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Liu X, Xiao W, Yang Y, Yan Y, Liang F. Augmented reality technology shortens aneurysm surgery learning curve for residents. Comput Assist Surg (Abingdon) 2024; 29:2311940. [PMID: 38315080 DOI: 10.1080/24699322.2024.2311940] [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: 02/07/2024] Open
Abstract
OBJECTIVES We aimed to prospectively investigate the benefit of using augmented reality (AR) for surgery residents learning aneurysm surgery. MATERIALS AND METHODS Eight residents were included, and divided into an AR group and a control group (4 in each group). Both groups were asked to locate an aneurysm with a blue circle on the same screenshot after their viewing of surgery videos from both AR and non-AR tests. Only the AR group was allowed to inspect and manipulate an AR holographic representation of the aneurysm in AR tests. The actual location of the aneurysm was defined by a yellow circle by an attending physician after each test. Localization deviation was determined by the distance between the blue and yellow circle. RESULTS Localization deviation was lower in the AR group than in the control group in the last 2 tests (AR Test 2: 2.7 ± 1.0 mm vs. 5.8 ± 4.1 mm, p = 0.01, non-AR Test 2: 2.1 ± 0.8 mm vs. 5.9 ± 5.8 mm, p < 0.001). The mean deviation was lower in non-AR Test 2 as compared to non-AR Test 1 in both groups (AR: p < 0.001, control: p = 0.391). The localization deviation of the AR group decreased from 8.1 ± 3.8 mm in Test 2 to 2.7 ± 1.0 mm in AR Test 2 (p < 0.001). CONCLUSION AR technology provides an effective and interactive way for neurosurgery training, and shortens the learning curve for residents in aneurysm surgery.
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Affiliation(s)
- Xinman Liu
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Weiping Xiao
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Yibing Yang
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Yan Yan
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Feng Liang
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
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Hee Koh C, Khawari S, Booker J, Choi D, Z Khan D, Layard Horsfall H, Sayal P, J Marcus H, Prezerakos G. Validation of a surgical simulator and establishment of quantitative performance thresholds - RealSpine simulation system for open lumbar decompressions. Spine J 2024:S1529-9430(24)00945-8. [PMID: 39173915 DOI: 10.1016/j.spinee.2024.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 08/09/2024] [Accepted: 08/11/2024] [Indexed: 08/24/2024]
Abstract
BACKGROUND CONTEXT The majority of surgical training is conducted in real-world operations. High-fidelity surgical simulators may provide a safer environment for surgical training. However, the extent that it reflects real-world operations and surgical ability is often poorly characterized. PURPOSE (1) Assess the validity and fidelity of a surgical simulator; (2) Examine the quantitative relationship between simulation performance and markers of real-world ability; (3) Establish thresholds for surgical expertise, and estimate their external validity and accuracy. STUDY DESIGN/SETTING A cohort study of surgeons at a British neurosurgical center. STUDY SAMPLE 10 early-career "novice" surgeons and 8 board-certified "expert" neurosurgeons. OUTCOMES MEASURES (1) Face and content validity, and visual and haptic fidelity; (2) Construct validity; (3) Predictive and discriminative utility of quantitative performance thresholds. METHODS Participants performed unilateral lumbar decompressions on high-fidelity spinal simulators that replicate the bony and soft tissue anatomy along with physiological processes such as bleeding and CSF leaks. Operating times, measured from first surgical action to either self-perceived satisfactory decompression or the end of allocated time, were recorded. The performance was also assessed independently by two blinded spinal subspecialist neurosurgeons using OSATS, a validated surgical assessment tool that utilizes five-point scales on a variety of technical domains to grade the overall technical proficiency. Validity and fidelity were assessed by expert neurosurgeons using quantitative questionnaires. Construct validity was assessed by ordinal regression of simulation performance against real-world surgical grade and portfolio. Thresholds of expert status by simulation performance was established, and their predictive and discriminative utility assessed by crossvalidation accuracy and AUC-ROC. RESULTS Operating time and expert assessments of simulation performance (OSATS) were strong and significant predictors of surrogate markers of real-world surgical ability. The thresholds for expert status were operating time of 15 minutes and modified OSATS score of 15/20. These thresholds predicted expert status with 84.2% and 71.4% accuracy respectively. Strong discriminative ability was demonstrated by AUC-ROC of 0.95 and 0.83 respectively. All expert surgeons agreed that RealSpine simulators demonstrate high face validity, and high visual and haptic fidelity, with overall scores showing statistically significant agreement on these items (all scores at least 4/5, P<.0001). There was less consensus on content validity, but with still significant overall agreement (average score: 3.75/5, P=0.023). CONCLUSIONS Real-world surgical ability and experience can be accurately predicted by defining objective quantitative thresholds on high-fidelity simulations. The thresholds established here, along with other data presented in this paper, may inform objectives and standards to be established in a spinal surgical training curriculum.
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Affiliation(s)
- Chan Hee Koh
- Queen Square Institute of Neurology, University College London, London, United Kingdom; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Neurosciences Department, Cleveland Clinic London, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.
| | - Sogha Khawari
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - James Booker
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - David Choi
- Queen Square Institute of Neurology, University College London, London, United Kingdom; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Danyal Z Khan
- Queen Square Institute of Neurology, University College London, London, United Kingdom; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Hugo Layard Horsfall
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Parag Sayal
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Hani J Marcus
- Queen Square Institute of Neurology, University College London, London, United Kingdom; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - George Prezerakos
- Queen Square Institute of Neurology, University College London, London, United Kingdom; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom; Neurosciences Department, Cleveland Clinic London, London, United Kingdom
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Xu J, Anastasiou D, Booker J, Burton OE, Layard Horsfall H, Salvadores Fernandez C, Xue Y, Stoyanov D, Tiwari MK, Marcus HJ, Mazomenos EB. A Deep Learning Approach to Classify Surgical Skill in Microsurgery Using Force Data from a Novel Sensorised Surgical Glove. SENSORS (BASEL, SWITZERLAND) 2023; 23:8947. [PMID: 37960645 PMCID: PMC10650455 DOI: 10.3390/s23218947] [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: 09/30/2023] [Revised: 10/26/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023]
Abstract
Microsurgery serves as the foundation for numerous operative procedures. Given its highly technical nature, the assessment of surgical skill becomes an essential component of clinical practice and microsurgery education. The interaction forces between surgical tools and tissues play a pivotal role in surgical success, making them a valuable indicator of surgical skill. In this study, we employ six distinct deep learning architectures (LSTM, GRU, Bi-LSTM, CLDNN, TCN, Transformer) specifically designed for the classification of surgical skill levels. We use force data obtained from a novel sensorized surgical glove utilized during a microsurgical task. To enhance the performance of our models, we propose six data augmentation techniques. The proposed frameworks are accompanied by a comprehensive analysis, both quantitative and qualitative, including experiments conducted with two cross-validation schemes and interpretable visualizations of the network's decision-making process. Our experimental results show that CLDNN and TCN are the top-performing models, achieving impressive accuracy rates of 96.16% and 97.45%, respectively. This not only underscores the effectiveness of our proposed architectures, but also serves as compelling evidence that the force data obtained through the sensorized surgical glove contains valuable information regarding surgical skill.
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Affiliation(s)
- Jialang Xu
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK; (J.X.); (D.A.); (J.B.); (O.E.B.); (H.L.H.); (C.S.F.); (Y.X.); (D.S.); (M.K.T.); (H.J.M.)
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - Dimitrios Anastasiou
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK; (J.X.); (D.A.); (J.B.); (O.E.B.); (H.L.H.); (C.S.F.); (Y.X.); (D.S.); (M.K.T.); (H.J.M.)
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - James Booker
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK; (J.X.); (D.A.); (J.B.); (O.E.B.); (H.L.H.); (C.S.F.); (Y.X.); (D.S.); (M.K.T.); (H.J.M.)
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
| | - Oliver E. Burton
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK; (J.X.); (D.A.); (J.B.); (O.E.B.); (H.L.H.); (C.S.F.); (Y.X.); (D.S.); (M.K.T.); (H.J.M.)
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
| | - Hugo Layard Horsfall
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK; (J.X.); (D.A.); (J.B.); (O.E.B.); (H.L.H.); (C.S.F.); (Y.X.); (D.S.); (M.K.T.); (H.J.M.)
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
| | - Carmen Salvadores Fernandez
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK; (J.X.); (D.A.); (J.B.); (O.E.B.); (H.L.H.); (C.S.F.); (Y.X.); (D.S.); (M.K.T.); (H.J.M.)
- Nanoengineered Systems Laboratory, UCL Mechanical Engineering, University College London, London WC1E 7JE, UK
| | - Yang Xue
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK; (J.X.); (D.A.); (J.B.); (O.E.B.); (H.L.H.); (C.S.F.); (Y.X.); (D.S.); (M.K.T.); (H.J.M.)
- Nanoengineered Systems Laboratory, UCL Mechanical Engineering, University College London, London WC1E 7JE, UK
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK; (J.X.); (D.A.); (J.B.); (O.E.B.); (H.L.H.); (C.S.F.); (Y.X.); (D.S.); (M.K.T.); (H.J.M.)
- Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Manish K. Tiwari
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK; (J.X.); (D.A.); (J.B.); (O.E.B.); (H.L.H.); (C.S.F.); (Y.X.); (D.S.); (M.K.T.); (H.J.M.)
- Nanoengineered Systems Laboratory, UCL Mechanical Engineering, University College London, London WC1E 7JE, UK
| | - Hani J. Marcus
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK; (J.X.); (D.A.); (J.B.); (O.E.B.); (H.L.H.); (C.S.F.); (Y.X.); (D.S.); (M.K.T.); (H.J.M.)
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
| | - Evangelos B. Mazomenos
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK; (J.X.); (D.A.); (J.B.); (O.E.B.); (H.L.H.); (C.S.F.); (Y.X.); (D.S.); (M.K.T.); (H.J.M.)
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
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Ahmed R, Muirhead W, Williams SC, Bagchi B, Datta P, Gupta P, Salvadores Fernandez C, Funnell JP, Hanrahan JG, Davids JD, Grover P, Tiwari MK, Murphy M, Marcus HJ. A synthetic model simulator for intracranial aneurysm clipping: validation of the UpSurgeOn AneurysmBox. Front Surg 2023; 10:1185516. [PMID: 37325417 PMCID: PMC10264641 DOI: 10.3389/fsurg.2023.1185516] [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: 03/13/2023] [Accepted: 05/17/2023] [Indexed: 06/17/2023] Open
Abstract
Background and objectives In recent decades, the rise of endovascular management of aneurysms has led to a significant decline in operative training for surgical aneurysm clipping. Simulation has the potential to bridge this gap and benchtop synthetic simulators aim to combine the best of both anatomical realism and haptic feedback. The aim of this study was to validate a synthetic benchtop simulator for aneurysm clipping (AneurysmBox, UpSurgeOn). Methods Expert and novice surgeons from multiple neurosurgical centres were asked to clip a terminal internal carotid artery aneurysm using the AneurysmBox. Face and content validity were evaluated using Likert scales by asking experts to complete a post-task questionnaire. Construct validity was evaluated by comparing expert and novice performance using the modified Objective Structured Assessment of Technical Skills (mOSATS), developing a curriculum-derived assessment of Specific Technical Skills (STS), and measuring the forces exerted using a force-sensitive glove. Results Ten experts and eighteen novices completed the task. Most experts agreed that the brain looked realistic (8/10), but far fewer agreed that the brain felt realistic (2/10). Half the expert participants (5/10) agreed that the aneurysm clip application task was realistic. When compared to novices, experts had a significantly higher median mOSATS (27 vs. 14.5; p < 0.01) and STS score (18 vs. 9; p < 0.01); the STS score was strongly correlated with the previously validated mOSATS score (p < 0.01). Overall, there was a trend towards experts exerting a lower median force than novices, however, these differences were not statistically significant (3.8 N vs. 4.0 N; p = 0.77). Suggested improvements for the model included reduced stiffness and the addition of cerebrospinal fluid (CSF) and arachnoid mater. Conclusion At present, the AneurysmBox has equivocal face and content validity, and future versions may benefit from materials that allow for improved haptic feedback. Nonetheless, it has good construct validity, suggesting it is a promising adjunct to training.
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Affiliation(s)
- Razna Ahmed
- Queen Square Institute of Neurology, University College London, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
| | - William Muirhead
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Simon C. Williams
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Biswajoy Bagchi
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
- Nanoengineered Systems Laboratory, Department of Mechanical Engineering, University College London, London, United Kingdom
| | - Priyankan Datta
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
- Nanoengineered Systems Laboratory, Department of Mechanical Engineering, University College London, London, United Kingdom
| | - Priya Gupta
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
- Nanoengineered Systems Laboratory, Department of Mechanical Engineering, University College London, London, United Kingdom
| | - Carmen Salvadores Fernandez
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
- Nanoengineered Systems Laboratory, Department of Mechanical Engineering, University College London, London, United Kingdom
| | - Jonathan P. Funnell
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - John G. Hanrahan
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Joseph D. Davids
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
- Institute of Global Health Innovation and Hamlyn Centre for Robotics Surgery, Imperial College London, London, United Kingdom
| | - Patrick Grover
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Manish K. Tiwari
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
- Nanoengineered Systems Laboratory, Department of Mechanical Engineering, University College London, London, United Kingdom
| | - Mary Murphy
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Hani J. Marcus
- Queen Square Institute of Neurology, University College London, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
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