
==== Front
Int J Surg
Int J Surg
JS9
International Journal of Surgery (London, England)
1743-9191
1743-9159
Lippincott Williams & Wilkins Hagerstown, MD

38728856
IJS-D-24-01751
10.1097/JS9.0000000000001615
00065
3
Correspondence
Letter to the Editor ‘A real-time augmented reality robot integrated with artificial intelligence for skin tumor surgery - experimental study and case series’
Wang Jie MD awangjie609025475@126.com

Li Ying PhD aliying_xx@163.com
*
Liu Jing PhD ab*liujing@dmu.edu.cn

a First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, People’s Republic of China
b Dalian Innovation Institute of Stem Cell and Precision Medicine, Dalian, Liaoning, People’s Republic of China
* Corresponding author. Address: First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, People’s Republic of China. Tel.: +86 180 988 733 89. E-mail: liying_xx@163.com) (Y. Li), and Tel.: +180 988 708 88. E-mail: liujing@dmu.edu.cn) (J. Liu).
9 2024
10 5 2024
110 9 58675868
29 4 2024
29 4 2024
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms. http://creativecommons.org/licenses/by-nc-sa/4.0/

OPEN-ACCESSTRUE
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pmc Dear Editor,

I have carefully read a recent publication titled ‘A real-time augmented reality robot integrated with artificial intelligence for skin tumor surgery - experimental study and case series,’ and I am deeply interested in the novel auxiliary system for dermatologic surgery proposed in this article1. This research holds significant implications for dermatologic surgery and paves the way for a new direction in the application of artificial intelligence (AI) to medicine. Utilizing AI and augmented reality (AR) technologies, this study trained an image processing system using clinical photos of skin lesions with dermatologists’ delineation of tumour margins, thereby developing an algorithm for surgical margin design that can differentiate between benign and malignant lesions. This innovative dermatologic surgery system automatically delineates tumour margins in the lesion area during surgery using a pre-trained segmentation network and the auto-focus feature of a projector. This system is of paramount importance to improve the precision of dermatologic surgery. While the authors discussed the limitations of this study in the discussion section, there are still additional noteworthy points for improvement that warrant consideration. The following opinions and insights may contribute to further optimizing this dermatologic surgery system and its better application into clinical practice, thereby assisting and enhancing dermatologic surgery in the future.

Firstly, the surgical margin design for malignant skin tumours requires the removal of the tumour during surgery and minimizing the risk of recurrence to the greatest extent possible. Given that recurrences of lesions after excision of malignant skin tumours tend to be more aggressive, the design of margins for malignancies such as melanoma and basal cell carcinoma is a crucial aspect of skin tumour surgery. However, for malignant skin tumours, dermoscopic features can provide clues about the histological subtype, which has implications for the actual margins of the lesion and the malignant extension of the tumour2. Compared to conventional cameras capturing skin lesions, skin microscope-guided surgical margins offer higher accuracy. A recent study indicated that perioperative dermoscopic examination enables precise surgical margin planning for primary basal cell carcinomas, predicting tumour depth and peripheral spread3. If feasible, incorporating dermoscopic images into the initial training algorithm of this surgical assistance system may significantly enhance the accuracy of surgical margin design for malignant skin tumours.

Secondly, when considering facial skin lesions, facial muscles, nerves, and blood vessels are highly abundant, and their anatomical structure is complex, requiring meticulous preoperative planning for facial surgeries. To ensure aesthetics and functionality while reducing the prominence of postoperative scars during the removal of facial skin lesions, incisions need to be made along the longitudinal axis of facial muscles and individual dynamic wrinkles. Compared to the skin on the trunk, facial skin is more three-dimensional, and thus the positioning accuracy should be further considered. A study by Topsakal et al.4 utilized open-source facial 3D models for deep learning training. Therefore, the authors could combine facial 3D models to achieve more precise designs of surgical margins for facial skin lesion excisions.

Thirdly, this study has a limited sample size. There are studies indicating that facial skin thickness varies among populations of different races and regions, which can impact surgical planning5. Future studies require the inclusion of cohorts from multiple countries to comprehensively evaluate the accuracy and applicability of this skin surgery assistance system.

In summary, the skin surgery assistance system proposed in this study, utilizing advanced AI and AR technologies, significantly enhances the accuracy and navigational capabilities of skin tumour surgeries. It provides patients with safer and more effective treatment options, thereby playing a crucial role in advancing skin tumour surgeries. Combined with the optimization strategies we have proposed here, this technology is likely to have an unprecedented impact and paradigm shift in the field of dermatologic surgery.

Ethical approval

Not applicable.

Consent

Not applicable.

Sources of funding

None.

Author contribution

J.W.: conception and manuscript writing; Y.L. and J.L.: manuscript revising. All authors were involved in the final approval of the manuscript.

Conflicts of interest disclosure

There are no conflicts of interest.

Research registration unique identifying number (UIN)

Not applicable.

Guarantor

Jie Wang.

Data availability statement

Not applicable.

Provenance and peer review

Our paper was not invited.

Acknowledgements

None.

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Published online 10 May 2024
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References

1 Huang K Liao J He J . A real-time augmented reality robot integrated with artificial intelligence for skin tumor surgery - experimental study and case series. Int J Surg 2024;Mar 28 [Epub ahead of print].
2 Álvarez-Salafranca M Ara M Zaballos P . Dermoscopy in basal cell carcinoma: an updated review. Actas Dermosifiliogr (Engl Ed) 2021;112 :330–338.33259816
3 Savant SS Jr . Use of preoperative and perioperative ex vivo dermoscopy for precise mapping of margins for standard surgical excision of primary basal cell carcinoma. Indian J Dermatol Venereol Leprol 2023;89 :793.
4 Topsakal O Glinton J Akbas MI . Open-source 3D morphing software for facial plastic surgery and facial landmark detection research and open access face data set based on deep learning (artificial intelligence) generated synthetic 3D models. Facial Plast Surg Aesthet Med 2024;26 :152–159.37751224
5 Eggerstedt M Rhee J Buranosky M . Nasal skin and soft tissue thickness variation among differing races and ethnicities: an objective radiographic analysis. Facial Plast Surg Aesthet Med 2020;22 :188–194.32212978
