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Liu Z, Hitchcock DB, Singapogu RB. Cannulation Skill Assessment Using Functional Data Analysis. IEEE J Biomed Health Inform 2023; 27:4512-4523. [PMID: 37310836 PMCID: PMC10519736 DOI: 10.1109/jbhi.2023.3283188] [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] [Indexed: 06/15/2023]
Abstract
OBJECTIVE A clinician's operative skill-the ability to safely and effectively perform a procedure-directly impacts patient outcomes and well-being. Therefore, it is necessary to accurately assess skill progression during medical training as well as develop methods to most efficiently train healthcare professionals. METHODS In this study, we explore whether time-series needle angle data recorded during cannulation on a simulator can be analyzed using functional data analysis methods to (1) identify skilled versus unskilled performance and (2) relate angle profiles to degree of success of the procedure. RESULTS Our methods successfully differentiated between types of needle angle profiles. In addition, the identified profile types were associated with degrees of skilled and unskilled behavior of subjects. Furthermore, the types of variability in the dataset were analyzed, providing particular insight into the overall range of needle angles used as well as the rate of change of angle as cannulation progressed in time. Finally, cannulation angle profiles also demonstrated an observable correlation with degree of cannulation success, a metric that is closely related to clinical outcome. CONCLUSION In summary, the methods presented here enable rich assessment of clinical skill since the functional (i.e., dynamic) nature of the data is duly considered.
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Bilgic E, Gorgy A, Yang A, Cwintal M, Ranjbar H, Kahla K, Reddy D, Li K, Ozturk H, Zimmermann E, Quaiattini A, Abbasgholizadeh-Rahimi S, Poenaru D, Harley JM. Exploring the roles of artificial intelligence in surgical education: A scoping review. Am J Surg 2021; 224:205-216. [PMID: 34865736 DOI: 10.1016/j.amjsurg.2021.11.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Technology-enhanced teaching and learning, including Artificial Intelligence (AI) applications, has started to evolve in surgical education. Hence, the purpose of this scoping review is to explore the current and future roles of AI in surgical education. METHODS Nine bibliographic databases were searched from January 2010 to January 2021. Full-text articles were included if they focused on AI in surgical education. RESULTS Out of 14,008 unique sources of evidence, 93 were included. Out of 93, 84 were conducted in the simulation setting, and 89 targeted technical skills. Fifty-six studies focused on skills assessment/classification, and 36 used multiple AI techniques. Also, increasing sample size, having balanced data, and using AI to provide feedback were major future directions mentioned by authors. CONCLUSIONS AI can help optimize the education of trainees and our results can help educators and researchers identify areas that need further investigation.
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Affiliation(s)
- Elif Bilgic
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Andrew Gorgy
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Alison Yang
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Michelle Cwintal
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Hamed Ranjbar
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Kalin Kahla
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Dheeksha Reddy
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Kexin Li
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Helin Ozturk
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Eric Zimmermann
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Andrea Quaiattini
- Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Canada; Institute of Health Sciences Education, McGill University, Montreal, Quebec, Canada
| | - Samira Abbasgholizadeh-Rahimi
- Department of Family Medicine, McGill University, Montreal, Quebec, Canada; Department of Electrical and Computer Engineering, McGill University, Montreal, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada; Mila Quebec AI Institute, Montreal, Canada
| | - Dan Poenaru
- Institute of Health Sciences Education, McGill University, Montreal, Quebec, Canada; Department of Pediatric Surgery, McGill University, Canada
| | - Jason M Harley
- Department of Surgery, McGill University, Montreal, Quebec, Canada; Institute of Health Sciences Education, McGill University, Montreal, Quebec, Canada; Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Steinberg Centre for Simulation and Interactive Learning, McGill University, Montreal, Quebec, Canada.
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Is Experience in Hemodialysis Cannulation Related to Expertise? A Metrics-based Investigation for Skills Assessment. Ann Biomed Eng 2021; 49:1688-1700. [PMID: 33417054 DOI: 10.1007/s10439-020-02708-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 12/08/2020] [Indexed: 12/19/2022]
Abstract
Cannulation is not only one of the most common medical procedures but also fraught with complications. The skill of the clinician performing cannulation directly impacts cannulation outcomes. However, current methods of teaching this skill are deficient, relying on subjective demonstrations and unrealistic manikins that have limited utility for skills training. Furthermore, of the factors that hinders effective continuing medical education is the assumption that clinical experience results in expertise. In this work, we examine if objective metrics acquired from a novel cannulation simulator are able to distinguish between experienced clinicians and established experts, enabling the measurement of true expertise. Twenty-two healthcare professionals, who practiced cannulation with varying experience, performed a simulated arteriovenous fistula cannulation task on the simulator. Four clinicians were peer-identified as experts while the others were designated to the experienced group. The simulator tracked the motion of the needle (via an electromagnetic sensor), rendered blood flashback function (via an infrared light sensor), and recorded pinch forces exerted on the needle (via force sensing elements). Metrics were computed based on motion, force, and other sensor data. Results indicated that, with near 80% of accuracy using both logistic regression and linear discriminant analysis, the objective metrics differentiated between experts and the experienced, including identifying needle motion and finger force as two prominent features that distinguished between the groups. Furthermore, results indicated that expertise was not correlated with years of experience, validating the central hypothesis of the study. These insights contribute to structured and standardized medical skills training by enabling a meaningful definition of expertise and could potentially lead to more effective skills training methods.
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Castillo-Segura P, Fernández-Panadero C, Alario-Hoyos C, Muñoz-Merino PJ, Delgado Kloos C. Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review. Artif Intell Med 2021; 112:102007. [PMID: 33581827 DOI: 10.1016/j.artmed.2020.102007] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 11/25/2020] [Accepted: 12/28/2020] [Indexed: 11/18/2022]
Abstract
The assessment of surgical technical skills to be acquired by novice surgeons has been traditionally done by an expert surgeon and is therefore of a subjective nature. Nevertheless, the recent advances on IoT (Internet of Things), the possibility of incorporating sensors into objects and environments in order to collect large amounts of data, and the progress on machine learning are facilitating a more objective and automated assessment of surgical technical skills. This paper presents a systematic literature review of papers published after 2013 discussing the objective and automated assessment of surgical technical skills. 101 out of an initial list of 537 papers were analyzed to identify: 1) the sensors used; 2) the data collected by these sensors and the relationship between these data, surgical technical skills and surgeons' levels of expertise; 3) the statistical methods and algorithms used to process these data; and 4) the feedback provided based on the outputs of these statistical methods and algorithms. Particularly, 1) mechanical and electromagnetic sensors are widely used for tool tracking, while inertial measurement units are widely used for body tracking; 2) path length, number of sub-movements, smoothness, fixation, saccade and total time are the main indicators obtained from raw data and serve to assess surgical technical skills such as economy, efficiency, hand tremor, or mind control, and distinguish between two or three levels of expertise (novice/intermediate/advanced surgeons); 3) SVM (Support Vector Machines) and Neural Networks are the preferred statistical methods and algorithms for processing the data collected, while new opportunities are opened up to combine various algorithms and use deep learning; and 4) feedback is provided by matching performance indicators and a lexicon of words and visualizations, although there is considerable room for research in the context of feedback and visualizations, taking, for example, ideas from learning analytics.
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Affiliation(s)
- Pablo Castillo-Segura
- Universidad Carlos III de Madrid, Av. Universidad 30, 28911, Leganés, Madrid, Spain.
| | | | - Carlos Alario-Hoyos
- Universidad Carlos III de Madrid, Av. Universidad 30, 28911, Leganés, Madrid, Spain.
| | - Pedro J Muñoz-Merino
- Universidad Carlos III de Madrid, Av. Universidad 30, 28911, Leganés, Madrid, Spain.
| | - Carlos Delgado Kloos
- Universidad Carlos III de Madrid, Av. Universidad 30, 28911, Leganés, Madrid, Spain.
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Mohamadipanah H, Perrone KH, Peterson K, Nathwani J, Huang F, Garren A, Garren M, Witt A, Pugh C. Sensors and Psychomotor Metrics: A Unique Opportunity to Close the Gap on Surgical Processes and Outcomes. ACS Biomater Sci Eng 2020; 6:2630-2640. [DOI: 10.1021/acsbiomaterials.9b01019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hossein Mohamadipanah
- Department of Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, California 94305, United States
| | - Kenneth H. Perrone
- Department of Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, California 94305, United States
| | - Katherine Peterson
- Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, 600 Highland Avenue, Madison, Wisconsin 53726, United States
| | - Jay Nathwani
- Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, 600 Highland Avenue, Madison, Wisconsin 53726, United States
| | - Felix Huang
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, 710 North Lake Shore Drive, #1022, Chicago, Illinois 60611, United States
| | - Anna Garren
- Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, 600 Highland Avenue, Madison, Wisconsin 53726, United States
| | - Margaret Garren
- Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, 600 Highland Avenue, Madison, Wisconsin 53726, United States
| | - Anna Witt
- Department of Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, California 94305, United States
| | - Carla Pugh
- Department of Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, California 94305, United States
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Mohamadipanah H, Perrone K, Peterson K, Garren M, Parthiban C, Sunkara A, Zinn M, Pugh C. Can Virtual Reality Be Used to Track Skills Decay During the Research Years? J Surg Res 2019; 247:150-155. [PMID: 31776024 DOI: 10.1016/j.jss.2019.10.030] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 09/21/2019] [Accepted: 10/01/2019] [Indexed: 10/25/2022]
Abstract
BACKGROUND Time away from surgical practice can lead to skills decay. Research residents are thought to be prone to skills decay, given their limited experience and reduced exposure to clinical activities during their research training years. This study takes a cross-sectional approach to assess differences in residents' skills at the beginning and end of their research years using virtual reality. We hypothesized that research residents will have measurable decay in psychomotor skills when evaluated using virtual reality. METHODS Surgical residents (n = 28) were divided into two groups; the first group was just beginning their research time (clinical residents: n = 19) and the second group (research residents: n = 9) had just finished at least 2 y of research. All participants were asked to perform a target-tracking task using a haptic device, and their performance was compared using Welch's t-test. RESULTS Research residents showed a higher level of "tracking error" (1.69 ± 0.44 cm versus 1.40 ± 0.19 cm; P = 0.04) and a similar level of "path length" (62.5 ± 10.5 cm versus 62.1 ± 5.2 cm; P = 0.92) when compared with clinical residents. CONCLUSIONS The increased "tracking error" among residents at the end of their research time suggests fine psychomotor skills decay in residents who spend time away from clinical duties during laboratory time. This decay demonstrates the need for research residents to regularly participate in clinical activities, simulation, or assessments to minimize and monitor skills decay while away from clinical practice. Additional longitudinal studies may help better map learning and decay curves for residents who spend time away from clinical practice.
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Affiliation(s)
| | - Kenneth Perrone
- Department of Surgery, Stanford University School of Medicine, Stanford, California
| | - Katherine Peterson
- Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | - Margaret Garren
- Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | - Chembian Parthiban
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Adhira Sunkara
- Department of Surgery, Stanford University School of Medicine, Stanford, California
| | - Michael Zinn
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Carla Pugh
- Department of Surgery, Stanford University School of Medicine, Stanford, California.
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