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  1. Article ; Online: Introducing surgical intelligence in gynecology: Automated identification of key steps in hysterectomy.

    Levin, Ishai / Rapoport Ferman, Judith / Bar, Omri / Ben Ayoun, Danielle / Cohen, Aviad / Wolf, Tamir

    International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics

    2024  

    Abstract: Objective: The analysis of surgical videos using artificial intelligence holds great promise for the future of surgery by facilitating the development of surgical best practices, identifying key pitfalls, enhancing situational awareness, and ... ...

    Abstract Objective: The analysis of surgical videos using artificial intelligence holds great promise for the future of surgery by facilitating the development of surgical best practices, identifying key pitfalls, enhancing situational awareness, and disseminating that information via real-time, intraoperative decision-making. The objective of the present study was to examine the feasibility and accuracy of a novel computer vision algorithm for hysterectomy surgical step identification.
    Methods: This was a retrospective study conducted on surgical videos of laparoscopic hysterectomies performed in 277 patients in five medical centers. We used a surgical intelligence platform (Theator Inc.) that employs advanced computer vision and AI technology to automatically capture video data during surgery, deidentify, and upload procedures to a secure cloud infrastructure. Videos were manually annotated with sequential steps of surgery by a team of annotation specialists. Subsequently, a computer vision system was trained to perform automated step detection in hysterectomy. Analyzing automated video annotations in comparison to manual human annotations was used to determine accuracy.
    Results: The mean duration of the videos was 103 ± 43 min. Accuracy between AI-based predictions and manual human annotations was 93.1% on average. Accuracy was highest for the dissection and mobilization step (96.9%) and lowest for the adhesiolysis step (70.3%).
    Conclusion: The results of the present study demonstrate that a novel AI-based model achieves high accuracy for automated steps identification in hysterectomy. This lays the foundations for the next phase of AI, focused on real-time clinical decision support and prediction of outcome measures, to optimize surgeon workflow and elevate patient care.
    Language English
    Publishing date 2024-03-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 80149-5
    ISSN 1879-3479 ; 0020-7292
    ISSN (online) 1879-3479
    ISSN 0020-7292
    DOI 10.1002/ijgo.15490
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 355: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 183: Show issues

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  2. Article ; Online: A novel high accuracy model for automatic surgical workflow recognition using artificial intelligence in laparoscopic totally extraperitoneal inguinal hernia repair (TEP).

    Ortenzi, Monica / Rapoport Ferman, Judith / Antolin, Alenka / Bar, Omri / Zohar, Maya / Perry, Ori / Asselmann, Dotan / Wolf, Tamir

    Surgical endoscopy

    2023  Volume 37, Issue 11, Page(s) 8818–8828

    Abstract: Introduction: Artificial intelligence and computer vision are revolutionizing the way we perceive video analysis in minimally invasive surgery. This emerging technology has increasingly been leveraged successfully for video segmentation, documentation, ... ...

    Abstract Introduction: Artificial intelligence and computer vision are revolutionizing the way we perceive video analysis in minimally invasive surgery. This emerging technology has increasingly been leveraged successfully for video segmentation, documentation, education, and formative assessment. New, sophisticated platforms allow pre-determined segments chosen by surgeons to be automatically presented without the need to review entire videos. This study aimed to validate and demonstrate the accuracy of the first reported AI-based computer vision algorithm that automatically recognizes surgical steps in videos of totally extraperitoneal (TEP) inguinal hernia repair.
    Methods: Videos of TEP procedures were manually labeled by a team of annotators trained to identify and label surgical workflow according to six major steps. For bilateral hernias, an additional change of focus step was also included. The videos were then used to train a computer vision AI algorithm. Performance accuracy was assessed in comparison to the manual annotations.
    Results: A total of 619 full-length TEP videos were analyzed: 371 were used to train the model, 93 for internal validation, and the remaining 155 as a test set to evaluate algorithm accuracy. The overall accuracy for the complete procedure was 88.8%. Per-step accuracy reached the highest value for the hernia sac reduction step (94.3%) and the lowest for the preperitoneal dissection step (72.2%).
    Conclusions: These results indicate that the novel AI model was able to provide fully automated video analysis with a high accuracy level. High-accuracy models leveraging AI to enable automation of surgical video analysis allow us to identify and monitor surgical performance, providing mathematical metrics that can be stored, evaluated, and compared. As such, the proposed model is capable of enabling data-driven insights to improve surgical quality and demonstrate best practices in TEP procedures.
    MeSH term(s) Humans ; Hernia, Inguinal/surgery ; Laparoscopy/methods ; Artificial Intelligence ; Workflow ; Minimally Invasive Surgical Procedures ; Herniorrhaphy/methods ; Surgical Mesh
    Language English
    Publishing date 2023-08-25
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 639039-0
    ISSN 1432-2218 ; 0930-2794
    ISSN (online) 1432-2218
    ISSN 0930-2794
    DOI 10.1007/s00464-023-10375-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2214: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

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