scholarly journals Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision

2021 ◽  
Author(s):  
Daichi Kitaguchi ◽  
Nobuyoshi Takeshita ◽  
Hiroki Matsuzaki ◽  
Hiro Hasegawa ◽  
Takahiro Igaki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Mesorectal Excision ◽  
Rectal Wall ◽  
Learning Model ◽  
Video Indexing ◽  
Video Data ◽  
Transanal Total Mesorectal Excision ◽  
Mesorectal Excision ◽  
Purse String ◽  
Deep Learning Model

Abstract Background Dividing a surgical procedure into a sequence of identifiable and meaningful steps facilitates intraoperative video data acquisition and storage. These efforts are especially valuable for technically challenging procedures that require intraoperative video analysis, such as transanal total mesorectal excision (TaTME); however, manual video indexing is time-consuming. Thus, in this study, we constructed an annotated video dataset for TaTME with surgical step information and evaluated the performance of a deep learning model in recognizing the surgical steps in TaTME. Methods This was a single-institutional retrospective feasibility study. All TaTME intraoperative videos were divided into frames. Each frame was manually annotated as one of the following major steps: (1) purse-string closure; (2) full thickness transection of the rectal wall; (3) down-to-up dissection; (4) dissection after rendezvous; and (5) purse-string suture for stapled anastomosis. Steps 3 and 4 were each further classified into four sub-steps, specifically, for dissection of the anterior, posterior, right, and left planes. A convolutional neural network-based deep learning model, Xception, was utilized for the surgical step classification task. Results Our dataset containing 50 TaTME videos was randomly divided into two subsets for training and testing with 40 and 10 videos, respectively. The overall accuracy obtained for all classification steps was 93.2%. By contrast, when sub-step classification was included in the performance analysis, a mean accuracy (± standard deviation) of 78% (± 5%), with a maximum accuracy of 85%, was obtained. Conclusions To the best of our knowledge, this is the first study based on automatic surgical step classification for TaTME. Our deep learning model self-learned and recognized the classification steps in TaTME videos with high accuracy after training. Thus, our model can be applied to a system for intraoperative guidance or for postoperative video indexing and analysis in TaTME procedures.

Purse-string failure at the beginning of the learning curve in transanal total mesorectal excision

2021 ◽  
Vol 47 (2) ◽  
pp. e49
Author(s):  
Martin Karamanliev ◽  
Tsvetomir Ivanov ◽  
Tsanko Yotsov ◽  
Emil Filipov ◽  
Tashko Deliyski ◽  
...  
Keyword(s):  
Learning Curve ◽  
Total Mesorectal Excision ◽  
Transanal Total Mesorectal Excision ◽  
Mesorectal Excision ◽  
Purse String

Wasmuth HH, Færden AE, Myklebust TÅ, et al (2020) Transanal total mesorectal excision for rectal cancer has been suspended in Norway. Br J Surg 107:121–30

2020 ◽  
Vol 14 (3) ◽  
pp. 155-158
Author(s):  
M. Aubert ◽  
Y. Panis
Keyword(s):  
Rectal Cancer ◽  
Total Mesorectal Excision ◽  
Cancer Du Rectum ◽  
Prise En Charge ◽  
Transanal Total Mesorectal Excision ◽  
Mesorectal Excision ◽  
Récidive Locale ◽  
Kaplan Meier ◽  
Cancers Colorectaux ◽  
Technique Chirurgicale

Contexte : L’exérèse totale du mésorectum par voie transanale (TaTME) pour la prise en charge du cancer du rectum est récemment apparue comme alternative à l’exérèse totale du mésorectum par voie abdominale. Cependant, certaines inquiétudes à propos des résultats oncologiques de cette technique chirurgicale ont émergé. Le but de cette étude était d’évaluer le taux de récidives locales après TaTME. Les objectifs secondaires s’intéressaient à la mortalité postopératoire, au taux de fistule anastomotique et au taux de stomie définitive. Méthodes : Les données de tous les patients opérés par TaTME ont été rapportées et comparées aux données issues des registres nationaux norvégiens de cancers colorectaux (NCCR) et de chirurgie gastro-intestinale (NoRGast). Les taux de récidive locale étaient estimés selon Kaplan-Meier. Résultats : En Norvège, 157 patients ont été opérés par TaTME pour un cancer du rectum entre octobre 2014 et octobre 2018. Trois des sept centres hospitaliers participants ont abandonné la réalisation de cette intervention après cinq procédures. Le taux de récidive locale était de 12 sur 157 patients (7,6 %) ; huit récidives locales étaient multifocales ou étendues. Le taux de récidive locale après un suivi de à 2,4 ans était estimé à 11,6 % (IC 95 % : [6,6‒19,9]) après TaTME contre 2,4 % (IC 95 % : [1,4‒4,4]) dans le registre NCCR (p < 0,001). Le hasard ratio était estimé à 6,71 (IC 95 % : [2,94‒15,32]). Le taux de fistule anastomotique nécessitant une réintervention était de 8,4 % dans le groupe TaTME contre 4,5 % dans le registre NoRGast (p = 0,047). Cinquante-six patients (35,7 %) étaient porteurs d’une stomie à la fin du suivi, dont 39 (24,8 %) étaient définitives. Conclusion : Le taux de fistule anastomotique était plus élevé après TaTME en comparaison aux données des registres nationaux norvégiens. Le taux de récidive locale ainsi que les caractéristiques de cette récidive après TaTME étaient défavorables.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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