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Article Navigation >  CHINESE JOURNAL OF BURNS AND WOUNDS  >  > Accepted Manuscript
Hou Xinwei,Dong Shuai,Zhu Bo,et al.Establishment and preliminary validation of an artificial intelligence-assisted model for identification of perforators of anterolateral thigh perforator flaps based on preoperative indocyanine green angiography (ICGA) videos[J].Chin J Burns Wounds,2026,42(9):1-10.DOI: 10.3760/cma.j.cn501225-20260531-00222.
Citation: Hou Xinwei,Dong Shuai,Zhu Bo,et al.Establishment and preliminary validation of an artificial intelligence-assisted model for identification of perforators of anterolateral thigh perforator flaps based on preoperative indocyanine green angiography (ICGA) videos[J].Chin J Burns Wounds,2026,42(9):1-10.DOI: 10.3760/cma.j.cn501225-20260531-00222.
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Establishment and preliminary validation of an artificial intelligence-assisted model for identification of perforators of anterolateral thigh perforator flaps based on preoperative indocyanine green angiography (ICGA) videos

doi: 10.3760/cma.j.cn501225-20260531-00222

  • Department of Hand Surgery, Suzhou Ruihua Orthopedic Hospital of Soochow University, Suzhou 215104, China

Funds:

Suzhou Municipal Key Medical Discipline SZXK202532

Suzhou Municipal Science and Technology Research Program SYWD2025069

2026 Jiangsu Province Graduate Research and Practice Innovation Program 26CXJH5988

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  • Corresponding author: Wang Kai, Email: 15962200201@163.com
  • Received Date: 2026-05-31
    Available Online: 2026-08-31
    Abstract
  •   Objective  To establish and preliminarily validate an artificial intelligence-assisted model for identification of perforators of anterolateral thigh perforator flaps based on preoperative indocyanine green angiography (ICGA) videos.  Methods  This study was a cross-sectional study. From January to July 2024, 70 patients who met the inclusion criteria and underwent anterolateral thigh perforator flap repair surgery were admitted to Suzhou Ruihua Orthopedic Hospital of Soochow University. All patients received preoperative color Doppler ultrasound (CDU) and ICGA examinations. Fifty patients were selected as the training set for model parameter training using the random function in Python 3.13, and the remaining 20 patients were assigned as the validation set for independent validation. With the intraoperative exploration result as the gold standard, 4 perforator sites were preset in the anterolateral thigh perforator flap area of each patient. In the training set and the validation set of patients, cases where the distance between the perforator localization points of CDU, ICGA-manual interpretation, and ICGA-based artificial intelligence (ICGA-AI) and the actual intraoperative cutaneous perforator entry point was ≤1.0 cm (defined as true positive) were calculated, and the positive rate was calculate. In the training set and validation set of patients, the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and balanced accuracy of the three methods for perforator localization were calculated, the consistency of the perforator localization results between the three methods and the intraoperative exploration; the distance between the perforator localization points determined by the three methods and the corresponding actual intraoperative cutaneous perforator entry point (error distance) was measured; the consistency between the dominant perforator localization by ICGA-AI and that determined intraoperatively was recorded, and the localization accuracy of the dominant perforator was calculated.  Results  There were no statistically significant differences in the positive rates of perforator localization among the three methods in both the training set and the validation set of patients (P>0.05). In the training set of patients, the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and balanced accuracy of CDU, ICGA-manual interpretation, and ICGA-AI were 88.70%, 85.88%, 89.47%, 84.88%, 87.50%, and 87.29%, 93.91%, 89.41%, 92.31%, 91.57%, 92.00%, and 91.66%, and 82.61%, 83.53%, 87.16%, 78.02%, 83.00%, and 83.07%, respectively; the consistency of the perforator localization results between the three methods and the intraoperative exploration was strong or almost perfect (with κ of 0.74, 0.84, and 0.66, respectively, P<0.05). In the validation set of patients, the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and balanced accuracy of CDU, ICGA-manual interpretation, and ICGA-AI were 95.24%, 78.95%, 83.33%, 93.75%, 87.50%, and 87.09%, 95.24%, 92.11%, 93.02%, 94.59%, 93.75%, and 93.67%, 95.24%, 94.74%, 95.24%, 94.74%, 95.00%, and 94.99%, respectively; the consistency of the perforator localization results between the three methods and the intraoperative exploration was strong or almost perfect (with κ of 0.75, 0.88, and 0.90, respectively, P<0.05). In the training set of patients, the error distance of perforator localization of ICGA-AI was 0.84 (0.70, 0.98) cm, which was significantly shorter than 1.41 (0.80, 2.00) cm of CDU (Z=-2.56, P<0.05) and 1.50 (1.03, 1.80) cm of ICGA-manual interpretation (Z=-5.88, P<0.05). In the validation set of patients, the error distance of perforator localization of ICGA-AI was 0.52 (0.35, 0.71) cm, which was significantly shorter than 1.50 (1.00, 2.25) cm of CDU (Z=-3.23, P<0.05) and 1.56 (1.41, 2.00) cm of ICGA-manual interpretation (Z=-3.52, P<0.05). In the training set and the validation set of patients, the accuracy of ICGA-AI for dominant perforator localization was 82.00% (41/50) and 90.00% (18/20), respectively.  Conclusions  This study preliminarily establishes an artificial intelligence-assisted method for identification of perforators of anterolateral thigh perforator flaps based on ICGA videos. In the validation set of patients, this method showed smaller localization errors and higher accuracy of dominant perforator localization, which can assist in preoperative perforator localization.

     

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