Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills

Alonso-Silverio, Gustavo A.; Pérez-Escamirosa, Fernando; Bruno-Sanchez, Raúl; Ortiz-Simón, José L.; Muñoz, Robert; Minor-Martínez, Arturo; Alarcón-Paredes, Antonio

doi:10.1177/1553350618777045

Cited by 43 publications

(43 citation statements)

References 38 publications

(36 reference statements)

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“…Boulet and Durning [ 24 ] discussed the application of AI to replace human ratings and assess medical education competencies. Another study that used augmented reality and AI algorithms evaluated a case study about the development of a laparoscopic box trainer to assess surgical psychomotor skills, and concluded the proposed system had potential benefits [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review

Sapci

2020

JMIR Med Educ

117

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Background The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice. Objective The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience. Methods A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments. Results This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula. Conclusions To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review

Sapci

2020

JMIR Med Educ

117

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“…Surgical simulators are interesting and useful tools for future cardiac surgeons' training. Surgical simulators increase dexterity, 11,12 which is an indispensable component of surgical skills.…”

Section: Discussionmentioning

confidence: 99%

Assessing a new coarctation repair simulator based on real patient’s anatomy

et al. 2019

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Objectives:To perform the preliminary tests of coarctation of aorta repair trainer, evaluate the surgical properties of the simulation and to assess and enhance residents’ skills.Methods:Single patient’s angio-CT anatomy data were converted into magnified 3D-printed model of aortic coarctation with hypoplastic aortic arch, serving for creation of a mould used during wax copies casting. Wax cores were painted with six layers of elastic silicone and melted, yielding phantoms that were consecutively fixed in a mounting with and without a thoracic wall. Simulation included: proximal and distal aortic arch clamping, incision of its lesser curvature, extended end-to-end anastomosis with 7-0 suture. A head-mounted camera video recording enabled anastomosis time and mean one suture bite time evaluation. Leakage assessment was done by a water test.Results:Two residents performed nine simulations each. Last four runs were performed with thoracic wall attached. All phantoms performed well, enabling tissue-like handling and cutting, excellent suture retention, and satisfactory elasticity. Median anastomosis times were 22′33″ and 24′47″ for phantoms without and with thoracic wall (p = not significant (NS)). Median times needed to pass suture through one side of anastomosis and regrasp needle were, respectively, 9″ and 13″ (p < 0.001). Median total number of leakages per phantom equalled 2 for both difficulty levels. There were no significant inter-resident differences in all assessed parameters.Conclusions:This medium-fidelity aortic coarctation repair trainer showed its feasibility in replication of major critical steps of the real operation. Objective surgical efficiency parameters could be obtained from each simulation and compared between trainees and at different adjustable difficulty levels.

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“…This study evaluated the effect of a laparoscopic trainer system that uses an AI algorithm. [29] This perspective paper only provided an outline and did not provide any evidence. This perspective article discussed the lack of student access to machine learning content and makes some suggestions to instructors.…”

Section: The Authors Investigated the Integration Of Nlp To Medicalmentioning

confidence: 99%

Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review (Preprint)

Sapci¹,

Sapci²

2020

Preprint

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BACKGROUND The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice. OBJECTIVE The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience. METHODS A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments. RESULTS This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula. CONCLUSIONS To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.

show abstract

Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills

Cited by 43 publications

References 38 publications

Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review

Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review

Assessing a new coarctation repair simulator based on real patient’s anatomy

Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review (Preprint)

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