS Open 2023; 7:7162090. [PMID: 37183598 PMCID: PMC10183714 DOI: 10.1093/bjsopen/zrad049] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/02/2023] [Accepted: 04/03/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Despite significant improvements in preoperative workup and surgical planning, surgeons often rely on their eyes and hands during surgery. Although this can be sufficient in some patients, intraoperative guidance is highly desirable. Near-infrared fluorescence has been advocated as a potential technique to guide surgeons during surgery. METHODS A literature search was conducted to identify relevant articles for fluorescence-guided surgery. The literature search was performed using Medical Subject Headings on PubMed for articles in English until November 2022 and a narrative review undertaken. RESULTS The use of invisible light, enabling real-time imaging, superior penetration depth, and the possibility to use targeted imaging agents, makes this optical imaging technique increasingly popular. Four main indications are described in this review: tissue perfusion, lymph node assessment, anatomy of vital structures, and tumour tissue imaging. Furthermore, this review provides an overview of future opportunities in the field of fluorescence-guided surgery. CONCLUSION Fluorescence-guided surgery has proven to be a widely innovative technique applicable in many fields of surgery. The potential indications for its use are diverse and can be combined. The big challenge for the future will be in bringing experimental fluorophores and conjugates through trials and into clinical practice, as well as validation of computer visualization with large data sets. This will require collaborative surgical groups focusing on utility, efficacy, and outcomes for these techniques.
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Affiliation(s)
- Paul A Sutton
- The Colorectal and Peritoneal Oncology Centre, Christie Hospital, Manchester, UK
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Martijn A van Dam
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Ronan A Cahill
- RAC, UCD Centre for Precision Surgery, University College Dublin, Dublin, Ireland
- RAC, Department of Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Sven Mieog
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Karol Polom
- Clinic of Oncological, Transplantation and General Surgery, Gdansk Medical University, Gdansk, Poland
| | | | - Joost van der Vorst
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
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Avram MF, Lazăr DC, Mariş MI, Olariu S. Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer. Front Oncol 2023; 13:1116761. [PMID: 36733307 PMCID: PMC9886660 DOI: 10.3389/fonc.2023.1116761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/03/2023] [Indexed: 01/19/2023] Open
Abstract
Background A considerable number of recent research have used artificial intelligence (AI) in the area of colorectal cancer (CRC). Surgical treatment of CRC still remains the most important curative component. Artificial intelligence in CRC surgery is not nearly as advanced as it is in screening (colonoscopy), diagnosis and prognosis, especially due to the increased complexity and variability of structures and elements in all fields of view, as well as a general shortage of annotated video banks for utilization. Methods A literature search was made and relevant studies were included in the minireview. Results The intraoperative steps which, at this moment, can benefit from AI in CRC are: phase and action recognition, excision plane navigation, endoscopy control, real-time circulation analysis, knot tying, automatic optical biopsy and hyperspectral imaging. This minireview also analyses the current advances in robotic treatment of CRC as well as the present possibility of automated CRC robotic surgery. Conclusions The use of AI in CRC surgery is still at its beginnings. The development of AI models capable of reproducing a colorectal expert surgeon's skill, the creation of large and complex datasets and the standardization of surgical colorectal procedures will contribute to the widespread use of AI in CRC surgical treatment.
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Affiliation(s)
- Mihaela Flavia Avram
- Department of Surgery X, 1st Surgery Discipline, “Victor Babeş” University of Medicine and Pharmacy Timişoara, Timişoara, Romania,Department of Mathematics, Politehnica University Timisoara, Timişoara, Romania,*Correspondence: Mihaela Flavia Avram,
| | - Daniela Cornelia Lazăr
- Department V of Internal Medicine I, Discipline of Internal Medicine IV, “Victor Babeş” University of Medicine and Pharmacy Timişoara, Timişoara, Romania
| | - Mihaela Ioana Mariş
- Department of Functional Sciences, Division of Physiopathology, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania,Center for Translational Research and Systems Medicine, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania
| | - Sorin Olariu
- Department of Surgery X, 1st Surgery Discipline, “Victor Babeş” University of Medicine and Pharmacy Timişoara, Timişoara, Romania
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Okamoto N, Rodríguez-Luna MR, Bencteux V, Al-Taher M, Cinelli L, Felli E, Urade T, Nkusi R, Mutter D, Marescaux J, Hostettler A, Collins T, Diana M. Computer-Assisted Differentiation between Colon-Mesocolon and Retroperitoneum Using Hyperspectral Imaging (HSI) Technology. Diagnostics (Basel) 2022; 12:diagnostics12092225. [PMID: 36140626 PMCID: PMC9497769 DOI: 10.3390/diagnostics12092225] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/10/2022] [Accepted: 09/12/2022] [Indexed: 12/01/2022] Open
Abstract
Complete mesocolic excision (CME), which involves the adequate resection of the tumor-bearing colonic segment with “en bloc” removal of its mesocolon along embryological fascial planes is associated with superior oncological outcomes. However, CME presents a higher complication rate compared to non-CME resections due to a higher risk of vascular injury. Hyperspectral imaging (HSI) is a contrast-free optical imaging technology, which facilitates the quantitative imaging of physiological tissue parameters and the visualization of anatomical structures. This study evaluates the accuracy of HSI combined with deep learning (DL) to differentiate the colon and its mesenteric tissue from retroperitoneal tissue. In an animal study including 20 pig models, intraoperative hyperspectral images of the sigmoid colon, sigmoid mesentery, and retroperitoneum were recorded. A convolutional neural network (CNN) was trained to distinguish the two tissue classes using HSI data, validated with a leave-one-out cross-validation process. The overall recognition sensitivity of the tissues to be preserved (retroperitoneum) and the tissues to be resected (colon and mesentery) was 79.0 ± 21.0% and 86.0 ± 16.0%, respectively. Automatic classification based on HSI and CNNs is a promising tool to automatically, non-invasively, and objectively differentiate the colon and its mesentery from retroperitoneal tissue.
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Affiliation(s)
- Nariaki Okamoto
- Research Institute against Digestive Cancer (IRCAD), 67091 Strasbourg, France
- ICube Laboratory, Photonics Instrumentation for Health, 67081 Strasbourg, France
- Correspondence:
| | - María Rita Rodríguez-Luna
- Research Institute against Digestive Cancer (IRCAD), 67091 Strasbourg, France
- ICube Laboratory, Photonics Instrumentation for Health, 67081 Strasbourg, France
| | - Valentin Bencteux
- ICube Laboratory, Photonics Instrumentation for Health, 67081 Strasbourg, France
| | - Mahdi Al-Taher
- Research Institute against Digestive Cancer (IRCAD), 67091 Strasbourg, France
- Department of Surgery, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
| | - Lorenzo Cinelli
- Research Institute against Digestive Cancer (IRCAD), 67091 Strasbourg, France
- Department of Gastrointestinal Surgery, San Raffaele Hospital IRCCS, 20132 Milan, Italy
| | - Eric Felli
- Research Institute against Digestive Cancer (IRCAD), 67091 Strasbourg, France
| | - Takeshi Urade
- Department of Surgery, Division of Hepato-Biliary-Pancreatic Surgery, Kobe University Graduate School of Medicine, Kobe 6500017, Japan
| | - Richard Nkusi
- Research Institute against Digestive Cancer (IRCAD), Kigali, Rwanda
| | - Didier Mutter
- Research Institute against Digestive Cancer (IRCAD), 67091 Strasbourg, France
- Department of Digestive and Endocrine Surgery, Nouvel Hôpital Civil, University of Strasbourg, 67091 Strasbourg, France
- IHU-Strasbourg—Institut de Chirurgie Guidée par L’image, 67091 Strasbourg, France
| | - Jacques Marescaux
- Research Institute against Digestive Cancer (IRCAD), 67091 Strasbourg, France
| | - Alexandre Hostettler
- Research Institute against Digestive Cancer (IRCAD), 67091 Strasbourg, France
- Research Institute against Digestive Cancer (IRCAD), Kigali, Rwanda
| | - Toby Collins
- Research Institute against Digestive Cancer (IRCAD), 67091 Strasbourg, France
- Research Institute against Digestive Cancer (IRCAD), Kigali, Rwanda
| | - Michele Diana
- Research Institute against Digestive Cancer (IRCAD), 67091 Strasbourg, France
- ICube Laboratory, Photonics Instrumentation for Health, 67081 Strasbourg, France
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