Annals of Coloproctology

Reviews

Minimally invasive surgery

Robotic colorectal surgery training: Portsmouth perspective: Guglielmo Niccolò Piozzi, Sentilnathan Subramaniam, Diana Ronconi Di Giuseppe, Rauand Duhoky, Jim S. Khan; Ann Coloproctol. 2024;40(4):350-362. Published online August 30, 2024; DOI: https://doi.org/10.3393/ac.2024.00444.0063

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Abstract PDF: This study aims to discuss the principles and pillars of robotic colorectal surgery training and share the training pathway at Portsmouth Hospitals University NHS Trust. A narrative review is presented to discuss all the relevant and critical steps in robotic surgical training. Robotic training requires a stepwise approach, including theoretical knowledge, case observation, simulation, dry lab, wet lab, tutored programs, proctoring (in person or telementoring), procedure-specific training, and follow-up. Portsmouth Colorectal has an established robotic training model with a safe stepwise approach that has been demonstrated through perioperative and oncological results. Robotic surgery training should enable a trainee to use the robotic platform safely and effectively, minimize errors, and enhance performance with improved outcomes. Portsmouth Colorectal has provided such a stepwise training program since 2015 and continues to promote and augment safe robotic training in its field. Safe and efficient training programs are essential to upholding the optimal standard of care.

Colorectal cancer

Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review: Minsung Kim, Taeyong Park, Bo Young Oh, Min Jeong Kim, Bum-Joo Cho, Il Tae Son; Ann Coloproctol. 2024;40(1):13-26. Published online February 28, 2024; DOI: https://doi.org/10.3393/ac.2023.00892.0127

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2 Web of Science
4 Citations

Abstract PDF

Purpose
The integration of artificial intelligence (AI) and magnetic resonance imaging in rectal cancer has the potential to enhance diagnostic accuracy by identifying subtle patterns and aiding tumor delineation and lymph node assessment. According to our systematic review focusing on convolutional neural networks, AI-driven tumor staging and the prediction of treatment response facilitate tailored treatment strategies for patients with rectal cancer.
Methods
This paper summarizes the current landscape of AI in the imaging field of rectal cancer, emphasizing the performance reporting design based on the quality of the dataset, model performance, and external validation.
Results
AI-driven tumor segmentation has demonstrated promising results using various convolutional neural network models. AI-based predictions of staging and treatment response have exhibited potential as auxiliary tools for personalized treatment strategies. Some studies have indicated superior performance than conventional models in predicting microsatellite instability and KRAS status, offering noninvasive and cost-effective alternatives for identifying genetic mutations.
Conclusion
Image-based AI studies for rectal cancer have shown acceptable diagnostic performance but face several challenges, including limited dataset sizes with standardized data, the need for multicenter studies, and the absence of oncologic relevance and external validation for clinical implantation. Overcoming these pitfalls and hurdles is essential for the feasible integration of AI models in clinical settings for rectal cancer, warranting further research.

Citations

Citations to this article as recorded by

L’intelligence artificielle pourrait-elle aider le chirurgien digestif dans la prise en charge du cancer du rectum ?
Arnaud Alves, Karem Slim
Journal de Chirurgie Viscérale.2024; 161(4): 253. CrossRef
Can artificial intelligence help a digestive surgeon in management of rectal cancer?
Arnaud Alves, Karem Slim
Journal of Visceral Surgery.2024; 161(4): 231. CrossRef
Artificial intelligence for the colorectal surgeon in 2024 – A narrative review of Prevalence, Policies, and (needed) Protections
Kurt S. Schultz, Michelle L. Hughes, Warqaa M. Akram, Anne K. Mongiu
Seminars in Colon and Rectal Surgery.2024; 35(3): 101037. CrossRef
Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications
Joana Mota, Maria João Almeida, Miguel Martins, Francisco Mendes, Pedro Cardoso, João Afonso, Tiago Ribeiro, João Ferreira, Filipa Fonseca, Manuel Limbert, Susana Lopes, Guilherme Macedo, Fernando Castro Poças, Miguel Mascarenhas
Journal of Clinical Medicine.2024; 13(19): 5842. CrossRef

AI colonoscopy

The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists’ perceptions thereof: Sarah Tham, Frederick H. Koh, Jasmine Ladlad, Koy-Min Chue, SKH Endoscopy Centre, Cui-Li Lin, Eng-Kiong Teo, Fung-Joon Foo; Ann Coloproctol. 2023;39(5):385-394. Published online March 10, 2023; DOI: https://doi.org/10.3393/ac.2022.00878.0125

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113 Download

Abstract PDF: The development of deep learning systems in artificial intelligence (AI) has enabled advances in endoscopy, and AI-aided colonoscopy has recently been ushered into clinical practice as a clinical decision-support tool. This has enabled real-time AI-aided detection of polyps with a higher sensitivity than the average endoscopist, and evidence to support its use has been promising thus far. This review article provides a summary of currently published data relating to AI-aided colonoscopy, discusses current clinical applications, and introduces ongoing research directions. We also explore endoscopists’ perceptions and attitudes toward the use of this technology, and discuss factors influencing its uptake in clinical practice.

The Future Medical Science and Colorectal Surgeons: Young Jin Kim; Ann Coloproctol. 2017;33(6):207-209. Published online December 31, 2017; DOI: https://doi.org/10.3393/ac.2017.33.6.207

3,665 View
63 Download
6 Web of Science
7 Citations

Abstract PDF

Future medical technology breakthroughs will build from the incredible progress made in computers, biotechnology, and nanotechnology and from the information learned from the human genome. With such technology and information, computer-aided diagnoses, organ replacement, gene therapy, personalized drugs, and even age reversal will become possible. True 3-dimensional system technology will enable surgeons to envision key clinical features and will help them in planning complex surgery. Surgeons will enter surgical instructions in a virtual space from a remote medical center, order a medical robot to perform the operation, and review the operation in real time on a monitor. Surgeons will be better than artificial intelligence or automated robots when surgeons (or we) love patients and ask questions for a better future. The purpose of this paper is looking at the future medical science and the changes of colorectal surgeons.

Citations

Citations to this article as recorded by

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer
Feng Liang, Shu Wang, Kai Zhang, Tong-Jun Liu, Jian-Nan Li
World Journal of Gastrointestinal Oncology.2022; 14(1): 124. CrossRef
Modern Machine Learning Practices in Colorectal Surgery: A Scoping Review
Stephanie Taha-Mehlitz, Silvio Däster, Laura Bach, Vincent Ochs, Markus von Flüe, Daniel Steinemann, Anas Taha
Journal of Clinical Medicine.2022; 11(9): 2431. CrossRef
Surgical safety in the COVID-19 era: present and future considerations
Young Il Kim, In Ja Park
Annals of Surgical Treatment and Research.2022; 102(6): 295. CrossRef
Introducing Mobile Collaborative Robots into Bioprocessing Environments: Personalised Drug Manufacturing and Environmental Monitoring
Robins Mathew, Robert McGee, Kevin Roche, Shada Warreth, Nikolaos Papakostas
Applied Sciences.2022; 12(21): 10895. CrossRef
7P pediatrics — Medicine of Development and Health Programming
Leyla S. Namazova-Baranova, Alexandr A. Baranov, Elena A. Vishneva, Anna A. Alekseeva, Valerii Y. Albitskiy, Irina A. Belyaeva, Viliya A. Bulgakova, Nato D. Vashakmadze, Olga B. Gordeeva, Irina V. Zelenkova, Elena V. Kaitukova, Georgii A. Karkashadze, Ele
Annals of the Russian academy of medical sciences.2021; 76(6): 622. CrossRef
Application and Prospect of a Mobile Hospital in Disaster Response
Xinlin Chen, Lu Lu, Jie Shi, Xin Zhang, Haojun Fan, Bin Fan, Bo Qu, Qi Lv, Shike Hou
Disaster Medicine and Public Health Preparedness.2020; 14(3): 377. CrossRef
The effect of diets delivered into the gastrointestinal tract on gut motility after colorectal surgery—a systematic review and meta-analysis of randomised controlled trials
Sophie Hogan, Daniel Steffens, Anna Rangan, Michael Solomon, Sharon Carey
European Journal of Clinical Nutrition.2019; 73(10): 1331. CrossRef

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