DEL MAESTRO, Rolando Fausto
A history of neuro-oncology.Montréal: DW Consulting, 2008.
Subjects: NEUROLOGY › History of Neurology, NEUROSURGERY › History of Neurosurgery, NEUROSURGERY › Neuro-oncology
A physics-based virtual simulator for cranial microneurosurgery training.Operative Neurosurgery, 71, 32-42, 2012.
"A virtual reality neurosurgery simulator with haptic feedback may help in the training and assessment of technical skills requiring the use of tactile and visual cues.
"To develop a simulator for craniotomy-based procedures with haptic and graphics feedback for implementation by universities and hospitals in the neurosurgery training curriculum.
"NeuroTouch was developed by a team of more than 50 experts from the National Research Council Canada in collaboration with surgeons from more than 20 teaching hospitals across Canada. Its main components are a stereovision system, bimanual haptic tool manipulators, and a high-end computer. The simulation software engine runs 3 processes for computing graphics, haptics, and mechanics. Training tasks were built from magnetic resonance imaging scans of patients with brain tumors.
"Two training tasks were implemented for practicing skills with 3 different surgical tools. In the tumor-debulking task, the objective is complete tumor removal without removing normal tissue, using the regular surgical aspirator (suction) and the ultrasonic aspirator. The objective of the tumor cauterization task is to remove a vascularized tumor with an aspirator while controlling blood loss using bipolar electrocautery."
Subjects: COMPUTING/MATHEMATICS in Medicine & Biology › Computer Simulation, COMPUTING/MATHEMATICS in Medicine & Biology › Visualization, Education, Biomedical, & Biomedical Profession, NEUROSURGERY › Neuro-oncology
Sir William Osler's Leonardo da Vinci collection: Flight, anatomy and art.Montréal: [Privately Printed], 2019.
Subjects: ANATOMY › 16th Century, ART & Medicine & Biology
Machine learning identification of surgical and operative factors associated with surgical expertise in virtual reality simulation.JAMA Network Open, 2 (8): e198363., 2019.
"Importance Despite advances in the assessment of technical skills in surgery, a clear understanding of the composites of technical expertise is lacking. Surgical simulation allows for the quantitation of psychomotor skills, generating data sets that can be analyzed using machine learning algorithms.
"Objective To identify surgical and operative factors selected by a machine learning algorithm to accurately classify participants by level of expertise in a virtual reality surgical procedure.
"Design, Setting, and Participants Fifty participants from a single university were recruited between March 1, 2015, and May 31, 2016, to participate in a case series study at McGill University Neurosurgical Simulation and Artificial Intelligence Learning Centre. Data were collected at a single time point and no follow-up data were collected. Individuals were classified a priori as expert (neurosurgery staff), seniors (neurosurgical fellows and senior residents), juniors (neurosurgical junior residents), and medical students, all of whom participated in 250 simulated tumor resections.
"Exposures All individuals participated in a virtual reality neurosurgical tumor resection scenario. Each scenario was repeated 5 times.
"Main Outcomes and Measures Through an iterative process, performance metrics associated with instrument movement and force, resection of tissues, and bleeding generated from the raw simulator data output were selected by K-nearest neighbor, naive Bayes, discriminant analysis, and support vector machine algorithms to most accurately determine group membership.
"Results A total of 50 individuals (9 women and 41 men; mean [SD] age, 33.6 [9.5] years; 14 neurosurgeons, 4 fellows, 10 senior residents, 10 junior residents, and 12 medical students) participated. Neurosurgeons were in practice between 1 and 25 years, with 9 (64%) involving a predominantly cranial practice. The K-nearest neighbor algorithm had an accuracy of 90% (45 of 50), the naive Bayes algorithm had an accuracy of 84% (42 of 50), the discriminant analysis algorithm had an accuracy of 78% (39 of 50), and the support vector machine algorithm had an accuracy of 76% (38 of 50). The K-nearest neighbor algorithm used 6 performance metrics to classify participants, the naive Bayes algorithm used 9 performance metrics, the discriminant analysis algorithm used 8 performance metrics, and the support vector machine algorithm used 8 performance metrics. Two neurosurgeons, 1 fellow or senior resident, 1 junior resident, and 1 medical student were misclassified.
"Conclusions and Relevance In a virtual reality neurosurgical tumor resection study, a machine learning algorithm successfully classified participants into 4 levels of expertise with 90% accuracy. These findings suggest that algorithms may be capable of classifying surgical expertise with greater granularity and precision than has been previously demonstrated in surgery."Available at doi:10.1001/jamanetworkopen.2019.8363.
Subjects: Artificial Intelligence in Medicine , COMPUTING/MATHEMATICS in Medicine & Biology › Computer Simulation, COMPUTING/MATHEMATICS in Medicine & Biology › Visualization, Education, Biomedical, & Biomedical Profession, NEUROSURGERY › Neuro-oncology