Latent Graph Representations for Critical View of Safety Assessment DOI Creative Commons
Aditya Murali, Deepak Alapatt, Pietro Mascagni

et al.

arXiv (Cornell University), Journal Year: 2022, Volume and Issue: unknown

Published: Jan. 1, 2022

Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization key anatomical structures, reasoning about their geometric relationships to one another, determining quality exposure. Prior works have approached this task by including semantic segmentation as an intermediate step, using predicted masks then predict CVS. While these methods are effective, they rely on extremely expensive ground-truth annotations tend fail when is incorrect, limiting generalization. In work, we propose a method for CVS prediction wherein first represent surgical image disentangled latent scene graph, process representation graph neural network. Our representations explicitly encode information - object location, class information, relations improve anatomy-driven reasoning, well visual features retain differentiability thereby provide robustness errors. Finally, address annotation cost, train our only bounding box annotations, incorporating auxiliary reconstruction objective learn fine-grained boundaries. We show that not outperforms several baseline trained with but also scales effectively masks, maintaining state-of-the-art performance.

Language: Английский

Der digitale Operationssaal DOI

Ann Wierick,

A. Schulze,

Sebastian Bodenstedt

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 95(6), P. 429 - 435

Published: March 5, 2024

At the central workplace of surgeon digitalization operating room has particular consequences for surgical work. Starting with intraoperative cross-sectional imaging and sonography, through functional imaging, minimally invasive robot-assisted surgery up to digital anesthesiological documentation, vast majority rooms are now at least partially digitalized. The increasing whole process chain enables not only collection but also analysis big data. Current research focuses on artificial intelligence data as prerequisite assistance systems that support decision making or warn risks; however, these technologies raise new ethical questions community affect core

Language: Английский

Citations

1

Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies DOI Open Access
Ayrton Bangolo, Nikita Wadhwani, Vignesh Krishnan Nagesh

et al.

Artificial Intelligence in Gastrointestinal Endoscopy, Journal Year: 2024, Volume and Issue: 5(2)

Published: May 11, 2024

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are third fifth most commonly diagnosed worldwide but cited as second leading causes mortality. Early institution appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, therapeutic tools assist in expeditious diagnosis, treatment planning/response prediction, post-surgical prognostication. AI intercept neoplastic lesions their primordial stages, accurately flag suspicious and/or inconspicuous with greater accuracy on radiologic, histopathological, endoscopic analyses, eliminate over-dependence clinicians. AI-based models have shown to be par, sometimes even outperformed experienced gastroenterologists radiologists. Convolutional neural networks (state-of-the-art deep learning models) powerful computational models, invaluable field precision oncology. These not only reliably classify images, also predict response chemotherapy, tumor recurrence, metastasis, survival rates post-treatment. In this systematic review, we analyze available evidence about utility artificial

Language: Английский

Citations

1

Artificial Intelligence for the Colorectal Surgeon in 2024 – A Narrative Review of Prevalence, Policies, and (needed) Protections DOI

Kurt S. Schultz,

Michelle Hughes,

Warqaa M. Akram

et al.

Seminars in Colon and Rectal Surgery, Journal Year: 2024, Volume and Issue: 35(3), P. 101037 - 101037

Published: July 23, 2024

Language: Английский

Citations

1

Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models DOI
Fiona R. Kolbinger, Jiangpeng He, Jinge Ma

et al.

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Journal Year: 2024, Volume and Issue: unknown, P. 2275 - 2284

Published: June 17, 2024

Language: Английский

Citations

1

Machines with vision for intraoperative guidance during gastrointestinal cancer surgery DOI Creative Commons
Muhammad Uzair Khalid, Simon Laplante, Amin Madani

et al.

Frontiers in Medicine, Journal Year: 2022, Volume and Issue: 9

Published: Sept. 30, 2022

OPINION article Front. Med., 30 September 2022Sec. Gastroenterology Volume 9 - 2022 | https://doi.org/10.3389/fmed.2022.1025382

Language: Английский

Citations

6

Applications of Artificial Intelligence in Surgery: clinical, technical, and governance considerations DOI

Pietro Mascagni,

Deepak Alapatt, Luca Sestini

et al.

Cirugía Española (English Edition), Journal Year: 2024, Volume and Issue: 102, P. S66 - S71

Published: May 2, 2024

Citations

0

UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation DOI Creative Commons
Md. Riad Hassan, M. Rubaiyat Hossain Mondal, Sheikh Iqbal Ahamed

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(6), P. e0304771 - e0304771

Published: June 17, 2024

Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiation therapy, and critical robotic surgery. Automatic organ from medical images is challenging due to the inconsistent shape size of different organs. Besides this, low contrast at edges organs similar types tissue confuses network’s ability segment contour properly. In this paper, we propose novel convolution neural network based uncertainty-driven boundary-refined (UDBRNet) that segments CT images. The are segmented first produce multiple masks multi-line decoder. Uncertain regions identified boundaries refined on uncertainty data. Our method achieves remarkable performance, boasting dice accuracies 0.80, 0.95, 0.92, 0.94 Esophagus, Heart, Trachea, Aorta respectively SegThor dataset, 0.71, 0.89, 0.85, 0.97, 0.97 Spinal Cord, Left-Lung, Right-Lung LCTSC dataset. These results demonstrate superiority our boundary refinement technique over state-of-the-art networks such as UNet, Attention FC-denseNet, BASNet, UNet++, R2UNet, TransUNet, DS-TransUNet. UDBRNet presents promising more precise segmentation, particularly in challenging, uncertain conditions. source code proposed will be available https://github.com/riadhassan/UDBRNet .

Language: Английский

Citations

0

Application and use of artificial intelligence in colorectal cancer surgery: where are we? DOI Open Access
Francesco Celotto, Giulia Capelli, Stefania Ferrari

et al.

Artificial Intelligence Surgery, Journal Year: 2024, Volume and Issue: 4(4), P. 348 - 63

Published: Oct. 31, 2024

AI is revolutionizing the landscape of colorectal cancer (CRC) surgery, permeating diverse facets ranging from intraoperative guidance to predictive modeling postoperative outcomes. This scoping review aims comprehensively delineate breadth artificial intelligence (AI) applications in CRC surgery. A search PubMed, Embase, and Ebsco databases up December 2023 was conducted, with registration international prospective register systematic reviews (PROSPERO) (CRD42024502107). Sixty-two studies meeting stringent inclusion criteria were scrutinized, encompassing utilization surgery or development AI-driven tools for surgical practice. Five principal domains application emerged: (i) Intraoperative guidance, leveraging real-time navigation, indocyanine green (ICG) angiography, hyperspectral imaging (HSI) enhance precision; (ii) Image segmentation, facilitating phase recognition, anatomical identification optimize visualization; (iii) Training performance assessment, enabling objective evaluation enhancement skills through simulations feedback mechanisms; (iv) Prediction complications, prognostication anastomotic leakage (AL) stricture, stoma requirements, prediction low anterior resection syndrome (LARS) short-term complications; (v) Utilization electronic health records (EHRs), harnessing algorithms streamline data analysis inform decision-making processes. underscores paradigm-shifting impact transcending conventional boundaries catalyzing advancements across domains. Although many are still experimental, as continues evolve, it promises transform practice, outcomes, revolutionize patient care. Embracing technologies imperative surgeons remain at vanguard innovation deliver superior outcomes patients.

Language: Английский

Citations

0

Applications of artificial intelligence in surgery: clinical, technical, and governance considerations DOI

Pietro Mascagni,

Deepak Alapatt, Luca Sestini

et al.

Cirugía Española, Journal Year: 2024, Volume and Issue: 102, P. S66 - S71

Published: July 1, 2024

Citations

0

Use of artificial intelligence in total mesorectal excision in rectal cancer surgery: State of the art and perspectives DOI Open Access
Vinicio Mosca, Giacomo Fuschillo, Guido Sciaudone

et al.

Artificial Intelligence in Gastroenterology, Journal Year: 2023, Volume and Issue: 4(3), P. 64 - 71

Published: Dec. 7, 2023

BACKGROUND Colorectal cancer is a major public health problem, with 1.9 million new cases and 953000 deaths worldwide in 2020. Total mesorectal excision (TME) the standard of care for treatment rectal crucial to prevent local recurrence, but it technically challenging surgery. The use artificial intelligence (AI) could help improve performance safety TME AIM To review literature on AI machine learning surgery potential future developments. METHODS Online scientific databases were searched articles between 2020 2023. RESULTS search yielded 876 results, only 13 studies selected review. specifically rapidly evolving field. There are number different algorithms that have been developed TME, including instrument detection, anatomical structure identification, image-guided navigation systems. CONCLUSION has revolutionize by providing real-time surgical guidance, preventing complications, improving training. However, further research needed fully understand benefits risks

Language: Английский

Citations

0