Introduction: Near-infrared autofluorescence imaging (NIFI) can be used to identify parathyroid gland (PG) during surgery. The purpose of the study is to establish a new model, help surgeons better identify, and protect PGs.
Methods: Five hundred and twenty three NIFI images were selected. The PGs were recorded by NIFI and marked with artificial intelligence (AI) model. The recognition rate for PGs was calculated. Analyze the differences between surgeons of different years of experience and AI recognition, and evaluate the diagnostic and therapeutic efficacy of AI model.
Results: Our model achieved 83.5% precision and 57.8% recall in the internal validation set. The visual recognition rate of AI model was 85.2% and 82.4% on internal and external sets. The PG recognition rate of AI model is higher than that of junior surgeons (p < 0.05).
Conclusions: This AI model will help surgeons identify PGs, and develop their learning ability and self-confidence.
Keywords: artificial intelligence; medical segmentation; near-infrared autofluorescence imaging; parathyroid gland; thyroidectomy.
© 2024 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.