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      Machine and deep learning for workflow recognition during surgery

      Author(s): 1
      Publication date Created:
      Publication date (Electronic):
      Journal: Minimally Invasive Therapy & Allied Technologies
      Publisher: Informa UK Limited

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          Abstract

          <p class="first" id="d5705783e51">Recent years have seen tremendous progress in artificial intelligence (AI), such as with the automatic and real-time recognition of objects and activities in videos in the field of computer vision. Due to its increasing digitalization, the operating room (OR) promises to directly benefit from this progress in the form of new assistance tools that can enhance the abilities and performance of surgical teams. Key for such tools is the recognition of the surgical workflow, because efficient assistance by an AI system requires this system to be aware of the surgical context, namely of all activities taking place inside the operating room. We present here how several recent techniques relying on machine and deep learning can be used to analyze the activities taking place during surgery, using videos captured from either endoscopic or ceiling-mounted cameras. We also present two potential clinical applications that we are developing at the University of Strasbourg with our clinical partners. </p>

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          EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos.

          Nicolas Padoy, Michel De Mathelin, Jacques Marescaux … (2017)
          Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, surgical phase recognition has been studied in the context of several kinds of surgeries, such as cataract, neurological, and laparoscopic surgeries. In the literature, two types of features are typically used to perform this task: visual features and tool usage signals. However, the used visual features are mostly handcrafted. Furthermore, the tool usage signals are usually collected via a manual annotation process or by using additional equipment. In this paper, we propose a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information. In previous studies, it has been shown that the tool usage signals can provide valuable information in performing the phase recognition task. Thus, we present a novel CNN architecture, called EndoNet, that is designed to carry out the phase recognition and tool presence detection tasks in a multi-task manner. To the best of our knowledge, this is the first work proposing to use a CNN for multiple recognition tasks on laparoscopic videos. Experimental comparisons to other methods show that EndoNet yields state-of-the-art results for both tasks.
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            Brain and neck tumors among physicians performing interventional procedures.

            Ariel Roguin, Jacob Goldstein, Olivier Bar … (2013)
            Physicians performing interventional procedures are chronically exposed to ionizing radiation, which is known to pose increased cancer risks. We recently reported 9 cases of brain cancer in interventional cardiologists. Subsequently, we received 22 additional cases from around the world, comprising an expanded 31 case cohort. Data were transmitted to us during the past few months. For all cases, where possible, we endeavored to obtain the baseline data, including age, gender, tumor type, and side involved, specialty (cardiologist vs radiologist), and number of years in practice. These data were obtained from the medical records, interviews with patients, when possible, or with family members and/or colleagues. The present report documented brain and neck tumors occurring in 31 physicians: 23 interventional cardiologists, 2 electrophysiologists, and 6 interventional radiologists. All physicians had worked for prolonged periods (latency period 12 to 32 years, mean 23.5 ± 5.9) in active interventional practice with exposure to ionizing radiation in the catheterization laboratory. The tumors included 17 cases (55%) of glioblastoma multiforme (GBM), 2 astrocytomas (7%), and 5 meningiomas (16%). In 26 of 31 cases, data were available regarding the side of the brain involved. The malignancy was left sided in 22 (85%), midline in 1, and right sided in 3 operators. In conclusion, these results raise additional concerns regarding brain cancer developing in physicians performing interventional procedures. Given that the brain is relatively unprotected and the left side of the head is known to be more exposed to radiation than the right, these findings of disproportionate reports of left-sided tumors suggest the possibility of a causal relation to occupational radiation exposure.
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              Disruptions in surgical flow and their relationship to surgical errors: an exploratory investigation.

              Andrew ElBardissi, Joseph A Dearani, Douglas Daly … (2007)
              Disruptions in surgical flow have the potential to increase the occurrence of surgical errors; however, little is known about the frequency and nature of surgical flow disruptions and their effect on the etiology of errors, which makes the development of evidence-based interventions extremely difficult. The goal of this project was to study surgical errors and their relationship to surgical flow disruptions in cardiovascular surgery prospectively to understand better the effect of these disruptions on surgical errors and ultimately patient safety. A trained observer recorded surgical errors and flow disruptions during 31 cardiac surgery operations over a 3-week period and categorized them by a classification system of human factors. Flow disruptions were then reviewed and analyzed by an interdisciplinary team of experts in operative and human factors. Flow disruptions consisted of teamwork/communication failures, equipment and technology problems, extraneous interruptions, training-related distractions, and issues in resource accessibility. Surgical errors increased significantly with increases in flow disruptions. Teamwork/communication failures were the strongest predictor of surgical errors. These findings provide preliminary data to develop evidence-based error management and patient safety programs within cardiac surgery with implications to other related surgical programs.
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                Author and article information

                Journal
                Title: Minimally Invasive Therapy & Allied Technologies
                Abbreviated Title: Minimally Invasive Therapy & Allied Technologies
                Publisher: Informa UK Limited
                ISSN (Print): 1364-5706
                ISSN (Electronic): 1365-2931
                Publication date Created: March 08 2019
                Publication date Created: March 04 2019
                Publication date (Electronic): March 08 2019
                Publication date (Print): March 04 2019
                Volume: 28
                Issue: 2
                Pages: 82-90
                Affiliations
                [1 ]ICube, IHU Strasbourg, CNRS, University of Strasbourg, Strasbourg, France
                Article
                DOI: 10.1080/13645706.2019.1584116
                PubMed ID: 30849261
                SO-VID: f5a498fc-7b6d-4857-a3c9-d5f9fc39950f
                Copyright © © 2019
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