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Andersen AG, Riparbelli AC, Siebner HR, Konge L, Bjerrum F. Using neuroimaging to assess brain activity and areas associated with surgical skills: a systematic review. Surg Endosc 2024; 38:3004-3026. [PMID: 38653901 DOI: 10.1007/s00464-024-10830-x] [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: 01/02/2024] [Accepted: 03/24/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Surgical skills acquisition is under continuous development due to the emergence of new technologies, and there is a need for assessment tools to develop along with these. A range of neuroimaging modalities has been used to map the functional activation of brain networks while surgeons acquire novel surgical skills. These have been proposed as a method to provide a deeper understanding of surgical expertise and offer new possibilities for the personalized training of future surgeons. With studies differing in modalities, outcomes, and surgical skills there is a need for a systematic review of the evidence. This systematic review aims to summarize the current knowledge on the topic and evaluate the potential use of neuroimaging in surgical education. METHODS We conducted a systematic review of neuroimaging studies that mapped functional brain activation while surgeons with different levels of expertise learned and performed technical and non-technical surgical tasks. We included all studies published before July 1st, 2023, in MEDLINE, EMBASE and WEB OF SCIENCE. RESULTS 38 task-based brain mapping studies were identified, consisting of randomized controlled trials, case-control studies, and observational cohort or cross-sectional studies. The studies employed a wide range of brain mapping modalities, including electroencephalography, functional magnetic resonance imaging, positron emission tomography, and functional near-infrared spectroscopy, activating brain areas involved in the execution and sensorimotor or cognitive control of surgical skills, especially the prefrontal cortex, supplementary motor area, and primary motor area, showing significant changes between novices and experts. CONCLUSION Functional neuroimaging can reveal how task-related brain activity reflects technical and non-technical surgical skills. The existing body of work highlights the potential of neuroimaging to link task-related brain activity patterns with the individual level of competency or improvement in performance after training surgical skills. More research is needed to establish its validity and usefulness as an assessment tool.
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Affiliation(s)
- Annarita Ghosh Andersen
- Copenhagen Academy for Medical Education and Simulation (CAMES), Center for Human Resources and Education, The Capital Region of Denmark, Ryesgade 53B, 2100, Copenhagen, Denmark.
- Department of Cardiothoracic Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
| | - Agnes Cordelia Riparbelli
- Copenhagen Academy for Medical Education and Simulation (CAMES), Center for Human Resources and Education, The Capital Region of Denmark, Ryesgade 53B, 2100, Copenhagen, Denmark
| | - Hartwig Roman Siebner
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark
- Department of Neurology, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Lars Konge
- Copenhagen Academy for Medical Education and Simulation (CAMES), Center for Human Resources and Education, The Capital Region of Denmark, Ryesgade 53B, 2100, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Flemming Bjerrum
- Copenhagen Academy for Medical Education and Simulation (CAMES), Center for Human Resources and Education, The Capital Region of Denmark, Ryesgade 53B, 2100, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Gastrounit, Surgical Section, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark
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Kamat A, Eastmond C, Gao Y, Nemani A, Yanik E, Cavuoto L, Hackett M, Norfleet J, Schwaitzberg S, De S, Intes X. Assessment of Surgical Tasks Using Neuroimaging Dataset (ASTaUND). Sci Data 2023; 10:699. [PMID: 37838752 PMCID: PMC10576768 DOI: 10.1038/s41597-023-02603-3] [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: 02/16/2023] [Accepted: 09/28/2023] [Indexed: 10/16/2023] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool for studying brain activity in mobile subjects. Open-access fNIRS datasets are limited to simple and/or motion-restricted tasks. Here, we report a fNIRS dataset acquired on mobile subjects performing Fundamentals of Laparoscopic Surgery (FLS) tasks in a laboratory environment. Demonstrating competency in the FLS tasks is a prerequisite for board certification in general surgery in the United States. The ASTaUND data set was acquired over four different studies. We provide the relevant information about the hardware, FLS task execution protocols, and subject demographics to facilitate the use of this open-access data set. We also provide the concurrent FLS scores, a quantitative metric for surgical skill assessment developed by the FLS committee. This data set is expected to support the growing field of assessing surgical skills via neuroimaging data and provide an example of data processing pipeline for use in realistic, non-restrictive environments.
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Affiliation(s)
- Anil Kamat
- Center for Modeling, Simulation, and Imaging for Medicine, Rensselaer Polytechnic Institute, Troy, New York, 12180, USA.
| | - Condell Eastmond
- Center for Modeling, Simulation, and Imaging for Medicine, Rensselaer Polytechnic Institute, Troy, New York, 12180, USA.
| | - Yuanyuan Gao
- Boston University Neurophotonics Center, Boston, Massachusetts, 02215, USA
| | - Arun Nemani
- Center for Modeling, Simulation, and Imaging for Medicine, Rensselaer Polytechnic Institute, Troy, New York, 12180, USA
| | - Erim Yanik
- Florida A&M University-Florida State University College of Engineering, Tallahassee, FL, 32310, USA
| | - Lora Cavuoto
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, 14260, USA
| | - Matthew Hackett
- University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY, 14260, USA
| | - Jack Norfleet
- University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY, 14260, USA
| | - Steven Schwaitzberg
- U.S. Army Combat Capabilities Development Command - Soldier Center (CCDC SC), Orlando, FL, USA
| | - Suvranu De
- Florida A&M University-Florida State University College of Engineering, Tallahassee, FL, 32310, USA
| | - Xavier Intes
- Center for Modeling, Simulation, and Imaging for Medicine, Rensselaer Polytechnic Institute, Troy, New York, 12180, USA
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Liu Z, Bible J, Petersen L, Zhang Z, Roy-Chaudhury P, Singapogu R. Relating process and outcome metrics for meaningful and interpretable cannulation skill assessment: A machine learning paradigm. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107429. [PMID: 37119772 PMCID: PMC10291517 DOI: 10.1016/j.cmpb.2023.107429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 02/06/2023] [Accepted: 02/15/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND AND OBJECTIVES The quality of healthcare delivery depends directly on the skills of clinicians. For patients on hemodialysis, medical errors or injuries caused during cannulation can lead to adverse outcomes, including potential death. To promote objective skill assessment and effective training, we present a machine learning approach, which utilizes a highly-sensorized cannulation simulator and a set of objective process and outcome metrics. METHODS In this study, 52 clinicians were recruited to perform a set of pre-defined cannulation tasks on the simulator. Based on data collected by sensors during their task performance, the feature space was then constructed based on force, motion, and infrared sensor data. Following this, three machine learning models- support vector machine (SVM), support vector regression (SVR), and elastic net (EN)- were constructed to relate the feature space to objective outcome metrics. Our models utilize classification based on the conventional skill classification labels as well as a new method that represents skill on a continuum. RESULTS With less than 5% of trials misplaced by two classes, the SVM model was effective in predicting skill based on the feature space. In addition, the SVR model effectively places both skill and outcome on a fine-grained continuum (versus discrete divisions) that is representative of reality. As importantly, the elastic net model enabled the identification of a set of process metrics that highly impact outcomes of the cannulation task, including smoothness of motion, needle angles, and pinch forces. CONCLUSIONS The proposed cannulation simulator, paired with machine learning assessment, demonstrates definite advantages over current cannulation training practices. The methods presented here can be adopted to drastically increase the effectiveness of skill assessment and training, thereby potentially improving clinical outcomes of hemodialysis treatment.
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Affiliation(s)
- Zhanhe Liu
- Department of Bioengineering, Clemson University, 301 Rhodes Research Center, Clemson, 29634, SC, USA
| | - Joe Bible
- School of Mathematical and Statistical Sciences, Clemson University, O-110 Martin Hall, Clemson, 29634, SC, USA
| | - Lydia Petersen
- Department of Bioengineering, Clemson University, 301 Rhodes Research Center, Clemson, 29634, SC, USA
| | - Ziyang Zhang
- Department of Bioengineering, Clemson University, 301 Rhodes Research Center, Clemson, 29634, SC, USA
| | - Prabir Roy-Chaudhury
- UNC Kidney Center, University of North Carolina, Chapel Hill, NC, 28144, USA; (Bill Hefner) VA Medical Center, Salisbury, NC, 28144, USA
| | - Ravikiran Singapogu
- Department of Bioengineering, Clemson University, 301 Rhodes Research Center, Clemson, 29634, SC, USA.
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Goble M, Caddick V, Patel R, Modi H, Darzi A, Orihuela-Espina F, Leff DR. Optical neuroimaging and neurostimulation in surgical training and assessment: A state-of-the-art review. FRONTIERS IN NEUROERGONOMICS 2023; 4:1142182. [PMID: 38234498 PMCID: PMC10790870 DOI: 10.3389/fnrgo.2023.1142182] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 03/03/2023] [Indexed: 01/19/2024]
Abstract
Introduction Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical neuroimaging technique used to assess surgeons' brain function. The aim of this narrative review is to outline the effect of expertise, stress, surgical technology, and neurostimulation on surgeons' neural activation patterns, and highlight key progress areas required in surgical neuroergonomics to modulate training and performance. Methods A literature search of PubMed and Embase was conducted to identify neuroimaging studies using fNIRS and neurostimulation in surgeons performing simulated tasks. Results Novice surgeons exhibit greater haemodynamic responses across the pre-frontal cortex than experts during simple surgical tasks, whilst expert surgical performance is characterized by relative prefrontal attenuation and upregulation of activation foci across other regions such as the supplementary motor area. The association between PFC activation and mental workload follows an inverted-U shaped curve, activation increasing then attenuating past a critical inflection point at which demands outstrip cognitive capacity Neuroimages are sensitive to the impact of laparoscopic and robotic tools on cognitive workload, helping inform the development of training programs which target neural learning curves. FNIRS differs in comparison to current tools to assess proficiency by depicting a cognitive state during surgery, enabling the development of cognitive benchmarks of expertise. Finally, neurostimulation using transcranial direct-current-stimulation may accelerate skill acquisition and enhance technical performance. Conclusion FNIRS can inform the development of surgical training programs which modulate stress responses, cognitive learning curves, and motor skill performance. Improved data processing with machine learning offers the possibility of live feedback regarding surgeons' cognitive states during operative procedures.
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Affiliation(s)
- Mary Goble
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
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Kamat A, Makled B, Norfleet J, Schwaitzberg SD, Intes X, De S, Dutta A. Directed information flow during laparoscopic surgical skill acquisition dissociated skill level and medical simulation technology. NPJ SCIENCE OF LEARNING 2022; 7:19. [PMID: 36008451 PMCID: PMC9411170 DOI: 10.1038/s41539-022-00138-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 08/04/2022] [Indexed: 05/11/2023]
Abstract
Virtual reality (VR) simulator has emerged as a laparoscopic surgical skill training tool that needs validation using brain-behavior analysis. Therefore, brain network and skilled behavior relationship were evaluated using functional near-infrared spectroscopy (fNIRS) from seven experienced right-handed surgeons and six right-handed medical students during the performance of Fundamentals of Laparoscopic Surgery (FLS) pattern of cutting tasks in a physical and a VR simulator. Multiple regression and path analysis (MRPA) found that the FLS performance score was statistically significantly related to the interregional directed functional connectivity from the right prefrontal cortex to the supplementary motor area with F (2, 114) = 9, p < 0.001, and R2 = 0.136. Additionally, a two-way multivariate analysis of variance (MANOVA) found a statistically significant effect of the simulator technology on the interregional directed functional connectivity from the right prefrontal cortex to the left primary motor cortex (F (1, 15) = 6.002, p = 0.027; partial η2 = 0.286) that can be related to differential right-lateralized executive control of attention. Then, MRPA found that the coefficient of variation (CoV) of the FLS performance score was statistically significantly associated with the CoV of the interregionally directed functional connectivity from the right primary motor cortex to the left primary motor cortex and the left primary motor cortex to the left prefrontal cortex with F (2, 22) = 3.912, p = 0.035, and R2 = 0.262. This highlighted the importance of the efference copy information from the motor cortices to the prefrontal cortex for postulated left-lateralized perceptual decision-making to reduce behavioral variability.
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Affiliation(s)
- Anil Kamat
- Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Basiel Makled
- US Army Futures Command, Combat Capabilities Development Command Soldier Center STTC, Orlando, FL, USA
| | - Jack Norfleet
- US Army Futures Command, Combat Capabilities Development Command Soldier Center STTC, Orlando, FL, USA
| | | | - Xavier Intes
- Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, USA
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Suvranu De
- Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, USA
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Anirban Dutta
- Neuroengineering and Informatics for Rehabilitation Laboratory, Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, USA.
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Kamat A, Makled B, Norfleet J, Intes X, Dutta A, De S. Brain network effects related to physical and virtual surgical training revealed by Granger causality. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1014-1017. [PMID: 34891460 DOI: 10.1109/embc46164.2021.9629680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
this study investigates the difference in effective connectivity among novice medical students trained on physical and virtual simulators to perform the Fundamental laparoscopic surgery (FLS) pattern cutting task (PC). We propose using dynamic spectral Granger causality (GC) in the frequency band of [0.01-0.07]Hz to measure the effect of surgical training on effective brain connectivity. To obtain the dynamics relationship between the cortical regions, we propose to use the short-time Fourier transform (STFT) method. FLS pattern cutting is a complex bimanual task requiring fine motor skills and increased brain activity. With this in mind, we have used high resolution functional near-infrared spectroscopy to leverage its high temporal resolution for capturing the change in hemodynamics (HbO2) in 14 healthy subjects. Analysis of variance (ANOVA) found a statistically significant difference in "LPMC granger causes RPMC" (LPMC→ RPMC) in the subject trained on these two simulator in the first 40 sec of the task. We showed that the directed brain connectivity was affected by the type of surgical simulator used for training the medical students.
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Walia P, Kumar KN, Dutta A. Neuroimaging Guided Transcranial Electrical Stimulation in Enhancing Surgical Skill Acquisition. Comment on Hung et al. The Efficacy of Transcranial Direct Current Stimulation in Enhancing Surgical Skill Acquisition: A Preliminary Meta-Analysis of Randomized Controlled Trials. Brain Sci. 2021, 11, 707. Brain Sci 2021; 11:1078. [PMID: 34439698 PMCID: PMC8395024 DOI: 10.3390/brainsci11081078] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 08/02/2021] [Indexed: 01/02/2023] Open
Abstract
Surgical skill acquisition may be facilitated with a safe application of transcranial direct current stimulation (tDCS). A preliminary meta-analysis of randomized control trials showed that tDCS was associated with significantly better improvement in surgical performance than the sham control; however, meta-analysis does not address the mechanistic understanding. It is known from skill learning studies that the hierarchy of cognitive control shows a rostrocaudal axis in the frontal lobe where a shift from posterior to anterior is postulated to mediate progressively abstract, higher-order control. Therefore, optimizing the transcranial electrical stimulation to target surgical task-related brain activation at different stages of motor learning may provide the causal link to the learning behavior. This comment paper presents the computational approach for neuroimaging guided tDCS based on open-source software pipelines and an open-data of functional near-infrared spectroscopy (fNIRS) for complex motor tasks. We performed an fNIRS-based cortical activation analysis using AtlasViewer software that was used as the target for tDCS of the motor complexity-related brain regions using ROAST software. For future studies on surgical skill training, it is postulated that the higher complexity laparoscopic suturing with intracorporeal knot tying task may result in more robust activation of the motor complexity-related brain areas when compared to the lower complexity laparoscopic tasks.
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Affiliation(s)
- Pushpinder Walia
- Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY 14260, USA;
| | - Kavya Narendra Kumar
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260, USA;
| | - Anirban Dutta
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260, USA;
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