Artificial Intelligence–Assisted Indocyanine Green Angiography for Perforators Identification in the Anterolateral Thigh Flap
AI-assisted ICG angiography is an emerging tool with potential to support perforator mapping. Although the current AI model demonstrated limited sensitivity, its accuracy can be enhanced by expanding training datasets, integrating temporal fluorescence dynamics, and refining fluorescence-time curve analysis. Future advancements in AI-driven image processing may further optimize intraoperative perforator identification, ultimately improving surgical precision and patient outcomes.
Authors
- Chia-Yuan Hsu
- Ming-Jui Chung (ORCID: https://orcid.org/0009-0000-5417-3247)
- Chen‐Hsiang Kuan (ORCID: https://orcid.org/0000-0001-5599-7125)
- Jung‐Hsien Hsieh (ORCID: https://orcid.org/0000-0002-3157-3560)
- W.J Chen
- Meng‐Kun Tsai (ORCID: https://orcid.org/0000-0002-2962-8913)
- Hao‐Chih Tai (ORCID: https://orcid.org/0000-0001-8155-3077)
- Yuchao Lu (ORCID: https://orcid.org/0000-0003-4523-2342)
- Nai‐Chen Cheng (ORCID: https://orcid.org/0000-0003-4071-5628)
Institutions
- National Taiwan University (TW)
- Cathay General Hospital (TW)
- National Taiwan University Hospital (TW)
Publication Details
- Journal
- Annals of Plastic Surgery
- Published
- 2025-11-10
- DOI
- https://doi.org/10.1097/sap.0000000000004545
- Primary Topic
- Reconstructive Surgery and Microvascular Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00