REFERENCES

1. Huerta S, Varshney A, Patel PM, Mayo HG, Livingston EH. Biological mesh implants for abdominal hernia repair: US Food and Drug Administration approval process and systematic review of its efficacy. JAMA Surg. 2016;151:374-81.

2. Gillies M, Anthony L, Al-Roubaie A, Rockliff A, Phong J. Trends in incisional and ventral hernia repair: a population analysis from 2001 to 2021. Cureus. 2023;15:e35744.

3. Talwar AA, Desai AA, McAuliffe PB, et al. Optimal computed tomography-based biomarkers for prediction of incisional hernia formation. Hernia. 2024;28:17-24.

4. McAuliffe PB, Desai AA, Talwar AA, et al. Preoperative computed tomography morphological features indicative of incisional hernia formation after abdominal surgery. Ann Surg. 2022;276:616-25.

5. Love MW, Warren JA, Davis S, et al. Computed tomography imaging in ventral hernia repair: can we predict the need for myofascial release? Hernia. 2021;25:471-7.

6. Elhage SA, Deerenberg EB, Ayuso SA, et al. Development and validation of image-based deep learning models to predict surgical complexity and complications in abdominal wall reconstruction. JAMA Surg. 2021;156:933-40.

7. Taye MM. Theoretical understanding of convolutional neural network: concepts, architectures, applications, future directions. Computation. 2023;11:52.

8. O’Shea K, Nash R. An introduction to convolutional neural networks. arXiv. 2015;arXiv:1511.08458. Available from http://arxiv.org/abs/1511.08458 [accessed 4 August 2025].

9. Ayuso SA, Elhage SA, Zhang Y, et al. Predicting rare outcomes in abdominal wall reconstruction using image-based deep learning models. Surgery. 2023;173:748-55.

10. Wilson HH, Ma C, Ku D, et al. Deep learning model utilizing clinical data alone outperforms image-based model for hernia recurrence following abdominal wall reconstruction with long-term follow up. Surg Endosc. 2024;38:3984-91.

11. Bamba Y, Ogawa S, Itabashi M, et al. Object and anatomical feature recognition in surgical video images based on a convolutional neural network. Int J Comput Assist Radiol Surg. 2021;16:2045-54.

12. Tabja Bortesi JP, Ranisau J, Di S, et al. Machine learning approaches for the image-based identification of surgical wound infections: scoping review. J Med Internet Res. 2024;26:e52880.

13. Tanner J, Rochon M, Harris R, et al. Digital wound monitoring with artificial intelligence to prioritise surgical wounds in cardiac surgery patients for priority or standard review: protocol for a randomised feasibility trial (WISDOM). BMJ Open. 2024;14:e086486.

14. McLean KA, Sgrò A, Brown LR, et al. Evaluation of remote digital postoperative wound monitoring in routine surgical practice. NPJ Digit Med. 2023;6:85.

15. Rochon M, Tanner J, Jurkiewicz J, et al. Wound imaging software and digital platform to assist review of surgical wounds using patient smartphones: the development and evaluation of artificial intelligence (WISDOM AI study). PLoS One. 2024;19:e0315384.

16. Fletcher RR, Schneider G, Bikorimana L, et al. The use of mobile thermal imaging and deep learning for prediction of surgical site infection. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); 2021 Nov 1-5; Mexico. New York: IEEE; 2021. pp. 5059-62.

17. Mayol J. Transforming abdominal wall surgery with generative artificial intelligence. J Abdom Wall Surg. 2023;2:12419.

18. Talwar A, Shen C, Shin JH. Natural language processing in plastic surgery patient consultations. Art Int Surg. 2025;5:46-52.

19. Lancet. AI in medicine: creating a safe and equitable future. Lancet. 2023;402:503.

20. Castelvecchi D. Can we open the black box of AI? Nature. 2016;538:20-3.

21. Pierce RL, Van Biesen W, Van Cauwenberge D, Decruyenaere J, Sterckx S. Explainability in medicine in an era of AI-based clinical decision support systems. Front Genet. 2022;13:903600.

22. Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3:e745-50.

23. Reddy S. Explainability and artificial intelligence in medicine. Lancet Digit Health. 2022;4:e214-5.

24. Gaetani M, Mazwi M, Balaci H, Greer R, Maratta C. Artificial intelligence in medicine and the pursuit of environmentally responsible science. Lancet Digit Health. 2024;6:e438-40.

25. World Health Organization. Ethics and governance of artificial intelligence for health. Geneva: World Health Organization; 2021. pp. xi, 34. Available from https://www.who.int/publications/i/item/9789240029200. [Last accessed on 4 Aug 2025].

26. Thompson N, Greenewald K, Lee K, Manso GF. The computational limits of deep learning. Proceedings of the Ninth Computing within Limits Conference; 2023 June 13-15; Virtual. New York: ACM; 2023.

27. Abid A, Sinha P, Harpale A, Gichoya J, Purkayastha S. Optimizing medical image classification models for edge devices. In: Matsui K, Omatu S, Yigitcanlar T, González SR, Editors. Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference. Proceedings of the 18th International Conference on Distributed Computing and Artificial Intelligence; 2021 Oct 6-8; Salamanca, Spain. Cham: Springer; 2022. pp 77-87.

28. Allam K. Adoption of artificial intelligence in cloud computing. Int J Comput Trends Technol. 2023;71:91-5.

Artificial Intelligence Surgery
ISSN 2771-0408 (Online)
Follow Us

Portico

All published articles will be preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles will be preserved here permanently:

https://www.portico.org/publishers/oae/