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Wang B, Yu JF, Lin SY, Li YJ, Huang WY, Yan SY, Wang SS, Zhang LY, Cai SJ, Wu SB, Li MY, Wang TY, Abdelhamid Ahmed AH, Randolph GW, Chen F, Zhao WX. Intraoperative AI-assisted early prediction of parathyroid and ischemia alert in endoscopic thyroid surgery. Head Neck 2024; 46:1975-1987. [PMID: 38348564 DOI: 10.1002/hed.27629] [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: 09/24/2023] [Revised: 12/20/2023] [Accepted: 12/31/2023] [Indexed: 07/07/2024] Open
Abstract
BACKGROUND The preservation of parathyroid glands is crucial in endoscopic thyroid surgery to prevent hypocalcemia and related complications. However, current methods for identifying and protecting these glands have limitations. We propose a novel technique that has the potential to improve the safety and efficacy of endoscopic thyroid surgery. PURPOSE Our study aims to develop a deep learning model called PTAIR 2.0 (Parathyroid gland Artificial Intelligence Recognition) to enhance parathyroid gland recognition during endoscopic thyroidectomy. We compare its performance against traditional surgeon-based identification methods. MATERIALS AND METHODS Parathyroid tissues were annotated in 32 428 images extracted from 838 endoscopic thyroidectomy videos, forming the internal training cohort. An external validation cohort comprised 54 full-length videos. Six candidate algorithms were evaluated to select the optimal one. We assessed the model's performance in terms of initial recognition time, identification duration, and recognition rate and compared it with the performance of surgeons. RESULTS Utilizing the YOLOX algorithm, we developed PTAIR 2.0, which demonstrated superior performance with an AP50 score of 92.1%. The YOLOX algorithm achieved a frame rate of 25.14 Hz, meeting real-time requirements. In the internal training cohort, PTAIR 2.0 achieved AP50 values of 94.1%, 98.9%, and 92.1% for parathyroid gland early prediction, identification, and ischemia alert, respectively. Additionally, in the external validation cohort, PTAIR outperformed both junior and senior surgeons in identifying and tracking parathyroid glands (p < 0.001). CONCLUSION The AI-driven PTAIR 2.0 model significantly outperforms both senior and junior surgeons in parathyroid gland identification and ischemia alert during endoscopic thyroid surgery, offering potential for enhanced surgical precision and patient outcomes.
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Affiliation(s)
- Bo Wang
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jia-Fan Yu
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Si-Ying Lin
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yi-Jian Li
- College of Computer and Data Science, Fuzhou University, Fuzhou, China
| | - Wen-Yu Huang
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Shou-Yi Yan
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Si-Si Wang
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Li-Yong Zhang
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Shao-Jun Cai
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Si-Bin Wu
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Meng-Yao Li
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Ting-Yi Wang
- Department of Leading Cadre, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Amr H Abdelhamid Ahmed
- Division of Thyroid and Parathyroid Endocrine Surgery, Department of Otolaryngology - Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, USA
| | - Gregory W Randolph
- Division of Thyroid and Parathyroid Endocrine Surgery, Department of Otolaryngology - Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, USA
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Fei Chen
- College of Computer and Data Science, Fuzhou University, Fuzhou, China
| | - Wen-Xin Zhao
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Clinical Research Center for Precision Management of Thyroid Cancer of Fujian Province, Fuzhou, China
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Tolentino R, Baradaran A, Gore G, Pluye P, Abbasgholizadeh-Rahimi S. Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review. JMIR MEDICAL EDUCATION 2024; 10:e54793. [PMID: 39023999 DOI: 10.2196/54793] [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: 11/22/2023] [Revised: 03/26/2024] [Accepted: 04/29/2024] [Indexed: 07/20/2024]
Abstract
BACKGROUND The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians' comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process. OBJECTIVE The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians. METHODS We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. RESULTS Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs. CONCLUSIONS This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.11124/JBIES-22-00374.
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Affiliation(s)
- Raymond Tolentino
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Ashkan Baradaran
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Genevieve Gore
- Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montreal, QC, Canada
| | - Pierre Pluye
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Samira Abbasgholizadeh-Rahimi
- Department of Family Medicine, McGill University, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Lady Davis Institute for Medical Research, Herzl Family Practice Centre, Jewish General Hospital, Montreal, QC, Canada
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada
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3
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Fares R, Atlan LD, Druckmann I, Factor S, Gortzak Y, Segal O, Artzi M, Sternheim A. Imaging-Based Deep Learning for Predicting Desmoid Tumor Progression. J Imaging 2024; 10:122. [PMID: 38786576 PMCID: PMC11122104 DOI: 10.3390/jimaging10050122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
Desmoid tumors (DTs) are non-metastasizing and locally aggressive soft-tissue mesenchymal neoplasms. Those that become enlarged often become locally invasive and cause significant morbidity. DTs have a varied pattern of clinical presentation, with up to 50-60% not growing after diagnosis and 20-30% shrinking or even disappearing after initial progression. Enlarging tumors are considered unstable and progressive. The management of symptomatic and enlarging DTs is challenging, and primarily consists of chemotherapy. Despite wide surgical resection, DTs carry a rate of local recurrence as high as 50%. There is a consensus that contrast-enhanced magnetic resonance imaging (MRI) or, alternatively, computerized tomography (CT) is the preferred modality for monitoring DTs. Each uses Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1), which measures the largest diameter on axial, sagittal, or coronal series. This approach, however, reportedly lacks accuracy in detecting response to therapy and fails to detect tumor progression, thus calling for more sophisticated methods. The objective of this study was to detect unique features identified by deep learning that correlate with the future clinical course of the disease. Between 2006 and 2019, 51 patients (mean age 41.22 ± 15.5 years) who had a tissue diagnosis of DT were included in this retrospective single-center study. Each had undergone at least three MRI examinations (including a pretreatment baseline study), and each was followed by orthopedic oncology specialists for a median of 38.83 months (IQR 44.38). Tumor segmentations were performed on a T2 fat-suppressed treatment-naive MRI sequence, after which the segmented lesion was extracted to a three-dimensional file together with its DICOM file and run through deep learning software. The results of the algorithm were then compared to clinical data collected from the patients' medical files. There were 28 males (13 stable) and 23 females (15 stable) whose ages ranged from 19.07 to 83.33 years. The model was able to independently predict clinical progression as measured from the baseline MRI with an overall accuracy of 93% (93 ± 0.04) and ROC of 0.89 ± 0.08. Artificial intelligence may contribute to risk stratification and clinical decision-making in patients with DT by predicting which patients are likely to progress.
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Affiliation(s)
- Rabih Fares
- Department of Radiology, Tel Aviv Sourasky Medical Center, Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel
| | - Lilian D. Atlan
- Department of Radiology, Tel Aviv Sourasky Medical Center, Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel
| | - Ido Druckmann
- Department of Radiology, Tel Aviv Sourasky Medical Center, Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel
| | - Shai Factor
- Division of Orthopedics, Tel Aviv Sourasky Medical Center, Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel
| | - Yair Gortzak
- Division of Orthopedics, Tel Aviv Sourasky Medical Center, Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel
| | - Ortal Segal
- Division of Orthopedics, Tel Aviv Sourasky Medical Center, Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel
| | - Moran Artzi
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel
| | - Amir Sternheim
- Division of Orthopedics, Tel Aviv Sourasky Medical Center, Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel
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González-González MA, Conde SV, Latorre R, Thébault SC, Pratelli M, Spitzer NC, Verkhratsky A, Tremblay MÈ, Akcora CG, Hernández-Reynoso AG, Ecker M, Coates J, Vincent KL, Ma B. Bioelectronic Medicine: a multidisciplinary roadmap from biophysics to precision therapies. Front Integr Neurosci 2024; 18:1321872. [PMID: 38440417 PMCID: PMC10911101 DOI: 10.3389/fnint.2024.1321872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/10/2024] [Indexed: 03/06/2024] Open
Abstract
Bioelectronic Medicine stands as an emerging field that rapidly evolves and offers distinctive clinical benefits, alongside unique challenges. It consists of the modulation of the nervous system by precise delivery of electrical current for the treatment of clinical conditions, such as post-stroke movement recovery or drug-resistant disorders. The unquestionable clinical impact of Bioelectronic Medicine is underscored by the successful translation to humans in the last decades, and the long list of preclinical studies. Given the emergency of accelerating the progress in new neuromodulation treatments (i.e., drug-resistant hypertension, autoimmune and degenerative diseases), collaboration between multiple fields is imperative. This work intends to foster multidisciplinary work and bring together different fields to provide the fundamental basis underlying Bioelectronic Medicine. In this review we will go from the biophysics of the cell membrane, which we consider the inner core of neuromodulation, to patient care. We will discuss the recently discovered mechanism of neurotransmission switching and how it will impact neuromodulation design, and we will provide an update on neuronal and glial basis in health and disease. The advances in biomedical technology have facilitated the collection of large amounts of data, thereby introducing new challenges in data analysis. We will discuss the current approaches and challenges in high throughput data analysis, encompassing big data, networks, artificial intelligence, and internet of things. Emphasis will be placed on understanding the electrochemical properties of neural interfaces, along with the integration of biocompatible and reliable materials and compliance with biomedical regulations for translational applications. Preclinical validation is foundational to the translational process, and we will discuss the critical aspects of such animal studies. Finally, we will focus on the patient point-of-care and challenges in neuromodulation as the ultimate goal of bioelectronic medicine. This review is a call to scientists from different fields to work together with a common endeavor: accelerate the decoding and modulation of the nervous system in a new era of therapeutic possibilities.
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Affiliation(s)
- María Alejandra González-González
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, United States
- Department of Pediatric Neurology, Baylor College of Medicine, Houston, TX, United States
| | - Silvia V. Conde
- iNOVA4Health, NOVA Medical School, Faculdade de Ciências Médicas, NOVA University, Lisbon, Portugal
| | - Ramon Latorre
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Stéphanie C. Thébault
- Laboratorio de Investigación Traslacional en salud visual (D-13), Instituto de Neurobiología, Universidad Nacional Autónoma de México (UNAM), Querétaro, Mexico
| | - Marta Pratelli
- Neurobiology Department, Kavli Institute for Brain and Mind, UC San Diego, La Jolla, CA, United States
| | - Nicholas C. Spitzer
- Neurobiology Department, Kavli Institute for Brain and Mind, UC San Diego, La Jolla, CA, United States
| | - Alexei Verkhratsky
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
- Achucarro Centre for Neuroscience, IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
- Department of Forensic Analytical Toxicology, School of Forensic Medicine, China Medical University, Shenyang, China
- International Collaborative Center on Big Science Plan for Purinergic Signaling, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Department of Stem Cell Biology, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
| | - Marie-Ève Tremblay
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Molecular Medicine, Université Laval, Québec City, QC, Canada
- Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, BC, Canada
| | - Cuneyt G. Akcora
- Department of Computer Science, University of Central Florida, Orlando, FL, United States
| | | | - Melanie Ecker
- Department of Biomedical Engineering, University of North Texas, Denton, TX, United States
| | | | - Kathleen L. Vincent
- Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, TX, United States
| | - Brandy Ma
- Stanley H. Appel Department of Neurology, Houston Methodist Hospital, Houston, TX, United States
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Bellini V, Russo M, Domenichetti T, Panizzi M, Allai S, Bignami EG. Artificial Intelligence in Operating Room Management. J Med Syst 2024; 48:19. [PMID: 38353755 PMCID: PMC10867065 DOI: 10.1007/s10916-024-02038-2] [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: 11/29/2023] [Accepted: 02/05/2024] [Indexed: 02/16/2024]
Abstract
This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Michele Russo
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Tania Domenichetti
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Matteo Panizzi
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Simone Allai
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Elena Giovanna Bignami
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy.
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6
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Men Y, Zhao Z, Chen W, Wu H, Zhang G, Luo F, Yu M. Research on workflow recognition for liver rupture repair surgery. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:1844-1856. [PMID: 38454663 DOI: 10.3934/mbe.2024080] [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: 03/09/2024]
Abstract
Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver rupture can catalyze critical conditions such as hemorrhage and shock. Surgical workflow recognition in liver rupture repair surgery videos presents a significant task aimed at reducing surgical mistakes and enhancing the quality of surgeries conducted by surgeons. A liver rupture repair simulation surgical dataset is proposed in this paper which consists of 45 videos collaboratively completed by nine surgeons. Furthermore, an end-to-end SA-RLNet, a self attention-based recurrent convolutional neural network, is introduced in this paper. The self-attention mechanism is used to automatically identify the importance of input features in various instances and associate the relationships between input features. The accuracy of the surgical phase classification of the SA-RLNet approach is 90.6%. The present study demonstrates that the SA-RLNet approach shows strong generalization capabilities on the dataset. SA-RLNet has proved to be advantageous in capturing subtle variations between surgical phases. The application of surgical workflow recognition has promising feasibility in liver rupture repair surgery.
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Affiliation(s)
- Yutao Men
- Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China
- National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China
| | - Zixian Zhao
- Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China
- Medical Support Technology Research Department, Systems Engineering Institute, Academy of Military Sciences, People's Liberation Army, Tianjin 300161, China
- National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China
| | - Wei Chen
- Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China
- National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China
| | - Hang Wu
- Medical Support Technology Research Department, Systems Engineering Institute, Academy of Military Sciences, People's Liberation Army, Tianjin 300161, China
| | - Guang Zhang
- Medical Support Technology Research Department, Systems Engineering Institute, Academy of Military Sciences, People's Liberation Army, Tianjin 300161, China
| | - Feng Luo
- Medical Support Technology Research Department, Systems Engineering Institute, Academy of Military Sciences, People's Liberation Army, Tianjin 300161, China
| | - Ming Yu
- Medical Support Technology Research Department, Systems Engineering Institute, Academy of Military Sciences, People's Liberation Army, Tianjin 300161, China
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Wise PA, Studier-Fischer A, Nickel F, Hackert T. [Status Quo of Surgical Navigation]. Zentralbl Chir 2023. [PMID: 38056501 DOI: 10.1055/a-2211-4898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Surgical navigation, also referred to as computer-assisted or image-guided surgery, is a technique that employs a variety of methods - such as 3D imaging, tracking systems, specialised software, and robotics to support surgeons during surgical interventions. These emerging technologies aim not only to enhance the accuracy and precision of surgical procedures, but also to enable less invasive approaches, with the objective of reducing complications and improving operative outcomes for patients. By harnessing the integration of emerging digital technologies, surgical navigation holds the promise of assisting complex procedures across various medical disciplines. In recent years, the field of surgical navigation has witnessed significant advances. Abdominal surgical navigation, particularly endoscopy, laparoscopic, and robot-assisted surgery, is currently undergoing a phase of rapid evolution. Emphases include image-guided navigation, instrument tracking, and the potential integration of augmented and mixed reality (AR, MR). This article will comprehensively delve into the latest developments in surgical navigation, spanning state-of-the-art intraoperative technologies like hyperspectral and fluorescent imaging, to the integration of preoperative radiological imaging within the intraoperative setting.
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Affiliation(s)
- Philipp Anthony Wise
- Klinik für Allgemein-, Viszeral- und Transplantationschirurgie, Universitätsklinikum Heidelberg, Heidelberg, Deutschland
| | - Alexander Studier-Fischer
- Klinik für Allgemein-, Viszeral- und Transplantationschirurgie, Universitätsklinikum Heidelberg, Heidelberg, Deutschland
| | - Felix Nickel
- Klinik für Allgemein-, Viszeral- und Thoraxchirurgie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
- Klinik für Allgemein-, Viszeral- und Transplantationschirurgie, Universitätsklinikum Heidelberg, Heidelberg, Deutschland
| | - Thilo Hackert
- Klinik für Allgemein-, Viszeral- und Thoraxchirurgie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
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Park JJ, Doiphode N, Zhang X, Pan L, Blue R, Shi J, Buch VP. Developing the surgeon-machine interface: using a novel instance-segmentation framework for intraoperative landmark labelling. Front Surg 2023; 10:1259756. [PMID: 37936949 PMCID: PMC10626480 DOI: 10.3389/fsurg.2023.1259756] [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: 07/17/2023] [Accepted: 09/20/2023] [Indexed: 11/09/2023] Open
Abstract
Introduction The utilisation of artificial intelligence (AI) augments intraoperative safety, surgical training, and patient outcomes. We introduce the term Surgeon-Machine Interface (SMI) to describe this innovative intersection between surgeons and machine inference. A custom deep computer vision (CV) architecture within a sparse labelling paradigm was developed, specifically tailored to conceptualise the SMI. This platform demonstrates the ability to perform instance segmentation on anatomical landmarks and tools from a single open spinal dural arteriovenous fistula (dAVF) surgery video dataset. Methods Our custom deep convolutional neural network was based on SOLOv2 architecture for precise, instance-level segmentation of surgical video data. Test video consisted of 8520 frames, with sparse labelling of only 133 frames annotated for training. Accuracy and inference time, assessed using F1-score and mean Average Precision (mAP), were compared against current state-of-the-art architectures on a separate test set of 85 additionally annotated frames. Results Our SMI demonstrated superior accuracy and computing speed compared to these frameworks. The F1-score and mAP achieved by our platform were 17% and 15.2% respectively, surpassing MaskRCNN (15.2%, 13.9%), YOLOv3 (5.4%, 11.9%), and SOLOv2 (3.1%, 10.4%). Considering detections that exceeded the Intersection over Union threshold of 50%, our platform achieved an impressive F1-score of 44.2% and mAP of 46.3%, outperforming MaskRCNN (41.3%, 43.5%), YOLOv3 (15%, 34.1%), and SOLOv2 (9%, 32.3%). Our platform demonstrated the fastest inference time (88ms), compared to MaskRCNN (90ms), SOLOV2 (100ms), and YOLOv3 (106ms). Finally, the minimal amount of training set demonstrated a good generalisation performance -our architecture successfully identified objects in a frame that were not included in the training or validation frames, indicating its ability to handle out-of-domain scenarios. Discussion We present our development of an innovative intraoperative SMI to demonstrate the future promise of advanced CV in the surgical domain. Through successful implementation in a microscopic dAVF surgery, our framework demonstrates superior performance over current state-of-the-art segmentation architectures in intraoperative landmark guidance with high sample efficiency, representing the most advanced AI-enabled surgical inference platform to date. Our future goals include transfer learning paradigms for scaling to additional surgery types, addressing clinical and technical limitations for performing real-time decoding, and ultimate enablement of a real-time neurosurgical guidance platform.
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Affiliation(s)
- Jay J. Park
- Department of Neurosurgery, The Surgical Innovation and Machine Interfacing (SIMI) Lab, Stanford University School of Medicine, Stanford, CA, United States
- Centre for Global Health, Usher Institute, Edinburgh Medical School, The University of Edinburgh, Edinburgh, United Kingdom
| | - Nehal Doiphode
- Department of Neurosurgery, The Surgical Innovation and Machine Interfacing (SIMI) Lab, Stanford University School of Medicine, Stanford, CA, United States
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Xiao Zhang
- Department of Computer Science, University of Chicago, Chicago, IL, United States
| | - Lishuo Pan
- Department of Computer Science, Brown University, Providence, RI, United States
| | - Rachel Blue
- Department of Neurosurgery, Perelman School of Medicine at The University of Pennsylvania, Philadelphia, PA, United States
| | - Jianbo Shi
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Vivek P. Buch
- Department of Neurosurgery, The Surgical Innovation and Machine Interfacing (SIMI) Lab, Stanford University School of Medicine, Stanford, CA, United States
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Akinrinmade AO, Adebile TM, Ezuma-Ebong C, Bolaji K, Ajufo A, Adigun AO, Mohammad M, Dike JC, Okobi OE. Artificial Intelligence in Healthcare: Perception and Reality. Cureus 2023; 15:e45594. [PMID: 37868407 PMCID: PMC10587915 DOI: 10.7759/cureus.45594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/19/2023] [Indexed: 10/24/2023] Open
Abstract
Artificial intelligence (AI) has birthed the new "big thing" in modern medicine. It promises to bring about safer and improved care that will be beneficial to patients and become a helpful tool in the hands of a skilled physician. Despite its anticipation, however, the implementation and usage of AI are still in their elementary phases, particularly due to legal and ethical considerations that border on "data." These challenges should not be brushed aside but rather be recognized and resolved to enable acceptance by all relevant stakeholders without prejudice. Once these challenges can be overcome, AI will truly revolutionize the field of medicine with improved diagnostic accuracy, a reduction in physician burnout, and an enhanced treatment modality. It is therefore paramount that AI be embraced by physicians and integrated into medical education in order to be well-prepared for our role in the future of medicine.
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Affiliation(s)
- Abidemi O Akinrinmade
- Medicine and Surgery, Benjamin S. Carson School of Medicine, Babcock University, Ilishan-Remo, NGA
| | - Temitayo M Adebile
- Public Health, Georgia Southern University, Statesboro, USA
- Nephrology, Boston Medical Center, Malden, USA
| | | | | | | | - Aisha O Adigun
- Infectious Diseases, University of Louisville, Louisville, USA
| | - Majed Mohammad
- Geriatrics, Mount Carmel Grove City Hospital, Grove City, USA
| | - Juliet C Dike
- Internal Medicine, University of Calabar, Calabar, NGA
| | - Okelue E Okobi
- Family Medicine, Larkin Community Hospital Palm Springs Campus, Miami, USA
- Family Medicine, Medficient Health Systems, Laurel, USA
- Family Medicine, Lakeside Medical Center, Belle Glade, USA
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10
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van der Meijden S, Arbous M, Geerts B. Possibilities and challenges for artificial intelligence and machine learning in perioperative care. BJA Educ 2023; 23:288-294. [PMID: 37465235 PMCID: PMC10350557 DOI: 10.1016/j.bjae.2023.04.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2023] [Indexed: 07/20/2023] Open
Affiliation(s)
- S.L. van der Meijden
- Healthplus.ai-R&D B.V., Amsterdam, The Netherlands
- Intensive Care Unit, Leiden University Medical Centre, Leiden, The Netherlands
| | - M.S. Arbous
- Intensive Care Unit, Leiden University Medical Centre, Leiden, The Netherlands
| | - B.F. Geerts
- Healthplus.ai-R&D B.V., Amsterdam, The Netherlands
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11
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Hernández-Aceituno J, Méndez-Pérez JA, González-Cava JM, Reboso-Morales JA. Towards intelligent supervision of operating rooms using stencil-based character recognition. Comput Biol Med 2023; 162:107071. [PMID: 37301096 DOI: 10.1016/j.compbiomed.2023.107071] [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: 02/22/2023] [Revised: 05/04/2023] [Accepted: 05/27/2023] [Indexed: 06/12/2023]
Abstract
The development of intelligent operating rooms is an example of a cyber-physical system resulting from the symbiosis of Industry 4.0 and medicine. A problem with this type of systems is that it requires demanding solutions that allow the real time acquisition of heterogeneous data in an efficient way. The aim of the presented work is the development of a data acquisition system, based on a real-time artificial vision algorithm which can capture information from different clinical monitors. The system was designed for the registration, pre-processing, and communication of clinical data recorded in an operating room. The methods for this proposal are based on a mobile device running a Unity application, which extracts information from clinical monitors and transmits the data to a supervision system through a wireless Bluetooth connection. The software implements a character detection algorithm and allows online correction of identified outliers. The results validate the system with real data obtained during surgical interventions, where only 0.42% values were missed and 0.89% were misread. The outlier detection algorithm was able to correct all the reading errors. In conclusion, the development of a low-cost compact solution to supervise operating rooms in real-time, collecting visual information non-intrusively and communicating data wirelessly, can be a very useful tool to overcome the lack of expensive data recording and processing technology in many clinical situations. The acquisition and pre-processing method presented in this article constitutes a key element towards the development of a cyber-physical system for the development of intelligent operating rooms.
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Affiliation(s)
- Javier Hernández-Aceituno
- Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Avda. Astrofísico Fco. Sánchez s/n, La Laguna, 38204, Canary Islands, Spain.
| | - Juan Albino Méndez-Pérez
- Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Avda. Astrofísico Fco. Sánchez s/n, La Laguna, 38204, Canary Islands, Spain.
| | - José M González-Cava
- Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Avda. Astrofísico Fco. Sánchez s/n, La Laguna, 38204, Canary Islands, Spain.
| | - José Antonio Reboso-Morales
- Hospital Universitario de Canarias, Servicio Canario de Salud, Ctra. Ofra s/n, La Cuesta, 38320, Canary Islands, Spain.
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12
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Kojima S, Kitaguchi D, Igaki T, Nakajima K, Ishikawa Y, Harai Y, Yamada A, Lee Y, Hayashi K, Kosugi N, Hasegawa H, Ito M. Deep-learning-based semantic segmentation of autonomic nerves from laparoscopic images of colorectal surgery: an experimental pilot study. Int J Surg 2023; 109:813-820. [PMID: 36999784 PMCID: PMC10389575 DOI: 10.1097/js9.0000000000000317] [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: 09/26/2022] [Accepted: 02/21/2023] [Indexed: 04/01/2023]
Abstract
BACKGROUND The preservation of autonomic nerves is the most important factor in maintaining genitourinary function in colorectal surgery; however, these nerves are not clearly recognisable, and their identification is strongly affected by the surgical ability. Therefore, this study aimed to develop a deep learning model for the semantic segmentation of autonomic nerves during laparoscopic colorectal surgery and to experimentally verify the model through intraoperative use and pathological examination. MATERIALS AND METHODS The annotation data set comprised videos of laparoscopic colorectal surgery. The images of the hypogastric nerve (HGN) and superior hypogastric plexus (SHP) were manually annotated under a surgeon's supervision. The Dice coefficient was used to quantify the model performance after five-fold cross-validation. The model was used in actual surgeries to compare the recognition timing of the model with that of surgeons, and pathological examination was performed to confirm whether the samples labelled by the model from the colorectal branches of the HGN and SHP were nerves. RESULTS The data set comprised 12 978 video frames of the HGN from 245 videos and 5198 frames of the SHP from 44 videos. The mean (±SD) Dice coefficients of the HGN and SHP were 0.56 (±0.03) and 0.49 (±0.07), respectively. The proposed model was used in 12 surgeries, and it recognised the right HGN earlier than the surgeons did in 50.0% of the cases, the left HGN earlier in 41.7% of the cases and the SHP earlier in 50.0% of the cases. Pathological examination confirmed that all 11 samples were nerve tissue. CONCLUSION An approach for the deep-learning-based semantic segmentation of autonomic nerves was developed and experimentally validated. This model may facilitate intraoperative recognition during laparoscopic colorectal surgery.
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Affiliation(s)
- Shigehiro Kojima
- Surgical Device Innovation
- Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba
- Division of Frontier Surgery, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Daichi Kitaguchi
- Surgical Device Innovation
- Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba
| | - Takahiro Igaki
- Surgical Device Innovation
- Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba
| | - Kei Nakajima
- Surgical Device Innovation
- Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba
| | | | | | | | | | | | | | - Hiro Hasegawa
- Surgical Device Innovation
- Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba
| | - Masaaki Ito
- Surgical Device Innovation
- Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba
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13
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Møller KE, Sørensen JL, Topperzer MK, Koerner C, Ottesen B, Rosendahl M, Grantcharov T, Strandbygaard J. Implementation of an Innovative Technology Called the OR Black Box: A Feasibility Study. Surg Innov 2023; 30:64-72. [PMID: 36112770 PMCID: PMC9925891 DOI: 10.1177/15533506221106258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Introduction. The operating room (OR) Black Box is an innovative technology that captures and compiles extensive real-time data from the OR, allowing identification and analysis of factors that influence intraoperative procedures and performances - ultimately improving patient safety. Implementation of this kind of technology is still an emerging research area and prone to face challenges. Methods. Observational study running from May 2017 to May 2021 conducted at Copenhagen University Hospital - Rigshospitalet, Denmark, involving 152 OR staff and 306 patients. Feasibility of the OR Black Box was assessed in accordance with Bowen's framework with 8 focus areas. Results. The OR Black Box had a high level of acceptability among stakeholders with 100% participation from management, 93% from OR staff, and 98% from patients. The implementation process improved over time, and an average of 80% of the surgeries conducted were captured. The practical aspects such as numerous formal and informal meetings, ethical and legal approval, recruitment of patients were acceptable, albeit time-consuming. The OR Black Box was adopted without any changes in scheduled surgery program, but capturing hours were adjusted to match the surgery program and relocation of OR staff declining to provide consent was possible. Conclusions. Implementation of the OR Black Box was feasible yet challenging. Management, nearly all staff, and patients embraced the initiative; however, ongoing evaluation, information meetings, and commitment from stakeholders are required and crucial to sustain momentum, continue implementation and expansion. Ideas from this study can be useful in the implementation of similar initiatives.
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Affiliation(s)
- Kjestine Emilie Møller
- Department of Gynecology and
Obstetrics, Copenhagen University Hospital –
Rigshospitalet, Copenhagen, Denmark,Kjestine Emilie Møller, Department of
Gynecology and Obstetrics, Juliane Marie Centre, Copenhagen University Hospital
– Rigshospitalet, Copenhagen, Blegdamsvej 9, 2100 Copenhagen, Denmark.
| | - Jette Led Sørensen
- Juliane Marie Centre, Children’s
Hospital Copenhagen, Copenhagen University Hospital –
Rigshospitalet and University of Copenhagen, Copenhagen, Denmark,Department of Clinical Medicine,
Faculty of Health and Medical Sciences, University of
Copenhagen, Copenhagen, Denmark
| | - Martha Krogh Topperzer
- Juliane Marie Centre, Children’s
Hospital Copenhagen, Copenhagen University Hospital –
Rigshospitalet and University of Copenhagen, Copenhagen, Denmark
| | - Christian Koerner
- Department of Improvement and
Digitalization, Copenhagen University Hospital –
Rigshospitalet, Copenhagen, Denmark
| | - Bent Ottesen
- Juliane Marie Centre, Children’s
Hospital Copenhagen, Copenhagen University Hospital –
Rigshospitalet and University of Copenhagen, Copenhagen, Denmark,Department of Clinical Medicine,
Faculty of Health and Medical Sciences, University of
Copenhagen, Copenhagen, Denmark
| | - Mikkel Rosendahl
- Department of Gynecology and
Obstetrics, Copenhagen University Hospital –
Rigshospitalet, Copenhagen, Denmark
| | - Teodor Grantcharov
- Department of General Surgery, University of Toronto, Toronto, ON, Canada,Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, ON, Canada
| | - Jeanett Strandbygaard
- Department of Gynecology and
Obstetrics, Copenhagen University Hospital –
Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine,
Faculty of Health and Medical Sciences, University of
Copenhagen, Copenhagen, Denmark
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14
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Ma S, Li L, Yang C, Liu B, Zhang X, Liao T, Liu S, Jin H, Cai H, Guo T. Advances in the application of robotic surgical systems in gastric cancer: A narrative review. Asian J Surg 2022:S1015-9584(22)01484-1. [PMID: 36334999 DOI: 10.1016/j.asjsur.2022.10.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 09/15/2022] [Accepted: 10/20/2022] [Indexed: 11/21/2022] Open
Abstract
Gastric cancer is one of the common malignant tumors in the gastrointestinal tract, and surgery is currently an important treatment for progressive gastric cancer. With the development of technology, the simultaneous maturation of artificial intelligence (AI), fifth-generation (5G) telecommunication networks and the internet of things (IOT) has brought significant efficacy and new opportunities for the surgical treatment of gastric malignancies. The combination of 5G network and remote surgical robotic system is the future trend of radical gastric cancer surgery, and the "unmanned" treatment mode of fully automated robotic gastric cancer radical surgery will be realized soon.
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15
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Irani CSS, Chu CH. Evolving with technology: Machine learning as an opportunity for operating room nurses to improve surgical care-A commentary. J Nurs Manag 2022; 30:3802-3805. [PMID: 35816560 DOI: 10.1111/jonm.13736] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 07/03/2022] [Indexed: 12/30/2022]
Abstract
AIMS To describe machine learning applications in an operating room setting, raise awareness of the lack of nursing inclusion on machine learning algorithm development, and show how operating room nurses can co-create this new technology. BACKGROUND Operating room nurses and managers perform anticipatory work on a daily basis to manage intrinsic and extrinsic factors that can cause surgical delays. EVALUATION Recent literature on machine learning and its potential use in operating room settings was reviewed along with literature on the role of the nurse in co-creating novel technology. KEY ISSUE Machine learning technology is rapidly evolving and being created for the operating room environment to improve patient safety and flow. Operating room nurses and managers are not being included in the development of machine learning algorithms, meaning products may be created that are not usable for all members of the surgical team. CONCLUSION This commentary highlights the ways machine learning effectively assists nurses and nursing managers, suggesting a pathway forward for surgical nursing as co-creators and implementers. IMPLICATION FOR NURSING MANAGEMENT Nursing managers will be exposed to machine learning programmes in the near future and need to understand the benefits they have for patient safety and patient flow.
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Affiliation(s)
- Cameron S S Irani
- Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada
| | - Charlene H Chu
- Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada.,KITE- Toronto Rehab Institution, University Health Network, Toronto, Ontario, Canada
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16
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Pennestrì F, Banfi G. Artificial intelligence in laboratory medicine: fundamental ethical issues and normative key-points. Clin Chem Lab Med 2022; 60:1867-1874. [PMID: 35413163 DOI: 10.1515/cclm-2022-0096] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/18/2022] [Indexed: 12/15/2022]
Abstract
The contribution of laboratory medicine in delivering value-based care depends on active cooperation and trust between pathologist and clinician. The effectiveness of medicine more in general depends in turn on active cooperation and trust between clinician and patient. From the second half of the 20th century, the art of medicine is challenged by the spread of artificial intelligence (AI) technologies, recently showing comparable performances to flesh-and-bone doctors in some diagnostic specialties. Being the principle source of data in medicine, the laboratory is a natural ground where AI technologies can disclose the best of their potential. In order to maximize the expected outcomes and minimize risks, it is crucial to define ethical requirements for data collection and interpretation by-design, clarify whether they are enhanced or challenged by specific uses of AI technologies, and preserve these data under rigorous but feasible norms. From 2018 onwards, the European Commission (EC) is making efforts to lay the foundations of sustainable AI development among European countries and partners, both from a cultural and a normative perspective. Alongside with the work of the EC, the United Kingdom provided worthy-considering complementary advice in order to put science and technology at the service of patients and doctors. In this paper we discuss the main ethical challenges associated with the use of AI technologies in pathology and laboratory medicine, and summarize the most pertaining key-points from the guidelines and frameworks before-mentioned.
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Affiliation(s)
| | - Giuseppe Banfi
- IRCCS Istituto Ortopedico Galeazzi, Milan, Lombardia, Italy.,Università Vita-Salute San Raffaele, Milan, Italy
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17
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Bellini V, Valente M, Del Rio P, Bignami E. Artificial intelligence in thoracic surgery: a narrative review. J Thorac Dis 2022; 13:6963-6975. [PMID: 35070380 PMCID: PMC8743413 DOI: 10.21037/jtd-21-761] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022]
Abstract
Objective The aim of this article is to review the current applications of artificial intelligence in thoracic surgery, from diagnosis and pulmonary disease management, to preoperative risk-assessment, surgical planning, and outcomes prediction. Background Artificial intelligence implementation in healthcare settings is rapidly growing, though its widespread use in clinical practice is still limited. The employment of machine learning algorithms in thoracic surgery is wide-ranging, including all steps of the clinical pathway. Methods We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases, including all the relevant studies published in the last ten years, until March 2021. Conclusion Machine learning methods are promising encouraging results throughout the key issues of thoracic surgery, both clinical, organizational, and educational. Artificial intelligence-based technologies showed remarkable efficacy to improve the perioperative evaluation of the patient, to assist the decision-making process, to enhance the surgical performance, and to optimize the operating room scheduling. Still, some concern remains about data supply, protection, and transparency, thus further studies and specific consensus guidelines are needed to validate these technologies for daily common practice. Keywords Artificial intelligence (AI); thoracic surgery; machine learning; lung resection; perioperative medicine
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Paolo Del Rio
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
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18
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Uncharted Waters of Machine and Deep Learning for Surgical Phase Recognition in Neurosurgery. World Neurosurg 2022; 160:4-12. [PMID: 35026457 DOI: 10.1016/j.wneu.2022.01.020] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/05/2022] [Accepted: 01/05/2022] [Indexed: 12/20/2022]
Abstract
Recent years have witnessed artificial intelligence (AI) make meteoric leaps in both medicine and surgery, bridging the gap between the capabilities of humans and machines. Digitization of operating rooms and the creation of massive quantities of data have paved the way for machine learning and computer vision applications in surgery. Surgical phase recognition (SPR) is a newly emerging technology that uses data derived from operative videos to train machine and deep learning algorithms to identify the phases of surgery. Advancement of this technology will be key in establishing context-aware surgical systems in the future. By automatically recognizing and evaluating the current surgical scenario, these intelligent systems are able to provide intraoperative decision support, improve operating room efficiency, assess surgical skills, and aid in surgical training and education. Still in its infancy, SPR has been mainly studied in laparoscopic surgeries, with a contrasting stark lack of research within neurosurgery. Given the high-tech and rapidly advancing nature of neurosurgery, we believe SPR has a tremendous untapped potential in this field. Herein, we present an overview of the SPR technology, its potential applications in neurosurgery, and the challenges that lie ahead.
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19
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Mazaheri S, Loya MF, Newsome J, Lungren M, Gichoya JW. Challenges of Implementing Artificial Intelligence in Interventional Radiology. Semin Intervent Radiol 2021; 38:554-559. [PMID: 34853501 DOI: 10.1055/s-0041-1736659] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and "learning" patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most.
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Affiliation(s)
- Sina Mazaheri
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Mohammed F Loya
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Janice Newsome
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia.,Department of Interventional Radiology, Emory University School of Medicine, Atlanta, Georgia
| | - Mathew Lungren
- LPCH Pediatric Interventional Radiology, Stanford University, Stanford, California
| | - Judy Wawira Gichoya
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
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20
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Gumbs AA, Frigerio I, Spolverato G, Croner R, Illanes A, Chouillard E, Elyan E. Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery? SENSORS (BASEL, SWITZERLAND) 2021; 21:5526. [PMID: 34450976 PMCID: PMC8400539 DOI: 10.3390/s21165526] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/03/2021] [Accepted: 08/11/2021] [Indexed: 12/30/2022]
Abstract
Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.
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Affiliation(s)
- Andrew A. Gumbs
- Centre Hospitalier Intercommunal de POISSY/SAINT-GERMAIN-EN-LAYE 10, Rue Champ de Gaillard, 78300 Poissy, France;
| | - Isabella Frigerio
- Department of Hepato-Pancreato-Biliary Surgery, Pederzoli Hospital, 37019 Peschiera del Garda, Italy;
| | - Gaya Spolverato
- Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, 35122 Padova, Italy;
| | - Roland Croner
- Department of General-, Visceral-, Vascular- and Transplantation Surgery, University of Magdeburg, Haus 60a, Leipziger Str. 44, 39120 Magdeburg, Germany;
| | - Alfredo Illanes
- INKA–Innovation Laboratory for Image Guided Therapy, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany;
| | - Elie Chouillard
- Centre Hospitalier Intercommunal de POISSY/SAINT-GERMAIN-EN-LAYE 10, Rue Champ de Gaillard, 78300 Poissy, France;
| | - Eyad Elyan
- School of Computing, Robert Gordon University, Aberdeen AB10 7JG, UK;
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21
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Condello I, Santarpino G, Nasso G, Moscarelli M, Fiore F, Speziale G. Management algorithms and artificial intelligence systems for cardiopulmonary bypass. Perfusion 2021; 37:765-772. [PMID: 34250858 DOI: 10.1177/02676591211030762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This article introduces management algorithms to support operators in choosing the best strategy for metabolic management during cardiopulmonary bypass using artificial intelligence systems. We developed algorithms for the identification of the optimal way for assessing metabolic parameters. Different management algorithms for extracorporeal procedures interfaced with metabolic monitoring systems already exist on the market and are applied in clinical practice. These algorithms could provide guidance for selecting the best metabolic strategy with the aim at reducing human error and optimizing management.
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Affiliation(s)
- Ignazio Condello
- Department of Cardiac Surgery, Anthea Hospital - GVM Care & Research, Bari, Italy
| | - Giuseppe Santarpino
- Department of Cardiac Surgery, Anthea Hospital - GVM Care & Research, Bari, Italy.,Department of Cardiac Surgery, Paracelsus Medical University, Nuremberg, Germany.,Cardiac Surgery Unit, Department of Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, Catanzaro, Italy
| | - Giuseppe Nasso
- Department of Cardiac Surgery, Anthea Hospital - GVM Care & Research, Bari, Italy
| | - Marco Moscarelli
- Department of Cardiac Surgery, Anthea Hospital - GVM Care & Research, Bari, Italy
| | - Flavio Fiore
- Department of Cardiac Surgery, Anthea Hospital - GVM Care & Research, Bari, Italy
| | - Giuseppe Speziale
- Department of Cardiac Surgery, Anthea Hospital - GVM Care & Research, Bari, Italy
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