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Younis R, Yamlahi A, Bodenstedt S, Scheikl PM, Kisilenko A, Daum M, Schulze A, Wise PA, Nickel F, Mathis-Ullrich F, Maier-Hein L, Müller-Stich BP, Speidel S, Distler M, Weitz J, Wagner M. A surgical activity model of laparoscopic cholecystectomy for co-operation with collaborative robots. Surg Endosc 2024; 38:4316-4328. [PMID: 38872018 PMCID: PMC11289174 DOI: 10.1007/s00464-024-10958-w] [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: 03/15/2024] [Accepted: 05/24/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND Laparoscopic cholecystectomy is a very frequent surgical procedure. However, in an ageing society, less surgical staff will need to perform surgery on patients. Collaborative surgical robots (cobots) could address surgical staff shortages and workload. To achieve context-awareness for surgeon-robot collaboration, the intraoperative action workflow recognition is a key challenge. METHODS A surgical process model was developed for intraoperative surgical activities including actor, instrument, action and target in laparoscopic cholecystectomy (excluding camera guidance). These activities, as well as instrument presence and surgical phases were annotated in videos of laparoscopic cholecystectomy performed on human patients (n = 10) and on explanted porcine livers (n = 10). The machine learning algorithm Distilled-Swin was trained on our own annotated dataset and the CholecT45 dataset. The validation of the model was conducted using a fivefold cross-validation approach. RESULTS In total, 22,351 activities were annotated with a cumulative duration of 24.9 h of video segments. The machine learning algorithm trained and validated on our own dataset scored a mean average precision (mAP) of 25.7% and a top K = 5 accuracy of 85.3%. With training and validation on our dataset and CholecT45, the algorithm scored a mAP of 37.9%. CONCLUSIONS An activity model was developed and applied for the fine-granular annotation of laparoscopic cholecystectomies in two surgical settings. A machine recognition algorithm trained on our own annotated dataset and CholecT45 achieved a higher performance than training only on CholecT45 and can recognize frequently occurring activities well, but not infrequent activities. The analysis of an annotated dataset allowed for the quantification of the potential of collaborative surgical robots to address the workload of surgical staff. If collaborative surgical robots could grasp and hold tissue, up to 83.5% of the assistant's tissue interacting tasks (i.e. excluding camera guidance) could be performed by robots.
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Affiliation(s)
- R Younis
- Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Centre for the Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology, Dresden, Germany
| | - A Yamlahi
- Division of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - S Bodenstedt
- Department for Translational Surgical Oncology, National Center for Tumor Diseases, Partner Site Dresden, Dresden, Germany
- Centre for the Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology, Dresden, Germany
| | - P M Scheikl
- Surgical Planning and Robotic Cognition (SPARC), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - A Kisilenko
- Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - M Daum
- Centre for the Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
| | - A Schulze
- Centre for the Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
| | - P A Wise
- Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - F Nickel
- Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg- Eppendorf, Hamburg, Germany
| | - F Mathis-Ullrich
- Surgical Planning and Robotic Cognition (SPARC), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - L Maier-Hein
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Division of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - B P Müller-Stich
- Department for Abdominal Surgery, University Center for Gastrointestinal and Liver Diseases, Basel, Switzerland
| | - S Speidel
- Department for Translational Surgical Oncology, National Center for Tumor Diseases, Partner Site Dresden, Dresden, Germany
- Centre for the Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology, Dresden, Germany
| | - M Distler
- Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
| | - J Weitz
- Centre for the Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology, Dresden, Germany
- Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
| | - M Wagner
- Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), Heidelberg, Germany.
- Department for Translational Surgical Oncology, National Center for Tumor Diseases, Partner Site Dresden, Dresden, Germany.
- Centre for the Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology, Dresden, Germany.
- Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
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Rau A, Bano S, Jin Y, Azagra P, Morlana J, Kader R, Sanderson E, Matuszewski BJ, Lee JY, Lee DJ, Posner E, Frank N, Elangovan V, Raviteja S, Li Z, Liu J, Lalithkumar S, Islam M, Ren H, Lovat LB, Montiel JMM, Stoyanov D. SimCol3D - 3D reconstruction during colonoscopy challenge. Med Image Anal 2024; 96:103195. [PMID: 38815359 DOI: 10.1016/j.media.2024.103195] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 02/08/2024] [Accepted: 05/02/2024] [Indexed: 06/01/2024]
Abstract
Colorectal cancer is one of the most common cancers in the world. While colonoscopy is an effective screening technique, navigating an endoscope through the colon to detect polyps is challenging. A 3D map of the observed surfaces could enhance the identification of unscreened colon tissue and serve as a training platform. However, reconstructing the colon from video footage remains difficult. Learning-based approaches hold promise as robust alternatives, but necessitate extensive datasets. Establishing a benchmark dataset, the 2022 EndoVis sub-challenge SimCol3D aimed to facilitate data-driven depth and pose prediction during colonoscopy. The challenge was hosted as part of MICCAI 2022 in Singapore. Six teams from around the world and representatives from academia and industry participated in the three sub-challenges: synthetic depth prediction, synthetic pose prediction, and real pose prediction. This paper describes the challenge, the submitted methods, and their results. We show that depth prediction from synthetic colonoscopy images is robustly solvable, while pose estimation remains an open research question.
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Affiliation(s)
- Anita Rau
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK; Stanford University, Stanford, CA, USA.
| | - Sophia Bano
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK.
| | - Yueming Jin
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK; National University of Singapore, Singapore.
| | | | | | - Rawen Kader
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK
| | - Edward Sanderson
- Computer Vision and Machine Learning (CVML) Group, University of Central Lancashire, Preston, UK
| | - Bogdan J Matuszewski
- Computer Vision and Machine Learning (CVML) Group, University of Central Lancashire, Preston, UK
| | - Jae Young Lee
- Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Dong-Jae Lee
- Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | | | | | | | - Sista Raviteja
- Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Zhengwen Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, China
| | - Jiquan Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, China
| | - Seenivasan Lalithkumar
- National University of Singapore, Singapore; The Chinese University of Hong Kong, Hong Kong, China
| | | | - Hongliang Ren
- National University of Singapore, Singapore; The Chinese University of Hong Kong, Hong Kong, China
| | - Laurence B Lovat
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK
| | | | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK
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Shkolyar E, Zhou SR, Carlson CJ, Chang S, Laurie MA, Xing L, Bowden AK, Liao JC. Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence. Nat Rev Urol 2024:10.1038/s41585-024-00904-9. [PMID: 38982304 DOI: 10.1038/s41585-024-00904-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2024] [Indexed: 07/11/2024]
Abstract
Diagnostic cystoscopy in combination with transurethral resection of the bladder tumour are the standard for the diagnosis, surgical treatment and surveillance of bladder cancer. The ability to inspect the bladder in its current form stems from a long chain of advances in imaging science and endoscopy. Despite these advances, bladder cancer recurrence and progression rates remain high after endoscopic resection. This stagnation is a result of the heterogeneity of cancer biology as well as limitations in surgical techniques and tools, as incomplete resection and provider-specific differences affect cancer persistence and early recurrence. An unmet clinical need remains for solutions that can improve tumour delineation and resection. Translational advances in enhanced cystoscopy technologies and artificial intelligence offer promising avenues to overcoming the progress plateau.
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Affiliation(s)
- Eugene Shkolyar
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Steve R Zhou
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Camella J Carlson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Shuang Chang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Mark A Laurie
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Audrey K Bowden
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Joseph C Liao
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA.
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
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4
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Wang J, Wang B, Liu YY, Luo YL, Wu YY, Xiang L, Yang XM, Qu YL, Tian TR, Man Y. Recent Advances in Digital Technology in Implant Dentistry. J Dent Res 2024; 103:787-799. [PMID: 38822563 DOI: 10.1177/00220345241253794] [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] [Indexed: 06/03/2024] Open
Abstract
Digital technology has emerged as a transformative tool in dental implantation, profoundly enhancing accuracy and effectiveness across multiple facets, such as diagnosis, preoperative treatment planning, surgical procedures, and restoration delivery. The multiple integration of radiographic data and intraoral data, sometimes with facial scan data or electronic facebow through virtual planning software, enables comprehensive 3-dimensional visualization of the hard and soft tissue and the position of future restoration, resulting in heightened diagnostic precision. In virtual surgery design, the incorporation of both prosthetic arrangement and individual anatomical details enables the virtual execution of critical procedures (e.g., implant placement, extended applications, etc.) through analysis of cross-sectional images and the reconstruction of 3-dimensional surface models. After verification, the utilization of digital technology including templates, navigation, combined techniques, and implant robots achieved seamless transfer of the virtual treatment plan to the actual surgical sites, ultimately leading to enhanced surgical outcomes with highly improved accuracy. In restoration delivery, digital techniques for impression, shade matching, and prosthesis fabrication have advanced, enabling seamless digital data conversion and efficient communication among clinicians and technicians. Compared with clinical medicine, artificial intelligence (AI) technology in dental implantology primarily focuses on diagnosis and prediction. AI-supported preoperative planning and surgery remain in developmental phases, impeded by the complexity of clinical cases and ethical considerations, thereby constraining widespread adoption.
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Affiliation(s)
- J Wang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - B Wang
- Department of Stomatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Sichuan, Henan
| | - Y Y Liu
- Department of Oral Implantology, The Affiliated Stomatological Hospital of Kunming Medical University, Kunming, Yunnan, Sichuan, China
| | - Y L Luo
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Y Wu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - L Xiang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - X M Yang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y L Qu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - T R Tian
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Man
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
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5
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Laleman W, Peiffer KH, Tischendorf M, Ullerich HJ, Praktiknjo M, Trebicka J. Role of endoscopy in hepatology. Dig Liver Dis 2024; 56:1185-1195. [PMID: 38151452 DOI: 10.1016/j.dld.2023.11.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/29/2023]
Abstract
The growing and evolving field of EUS and advanced hepatobiliary endoscopy has amplified traditional upper gastrointestinal endoscopy and unveiled novel options for remaining unsolved hepatobiliary issues, both diagnostically and therapeutically. This conceptually appealing and fascinating integration of endoscopy within the practice of hepatology is referred to as 'endo-hepatology'. Endo-hepatology focuses on the one hand on disorders of the liver parenchyma and liver vasculature and of the hepatobiliary tract on the other hand. Applications hanging under the umbrella of endohepatology involve amongst others EUS-guided liver biopsy, EUS-guided portal pressure measurement, EUS-guided portal venous blood sampling, EUS-guided coil & glue embolization of gastric varices and spontaneous portosystemic shunts as well as ERCP in the challenging context of (decompensated cirrhosis) and intraductal cholangioscopy for primary sclerosing cholangitis. Although endoscopic proficiency however does not necessarily equal in an actual straightforward end-solution for currently persisting (complex) hepatobiliary situations. Therefore, endohepatology continues to generate high-quality data to validate and standardize procedures against currently considered (best available) "golden standards" while continuing to search and trying to provide novel minimally invasive solutions for persisting hepatological stalemate situations. In the current review, we aim to critically appraise the status and potential future directions of endo-hepatology.
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Affiliation(s)
- Wim Laleman
- Department of Gastroenterology and Hepatology, Section of Liver and Biliopancreatic disorders, University Hospitals Leuven, KU Leuven, Leuven, Belgium; Department of Medicine B (Gastroenterology, Hepatology, Endocrinology, Clinical Infectiology), University Hospital Muenster, Muenster, Germany.
| | - Kai-Henrik Peiffer
- Department of Medicine B (Gastroenterology, Hepatology, Endocrinology, Clinical Infectiology), University Hospital Muenster, Muenster, Germany
| | - Michael Tischendorf
- Department of Medicine B (Gastroenterology, Hepatology, Endocrinology, Clinical Infectiology), University Hospital Muenster, Muenster, Germany
| | - Hans-Joerg Ullerich
- Department of Medicine B (Gastroenterology, Hepatology, Endocrinology, Clinical Infectiology), University Hospital Muenster, Muenster, Germany
| | - Michael Praktiknjo
- Department of Medicine B (Gastroenterology, Hepatology, Endocrinology, Clinical Infectiology), University Hospital Muenster, Muenster, Germany
| | - Jonel Trebicka
- Department of Medicine B (Gastroenterology, Hepatology, Endocrinology, Clinical Infectiology), University Hospital Muenster, Muenster, Germany; European Foundation of Chronic Liver Failure, EFCLIF, Barcelona, Spain
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Han K, Zhao P, Chen S, Bao Y, Li B, Du J, Wu J, Li H, Chai N, Du X, Linghu E, Liu M. Systematic analysis of levels of evidence supporting Chinese clinical practice guidelines for gastrointestinal disease. MED 2024:S2666-6340(24)00210-1. [PMID: 38889718 DOI: 10.1016/j.medj.2024.05.006] [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: 12/29/2023] [Revised: 03/12/2024] [Accepted: 05/11/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Clinical practice guidelines (CPGs) inform healthcare decisions and improve patient care. However, an evaluation of guidelines on gastrointestinal diseases (GIDs) is lacking. This study aimed to systematically analyze the level of evidence (LOE) supporting Chinese CPGs for GIDs. METHODS CPGs for GIDs were identified by systematically searching major databases. Data on LOEs and classes of recommendations (CORs) were extracted. According to the Grades of Recommendation, Assessment, Development, and Evaluation system, LOEs were categorized as high, moderate, low, or very low, whereas CORs were classified as strong or weak. Statistical analyses were conducted to determine the distribution of LOEs and CORs across different subtopics and assess changes in evidence quality over time. FINDINGS Only 27.9% of these recommendations were supported by a high LOE, whereas approximately 70% were strong recommendations. There was a significant disparity among different subtopics in the proportion of strong recommendations supported by a high LOE. The number of guidelines has increased in the past 5 years, but there has been a concomitant decline in the proportion of recommendations supported by a high LOE. CONCLUSIONS There is a general lack of high-quality evidence supporting Chinese CPGs for GIDs, and there are inconsistencies in strong recommendations that have not improved. This study identified areas requiring further research, emphasizing the need to bridge these gaps and promote the conduct of high-quality clinical trials. FUNDING This study was supported by the National Key R&D Program of China (2022YFC2503604 and 2022YFC2503605) and Special Topics in Military Health Care (22BJZ25).
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Affiliation(s)
- Ke Han
- Department of Gastroenterology and Hepatology, First Medical Center of the Chinese PLA General Hospital, Beijing, China
| | - Pengyue Zhao
- Department of General Surgery, First Medical Center of the Chinese PLA General Hospital, Beijing, China
| | - Shimin Chen
- Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yinghui Bao
- Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Boyan Li
- Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Jiajun Du
- Library of Graduate School, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Junwei Wu
- Library of Graduate School, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Huikai Li
- Department of Gastroenterology and Hepatology, First Medical Center of the Chinese PLA General Hospital, Beijing, China
| | - Ningli Chai
- Department of Gastroenterology and Hepatology, First Medical Center of the Chinese PLA General Hospital, Beijing, China
| | - Xiaohui Du
- Department of General Surgery, First Medical Center of the Chinese PLA General Hospital, Beijing, China.
| | - Enqiang Linghu
- Department of Gastroenterology and Hepatology, First Medical Center of the Chinese PLA General Hospital, Beijing, China.
| | - Miao Liu
- Department of Anti-NBC Medicine, Graduate School, Chinese PLA General Hospital, Beijing, China.
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7
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Schmidt A, Mohareri O, DiMaio S, Yip MC, Salcudean SE. Tracking and mapping in medical computer vision: A review. Med Image Anal 2024; 94:103131. [PMID: 38442528 DOI: 10.1016/j.media.2024.103131] [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: 10/16/2023] [Revised: 02/08/2024] [Accepted: 02/29/2024] [Indexed: 03/07/2024]
Abstract
As computer vision algorithms increase in capability, their applications in clinical systems will become more pervasive. These applications include: diagnostics, such as colonoscopy and bronchoscopy; guiding biopsies, minimally invasive interventions, and surgery; automating instrument motion; and providing image guidance using pre-operative scans. Many of these applications depend on the specific visual nature of medical scenes and require designing algorithms to perform in this environment. In this review, we provide an update to the field of camera-based tracking and scene mapping in surgery and diagnostics in medical computer vision. We begin with describing our review process, which results in a final list of 515 papers that we cover. We then give a high-level summary of the state of the art and provide relevant background for those who need tracking and mapping for their clinical applications. After which, we review datasets provided in the field and the clinical needs that motivate their design. Then, we delve into the algorithmic side, and summarize recent developments. This summary should be especially useful for algorithm designers and to those looking to understand the capability of off-the-shelf methods. We maintain focus on algorithms for deformable environments while also reviewing the essential building blocks in rigid tracking and mapping since there is a large amount of crossover in methods. With the field summarized, we discuss the current state of the tracking and mapping methods along with needs for future algorithms, needs for quantification, and the viability of clinical applications. We then provide some research directions and questions. We conclude that new methods need to be designed or combined to support clinical applications in deformable environments, and more focus needs to be put into collecting datasets for training and evaluation.
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Affiliation(s)
- Adam Schmidt
- Department of Electrical and Computer Engineering, University of British Columbia, 2329 West Mall, Vancouver V6T 1Z4, BC, Canada.
| | - Omid Mohareri
- Advanced Research, Intuitive Surgical, 1020 Kifer Rd, Sunnyvale, CA 94086, USA
| | - Simon DiMaio
- Advanced Research, Intuitive Surgical, 1020 Kifer Rd, Sunnyvale, CA 94086, USA
| | - Michael C Yip
- Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA
| | - Septimiu E Salcudean
- Department of Electrical and Computer Engineering, University of British Columbia, 2329 West Mall, Vancouver V6T 1Z4, BC, Canada
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Selvaraj V, Sudhakar S, Sekaran S, Rajamani Sekar SK, Warrier S. Enhancing precision and efficiency: harnessing robotics and artificial intelligence for endoscopic and surgical advancements. Int J Surg 2024; 110:1315-1316. [PMID: 38016128 PMCID: PMC10871611 DOI: 10.1097/js9.0000000000000936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 11/09/2023] [Indexed: 11/30/2023]
Affiliation(s)
- Vimalraj Selvaraj
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology – Madras
| | - Swathi Sudhakar
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology – Madras
| | - Saravanan Sekaran
- Department of Prosthodontics, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University
| | | | - Sudha Warrier
- Department of Biotechnology, Faculty of Biomedical Sciences and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai Tamil Nadu, India
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Laleman W, Vanderschueren E, Mehdi ZS, Wiest R, Cardenas A, Trebicka J. Endoscopic procedures in hepatology: Current trends and new developments. J Hepatol 2024; 80:124-139. [PMID: 37730125 DOI: 10.1016/j.jhep.2023.08.032] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 08/10/2023] [Accepted: 08/28/2023] [Indexed: 09/22/2023]
Abstract
Gastrointestinal endoscopy has long been a reliable backbone in the diagnosis and management of hepatobilary disorders and their complications. However, with evolving non-invasive testing, personalised medicine has reframed the utility and necessity of endoscopic screening. Conversely, the growing interest and use of endoscopic ultrasound (EUS) and advanced endoscopy within gastrointestinal units has also opened novel diagnostic and therapeutic avenues for patients with various hepatobiliary diseases. The integration of "advanced endoscopy" within the practice of hepatology is nowadays referred to as "endo-hepatology". In essence, endo-hepatology consists of two pillars: one focusing primarily on disorders of the liver parenchyma, vascular disorders, and portal hypertension, which is mainly captured via EUS, while the other targets the hepatobiliary tract via endoscopic retrograde cholangiopancreatography and advanced imaging. Applications under the umbrella of endo-hepatology include, amongst others, EUS-guided liver biopsy, EUS-guided portal pressure gradient measurement, coil and glue embolisation of gastric varices as well as cholangioscopy. As such endo-hepatology could become an attractive concept wherein advanced endoscopy might reinforce the medical management of patients with hepatobiliary disorders and their complications after initial basic work-up. In this review, we discuss current trends and future developments within endo-hepatology and the remaining hurdles to overcome.
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Affiliation(s)
- Wim Laleman
- Department of Gastroenterology and Hepatology, Section of Liver and Biliopancreatic Disorders, University Hospitals Leuven, KU LEUVEN, Leuven, Belgium; Medizinische Klinik B, Universitätsklinikum Münster, Münster University, Münster, Germany.
| | - Emma Vanderschueren
- Department of Gastroenterology and Hepatology, Section of Liver and Biliopancreatic Disorders, University Hospitals Leuven, KU LEUVEN, Leuven, Belgium
| | - Zain Seyad Mehdi
- Department of Mechanical Engineering, KU LEUVEN, Leuven, Belgium
| | - Reiner Wiest
- Department of Visceral Surgery and Medicine, University Inselspital, Bern, Switzerland
| | - Andres Cardenas
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centro de Investigacion Biomedica en Red Enfermedades Hepaticas y Digestivas (CIBERehd), Madrid, Spain; Institute of Digestive Disease and Metabolism, Hospital Clinic de Barcelona, Barcelona, Catalunya, Spain
| | - Jonel Trebicka
- Medizinische Klinik B, Universitätsklinikum Münster, Münster University, Münster, Germany; European Foundation of Chronic Liver Failure, EFCLIF, Barcelona, Spain
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10
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Mahoney LB, Huang JS, Lightdale JR, Walsh CM. Pediatric endoscopy: how can we improve patient outcomes and ensure best practices? Expert Rev Gastroenterol Hepatol 2024; 18:89-102. [PMID: 38465446 DOI: 10.1080/17474124.2024.2328229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 03/05/2024] [Indexed: 03/12/2024]
Abstract
INTRODUCTION Strategies to promote high-quality endoscopy in children require consensus around pediatric-specific quality standards and indicators. Using a rigorous guideline development process, the international Pediatric Endoscopy Quality Improvement Network (PEnQuIN) was developed to support continuous quality improvement efforts within and across pediatric endoscopy services. AREAS COVERED This review presents a framework, informed by the PEnQuIN guidelines, for assessing endoscopist competence, granting procedural privileges, audit and feedback, and for skill remediation, when required. As is critical for promoting quality, PEnQuIN indicators can be benchmarked at the individual endoscopist, endoscopy facility, and endoscopy community levels. Furthermore, efforts to incorporate technologies, including electronic medical records and artificial intelligence, into endoscopic quality improvement processes can aid in creation of large-scale networks to facilitate comparison and standardization of quality indicator reporting across sites. EXPERT OPINION PEnQuIN quality standards and indicators provide a framework for continuous quality improvement in pediatric endoscopy, benefiting individual endoscopists, endoscopy facilities, and the broader endoscopy community. Routine and reliable measurement of data, facilitated by technology, is required to identify and drive improvements in care. Engaging all stakeholders in endoscopy quality improvement processes is crucial to enhancing patient outcomes and establishing best practices for safe, efficient, and effective pediatric endoscopic care.
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Affiliation(s)
- Lisa B Mahoney
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, MA, USA
| | - Jeannie S Huang
- Rady Children's Hospital, San Diego, CA and University of California San Diego, La Jolla, CA, USA
| | - Jenifer R Lightdale
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, MA, USA
| | - Catharine M Walsh
- Division of Gastroenterology, Hepatology and Nutrition and the Research and Learning Institutes, The Hospital for Sick Children, Department of Paediatrics and the Wilson Centre, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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11
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Chu CH, Chia YH, Hsu HC, Vyas S, Tsai CM, Yamaguchi T, Tanaka T, Chen HW, Luo Y, Yang PC, Tsai DP. Intelligent Phase Contrast Meta-Microscope System. NANO LETTERS 2023; 23:11630-11637. [PMID: 38038680 DOI: 10.1021/acs.nanolett.3c03484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Phase contrast imaging techniques enable the visualization of disparities in the refractive index among various materials. However, these techniques usually come with a cost: the need for bulky, inflexible, and complicated configurations. Here, we propose and experimentally demonstrate an ultracompact meta-microscope, a novel imaging platform designed to accomplish both optical and digital phase contrast imaging. The optical phase contrast imaging system is composed of a pair of metalenses and an intermediate spiral phase metasurface located at the Fourier plane. The performance of the system in generating edge-enhanced images is validated by imaging a variety of human cells, including lung cell lines BEAS-2B, CLY1, and H1299 and other types. Additionally, we integrate the ResNet deep learning model into the meta-microscope to transform bright-field images into edge-enhanced images with high contrast accuracy. This technology promises to aid in the development of innovative miniature optical systems for biomedical and clinical applications.
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Affiliation(s)
- Cheng Hung Chu
- YongLin Institute of Health, National Taiwan University, Taipei 10672, Taiwan
| | - Yu-Hsin Chia
- Institute of Medical Device and Imaging, National Taiwan University, Taipei 10051, Taiwan
- Department of Biomedical Engineering, National Taiwan University, Taipei 10051, Taiwan
| | - Hung-Chuan Hsu
- Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Sunil Vyas
- Institute of Medical Device and Imaging, National Taiwan University, Taipei 10051, Taiwan
| | - Chen-Ming Tsai
- Institute of Medical Device and Imaging, National Taiwan University, Taipei 10051, Taiwan
| | - Takeshi Yamaguchi
- Innovative Photon Manipulation Research Team, RIKEN Center for Advanced Photonics, Saitama 351-0198, Japan
| | - Takuo Tanaka
- Innovative Photon Manipulation Research Team, RIKEN Center for Advanced Photonics, Saitama 351-0198, Japan
| | - Huei-Wen Chen
- Graduate Institute of Toxicology, College of Medicine, National Taiwan University, Taipei 100, Taiwan
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei 100, Taiwan
| | - Yuan Luo
- YongLin Institute of Health, National Taiwan University, Taipei 10672, Taiwan
- Institute of Medical Device and Imaging, National Taiwan University, Taipei 10051, Taiwan
- Department of Biomedical Engineering, National Taiwan University, Taipei 10051, Taiwan
- Program for Precision Health and Intelligent Medicine, National Taiwan University, Taipei 106319, Taiwan, R.O.C
| | - Pan-Chyr Yang
- YongLin Institute of Health, National Taiwan University, Taipei 10672, Taiwan
- Program for Precision Health and Intelligent Medicine, National Taiwan University, Taipei 106319, Taiwan, R.O.C
- Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University, Taipei 10002, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan
| | - Din Ping Tsai
- Department of Electrical Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong
- Centre for Biosystems, Neuroscience, and Nanotechnology, City University of Hong Kong, Kowloon 999077, Hong Kong
- The State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon 99907, Hong Kong
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12
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Daher R, Vasconcelos F, Stoyanov D. A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its Effect on Image Correspondence. Med Image Anal 2023; 90:102994. [PMID: 37812856 PMCID: PMC10958122 DOI: 10.1016/j.media.2023.102994] [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: 12/04/2021] [Revised: 08/31/2023] [Accepted: 10/02/2023] [Indexed: 10/11/2023]
Abstract
Video streams are utilised to guide minimally-invasive surgery and diagnosis in a wide range of procedures, and many computer-assisted techniques have been developed to automatically analyse them. These approaches can provide additional information to the surgeon such as lesion detection, instrument navigation, or anatomy 3D shape modelling. However, the necessary image features to recognise these patterns are not always reliably detected due to the presence of irregular light patterns such as specular highlight reflections. In this paper, we aim at removing specular highlights from endoscopic videos using machine learning. We propose using a temporal generative adversarial network (GAN) to inpaint the hidden anatomy under specularities, inferring its appearance spatially and from neighbouring frames, where they are not present in the same location. This is achieved using in-vivo data from gastric endoscopy (Hyper Kvasir) in a fully unsupervised manner that relies on the automatic detection of specular highlights. System evaluations show significant improvements to other methods through direct comparison and ablation studies that depict the importance of the network's temporal and transfer learning components. The generalisability of our system to different surgical setups and procedures was also evaluated qualitatively on in-vivo data of gastric endoscopy and ex-vivo porcine data (SERV-CT, SCARED). We also assess the effect of our method in comparison to other methods on computer vision tasks that underpin 3D reconstruction and camera motion estimation, namely stereo disparity, optical flow, and sparse point feature matching. These are evaluated quantitatively and qualitatively and results show a positive effect of our specular inpainting method on these tasks in a novel comprehensive analysis. Our code and dataset are made available at https://github.com/endomapper/Endo-STTN.
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Affiliation(s)
- Rema Daher
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Gower Street, London, WC1E 6BT, UK.
| | - Francisco Vasconcelos
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Gower Street, London, WC1E 6BT, UK.
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Gower Street, London, WC1E 6BT, UK.
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13
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Bobrow TL, Golhar M, Vijayan R, Akshintala VS, Garcia JR, Durr NJ. Colonoscopy 3D video dataset with paired depth from 2D-3D registration. Med Image Anal 2023; 90:102956. [PMID: 37713764 PMCID: PMC10591895 DOI: 10.1016/j.media.2023.102956] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 06/29/2023] [Accepted: 09/04/2023] [Indexed: 09/17/2023]
Abstract
Screening colonoscopy is an important clinical application for several 3D computer vision techniques, including depth estimation, surface reconstruction, and missing region detection. However, the development, evaluation, and comparison of these techniques in real colonoscopy videos remain largely qualitative due to the difficulty of acquiring ground truth data. In this work, we present a Colonoscopy 3D Video Dataset (C3VD) acquired with a high definition clinical colonoscope and high-fidelity colon models for benchmarking computer vision methods in colonoscopy. We introduce a novel multimodal 2D-3D registration technique to register optical video sequences with ground truth rendered views of a known 3D model. The different modalities are registered by transforming optical images to depth maps with a Generative Adversarial Network and aligning edge features with an evolutionary optimizer. This registration method achieves an average translation error of 0.321 millimeters and an average rotation error of 0.159 degrees in simulation experiments where error-free ground truth is available. The method also leverages video information, improving registration accuracy by 55.6% for translation and 60.4% for rotation compared to single frame registration. 22 short video sequences were registered to generate 10,015 total frames with paired ground truth depth, surface normals, optical flow, occlusion, six degree-of-freedom pose, coverage maps, and 3D models. The dataset also includes screening videos acquired by a gastroenterologist with paired ground truth pose and 3D surface models. The dataset and registration source code are available at https://durr.jhu.edu/C3VD.
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Affiliation(s)
- Taylor L Bobrow
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Mayank Golhar
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Rohan Vijayan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Venkata S Akshintala
- Division of Gastroenterology and Hepatology, Johns Hopkins Medicine, Baltimore, MD 21287, USA
| | - Juan R Garcia
- Department of Art as Applied to Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Nicholas J Durr
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
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14
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Qi C, Hu L, Zhang C, Wang K, Qiu B, Yi J, Shen Y. Role of surgery in T4N0-3M0 esophageal cancer. World J Surg Oncol 2023; 21:369. [PMID: 38008742 PMCID: PMC10680323 DOI: 10.1186/s12957-023-03239-8] [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: 07/10/2023] [Accepted: 11/13/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND This study aimed to investigate an unsettled issue that whether T4 esophageal cancer could benefit from surgery. METHODS Patients with T4N0-3M0 esophageal cancer from 2004 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. Kaplan-Meier method, Cox proportional hazard regression, and propensity score matching (PSM) were used to compare overall survival (OS) between the surgery and no-surgery group. RESULTS A total of 1822 patients were analyzed. The multivariable Cox regression showed the HR (95% CI) for surgery vs. no surgery was 0.492 (0.427-0.567) (P < 0.001) in T4N0-3M0 cohort, 0.471 (0.354-0.627) (P < 0.001) in T4aN0-3M0 cohort, and 0.480 (0.335-0.689) (P < 0.001) in T4bN0-3M0 cohort. The HR (95% CI) for neoadjuvant therapy plus surgery vs. no surgery and surgery without neoadjuvant therapy vs. no surgery were 0.548 (0.461-0.650) (P < 0.001) and 0.464 (0.375-0.574) (P < 0.001), respectively. No significant OS difference was observed between neoadjuvant therapy plus surgery and surgery without neoadjuvant therapy: 0.966 (0.686-1.360) (P = 0.843). Subgroup analyses and PSM-adjusted analyses showed consistent results. CONCLUSION Surgery might bring OS improvement for T4N0-3M0 esophageal cancer patients, no matter in T4a disease or in T4b disease. Surgery with and without neoadjuvant therapy might both achieve better OS than no surgery.
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Affiliation(s)
- Chen Qi
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Liwen Hu
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling Clinical Medical School, Nanjing Medical University, Nanjing, 210002, China
| | - Chi Zhang
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
| | - Kang Wang
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling Clinical Medical School, Nanjing Medical University, Nanjing, 210002, China
| | - Bingmei Qiu
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China
- Department of Anesthesiology, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, 210004, China
| | - Jun Yi
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China.
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling Clinical Medical School, Nanjing Medical University, Nanjing, 210002, China.
| | - Yi Shen
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China.
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling Clinical Medical School, Nanjing Medical University, Nanjing, 210002, China.
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15
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Brandenburg JM, Jenke AC, Stern A, Daum MTJ, Schulze A, Younis R, Petrynowski P, Davitashvili T, Vanat V, Bhasker N, Schneider S, Mündermann L, Reinke A, Kolbinger FR, Jörns V, Fritz-Kebede F, Dugas M, Maier-Hein L, Klotz R, Distler M, Weitz J, Müller-Stich BP, Speidel S, Bodenstedt S, Wagner M. Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study. Surg Endosc 2023; 37:8577-8593. [PMID: 37833509 PMCID: PMC10615926 DOI: 10.1007/s00464-023-10447-6] [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: 06/29/2023] [Accepted: 09/02/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotations by medical experts are crucial, but still a bottleneck, we prospectively investigate active learning (AL) to reduce annotation effort and present automatic recognition of surgomic features. METHODS To establish a process for development of surgomic features, ten video-based features related to bleeding, as highly relevant intraoperative complication, were chosen. They comprise the amount of blood and smoke in the surgical field, six instruments, and two anatomic structures. Annotation of selected frames from robot-assisted minimally invasive esophagectomies was performed by at least three independent medical experts. To test whether AL reduces annotation effort, we performed a prospective annotation study comparing AL with equidistant sampling (EQS) for frame selection. Multiple Bayesian ResNet18 architectures were trained on a multicentric dataset, consisting of 22 videos from two centers. RESULTS In total, 14,004 frames were tag annotated. A mean F1-score of 0.75 ± 0.16 was achieved for all features. The highest F1-score was achieved for the instruments (mean 0.80 ± 0.17). This result is also reflected in the inter-rater-agreement (1-rater-kappa > 0.82). Compared to EQS, AL showed better recognition results for the instruments with a significant difference in the McNemar test comparing correctness of predictions. Moreover, in contrast to EQS, AL selected more frames of the four less common instruments (1512 vs. 607 frames) and achieved higher F1-scores for common instruments while requiring less training frames. CONCLUSION We presented ten surgomic features relevant for bleeding events in esophageal surgery automatically extracted from surgical video using ML. AL showed the potential to reduce annotation effort while keeping ML performance high for selected features. The source code and the trained models are published open source.
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Affiliation(s)
- Johanna M Brandenburg
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Alexander C Jenke
- Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Antonia Stern
- Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany
| | - Marie T J Daum
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - André Schulze
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Rayan Younis
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Philipp Petrynowski
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Tornike Davitashvili
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Vincent Vanat
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Nithya Bhasker
- Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Sophia Schneider
- Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany
| | - Lars Mündermann
- Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany
| | - Annika Reinke
- Department of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Fiona R Kolbinger
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
- Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany
- Else Kröner-Fresenius Center for Digital Health, Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
| | - Vanessa Jörns
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Fleur Fritz-Kebede
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Dugas
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Lena Maier-Hein
- Department of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rosa Klotz
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
- The Study Center of the German Surgical Society (SDGC), Heidelberg University Hospital, Heidelberg, Germany
| | - Marius Distler
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
- Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
| | - Jürgen Weitz
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
- Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- Centre for Tactile Internet With Human-in-the-Loop (CeTI), Technische Universität Dresden, 01062, Dresden, Germany
| | - Beat P Müller-Stich
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, Basel, Switzerland
| | - Stefanie Speidel
- Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
- Centre for Tactile Internet With Human-in-the-Loop (CeTI), Technische Universität Dresden, 01062, Dresden, Germany
| | - Sebastian Bodenstedt
- Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
- Centre for Tactile Internet With Human-in-the-Loop (CeTI), Technische Universität Dresden, 01062, Dresden, Germany
| | - Martin Wagner
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), Heidelberg, Germany.
- German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
- Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany.
- Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany.
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany.
- Centre for Tactile Internet With Human-in-the-Loop (CeTI), Technische Universität Dresden, 01062, Dresden, Germany.
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16
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Li S, Huang L, Guo Y, Wang L, Xie RJ. A super-high brightness and excellent colour quality laser-driven white light source enables miniaturized endoscopy. MATERIALS HORIZONS 2023; 10:4581-4588. [PMID: 37584153 DOI: 10.1039/d3mh01170d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
A laser-driven white light source promises intrinsic advantages for miniaturized endoscopic illumination. However, it remains a great challenge to simultaneously achieve high brightness and excellent colour rendition due to the shortage of highly efficient and thermally robust red-emitting laser phosphor converters. Here, we designed CaAlSiN3:Eu@Al (CASN@Al) converters with neglectable efficiency loss by tightly bonding all-inorganic phosphor films on an aluminium substrate. A layer-by-layer phosphor converter (LuAG/CASN@Al), i.e., stacking a green-emitting Lu3Al5O12:Ce (LuAG) layer on CASN@Al, was constructed to enhance light conversion efficiency and reduce reabsorption loss under blue laser excitation, which thus produces an excellent white light source with a luminous efficacy of 258 lm W-1 and a colour rendering index of 91. A miniaturized endoscopy with a coupling efficiency twice that of the commercial white LEDs was demonstrated by using the laser-driven white light and showed a central illuminance as high as 52 730 lx, more vivid images and long-term reliability.
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Affiliation(s)
- Shuxing Li
- Fujian Provincial Key Laboratory of Surface and Interface Engineering for High Performance Materials, College of Materials, Xiamen University, Xiamen 361005, China
| | - Linhui Huang
- Fujian Provincial Key Laboratory of Surface and Interface Engineering for High Performance Materials, College of Materials, Xiamen University, Xiamen 361005, China
| | - Yunqin Guo
- Fujian Provincial Key Laboratory of Surface and Interface Engineering for High Performance Materials, College of Materials, Xiamen University, Xiamen 361005, China
| | - Le Wang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China.
| | - Rong-Jun Xie
- Fujian Provincial Key Laboratory of Surface and Interface Engineering for High Performance Materials, College of Materials, Xiamen University, Xiamen 361005, China
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China.
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17
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [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: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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18
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Wang Z, Tao H, Wang J, Zhu Y, Lin J, Fang C, Yang J. Laparoscopic right hemi-hepatectomy plus total caudate lobectomy for perihilar cholangiocarcinoma via anterior approach with augmented reality navigation: a feasibility study. Surg Endosc 2023; 37:8156-8164. [PMID: 37653158 DOI: 10.1007/s00464-023-10397-z] [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: 04/14/2023] [Accepted: 08/13/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND Right hemi-hepatectomy plus total caudate lobectomy is the appropriate procedure for type IIIa or partial type II pCCA. However, the laparoscopic implementation of this procedure remains technically challenging, especially hilar vascular dissection and en bloc resection of the total caudate lobe. Augmented reality navigation can provide intraoperative navigation to enhance visualization of invisible hilar blood vessels and guide the parenchymal transection plane. METHODS Eleven patients who underwent laparoscopic right hemi-hepatectomy plus total caudate lobectomy from January 2021 to January 2023 were enrolled in this study. Augmented reality navigation technology and the anterior approach were utilized in this operation. Routine operative and short-term postoperative outcomes were assessed to evaluate the feasibility of the novel navigation method in this operation. RESULTS Right hemi-hepatectomy plus total caudate lobectomy was successfully performed in all 11 enrolled patients. Among the 11 patients, the mean operation time was 454.5 ± 25.0 min and the mean estimated blood loss was 209.1 ± 56.1 ml. Negative surgical margins were achieved in all patients. The postoperative course of all the patients was uneventful, and the mean length of postoperative hospital stay was 10.5 ± 1.2 days. CONCLUSION Laparoscopic right hemi-hepatectomy plus total caudate lobectomy via the anterior approach may be feasible and safe for pCCA with the assistance of augmented reality navigation.
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Affiliation(s)
- Zhuangxiong Wang
- Department of Hepatobiliary Surgery I, General Surgery Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, China
| | - Haisu Tao
- Department of Hepatobiliary Surgery I, General Surgery Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, China
| | - Junfeng Wang
- Department of Hepatobiliary Surgery I, General Surgery Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, China
| | - Yilin Zhu
- Department of Hepatobiliary Surgery I, General Surgery Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, China
| | - Jinyu Lin
- Department of Hepatobiliary Surgery I, General Surgery Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, China
| | - Chihua Fang
- Department of Hepatobiliary Surgery I, General Surgery Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, China.
| | - Jian Yang
- Department of Hepatobiliary Surgery I, General Surgery Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Guangzhou, China.
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19
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Wang H, Wang K, Yan T, Zhou H, Cao E, Lu Y, Wang Y, Luo J, Pang Y. Endoscopic image classification algorithm based on Poolformer. Front Neurosci 2023; 17:1273686. [PMID: 37811325 PMCID: PMC10551176 DOI: 10.3389/fnins.2023.1273686] [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: 08/07/2023] [Accepted: 09/04/2023] [Indexed: 10/10/2023] Open
Abstract
Image desmoking is a significant aspect of endoscopic image processing, effectively mitigating visual field obstructions without the need for additional surgical interventions. However, current smoke removal techniques tend to apply comprehensive video enhancement to all frames, encompassing both smoke-free and smoke-affected images, which not only escalates computational costs but also introduces potential noise during the enhancement of smoke-free images. In response to this challenge, this paper introduces an approach for classifying images that contain surgical smoke within endoscopic scenes. This classification method provides crucial target frame information for enhancing surgical smoke removal, improving the scientific robustness, and enhancing the real-time processing capabilities of image-based smoke removal method. The proposed endoscopic smoke image classification algorithm based on the improved Poolformer model, augments the model's capacity for endoscopic image feature extraction. This enhancement is achieved by transforming the Token Mixer within the encoder into a multi-branch structure akin to ConvNeXt, a pure convolutional neural network. Moreover, the conversion to a single-path topology during the prediction phase elevates processing speed. Experiments use the endoscopic dataset sourced from the Hamlyn Centre Laparoscopic/Endoscopic Video Dataset, augmented by Blender software rendering. The dataset comprises 3,800 training images and 1,200 test images, distributed in a 4:1 ratio of smoke-free to smoke-containing images. The outcomes affirm the superior performance of this paper's approach across multiple parameters. Comparative assessments against existing models, such as mobilenet_v3, efficientnet_b7, and ViT-B/16, substantiate that the proposed method excels in accuracy, sensitivity, and inference speed. Notably, when contrasted with the Poolformer_s12 network, the proposed method achieves a 2.3% enhancement in accuracy, an 8.2% boost in sensitivity, while incurring a mere 6.4 frames per second reduction in processing speed, maintaining 87 frames per second. The results authenticate the improved performance of the refined Poolformer model in endoscopic smoke image classification tasks. This advancement presents a lightweight yet effective solution for the automatic detection of smoke-containing images in endoscopy. This approach strikes a balance between the accuracy and real-time processing requirements of endoscopic image analysis, offering valuable insights for targeted desmoking process.
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Affiliation(s)
- Huiqian Wang
- Postdoctoral Research Station, Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
- Chongqing Xishan Science & Technology Co., Ltd., Chongqing, China
| | - Kun Wang
- Postdoctoral Research Station, Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Tian Yan
- Postdoctoral Research Station, Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Hekai Zhou
- Postdoctoral Research Station, Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Enling Cao
- Postdoctoral Research Station, Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yi Lu
- Postdoctoral Research Station, Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yuanfa Wang
- Postdoctoral Research Station, Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
- Chongqing Xishan Science & Technology Co., Ltd., Chongqing, China
| | - Jiasai Luo
- Postdoctoral Research Station, Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yu Pang
- Postdoctoral Research Station, Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
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Langerman A, Hammack-Aviran C, Cohen IG, Agarwala AV, Cortez N, Feigenson NR, Fried GM, Grantcharov T, Greenberg CC, Mello MM, Shuman AG. Navigating a Path Toward Routine Recording in the Operating Room. Ann Surg 2023; 278:e474-e475. [PMID: 37212390 DOI: 10.1097/sla.0000000000005906] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Affiliation(s)
- Alexander Langerman
- Department of Otolaryngology - Head and Neck Surgery and Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, TN
| | | | | | - Aalok V Agarwala
- Department of Anesthesia, Massachusetts Eye and Ear Infirmary. Boston, MA
| | - Nathan Cortez
- Southern Methodist University Dedman School of Law, Dallas, TX
| | | | - Gerald M Fried
- Division of General Surgery, McGill University Faculty of Medicine and Health Sciences, Montreal, QC, Canada
| | | | | | - Michelle M Mello
- Stanford Law School and Department of Health Policy, Stanford University School of Medicine, Stanford, CA
| | - Andrew G Shuman
- Department of Otolaryngology - Head and Neck Surgery and Center for Bioethics and Social Sciences in Medicine, University of Michigan, and the Veterans Affairs Ann Arbor Health System, Ann Arbor, MI
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21
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Ungureanu BS, Gheonea DI, Florescu DN, Iordache S, Cazacu SM, Iovanescu VF, Rogoveanu I, Turcu-Stiolica A. Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning. Front Med (Lausanne) 2023; 10:1134835. [PMID: 36873879 PMCID: PMC9982090 DOI: 10.3389/fmed.2023.1134835] [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: 12/30/2022] [Accepted: 02/06/2023] [Indexed: 02/19/2023] Open
Abstract
Background Non-endoscopic risk scores, Glasgow Blatchford (GBS) and admission Rockall (Rock), are limited by poor specificity. The aim of this study was to develop an Artificial Neural Network (ANN) for the non-endoscopic triage of nonvariceal upper gastrointestinal bleeding (NVUGIB), with mortality as a primary outcome. Methods Four machine learning algorithms, namely, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), logistic regression (LR), K-Nearest Neighbor (K-NN), were performed with GBS, Rock, Beylor Bleeding score (BBS), AIM65, and T-score. Results A total of 1,096 NVUGIB hospitalized in the Gastroenterology Department of the County Clinical Emergency Hospital of Craiova, Romania, randomly divided into training and testing groups, were included retrospectively in our study. The machine learning models were more accurate at identifying patients who met the endpoint of mortality than any of the existing risk scores. AIM65 was the most important score in the detection of whether a NVUGIB would die or not, whereas BBS had no influence on this. Also, the greater AIM65 and GBS, and the lower Rock and T-score, the higher mortality will be. Conclusion The best accuracy was obtained by the hyperparameter-tuned K-NN classifier (98%), giving the highest precision and recall on the training and testing datasets among all developed models, showing that machine learning can accurately predict mortality in patients with NVUGIB.
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Affiliation(s)
- Bogdan Silviu Ungureanu
- Department of Gastroenterology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Dan Ionut Gheonea
- Department of Gastroenterology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Dan Nicolae Florescu
- Department of Gastroenterology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Sevastita Iordache
- Department of Gastroenterology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Sergiu Marian Cazacu
- Department of Gastroenterology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Vlad Florin Iovanescu
- Department of Gastroenterology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Ion Rogoveanu
- Department of Gastroenterology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Adina Turcu-Stiolica
- Department of Pharmacoeconomics, University of Medicine and Pharmacy of Craiova, Craiova, Romania
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