Thomas J On, Yuan Xu, Nicolas I Gonzalez-Romo, Gerardo Gomez-Castro, Oscar Alcantar-Garibay, Marco Santello, Michael T Lawton, Mark C Preul
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引用次数: 0
Abstract
Background: Surgical approaches that access the posterior temporal bone require careful drilling motions to achieve adequate exposure while avoiding injury to critical structures.
Objective: We assessed a deep learning hand motion detector to potentially refine hand motion and precision during power drill use in a cadaveric mastoidectomy procedure.
Methods: A deep-learning hand motion detector tracked the movement of a surgeon's hands during three cadaveric mastoidectomy procedures. The model provided horizontal and vertical coordinates of 21 landmarks on both hands, which were used to create vertical and horizontal plane tracking plots. Preliminary surgical performance metrics were calculated from the motion detections.
Results: 1,948,837 landmark detections were collected, with an overall 85.9% performance. There was similar detection of the dominant hand (48.2%) compared to the non-dominant hand (51.7%). A loss of tracking occurred due to the increased brightness caused by the microscope light at the center of the field and by movements of the hand outside the field of view of the camera. The mean (SD) time spent (seconds) during instrument changes was 21.5 (12.4) and 4.4 (5.7) during adjustments of the microscope.
Conclusion: A deep-learning hand motion detector can measure surgical motion without physical sensors attached to the hands during mastoidectomy simulations on cadavers. While preliminary metrics were developed to assess hand motion during mastoidectomy, further studies are needed to expand and validate these metrics for potential use in guiding and evaluating surgical training.
期刊介绍:
Evidence of surgical interventions go back to prehistoric times. Since then, the field of surgery has developed into a complex array of specialties and procedures, particularly with the advent of microsurgery, lasers and minimally invasive techniques. The advanced skills now required from surgeons has led to ever increasing specialization, though these still share important fundamental principles.
Frontiers in Surgery is the umbrella journal representing the publication interests of all surgical specialties. It is divided into several “Specialty Sections” listed below. All these sections have their own Specialty Chief Editor, Editorial Board and homepage, but all articles carry the citation Frontiers in Surgery.
Frontiers in Surgery calls upon medical professionals and scientists from all surgical specialties to publish their experimental and clinical studies in this journal. By assembling all surgical specialties, which nonetheless retain their independence, under the common umbrella of Frontiers in Surgery, a powerful publication venue is created. Since there is often overlap and common ground between the different surgical specialties, assembly of all surgical disciplines into a single journal will foster a collaborative dialogue amongst the surgical community. This means that publications, which are also of interest to other surgical specialties, will reach a wider audience and have greater impact.
The aim of this multidisciplinary journal is to create a discussion and knowledge platform of advances and research findings in surgical practice today to continuously improve clinical management of patients and foster innovation in this field.