Entwicklung eines Feedbacksystems zur Optimierung der laparoskopischen Instrumentenführung durch Integration einer automatisierten Bildklassifizierung

Während laparoskopischer Eingriffe kann es zu akzidentellen Verletzungen benachbarter Gewebestrukturen kommen, vor allem wenn sich das Arbeitsinstrument außerhalb des Sichtfeldes der laparoskopischen Kamera befindet. Ausgangspunkt der vorliegenden Arbeit war die quantitative sowie qualitative Untersuchung des Auftretens dieser als „Adverse Events“ (AE) bezeichneten Situationen während der laparoskopischen Cholezystektomie in einem realitätsnahen Trainingssetting. Des Weiteren sollte mit der Entwicklung eines Funktionsmusters die Machbarkeit eines kontextsensitiven, audiovisuellen Feedbacksystems durch Implementierung einer automatisierten binären Klassifizierung der zugrundeliegenden Bilddaten belegt werden. Das Ziel war dabei die Erkennung von AE während des Eingriffs in Echtzeit und deren Rückmeldung an das Operationsteam. Die Evaluation erfolgte im Rahmen einer randomisierten kontrollierten Probandenstudie mit 24 Medizinstudierenden (je 12 in Interventions- versus Kontrollgruppe), welche jeweils vier konsekutive laparoskopische Cholezystektomien in einer standardisierten Trainingsumgebung durchführten. Der Interventionsgruppe nutzte dabei das Feedbacksystem. Primärer Endpunkt war die Inzidenz von AE. Insgesamt wurden in der Gesamtpopulation 2895 AE registriert. Die mediane Anzahl der AE pro Eingriff lag bei 20,5. Die entwickelte Anwendung zur binären Bildklassifizierung konnte davon lediglich 33,9 % korrekt zuordnen. In der vergleichenden Auswertung von Interventions- und Kontrollgruppe ergaben sich hinsichtlich des primären Endpunkts keine statistisch signifikanten Unterschiede. Es wird geschlussfolgert, dass sich mit dem entwickelten Klassifizierungs- und Feedbacksystem das Auftreten von AE nicht beeinflussen lässt. Grundsätzlich deutet jedoch die hohe Anzahl an AE in Verbindung mit den aus der Literatur bekannten, teils schwerwiegenden Folgen für die betroffenen Patientinnen und Patienten nach iatrogenen Verletzungen im Rahmen laparoskopischer Eingriffe auf den Bedarf an zusätzlichen Sicherheitskonzepten hin. Diesbezüglich sind die angestoßenen Weiterentwicklungen unter Verwendung von Technologien auf dem Gebiet der künstlichen Intelligenz als vielversprechend zu beurteilen

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks

Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train The CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labelled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture

Quantification of electrosurgery-related critical events during laparoscopic cholecystectomy – a prospective experimental study among surgical novices

Uncontrolled movement of instruments in laparoscopic surgery can lead to inadvertent tissue damage, particularly when the dissecting or electrosurgical instrument is located outside the field of view of the laparoscopic camera. The incidence and relevance of such events are currently unknown. The present work aims to identify and quantify potentially dangerous situations using the example of laparoscopic cholecystectomy (LC). Twenty-four final year medical students were prompted to each perform four consecutive LC attempts on a well-established box trainer in a surgical training environment following a standardized protocol in a porcine model. The following situation was defined as a critical event (CE): the dissecting instrument was inadvertently located outside the laparoscopic camera’s field of view. Simultaneous activation of the electrosurgical unit was defined as a highly critical event (hCE). Primary endpoint was the incidence of CEs. While performing 96 LCs, 2895 CEs were observed. Of these, 1059 (36.6%) were hCEs. The median number of CEs per LC was 20.5 (range: 1–125; IQR: 33) and the median number of hCEs per LC was 8.0 (range: 0–54, IQR: 10). Mean total operation time was 34.7 min (range: 15.6–62.5 min, IQR: 14.3 min). Our study demonstrates the significance of CEs as a potential risk factor for collateral damage during LC. Further studies are needed to investigate the occurrence of CE in clinical practice, not just for laparoscopic cholecystectomy but also for other procedures. Systematic training of future surgeons as well as technical solutions address this safety issue