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Article ; Online: Treatment of complete rectal prolapse using the TEO® platform (transanal endoscopic operation) - a video vignette.

D'Urso, Antonio / Lapergola, Alfonso / Marescaux, Jacques / Mutter, Didier / Serra-Aracil, Xavier

Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland

2024

Language	English
Publishing date	2024-02-05
Publishing country	England
Document type	Letter
ZDB-ID	1440017-0
ISSN	1463-1318 ; 1462-8910
ISSN (online)	1463-1318
ISSN	1462-8910
DOI	10.1111/codi.16910
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Zs.A 5799: Show issues

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Article ; Online: Rendezvous in time: an attention-based temporal fusion approach for surgical triplet recognition.

Sharma, Saurav / Nwoye, Chinedu Innocent / Mutter, Didier / Padoy, Nicolas

International journal of computer assisted radiology and surgery

2023 Volume 18, Issue 6, Page(s) 1053–1059

Abstract: Purpose: One of the recent advances in surgical AI is the recognition of surgical activities as triplets of [Formula: see text]instrument, verb, target[Formula: see text]. Albeit providing detailed information for computer-assisted intervention, current ...

Abstract	Purpose: One of the recent advances in surgical AI is the recognition of surgical activities as triplets of [Formula: see text]instrument, verb, target[Formula: see text]. Albeit providing detailed information for computer-assisted intervention, current triplet recognition approaches rely only on single-frame features. Exploiting the temporal cues from earlier frames would improve the recognition of surgical action triplets from videos. Methods: In this paper, we propose Rendezvous in Time (RiT)-a deep learning model that extends the state-of-the-art model, Rendezvous, with temporal modeling. Focusing more on the verbs, our RiT explores the connectedness of current and past frames to learn temporal attention-based features for enhanced triplet recognition. Results: We validate our proposal on the challenging surgical triplet dataset, CholecT45, demonstrating an improved recognition of the verb and triplet along with other interactions involving the verb such as [Formula: see text]instrument, verb[Formula: see text]. Qualitative results show that the RiT produces smoother predictions for most triplet instances than the state-of-the-arts. Conclusion: We present a novel attention-based approach that leverages the temporal fusion of video frames to model the evolution of surgical actions and exploit their benefits for surgical triplet recognition.
Language	English
Publishing date	2023-04-25
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2365628-1
ISSN	1861-6429 ; 1861-6410
ISSN (online)	1861-6429
ISSN	1861-6410
DOI	10.1007/s11548-023-02914-1
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Zs.A 6615: Show issues

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Article ; Online: Live laparoscopic video retrieval with compressed uncertainty.

Yu, Tong / Mascagni, Pietro / Verde, Juan / Marescaux, Jacques / Mutter, Didier / Padoy, Nicolas

Medical image analysis

2023 Volume 88, Page(s) 102866

Abstract: Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care. However the primitive and most common approach to retrieval, involving text in the form of keywords, is severely limited ...

Abstract	Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care. However the primitive and most common approach to retrieval, involving text in the form of keywords, is severely limited when dealing with complex media formats. Content-based retrieval offers a way to overcome this limitation, by using rich media as the query itself. Surgical video-to-video retrieval in particular is a new and largely unexplored research problem with high clinical value, especially in the real-time case: using real-time video hashing, search can be achieved directly inside of the operating room. Indeed, the process of hashing converts large data entries into compact binary arrays or hashes, enabling large-scale search operations at a very fast rate. However, due to fluctuations over the course of a video, not all bits in a given hash are equally reliable. In this work, we propose a method capable of mitigating this uncertainty while maintaining a light computational footprint. We present superior retrieval results (3%-4% top 10 mean average precision) on a multi-task evaluation protocol for surgery, using cholecystectomy phases, bypass phases, and coming from an entirely new dataset introduced here, surgical events across six different surgery types. Success on this multi-task benchmark shows the generalizability of our approach for surgical video retrieval.
MeSH term(s)	Humans ; Algorithms ; Cholecystectomy ; Laparoscopy ; Uncertainty
Language	English
Publishing date	2023-06-15
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Video-Audio Media
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2023.102866
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Article ; Online: Preserving privacy in surgical video analysis using a deep learning classifier to identify out-of-body scenes in endoscopic videos.

Lavanchy, Joël L / Vardazaryan, Armine / Mascagni, Pietro / Mutter, Didier / Padoy, Nicolas

Scientific reports

2023 Volume 13, Issue 1, Page(s) 9235

Abstract: Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if the endoscopic camera is moved out of the body of patients and out-of-body scenes are ... ...

Abstract	Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if the endoscopic camera is moved out of the body of patients and out-of-body scenes are recorded. Therefore, identification of out-of-body scenes in endoscopic videos is of major importance to preserve the privacy of patients and operating room staff. This study developed and validated a deep learning model for the identification of out-of-body images in endoscopic videos. The model was trained and evaluated on an internal dataset of 12 different types of laparoscopic and robotic surgeries and was externally validated on two independent multicentric test datasets of laparoscopic gastric bypass and cholecystectomy surgeries. Model performance was evaluated compared to human ground truth annotations measuring the receiver operating characteristic area under the curve (ROC AUC). The internal dataset consisting of 356,267 images from 48 videos and the two multicentric test datasets consisting of 54,385 and 58,349 images from 10 and 20 videos, respectively, were annotated. The model identified out-of-body images with 99.97% ROC AUC on the internal test dataset. Mean ± standard deviation ROC AUC on the multicentric gastric bypass dataset was 99.94 ± 0.07% and 99.71 ± 0.40% on the multicentric cholecystectomy dataset, respectively. The model can reliably identify out-of-body images in endoscopic videos and is publicly shared. This facilitates privacy preservation in surgical video analysis.
MeSH term(s)	Humans ; Deep Learning ; Privacy ; Laparoscopy ; Video Recording ; Cholecystectomy
Language	English
Publishing date	2023-06-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-36453-1
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Article ; Online: Risk factors for in-hospital mortality after emergency colorectal surgery in octogenarians: results of a cohort study from a referral center.

Mathis, Guillaume / Lapergola, Alfonso / Alexandre, Florent / Philouze, Guillaume / Mutter, Didier / D'Urso, Antonio

International journal of colorectal disease

2023 Volume 38, Issue 1, Page(s) 270

Abstract: Purpose: The objective of this study was to investigate predictive factors of mortality in emergency colorectal surgery in octogenarian patients.: Methods: It is a retrospective cohort study conducted at a single-institution tertiary referral center. ...

Abstract	Purpose: The objective of this study was to investigate predictive factors of mortality in emergency colorectal surgery in octogenarian patients. Methods: It is a retrospective cohort study conducted at a single-institution tertiary referral center. Consecutive patients who underwent emergency colorectal surgery between January 2015 and January 2020 were identified. The primary endpoint was 30-day mortality. Univariate and multivariate analyses were performed using a logistic regression model. Results: A total of 111 patients were identified (43 men, 68 women). Mean age was 85.7 ± 3.7 years (80-96). Main diagnoses included complicated sigmoiditis in 38 patients (34.3%), cancer in 35 patients (31.5%), and ischemic colitis in 31 patients (27.9%). An ASA score of 3 or higher was observed in 88.3% of patients. The mean Charlson score was 5.9. The Possum score was 35.9% for mortality and 79.3% for morbidity. The 30-day mortality rate was 25.2%. Univariate analysis of preoperative risk factors for mortality shows that the history of valvular heart disease (p = 0.008), intensive care unit provenance (p = 0.003), preoperative sepsis (p < 0.001), diagnosis of ischemic colitis (p = 0.012), creatinine (p = 0.006) and lactate levels (p = 0.01) were significantly associated with 30-day mortality, and patients coming from home had a lower 30-day mortality rate (p = 0.018). Intraoperative variables associated with 30-day mortality included ileostomy creation (p = 0.022) and temporary laparostomy (p = 0.004). At multivariate analysis, only lactate (p = 0.032) and creatinine levels (p = 0.027) were found to be independent predictors of 30-day mortality, home provenance was an independent protective factor (p = 0.004). Mean follow-up was 3.4 years. Survival at 1 and 3 years was 57.6 and 47.7%. Conclusion: Emergency colorectal surgery is challenging. However, age should not be a contraindication. The 30-day mortality rate (25.2%) is one of the lowest in the literature. Hyperlactatemia (> 2mmol/L) and creatinine levels appear to be independent predictors of mortality.
MeSH term(s)	Male ; Aged, 80 and over ; Humans ; Female ; Cohort Studies ; Retrospective Studies ; Octogenarians ; Hospital Mortality ; Colitis, Ischemic ; Colorectal Surgery/adverse effects ; Creatinine ; Postoperative Complications/etiology ; Risk Factors ; Referral and Consultation ; Lactates
Chemical Substances	Creatinine (AYI8EX34EU) ; Lactates
Language	English
Publishing date	2023-11-21
Publishing country	Germany
Document type	Journal Article
ZDB-ID	84975-3
ISSN	1432-1262 ; 0179-1958
ISSN (online)	1432-1262
ISSN	0179-1958
DOI	10.1007/s00384-023-04565-9
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Zs.A 2121: Show issues

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Article ; Online: Latent Graph Representations for Critical View of Safety Assessment.

Murali, Aditya / Alapatt, Deepak / Mascagni, Pietro / Vardazaryan, Armine / Garcia, Alain / Okamoto, Nariaki / Mutter, Didier / Padoy, Nicolas

IEEE transactions on medical imaging

2024 Volume 43, Issue 3, Page(s) 1247–1258

Abstract: Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their ... ...

Abstract	Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure. Prior works have approached this task by including semantic segmentation as an intermediate step, using predicted segmentation masks to then predict the CVS. While these methods are effective, they rely on extremely expensive ground-truth segmentation annotations and tend to fail when the predicted segmentation is incorrect, limiting generalization. In this work, we propose a method for CVS prediction wherein we first represent a surgical image using a disentangled latent scene graph, then process this representation using a graph neural network. Our graph representations explicitly encode semantic information - object location, class information, geometric relations - to improve anatomy-driven reasoning, as well as visual features to retain differentiability and thereby provide robustness to semantic errors. Finally, to address annotation cost, we propose to train our method using only bounding box annotations, incorporating an auxiliary image reconstruction objective to learn fine-grained object boundaries. We show that our method not only outperforms several baseline methods when trained with bounding box annotations, but also scales effectively when trained with segmentation masks, maintaining state-of-the-art performance.
MeSH term(s)	Image Processing, Computer-Assisted ; Neural Networks, Computer ; Semantics
Language	English
Publishing date	2024-03-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2023.3333034
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Book ; Online: Surgical Action Triplet Detection by Mixed Supervised Learning of Instrument-Tissue Interactions

Sharma, Saurav / Nwoye, Chinedu Innocent / Mutter, Didier / Padoy, Nicolas

2023

Abstract: Surgical action triplets describe instrument-tissue interactions as (instrument, verb, target) combinations, thereby supporting a detailed analysis of surgical scene activities and workflow. This work focuses on surgical action triplet detection, which ... ...

Abstract	Surgical action triplets describe instrument-tissue interactions as (instrument, verb, target) combinations, thereby supporting a detailed analysis of surgical scene activities and workflow. This work focuses on surgical action triplet detection, which is challenging but more precise than the traditional triplet recognition task as it consists of joint (1) localization of surgical instruments and (2) recognition of the surgical action triplet associated with every localized instrument. Triplet detection is highly complex due to the lack of spatial triplet annotation. We analyze how the amount of instrument spatial annotations affects triplet detection and observe that accurate instrument localization does not guarantee better triplet detection due to the risk of erroneous associations with the verbs and targets. To solve the two tasks, we propose MCIT-IG, a two-stage network, that stands for Multi-Class Instrument-aware Transformer-Interaction Graph. The MCIT stage of our network models per class embedding of the targets as additional features to reduce the risk of misassociating triplets. Furthermore, the IG stage constructs a bipartite dynamic graph to model the interaction between the instruments and targets, cast as the verbs. We utilize a mixed-supervised learning strategy that combines weak target presence labels for MCIT and pseudo triplet labels for IG to train our network. We observed that complementing minimal instrument spatial annotations with target embeddings results in better triplet detection. We evaluate our model on the CholecT50 dataset and show improved performance on both instrument localization and triplet detection, topping the leaderboard of the CholecTriplet challenge in MICCAI 2022. Comment: Accepted at MICCAI, 2023. Project Page: https://github.com/CAMMA-public/mcit-ig
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2023-07-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Comment on "Short-term Outcomes of Ambulatory Colectomy for 157 Consecutive Patients." Evaluation of Organizational Innovations: Reconciliation Between Patients' Expectations and Doctors' Duties.

Pessaux, Patrick / Mutter, Didier / Marescaux, Jacques

Annals of surgery

2020 Volume 274, Issue 6, Page(s) e677–e678

MeSH term(s)	Colectomy ; Humans ; Motivation ; Physician-Patient Relations ; Physicians
Language	English
Publishing date	2020-03-21
Publishing country	United States
Document type	Letter ; Comment
ZDB-ID	340-2
ISSN	1528-1140 ; 0003-4932
ISSN (online)	1528-1140
ISSN	0003-4932
DOI	10.1097/SLA.0000000000003810
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Article: Hepar Lobatum carcinomatosum: A rare cause of portal hypertension.

Mathis, Guillaume / Felli, Emanuele / Mutter, Didier / Pessaux, Patrick

Clinical case reports

2020 Volume 8, Issue 10, Page(s) 2082–2083

Abstract: Hepar lobatum carcinomatosum is a rare form of major hepatic dysmorphia secondary to metastatic breast cancer. This condition seems to be related to the obstruction of portal vessels by tumor cells responsible of possible secondary portal hypertension ... ...

Abstract	Hepar lobatum carcinomatosum is a rare form of major hepatic dysmorphia secondary to metastatic breast cancer. This condition seems to be related to the obstruction of portal vessels by tumor cells responsible of possible secondary portal hypertension without underlying cirrhosis.
Language	English
Publishing date	2020-06-23
Publishing country	England
Document type	Journal Article
ZDB-ID	2740234-4
ISSN	2050-0904
ISSN	2050-0904
DOI	10.1002/ccr3.3052
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Article ; Online: Proposal and multicentric validation of a laparoscopic Roux-en-Y gastric bypass surgery ontology.

Lavanchy, Joël L / Gonzalez, Cristians / Kassem, Hasan / Nett, Philipp C / Mutter, Didier / Padoy, Nicolas

Surgical endoscopy

2022 Volume 37, Issue 3, Page(s) 2070–2077

Abstract: Background: Phase and step annotation in surgical videos is a prerequisite for surgical scene understanding and for downstream tasks like intraoperative feedback or assistance. However, most ontologies are applied on small monocentric datasets and lack ... ...

Abstract	Background: Phase and step annotation in surgical videos is a prerequisite for surgical scene understanding and for downstream tasks like intraoperative feedback or assistance. However, most ontologies are applied on small monocentric datasets and lack external validation. To overcome these limitations an ontology for phases and steps of laparoscopic Roux-en-Y gastric bypass (LRYGB) is proposed and validated on a multicentric dataset in terms of inter- and intra-rater reliability (inter-/intra-RR). Methods: The proposed LRYGB ontology consists of 12 phase and 46 step definitions that are hierarchically structured. Two board certified surgeons (raters) with > 10 years of clinical experience applied the proposed ontology on two datasets: (1) StraBypass40 consists of 40 LRYGB videos from Nouvel Hôpital Civil, Strasbourg, France and (2) BernBypass70 consists of 70 LRYGB videos from Inselspital, Bern University Hospital, Bern, Switzerland. To assess inter-RR the two raters' annotations of ten randomly chosen videos from StraBypass40 and BernBypass70 each, were compared. To assess intra-RR ten randomly chosen videos were annotated twice by the same rater and annotations were compared. Inter-RR was calculated using Cohen's kappa. Additionally, for inter- and intra-RR accuracy, precision, recall, F1-score, and application dependent metrics were applied. Results: The mean ± SD video duration was 108 ± 33 min and 75 ± 21 min in StraBypass40 and BernBypass70, respectively. The proposed ontology shows an inter-RR of 96.8 ± 2.7% for phases and 85.4 ± 6.0% for steps on StraBypass40 and 94.9 ± 5.8% for phases and 76.1 ± 13.9% for steps on BernBypass70. The overall Cohen's kappa of inter-RR was 95.9 ± 4.3% for phases and 80.8 ± 10.0% for steps. Intra-RR showed an accuracy of 98.4 ± 1.1% for phases and 88.1 ± 8.1% for steps. Conclusion: The proposed ontology shows an excellent inter- and intra-RR and should therefore be implemented routinely in phase and step annotation of LRYGB.
MeSH term(s)	Humans ; Gastric Bypass ; Obesity, Morbid/surgery ; Reproducibility of Results ; Treatment Outcome ; Laparoscopy ; Postoperative Complications/surgery
Language	English
Publishing date	2022-10-26
Publishing country	Germany
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	639039-0
ISSN	1432-2218 ; 0930-2794
ISSN (online)	1432-2218
ISSN	0930-2794
DOI	10.1007/s00464-022-09745-2
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